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<hr />
<div>== Introduction ==<br />
'''System archetypes''' are the '''patterns''' of behaviour that we can find in complex '''systems'''. Patterns help us to get an idea of how a particular system works. Even though each system is different, and at first look it may seem unique, but it is possible to find some similarities - repetitive structures and principles of how systems work. System archetypes help to find and identify these similarities. If we know the principle how the system works, we can better understand it and explore its weaknesses.<br />
<br />
System archetypes, that are part of '''systems thinking''' and [[System Dynamics]], is used to detect patterns of behaviour of '''social systems''' (businesses, communities, states, economies, etc.). They can be used in two areas, namely as a tool to determines the current state - '''a diagnostic tool''' or as a tool to look at the future development of the system - '''a prospective tool'''. As a diagnostic tool helps managers to obtain the perspective of the internal structure of the system, its functioning and help them to get a better idea of the current system state. As a prospective tool it is mainly used for planning. Managers can formulate their future goals and with the knowledge of the functioning of their organization, they can better determine the procedure by which this goal can be reached <ref name="braun">Braun, William. The System Archetypes [online]. 27. 2. 2002 [seen 21. 1. 2016]. Available at http://www.albany.edu/faculty/gpr/PAD724/724WebArticles/sys_archetypes.pdf</ref>.<br />
<br />
== Definition ==<br />
=== System ===<br />
The system consists of a '''set of elements''' and '''relationships''' between them. The individual elements and connections between them together form a larger value than the individual components alone. Each system can have inputs and outputs which form the interface between the system itself and its surroundings <ref name="palan">Palán, Zdeněk. Systém [online].[seen 21. 1. 2016]. Available at http://www.andromedia.cz/andragogicky-slovnik/system</ref>.<br />
<br />
=== Archetype ===<br />
Archetype, also called prototype, represents a typical example, ideal type or perfect example. Archetype can also be a symbol or a recurrent motif occurring for example in literature <ref name="slovnik">Slovník cizích slov ABZ. Pojem archetyp [online]. 2016 [seen 22. 1. 2016]. Available at http://slovnik-cizich-slov.abz.cz/web.php/slovo/archetyp</ref>.<br />
<br />
== Overview of System Archetypes ==<br />
[[File:Family_Tree.gif|thumb|Family Tree <ref name="isee">isee systems. Archetype Family Tree [online]. 2006 [seen 20. 1. 2016]. Available at http://www.iseesystems.com/Online_training/course/module6/6-03-0-0-tree.htm</ref> (If you don't see picture correctly click twice on it to reload)]]<br />
There are '''16 unique system archetypes''' in total. These 16 archetypes differ from each other and describe different kind of systems, but all have a common foundation. There are '''two basic archetypes''', from which the remaining 14 archetypes is derived. Respectively, there are two archetypes, which can be combined to form the remaining 14 archetypes. Two basic archetypes are called Balancing Loop and Reinforcing Loop. Archetypes can be also divided into two groups the first is '''Fixing a Problem group''' and second one is '''Influencing Change group''' <ref name="sherrer">Sherrer, J. Alex. A Project Manager's Guide to Systems Thinking: Part 2 [online]. Project Smart. 24. 7. 2010 [seen 21. 1. 2016]. Available at https://www.projectsmart.co.uk/project-managers-guide-to-systems-thinking-part-2.php</ref>.<br />
<br />
=== Fixing a Problem ===<br />
*Balancing Loop<br />
*Balancing Structure with Delay<br />
*Indecision<br />
*Drifting Goals<br />
*Escalation<br />
*Fixes That Fail<br />
*Shifting the Burden<br />
*Addiction<br />
<br />
=== Influencing Change ===<br />
*Reinforcing Loop<br />
*Limits to Growth<br />
*Accidental Adversaries<br />
*Success to the Successful<br />
*Tragedy of the Commons<br />
*Attractiveness Principle<br />
*Growth and Underinvestment<br />
*Growth and Underinvestment with Drifting Standard<br />
<br />
== System Archetype Family Tree ==<br />
To recognize the fact that the archetype is suitable for such a situation can be helpful the Family tree of system archetypes. Family tree is an ordered tree that based on simple questions is capable of directing the user to select the correct system archetype for concrete application.<br />
<br />
== Balancing Loop ==<br />
Balancing Loop contains a negative feedback, which is helping the current state to be closer to the target state. Loop is stabilizing due to the negative feedback. When current state is deflected from the target state it helps to return to a desired state. The greater the difference between the target state and current state cause the faster convergence to final state.<br />
<br />
[[File:Archetype-balancing-structure.jpg|thumb|centre|upright=0.5|Balancing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be filling of an empty glass of water. Initially, the glass is empty, and thus the difference between the actual water level (glass is empty) and the required water level (glass is full) is the maximum. Glass is filled with water and a difference of the current water level and the required water level continues to decrease. At the beginning the glass is filled quickly, and it is obvious that much water is missing, then the difference which lowers the intensity of the water flow. At the moment of achieving the required water level there is no longer any motivation to continue with glass filling. Desired state becomes state achieved –the process is terminated.<br />
<br />
== Balancing Loop with Delay ==<br />
This loop is based on the previous one, except there is a delay, which plays a role in overall system responsiveness. The combination of the negative feedback and the delay in the system creates oscillations. The system is trying to achieve the target state, but because of delay, the system detects the information later. The system thus exceeds the target state.<br />
<br />
[[File:Archetype-balancing-structure-with-delay.jpg|thumb|centre|upright=0.5|Balancing Loop with Delay <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a room heated by a heater equipped with a thermostat. The thermostat switches on the heating when the room temperature is lower than desired. Heating begins to heat up and the thermostat switches off the heating when the room is heated up to the desired temperature. Heating is switched off, but for some time after shutdown is still hot. The room air is heated to higher temperature than the desired one, and this principle is repeated over and over again. Room temperature oscillates around a desired temperature, and it is never stabilized on the desired temperature level.<br />
<br />
== Indecision ==<br />
Main character is an oscillation which is created by combining of two balancing loops with delays. One loop reaches the desired state, and the target state of the second loop moves and that in turn influences the first loop. Both loops never reach their goals together and constantly oscillate around their target states.<br />
<br />
[[File:Archetype-indecision.jpg|thumb|centre|upright=0.5|Indecision <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
Examples may be currency market. Currency prices on the stock exchange is determined by supply and demand. When the offer price is lower than the price that buyers are willing to pay, then the purchasers buy larger amount of the currency. The seller realizes that the price is lower than it might be, and thus they increase the price, it will discourage potential buyers and the price will again gradually decrease below what buyers are willing to pay. The whole process is continually repeated and price oscillates around an equilibrium market price.<br />
<br />
== Drifting Goals ==<br />
It is relatively simple archetype, in which two balancing loops are connected, but in this case there are no delays. Loops stand in opposite of each other and the effort made by one loop cause impossibility to balance the second loop and vice versa.<br />
<br />
[[File:Archetype-drifting-goals.jpg|thumb|centre|upright=0.5|Drifting Goals <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a company that offers trips to the sea. The company decided to offer an additional service to its customers. Due to additional service costs of tours increase and the final price of trips will be higher than before. Next season customers begin to migrate to the competition, and the company decides to reconsider its initial plans and choose a compromise solution. The original goal has not been met and there has been erosion of targets.<br />
<br />
== Escalation ==<br />
This archetype is built on two or more interconnected loops. Increasing the target value in one loop tends to increase the second loop, and possibly other loops. The loops have a negative feedback. Due to the relatively higher value of one loop the other loops values grows. In the system as a whole, the target values continuously increasing in a cycle. Growth of the values can only be stopped by external constraints such as economic constraint, time constraint or space constraint.<br />
<br />
[[File:Archetype-escalation.jpg|thumb|centre|upright=0.5|Escalation <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A typical example is the arms race. One country considers that it is threatened by another country. The first country raises its arms capacity and it forces the other country to also expand its weapons facilities. This again creates concerns among the first earth and once again raises his arms capacity, and the whole cycle repeats. Stop the escalation of the situation usually only lack of resources on one or both sides.<br />
<br />
== Fixes That Fail ==<br />
Archetype is a combination of stabilizing and reinforcing loops. Stabilizing loop trying to reduce the gap between current and desired state. The difference between these states is solved in a way which does not eliminate the problems. This solution only delays the inevitable result and situation as a whole is even worse. The more often undesirable states are solved by an intervention which does not remove the problem, the faster the actual solution gets more difficult.<br />
<br />
[[File:Archetype-fixes-that-fail.jpg|thumb|centre|upright=0.5|Fixes That Fail <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example is the so-called debt trap. Situation where one loan is repaid by another. The first loan is repaid by another loan but under worse conditions, which making the whole situation worse. The situation is temporarily solved, but in the long term, the overall situation deteriorates and more loans always brings a deterioration of the situation and the only apparent solution of the problem.<br />
<br />
== Shifting the Burden ==<br />
Archetype captures the situation when dealing with a problem is solved only by temporarily solution. This short-term also affects the fundamental solution. Attention is given only to short-term solution or a solution which solves only side effects.<br />
<br />
[[File:Archetype-shifting-burden.jpg|thumb|centre|upright=0.5|Shifting the Burden <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example may be the treatment of serious illness. The patient's doctor prescribes for example medication which only relieve pain caused by serious illness. Patient for a short time period feels better, but subsequently his condition gets. Disease gets to an advanced stage and it is significantly more difficult to cure it than at the beginning.<br />
<br />
== Addiction ==<br />
It is based on the archetype Fixes That Fail and it appear where short-term solutions are applied. Short-term solutions gradually lose their effect.<br />
<br />
[[File:Archetype-addiction.jpg|thumb|centre|upright=0.5|Addiction <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example is drug addiction. Drug addict person after a long abstinence feels unwell and he trying to improve his condition by taking another dose of drugs. Condition temporarily improved, but on the other hand, the addiction is than stronger. Drug addict person needs another dose but this dose must be stronger to trigger a similar denial of the original problem of addiction.<br />
<br />
== Reinforcing Loop ==<br />
Reinforcing Loop is the second of two basic archetypes. It serves as a building element, which appears in the other system archetypes. Reinforcing Loop describes a situation where there is a persistent increase or decrease. This loop always leads to a unidirectional development. It is basically the exact opposite of the Balancing Loop.<br />
<br />
[[File:Archetype-reinforcing.jpg|thumb|centre|upright=0.5|Reinforcing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a savings account, which is initially deposited by an amount. The account offers a constant interest rate. After each crediting of interest, the originally deposit increases. In the next period the interest is calculated based on higher amount of money. This situation, however, abstracts from the inflation.<br />
<br />
== Limits to Growth ==<br />
This archetype is a combination of two basic loops - Balancing Loop and Reinforcing Loop. Reinforcing Loop represents steady growth, which is limited by Balancing Loop. Because of Balancing Loop system cannot grow infinitely and always has its limitations.<br />
<br />
[[File:Archetype-limits-to-growth.jpg|thumb|centre|upright=0.5|Limits to Growth <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example might be global population growth. The population is bigger, it grows faster, but the faster it grows, the more resources it needs for its development. Demands on resources disproportionately increases in comparison with available resources on the planet. Growth is limited and cannot be infinite.<br />
<br />
== Accidental Adversaries ==<br />
This archetype is composed of two Reinforcing Loops and around them is the Balancing Loop. Archetype describes the situation where two or more parties try to work, but also trying to increase their own benefit. Efforts to increase their own benefit leads to a reduction in the benefit of the other party and thus of cooperating parties become party rival.<br />
<br />
[[File:Archetype-accidental-adversaries.jpg|thumb|centre|upright=0.5|Accidental Adversaries <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example might be a company represented by its owners and top managers. Owners and managers have a common interest, which is the prosperity of the company. Even though they have a common interest, the interest of each party can be dispersed by other factors. Managers, for example, want to gain bonuses for their performance so they try to artificially inflate the growth and performance of the company. Owners may actually want to realize their short-term gains and thus can choose to pay disproportionately large dividends. This behaviour lead to the fact that the teammates become opponents and their behaviour does not lead to a single common goal.<br />
<br />
== Success to the Successful ==<br />
This model consists of the two Reinforcing Loops which has mutually opposite tendency. One loop is growing and the other declining. The more one loop grows, the more the second loop decreases and vice versa.<br />
<br />
[[File:Archetype-success-to-the-successful.jpg|thumb|centre|upright=0.5|Success to the Successful <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example is Self-fulfilling prophecy. Imagine two people, a confident, successful, another vice versa lacking confidence and success. These two people are constantly reinforcing that there are those who they think they are, and the gulf between them constantly increase. First self-confidence person is going better and better which gives him the courage to push further, the second person on the contrary, confirms that he fails and his self-esteem drops.<br />
<br />
== Tragedy of the Commons ==<br />
Tragedy of the Commons is a situation where two or more parties fighting for a common and limited resource. The fewer the resources left, the more they try to get the share.<br />
<br />
[[File:Archetype-tragedy-of-the-commons.jpg|thumb|centre|upright=0.5|Tragedy of the Commons <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be global fight between various states and corporations about limited oil resources - oilfields. The less new oilfields appear and the faster the current are depleted, the more resources and effort is devoted to extraction. Due to limited resources and shrinking the whole situation escalates further, the situation is tense and there is more conflict and these are more serious than before.<br />
<br />
== Attractiveness Principle ==<br />
Archetype is derived from the Limits to Growth archetype. It is extended by the fact that it is listed more than one threshold. Whereby the limits may not be as big and does not have to limit the same parameter. The basis in this model are the Reinforcing Loops, which is identical with that appearing in the Limits to Growth archetype. Loop strengthens and accelerates time to grow. In the model, there are also Stabilizing Loops that will bring the current state of the coasts. Balancing act with a delay, thus it is possible to establish a short-term imbalance that is gradually stabilized.<br />
<br />
[[File:Archetype-attractiveness-principle.jpg|thumb|centre|upright=0.5|Attractiveness Principle <ref name="sherrer"></ref>]]<br />
<br />
== Growth and Underinvestment ==<br />
Archetype based on the Limits to Growth archetype. The difference here is that the limiting factor or factors dynamically develops along with the development of the entire system. Limitations are therefore not constant, but varies in time. The model shows the system which not enough to invests to itself and create its own limitations in future growth. Archetype is comprised of three loops, two Balancing Loops and one Reinforcing Loop.<br />
<br />
[[File:Archetype-growth-and-underinvestment.jpg|thumb|centre|upright=0.5|Growth and Underinvestment <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example from the life may be preparation of high school student for study at university. If the student does not pay sufficient attention to current high school studies, he can thus create restrictions of his future potential. For example, he will not be accepted to university which he wants to or will be dismissed because does not manage to keep pace with other students. Neglected the study of lower grade may bring future constraints in a higher degree.<br />
<br />
== Growth and Underinvestment with Drifting Standard ==<br />
Archetype based on previous archetype, which is enriched with fourth loop. The fourth loop represents a certain standard, which change over time and reduces the need for future changes. Overall, this tendency leads to the overall growth of the system decreases.<br />
<br />
[[File:Archetype-growth-and-underinvestment-with-drifting-standard.jpg|thumb|centre|upright=0.5|Growth and Underinvestment with Drifting Standard <ref name="sherrer"></ref>]]<br />
<br />
== Quiz ==<br />
Try to match each real example with correct system archetype. Quiz correct answers see below.<br />
<br />
'''Real examples:'''<br />
#Dam (with a constant water level)<br />
#Arms race<br />
#Word of mouth<br />
#Humanitarian assistance (sending food)<br />
#Burning rain forests for palm trees (palm oil)<br />
<br />
'''System archetypes:'''<br />
<ol type="a"><br />
<li>Shifting the Burden</li><br />
<li>Reinforcing Loop</li><br />
<li>Balancing Loop</li><br />
<li>Tragedy of Common</li><br />
<li>Escalation</li><br />
</ol><br />
<br />
== See Also ==<br />
*[[System Dynamics]]<br />
*[https://en.wikipedia.org/wiki/The_Fifth_Discipline The Fifth Discipline]<br />
== References ==<br />
<references/><br />
*Šalamon, Tomáš. (2011). [http://www.designofagentbasedmodels.info ''Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes'']. Řepín, Czech Republic: Bruckner Publishing<br />
*Taborga, Jorge. Systems Archetypes and Their Application.2016 [seen 23. 1. 2016]. Available at https://www.saybrook.edu/rethinkingcomplexity/posts/08-15-11/systems-archetypes-and-their-application<br />
*Continuous Improvement Associates. Systems Thinking Archetypes (Generic Structures). 2003 [seen 22. 1. 2016]. Available at http://www.exponentialimprovement.com/cms/uploads/ArchetypesGeneric02.pdf<br />
*Bellinger, Gene. Archetypes: Interaction Structures of the Universe. 2004 [seen 20. 1. 2016]. Available at http://www.systems-thinking.org/arch/arch.htm#archdg<br />
<br />
== External Links ==<br />
*[https://insightmaker.com/tag/Systems-Archetypes Insight Maker] - A free dynamic modelling and simulation web application.<br />
<br />
== Quiz Correct Answers ==<br />
There are correct answers from quiz hereinabove.<br />
<br />
'''Correct Answers:'''<br />
#c.<br />
#e.<br />
#b.<br />
#a.<br />
#d.<br />
<br />
<br />
[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 20:38, 24 January 2016 (CET)</div>Xkrep33http://www.simulace.info/index.php?title=System_Archetypes&diff=10826System Archetypes2016-01-24T19:37:33Z<p>Xkrep33: </p>
<hr />
<div>== Introduction ==<br />
'''System archetypes''' are the '''patterns''' of behaviour that we can find in complex '''systems'''. Patterns help us to get an idea of how a particular system works. Even though each system is different, and at first look it may seem unique, but it is possible to find some similarities - repetitive structures and principles of how systems work. System archetypes help to find and identify these similarities. If we know the principle how the system works, we can better understand it and explore its weaknesses.<br />
<br />
System archetypes, that are part of '''systems thinking''' and [[System Dynamics]], is used to detect patterns of behaviour of '''social systems''' (businesses, communities, states, economies, etc.). They can be used in two areas, namely as a tool to determines the current state - '''a diagnostic tool''' or as a tool to look at the future development of the system - '''a prospective tool'''. As a diagnostic tool helps managers to obtain the perspective of the internal structure of the system, its functioning and help them to get a better idea of the current system state. As a prospective tool it is mainly used for planning. Managers can formulate their future goals and with the knowledge of the functioning of their organization, they can better determine the procedure by which this goal can be reached <ref name="braun">Braun, William. The System Archetypes [online]. 27. 2. 2002 [seen 21. 1. 2016]. Available at http://www.albany.edu/faculty/gpr/PAD724/724WebArticles/sys_archetypes.pdf</ref>.<br />
<br />
== Definition ==<br />
=== System ===<br />
The system consists of a '''set of elements''' and '''relationships''' between them. The individual elements and connections between them together form a larger value than the individual components alone. Each system can have inputs and outputs which form the interface between the system itself and its surroundings <ref name="palan">Palán, Zdeněk. Systém [online].[seen 21. 1. 2016]. Available at http://www.andromedia.cz/andragogicky-slovnik/system</ref>.<br />
<br />
=== Archetype ===<br />
Archetype, also called prototype, represents a typical example, ideal type or perfect example. Archetype can also be a symbol or a recurrent motif occurring for example in literature <ref name="slovnik">Slovník cizích slov ABZ. Pojem archetyp [online]. 2016 [seen 22. 1. 2016]. Available at http://slovnik-cizich-slov.abz.cz/web.php/slovo/archetyp</ref>.<br />
<br />
== Overview of System Archetypes ==<br />
[[File:Family_Tree.gif|thumb|Family Tree <ref name="isee">isee systems. Archetype Family Tree [online]. 2006 [seen 20. 1. 2016]. Available at http://www.iseesystems.com/Online_training/course/module6/6-03-0-0-tree.htm</ref> (If you don't see picture correctly click twice on it to reload)]]<br />
There are '''16 unique system archetypes''' in total. These 16 archetypes differ from each other and describe different kind of systems, but all have a common foundation. There are '''two basic archetypes''', from which the remaining 14 archetypes is derived. Respectively, there are two archetypes, which can be combined to form the remaining 14 archetypes. Two basic archetypes are called Balancing Loop and Reinforcing Loop. Archetypes can be also divided into two groups the first is '''Fixing a Problem group''' and second one is '''Influencing Change group''' <ref name="sherrer">Sherrer, J. Alex. A Project Manager's Guide to Systems Thinking: Part 2 [online]. Project Smart. 24. 7. 2010 [seen 21. 1. 2016]. Available at https://www.projectsmart.co.uk/project-managers-guide-to-systems-thinking-part-2.php</ref>.<br />
<br />
=== Fixing a Problem ===<br />
*Balancing Loop<br />
*Balancing Structure with Delay<br />
*Indecision<br />
*Drifting Goals<br />
*Escalation<br />
*Fixes That Fail<br />
*Shifting the Burden<br />
*Addiction<br />
<br />
=== Influencing Change ===<br />
*Reinforcing Loop<br />
*Limits to Growth<br />
*Accidental Adversaries<br />
*Success to the Successful<br />
*Tragedy of the Commons<br />
*Attractiveness Principle<br />
*Growth and Underinvestment<br />
*Growth and Underinvestment with Drifting Standard<br />
<br />
== System Archetype Family Tree ==<br />
To recognize the fact that the archetype is suitable for such a situation can be helpful the Family tree of system archetypes. Family tree is an ordered tree that based on simple questions is capable of directing the user to select the correct system archetype for concrete application.<br />
<br />
== Balancing Loop ==<br />
Balancing Loop contains a negative feedback, which is helping the current state to be closer to the target state. Loop is stabilizing due to the negative feedback. When current state is deflected from the target state it helps to return to a desired state. The greater the difference between the target state and current state cause the faster convergence to final state.<br />
<br />
[[File:Archetype-balancing-structure.jpg|thumb|centre|upright=0.5|Balancing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be filling of an empty glass of water. Initially, the glass is empty, and thus the difference between the actual water level (glass is empty) and the required water level (glass is full) is the maximum. Glass is filled with water and a difference of the current water level and the required water level continues to decrease. At the beginning the glass is filled quickly, and it is obvious that much water is missing, then the difference which lowers the intensity of the water flow. At the moment of achieving the required water level there is no longer any motivation to continue with glass filling. Desired state becomes state achieved –the process is terminated.<br />
<br />
== Balancing Loop with Delay ==<br />
This loop is based on the previous one, except there is a delay, which plays a role in overall system responsiveness. The combination of the negative feedback and the delay in the system creates oscillations. The system is trying to achieve the target state, but because of delay, the system detects the information later. The system thus exceeds the target state.<br />
<br />
[[File:Archetype-balancing-structure-with-delay.jpg|thumb|centre|upright=0.5|Balancing Loop with Delay <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a room heated by a heater equipped with a thermostat. The thermostat switches on the heating when the room temperature is lower than desired. Heating begins to heat up and the thermostat switches off the heating when the room is heated up to the desired temperature. Heating is switched off, but for some time after shutdown is still hot. The room air is heated to higher temperature than the desired one, and this principle is repeated over and over again. Room temperature oscillates around a desired temperature, and it is never stabilized on the desired temperature level.<br />
<br />
== Indecision ==<br />
Main character is an oscillation which is created by combining of two balancing loops with delays. One loop reaches the desired state, and the target state of the second loop moves and that in turn influences the first loop. Both loops never reach their goals together and constantly oscillate around their target states.<br />
<br />
[[File:Archetype-indecision.jpg|thumb|centre|upright=0.5|Indecision <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
Examples may be currency market. Currency prices on the stock exchange is determined by supply and demand. When the offer price is lower than the price that buyers are willing to pay, then the purchasers buy larger amount of the currency. The seller realizes that the price is lower than it might be, and thus they increase the price, it will discourage potential buyers and the price will again gradually decrease below what buyers are willing to pay. The whole process is continually repeated and price oscillates around an equilibrium market price.<br />
<br />
== Drifting Goals ==<br />
It is relatively simple archetype, in which two balancing loops are connected, but in this case there are no delays. Loops stand in opposite of each other and the effort made by one loop cause impossibility to balance the second loop and vice versa.<br />
<br />
[[File:Archetype-drifting-goals.jpg|thumb|centre|upright=0.5|Drifting Goals <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a company that offers trips to the sea. The company decided to offer an additional service to its customers. Due to additional service costs of tours increase and the final price of trips will be higher than before. Next season customers begin to migrate to the competition, and the company decides to reconsider its initial plans and choose a compromise solution. The original goal has not been met and there has been erosion of targets.<br />
<br />
== Escalation ==<br />
This archetype is built on two or more interconnected loops. Increasing the target value in one loop tends to increase the second loop, and possibly other loops. The loops have a negative feedback. Due to the relatively higher value of one loop the other loops values grows. In the system as a whole, the target values continuously increasing in a cycle. Growth of the values can only be stopped by external constraints such as economic constraint, time constraint or space constraint.<br />
<br />
[[File:Archetype-escalation.jpg|thumb|centre|upright=0.5|Escalation <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A typical example is the arms race. One country considers that it is threatened by another country. The first country raises its arms capacity and it forces the other country to also expand its weapons facilities. This again creates concerns among the first earth and once again raises his arms capacity, and the whole cycle repeats. Stop the escalation of the situation usually only lack of resources on one or both sides.<br />
<br />
== Fixes That Fail ==<br />
Archetype is a combination of stabilizing and reinforcing loops. Stabilizing loop trying to reduce the gap between current and desired state. The difference between these states is solved in a way which does not eliminate the problems. This solution only delays the inevitable result and situation as a whole is even worse. The more often undesirable states are solved by an intervention which does not remove the problem, the faster the actual solution gets more difficult.<br />
<br />
[[File:Archetype-fixes-that-fail.jpg|thumb|centre|upright=0.5|Fixes That Fail <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example is the so-called debt trap. Situation where one loan is repaid by another. The first loan is repaid by another loan but under worse conditions, which making the whole situation worse. The situation is temporarily solved, but in the long term, the overall situation deteriorates and more loans always brings a deterioration of the situation and the only apparent solution of the problem.<br />
<br />
== Shifting the Burden ==<br />
Archetype captures the situation when dealing with a problem is solved only by temporarily solution. This short-term also affects the fundamental solution. Attention is given only to short-term solution or a solution which solves only side effects.<br />
<br />
[[File:Archetype-shifting-burden.jpg|thumb|centre|upright=0.5|Shifting the Burden <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example may be the treatment of serious illness. The patient's doctor prescribes for example medication which only relieve pain caused by serious illness. Patient for a short time period feels better, but subsequently his condition gets. Disease gets to an advanced stage and it is significantly more difficult to cure it than at the beginning.<br />
<br />
== Addiction ==<br />
It is based on the archetype Fixes That Fail and it appear where short-term solutions are applied. Short-term solutions gradually lose their effect.<br />
<br />
[[File:Archetype-addiction.jpg|thumb|centre|upright=0.5|Addiction <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example is drug addiction. Drug addict person after a long abstinence feels unwell and he trying to improve his condition by taking another dose of drugs. Condition temporarily improved, but on the other hand, the addiction is than stronger. Drug addict person needs another dose but this dose must be stronger to trigger a similar denial of the original problem of addiction.<br />
<br />
== Reinforcing Loop ==<br />
Reinforcing Loop is the second of two basic archetypes. It serves as a building element, which appears in the other system archetypes. Reinforcing Loop describes a situation where there is a persistent increase or decrease. This loop always leads to a unidirectional development. It is basically the exact opposite of the Balancing Loop.<br />
<br />
[[File:Archetype-reinforcing.jpg|thumb|centre|upright=0.5|Reinforcing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a savings account, which is initially deposited by an amount. The account offers a constant interest rate. After each crediting of interest, the originally deposit increases. In the next period the interest is calculated based on higher amount of money. This situation, however, abstracts from the inflation.<br />
<br />
== Limits to Growth ==<br />
This archetype is a combination of two basic loops - Balancing Loop and Reinforcing Loop. Reinforcing Loop represents steady growth, which is limited by Balancing Loop. Because of Balancing Loop system cannot grow infinitely and always has its limitations.<br />
<br />
[[File:Archetype-limits-to-growth.jpg|thumb|centre|upright=0.5|Limits to Growth <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example might be global population growth. The population is bigger, it grows faster, but the faster it grows, the more resources it needs for its development. Demands on resources disproportionately increases in comparison with available resources on the planet. Growth is limited and cannot be infinite.<br />
<br />
== Accidental Adversaries ==<br />
This archetype is composed of two Reinforcing Loops and around them is the Balancing Loop. Archetype describes the situation where two or more parties try to work, but also trying to increase their own benefit. Efforts to increase their own benefit leads to a reduction in the benefit of the other party and thus of cooperating parties become party rival.<br />
<br />
[[File:Archetype-accidental-adversaries.jpg|thumb|centre|upright=0.5|Accidental Adversaries <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example might be a company represented by its owners and top managers. Owners and managers have a common interest, which is the prosperity of the company. Even though they have a common interest, the interest of each party can be dispersed by other factors. Managers, for example, want to gain bonuses for their performance so they try to artificially inflate the growth and performance of the company. Owners may actually want to realize their short-term gains and thus can choose to pay disproportionately large dividends. This behaviour lead to the fact that the teammates become opponents and their behaviour does not lead to a single common goal.<br />
<br />
== Success to the Successful ==<br />
This model consists of the two Reinforcing Loops which has mutually opposite tendency. One loop is growing and the other declining. The more one loop grows, the more the second loop decreases and vice versa.<br />
<br />
[[File:Archetype-success-to-the-successful.jpg|thumb|centre|upright=0.5|Success to the Successful <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example is Self-fulfilling prophecy. Imagine two people, a confident, successful, another vice versa lacking confidence and success. These two people are constantly reinforcing that there are those who they think they are, and the gulf between them constantly increase. First self-confidence person is going better and better which gives him the courage to push further, the second person on the contrary, confirms that he fails and his self-esteem drops.<br />
<br />
== Tragedy of the Commons ==<br />
Tragedy of the Commons is a situation where two or more parties fighting for a common and limited resource. The fewer the resources left, the more they try to get the share.<br />
<br />
[[File:Archetype-tragedy-of-the-commons.jpg|thumb|centre|upright=0.5|Tragedy of the Commons <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be global fight between various states and corporations about limited oil resources - oilfields. The less new oilfields appear and the faster the current are depleted, the more resources and effort is devoted to extraction. Due to limited resources and shrinking the whole situation escalates further, the situation is tense and there is more conflict and these are more serious than before.<br />
<br />
== Attractiveness Principle ==<br />
Archetype is derived from the Limits to Growth archetype. It is extended by the fact that it is listed more than one threshold. Whereby the limits may not be as big and does not have to limit the same parameter. The basis in this model are the Reinforcing Loops, which is identical with that appearing in the Limits to Growth archetype. Loop strengthens and accelerates time to grow. In the model, there are also Stabilizing Loops that will bring the current state of the coasts. Balancing act with a delay, thus it is possible to establish a short-term imbalance that is gradually stabilized.<br />
<br />
[[File:Archetype-attractiveness-principle.jpg|thumb|centre|upright=0.5|Attractiveness Principle <ref name="sherrer"></ref>]]<br />
<br />
== Growth and Underinvestment ==<br />
Archetype based on the Limits to Growth archetype. The difference here is that the limiting factor or factors dynamically develops along with the development of the entire system. Limitations are therefore not constant, but varies in time. The model shows the system which not enough to invests to itself and create its own limitations in future growth. Archetype is comprised of three loops, two Balancing Loops and one Reinforcing Loop.<br />
<br />
[[File:Archetype-growth-and-underinvestment.jpg|thumb|centre|upright=0.5|Growth and Underinvestment <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example from the life may be preparation of high school student for study at university. If the student does not pay sufficient attention to current high school studies, he can thus create restrictions of his future potential. For example, he will not be accepted to university which he wants to or will be dismissed because does not manage to keep pace with other students. Neglected the study of lower grade may bring future constraints in a higher degree.<br />
<br />
== Growth and Underinvestment with Drifting Standard ==<br />
Archetype based on previous archetype, which is enriched with fourth loop. The fourth loop represents a certain standard, which change over time and reduces the need for future changes. Overall, this tendency leads to the overall growth of the system decreases.<br />
<br />
[[File:Archetype-growth-and-underinvestment-with-drifting-standard.jpg|thumb|centre|upright=0.5|Growth and Underinvestment with Drifting Standard <ref name="sherrer"></ref>]]<br />
<br />
== Quiz ==<br />
Try to match each real example with correct system archetype. Quiz correct answers see below.<br />
<br />
'''Real examples:'''<br />
#Dam (with a constant water level)<br />
#Arms race<br />
#Word of mouth<br />
#Humanitarian assistance (sending food)<br />
#Burning rain forests for palm trees (palm oil)<br />
<br />
'''System archetypes:'''<br />
<ol type="a"><br />
<li>Shifting the Burden</li><br />
<li>Reinforcing Loop</li><br />
<li>Balancing Loop</li><br />
<li>Tragedy of Common</li><br />
<li>Escalation</li><br />
</ol><br />
<br />
== See Also ==<br />
*[[System Dynamics]]<br />
*[https://en.wikipedia.org/wiki/The_Fifth_Discipline The Fifth Discipline]<br />
== References ==<br />
<references/><br />
*Šalamon, Tomáš. (2011). [http://www.designofagentbasedmodels.info ''Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes'']. Řepín, Czech Republic: Bruckner Publishing<br />
*Taborga, Jorge. Systems Archetypes and Their Application.2016 [seen 23. 1. 2016]. Available at https://www.saybrook.edu/rethinkingcomplexity/posts/08-15-11/systems-archetypes-and-their-application<br />
*Continuous Improvement Associates. Systems Thinking Archetypes (Generic Structures). 2003 [seen 22. 1. 2016]. Available at http://www.exponentialimprovement.com/cms/uploads/ArchetypesGeneric02.pdf<br />
*Bellinger, Gene. Archetypes: Interaction Structures of the Universe. 2004 [seen 20. 1. 2016]. Available at http://www.systems-thinking.org/arch/arch.htm#archdg<br />
<br />
== External Links ==<br />
*[https://insightmaker.com/tag/Systems-Archetypes Insight Maker] - A free dynamic modelling and simulation web application.<br />
<br />
== Quiz Correct Answers ==<br />
There are correct answers from quiz hereinabove.<br />
<br />
'''Correct Answers:'''<br />
#c.<br />
#e.<br />
#b.<br />
#a.<br />
#d.<br />
<br />
[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 20:37, 24 January 2016 (CET)<br />
<br />
<br />
[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 11:30, 24 January 2016 (CET)</div>Xkrep33http://www.simulace.info/index.php?title=System_Archetypes&diff=10812System Archetypes2016-01-24T10:30:37Z<p>Xkrep33: </p>
<hr />
<div>== Introduction ==<br />
'''System archetypes''' are the '''patterns''' of behaviour that we can find in complex '''systems'''. Patterns help us to get an idea of how a particular system works. Even though each system is different, and at first look it may seem unique, it is possible to find some similarities - repetitive structures and principles of how systems work. System archetypes help to find and identify these similarities. If we know the principle how the system works, we can better understand it and explore its weaknesses.<br />
<br />
System archetypes, that are part of '''systems thinking''' and [[System Dynamics]], is used to detect patterns of behaviour of '''social systems''' (businesses, communities, states, economies, etc.). They can be used in two areas, namely as a tool to determines the current state - '''a diagnostic tool''' or as a tool to look at the future development of the system - '''a prospective tool'''. As a diagnostic tool helps managers to obtain the perspective of the internal structure of the system, its functioning and help them to get a better idea of the current system state. As a prospective tool it is mainly used for planning. Managers can formulate their future goals and with the knowledge of the functioning of their organization, they can better determine the procedure by which this goal can be reached <ref name="braun">Braun, William. The System Archetypes [online]. 27. 2. 2002 [seen 21. 1. 2016]. Available at http://www.albany.edu/faculty/gpr/PAD724/724WebArticles/sys_archetypes.pdf</ref>.<br />
<br />
== Definition ==<br />
=== System ===<br />
The system consists of a '''set of elements''' and '''relationships''' between them. The individual elements and connections between them together form a larger value than the individual components alone. Each system can have inputs and outputs which form the interface between the system itself and its surroundings <ref name="palan">Palán, Zdeněk. Systém [online].[seen 21. 1. 2016]. Available at http://www.andromedia.cz/andragogicky-slovnik/system</ref>.<br />
<br />
=== Archetype ===<br />
Also called prototype represents a typical example, ideal type or perfect example. Archetype can also be a symbol or a recurrent motif occurring for example in literature <ref name="slovnik">Slovník cizích slov ABZ. Pojem archetyp [online]. 2016 [seen 22. 1. 2016]. Available at http://slovnik-cizich-slov.abz.cz/web.php/slovo/archetyp</ref>.<br />
<br />
== Overview of System Archetypes ==<br />
[[File:Family_Tree.gif|thumb|Family Tree <ref name="isee">isee systems. Archetype Family Tree [online]. 2006 [seen 20. 1. 2016]. Available at http://www.iseesystems.com/Online_training/course/module6/6-03-0-0-tree.htm</ref> (If you don't see picture correctly click twice on it to reload)]]<br />
There are '''16 unique system archetypes''' in total. These 16 archetypes differ from each other and describe different kind of systems, but all have a common foundation. There are '''two basic archetypes''', from which the remaining 14 archetypes is derived. Respectively, there are two archetypes, which can be combined to form the remaining 14 archetypes. Two basic archetypes are called Balancing Loop and Reinforcing Loop. Archetypes can be also divided into two groups the first is '''Fixing a Problem group''' and second one is '''Influencing Change group''' <ref name="sherrer">Sherrer, J. Alex. A Project Manager's Guide to Systems Thinking: Part 2 [online]. Project Smart. 24. 7. 2010 [seen 21. 1. 2016]. Available at https://www.projectsmart.co.uk/project-managers-guide-to-systems-thinking-part-2.php</ref>.<br />
<br />
=== Fixing a Problem ===<br />
*Balancing Loop<br />
*Balancing Structure with Delay<br />
*Indecision<br />
*Drifting Goals<br />
*Escalation<br />
*Fixes That Fail<br />
*Shifting the Burden<br />
*Addiction<br />
<br />
=== Influencing Change ===<br />
*Reinforcing Loop<br />
*Limits to Growth<br />
*Accidental Adversaries<br />
*Success to the Successful<br />
*Tragedy of the Commons<br />
*Attractiveness Principle<br />
*Growth and Underinvestment<br />
*Growth and Underinvestment with Drifting Standard<br />
<br />
== System Archetype Family Tree ==<br />
To recognize the fact that the archetype is suitable for such a situation can be helpful the Family tree of system archetypes. Family tree is an ordered tree that based on simple questions is capable of directing the user to select the correct system archetype for concrete application.<br />
<br />
== Balancing Loop ==<br />
Balancing Loop contains a negative feedback, which is helping the current state to be closer to the target state. Loop is stabilizing due to the negative feedback. When current state is deflected from the target state it helps to return to a desired state. The greater the difference between the target state and current state cause the faster convergence to final state.<br />
<br />
[[File:Archetype-balancing-structure.jpg|thumb|centre|upright=0.5|Balancing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be filling of an empty glass of water. Initially, the glass is empty, and thus the difference between the actual water level (glass is empty) and the required water level (glass is full) is the maximum. Glass is filled with water and a difference of the current water level and the required water level continues to decrease. At the beginning the glass is filled quickly, and it is obvious that much water is missing, then the difference which lowers the intensity of the water flow. At the moment of achieving the required water level there is no longer any motivation to continue with glass filling. Desired state becomes state achieved –the process is terminated.<br />
<br />
== Balancing Loop with Delay ==<br />
This loop is based on the previous one, except there is a delay, which plays a role in overall system responsiveness. The combination of the negative feedback and the delay in the system creates oscillations. The system is trying to achieve the target state, but because of delay, the system detects the information later. The system thus exceeds the target state.<br />
<br />
[[File:Archetype-balancing-structure-with-delay.jpg|thumb|centre|upright=0.5|Balancing Loop with Delay <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a room heated by a heater equipped with a thermostat. The thermostat switches on the heating when the room temperature is lower than desired. Heating begins to heat up and the thermostat switches off the heating when the room is heated up to the desired temperature. Heating is switched off, but for some time after shutdown is still hot. The room air is heated to higher temperature than the desired one, and this principle is repeated over and over again. Room temperature oscillates around a desired temperature, and it is never stabilized on the desired temperature level.<br />
<br />
== Indecision ==<br />
Main character is an oscillation which is created by combining of two balancing loops with delays. One loop reaches the desired state, and the target state of the second loop moves and that in turn influences the first loop. Both loops never reach their goals together and constantly oscillate around their target states.<br />
<br />
[[File:Archetype-indecision.jpg|thumb|centre|upright=0.5|Indecision <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
Examples may be currency market. Currency prices on the stock exchange is determined by supply and demand. When the offer price is lower than the price that buyers are willing to pay, then the purchasers buy larger amount of the currency. The seller realizes that the price is lower than it might be, and thus they increase the price, it will discourage potential buyers and the price will again gradually decrease below what buyers are willing to pay. The whole process is continually repeated and price oscillates around an equilibrium market price.<br />
<br />
== Drifting Goals ==<br />
It is relatively simple archetype, in which two balancing loops are connected, but in this case there are no delays. Loops stand in opposite of each other and the effort made by one loop cause impossibility to balance the second loop and vice versa.<br />
<br />
[[File:Archetype-drifting-goals.jpg|thumb|centre|upright=0.5|Drifting Goals <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a company that offers trips to the sea. The company decided to offer an additional service to its customers. Due to additional service costs of tours increase and the final price of trips will be higher than before. Next season customers begin to migrate to the competition, and the company decides to reconsider its initial plans and choose a compromise solution. The original goal has not been met and there has been erosion of targets.<br />
<br />
== Escalation ==<br />
This archetype is built on two or more interconnected loops. Increasing the target value in one loop tends to increase the second loop, and possibly other loops. The loops have a negative feedback. Due to the relatively higher value of one loop the other loops values grows. In the system as a whole, the target values continuously increasing in a cycle. Growth of the values can only be stopped by external constraints such as economic constraint, time constraint or space constraint.<br />
<br />
[[File:Archetype-escalation.jpg|thumb|centre|upright=0.5|Escalation <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A typical example is the arms race. One country considers that it is threatened by another country. The first country raises its arms capacity and it forces the other country to also expand its weapons facilities. This again creates concerns among the first earth and once again raises his arms capacity, and the whole cycle repeats. Stop the escalation of the situation usually only lack of resources on one or both sides.<br />
<br />
== Fixes That Fail ==<br />
Archetype is a combination of stabilizing and reinforcing loops. Stabilizing loop trying to reduce the gap between current and desired state. The difference between these states is solved in a way which does not eliminate the problems. This solution only delays the inevitable result and situation as a whole is even worse. The more often undesirable states are solved by an intervention which does not remove the problem, the faster the actual solution gets more difficult.<br />
<br />
[[File:Archetype-fixes-that-fail.jpg|thumb|centre|upright=0.5|Fixes That Fail <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example is the so-called debt trap. Situation where one loan is repaid by another. The first loan is repaid by another loan but under worse conditions, which making the whole situation worse. The situation is temporarily solved, but in the long term, the overall situation deteriorates and more loans always brings a deterioration of the situation and the only apparent solution of the problem.<br />
<br />
== Shifting the Burden ==<br />
Archetype captures the situation when dealing with a problem is solved only by temporarily solution. This short-term also affects the fundamental solution. Attention is given only to short-term solution or a solution which solves only side effects.<br />
<br />
[[File:Archetype-shifting-burden.jpg|thumb|centre|upright=0.5|Shifting the Burden <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example may be the treatment of serious illness. The patient's doctor prescribes for example medication which only relieve pain caused by serious illness. Patient for a short time period feels better, but subsequently his condition gets. Disease gets to an advanced stage and it is significantly more difficult to cure it than at the beginning.<br />
<br />
== Addiction ==<br />
It is based on the archetype Fixes That Fail and it appear where short-term solutions are applied. Short-term solutions gradually lose their effect.<br />
<br />
[[File:Archetype-addiction.jpg|thumb|centre|upright=0.5|Addiction <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example is drug addiction. Drug addict person after a long abstinence feels unwell and he trying to improve his condition by taking another dose of drugs. Condition temporarily improved, but on the other hand, the addiction is than stronger. Drug addict person needs another dose but this dose must be stronger to trigger a similar denial of the original problem of addiction.<br />
<br />
== Reinforcing Loop ==<br />
Reinforcing Loop is the second of two basic archetypes. It serves as a building element, which appears in the other system archetypes. Reinforcing Loop describes a situation where there is a persistent increase or decrease. This loop always leads to a unidirectional development. It is basically the exact opposite of the Balancing Loop.<br />
<br />
[[File:Archetype-reinforcing.jpg|thumb|centre|upright=0.5|Reinforcing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a savings account, which is initially deposited by an amount. The account offers a constant interest rate. After each crediting of interest, the originally deposit increases. In the next period the interest is calculated based on higher amount of money. This situation, however, abstracts from the inflation.<br />
<br />
== Limits to Growth ==<br />
This archetype is a combination of two basic loops - Balancing Loop and Reinforcing Loop. Reinforcing Loop represents steady growth, which is limited by Balancing Loop. Because of Balancing Loop system cannot grow infinitely and always has its limitations.<br />
<br />
[[File:Archetype-limits-to-growth.jpg|thumb|centre|upright=0.5|Limits to Growth <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example might be global population growth. The population is bigger, it grows faster, but the faster it grows, the more resources it needs for its development. Demands on resources disproportionately increases in comparison with available resources on the planet. Growth is limited and cannot be infinite.<br />
<br />
== Accidental Adversaries ==<br />
This archetype is composed of two Reinforcing Loops and around them is the Balancing Loop. Archetype describes the situation where two or more parties try to work, but also trying to increase their own benefit. Efforts to increase their own benefit leads to a reduction in the benefit of the other party and thus of cooperating parties become party rival.<br />
<br />
[[File:Archetype-accidental-adversaries.jpg|thumb|centre|upright=0.5|Accidental Adversaries <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example might be a company represented by its owners and top managers. Owners and managers have a common interest, which is the prosperity of the company. Even though they have a common interest, the interest of each party can be dispersed by other factors. Managers, for example, want to gain bonuses for their performance so they try to artificially inflate the growth and performance of the company. Owners may actually want to realize their short-term gains and thus can choose to pay disproportionately large dividends. This behaviour lead to the fact that the teammates become opponents and their behaviour does not lead to a single common goal.<br />
<br />
== Success to the Successful ==<br />
This model consists of the two Reinforcing Loops which has mutually opposite tendency. One loop is growing and the other declining. The more one loop grows, the more the second loop decreases and vice versa.<br />
<br />
[[File:Archetype-success-to-the-successful.jpg|thumb|centre|upright=0.5|Success to the Successful <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example is Self-fulfilling prophecy. Imagine two people, a confident, successful, another vice versa lacking confidence and success. These two people are constantly reinforcing that there are those who they think they are, and the gulf between them constantly increase. First self-confidence person is going better and better which gives him the courage to push further, the second person on the contrary, confirms that he fails and his self-esteem drops.<br />
<br />
== Tragedy of the Commons ==<br />
Tragedy of the Commons is a situation where two or more parties fighting for a common and limited resource. The fewer the resources left, the more they try to get the share.<br />
<br />
[[File:Archetype-tragedy-of-the-commons.jpg|thumb|centre|upright=0.5|Tragedy of the Commons <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be global fight between various states and corporations about limited oil resources - oilfields. The less new oilfields appear and the faster the current are depleted, the more resources and effort is devoted to extraction. Due to limited resources and shrinking the whole situation escalates further, the situation is tense and there is more conflict and these are more serious than before.<br />
<br />
== Attractiveness Principle ==<br />
Archetype is derived from the Limits to Growth archetype. It is extended by the fact that it is listed more than one threshold. Whereby the limits may not be as big and does not have to limit the same parameter. The basis in this model are the Reinforcing Loops, which is identical with that appearing in the Limits to Growth archetype. Loop strengthens and accelerates time to grow. In the model, there are also Stabilizing Loops that will bring the current state of the coasts. Balancing act with a delay, thus it is possible to establish a short-term imbalance that is gradually stabilized.<br />
<br />
[[File:Archetype-attractiveness-principle.jpg|thumb|centre|upright=0.5|Attractiveness Principle <ref name="sherrer"></ref>]]<br />
<br />
== Growth and Underinvestment ==<br />
Archetype based on the Limits to Growth archetype. The difference here is that the limiting factor or factors dynamically develops along with the development of the entire system. Limitations are therefore not constant, but varies in time. The model shows the system which not enough to invests to itself and create its own limitations in future growth. Archetype is comprised of three loops, two Balancing Loops and one Reinforcing Loop.<br />
<br />
[[File:Archetype-growth-and-underinvestment.jpg|thumb|centre|upright=0.5|Growth and Underinvestment <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example from the life may be preparation of high school student for study at university. If the student does not pay sufficient attention to current high school studies, he can thus create restrictions of his future potential. For example, he will not be accepted to university which he wants to or will be dismissed because does not manage to keep pace with other students. Neglected the study of lower grade may bring future constraints in a higher degree.<br />
<br />
== Growth and Underinvestment with Drifting Standard ==<br />
Archetype based on previous archetype, which is enriched with fourth loop. The fourth loop represents a certain standard, which change over time and reduces the need for future changes. Overall, this tendency leads to the overall growth of the system decreases.<br />
<br />
[[File:Archetype-growth-and-underinvestment-with-drifting-standard.jpg|thumb|centre|upright=0.5|Growth and Underinvestment with Drifting Standard <ref name="sherrer"></ref>]]<br />
<br />
== Quiz ==<br />
Try to match each real example with correct system archetype. Quiz correct answers see below.<br />
<br />
'''Real examples:'''<br />
#Dam (with a constant water level)<br />
#Arms race<br />
#Word of mouth<br />
#Humanitarian assistance (sending food)<br />
#Burning rain forests for palm trees (palm oil)<br />
<br />
'''System archetypes:'''<br />
<ol type="a"><br />
<li>Shifting the Burden</li><br />
<li>Reinforcing Loop</li><br />
<li>Balancing Loop</li><br />
<li>Tragedy of Common</li><br />
<li>Escalation</li><br />
</ol><br />
<br />
== See Also ==<br />
*[[System Dynamics]]<br />
*[https://en.wikipedia.org/wiki/The_Fifth_Discipline The Fifth Discipline]<br />
== References ==<br />
<references/><br />
*Šalamon, Tomáš. (2011). [http://www.designofagentbasedmodels.info ''Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes'']. Řepín, Czech Republic: Bruckner Publishing<br />
*Taborga, Jorge. Systems Archetypes and Their Application.2016 [seen 23. 1. 2016]. Available at https://www.saybrook.edu/rethinkingcomplexity/posts/08-15-11/systems-archetypes-and-their-application<br />
*Continuous Improvement Associates. Systems Thinking Archetypes (Generic Structures). 2003 [seen 22. 1. 2016]. Available at http://www.exponentialimprovement.com/cms/uploads/ArchetypesGeneric02.pdf<br />
*Bellinger, Gene. Archetypes: Interaction Structures of the Universe. 2004 [seen 20. 1. 2016]. Available at http://www.systems-thinking.org/arch/arch.htm#archdg<br />
<br />
== External Links ==<br />
*[https://insightmaker.com/tag/Systems-Archetypes Insight Maker] - A free dynamic modelling and simulation web application.<br />
<br />
== Quiz Correct Answers ==<br />
There are correct answers from quiz hereinabove.<br />
<br />
'''Correct Answers:'''<br />
#c.<br />
#e.<br />
#b.<br />
#a.<br />
#d.<br />
<br />
<br />
[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 11:30, 24 January 2016 (CET)</div>Xkrep33http://www.simulace.info/index.php?title=System_Archetypes&diff=10810System Archetypes2016-01-24T10:20:50Z<p>Xkrep33: </p>
<hr />
<div>== Introduction ==<br />
'''System archetypes''' are the '''patterns''' of behaviour that we can find in complex '''systems'''. Patterns help us to get an idea of how a particular system works. Even though each system is different, and at first look it may seem unique, it is possible to find some similarities - repetitive structures and principles of how systems work. System archetypes help to find and identify these similarities. If we know the principle how the system works, we can better understand it and explore its weaknesses.<br />
<br />
System archetypes, that are part of '''systems thinking''' and [[System Dynamics]], is used to detect patterns of behaviour of '''social systems''' (businesses, communities, states, economies, etc.). They can be used in two areas, namely as a tool to determines the current state - '''a diagnostic tool''' or as a tool to look at the future development of the system - '''a prospective tool'''. As a diagnostic tool helps managers to obtain the perspective of the internal structure of the system, its functioning and help them to get a better idea of the current system state. As a prospective tool it is mainly used for planning. Managers can formulate their future goals and with the knowledge of the functioning of their organization, they can better determine the procedure by which this goal can be reached <ref name="braun">Braun, William. The System Archetypes [online]. 27. 2. 2002 [seen 21. 1. 2016]. Available at http://www.albany.edu/faculty/gpr/PAD724/724WebArticles/sys_archetypes.pdf</ref>.<br />
<br />
== Definition ==<br />
=== System ===<br />
The system consists of a '''set of elements''' and '''relationships''' between them. The individual elements and connections between them together form a larger value than the individual components alone. Each system can have inputs and outputs which form the interface between the system itself and its surroundings <ref name="palan">Palán, Zdeněk. Systém [online].[seen 21. 1. 2016]. Available at http://www.andromedia.cz/andragogicky-slovnik/system</ref>.<br />
<br />
=== Archetype ===<br />
Also called prototype represents a typical example, ideal type or perfect example. Archetype can also be a symbol or a recurrent motif occurring for example in literature <ref name="slovnik">Slovník cizích slov ABZ. Pojem archetyp [online]. 2016 [seen 22. 1. 2016]. Available at http://slovnik-cizich-slov.abz.cz/web.php/slovo/archetyp</ref>.<br />
<br />
== Overview of System Archetypes ==<br />
[[File:Family_Tree.gif|thumb|Family Tree <ref name="isee">isee systems. Archetype Family Tree [online]. 2006 [seen 20. 1. 2016]. Available at http://www.iseesystems.com/Online_training/course/module6/6-03-0-0-tree.htm</ref>]]<br />
There are '''16 unique system archetypes''' in total. These 16 archetypes differ from each other and describe different kind of systems, but all have a common foundation. There are '''two basic archetypes''', from which the remaining 14 archetypes is derived. Respectively, there are two archetypes, which can be combined to form the remaining 14 archetypes. Two basic archetypes are called Balancing Loop and Reinforcing Loop. Archetypes can be also divided into two groups the first is '''Fixing a Problem group''' and second one is '''Influencing Change group''' <ref name="sherrer">Sherrer, J. Alex. A Project Manager's Guide to Systems Thinking: Part 2 [online]. Project Smart. 24. 7. 2010 [seen 21. 1. 2016]. Available at https://www.projectsmart.co.uk/project-managers-guide-to-systems-thinking-part-2.php</ref>.<br />
<br />
=== Fixing a Problem ===<br />
*Balancing Loop<br />
*Balancing Structure with Delay<br />
*Indecision<br />
*Drifting Goals<br />
*Escalation<br />
*Fixes That Fail<br />
*Shifting the Burden<br />
*Addiction<br />
<br />
=== Influencing Change ===<br />
*Reinforcing Loop<br />
*Limits to Growth<br />
*Accidental Adversaries<br />
*Success to the Successful<br />
*Tragedy of the Commons<br />
*Attractiveness Principle<br />
*Growth and Underinvestment<br />
*Growth and Underinvestment with Drifting Standard<br />
<br />
== System Archetype Family Tree ==<br />
To recognize the fact that the archetype is suitable for such a situation can be helpful the Family tree of system archetypes. Family tree is an ordered tree that based on simple questions is capable of directing the user to select the correct system archetype for concrete application.<br />
<br />
== Balancing Loop ==<br />
Balancing Loop contains a negative feedback, which is helping the current state to be closer to the target state. Loop is stabilizing due to the negative feedback. When current state is deflected from the target state it helps to return to a desired state. The greater the difference between the target state and current state cause the faster convergence to final state.<br />
<br />
[[File:Archetype-balancing-structure.jpg|thumb|centre|upright=0.5|Balancing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be filling of an empty glass of water. Initially, the glass is empty, and thus the difference between the actual water level (glass is empty) and the required water level (glass is full) is the maximum. Glass is filled with water and a difference of the current water level and the required water level continues to decrease. At the beginning the glass is filled quickly, and it is obvious that much water is missing, then the difference which lowers the intensity of the water flow. At the moment of achieving the required water level there is no longer any motivation to continue with glass filling. Desired state becomes state achieved –the process is terminated.<br />
<br />
== Balancing Loop with Delay ==<br />
This loop is based on the previous one, except there is a delay, which plays a role in overall system responsiveness. The combination of the negative feedback and the delay in the system creates oscillations. The system is trying to achieve the target state, but because of delay, the system detects the information later. The system thus exceeds the target state.<br />
<br />
[[File:Archetype-balancing-structure-with-delay.jpg|thumb|centre|upright=0.5|Balancing Loop with Delay <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a room heated by a heater equipped with a thermostat. The thermostat switches on the heating when the room temperature is lower than desired. Heating begins to heat up and the thermostat switches off the heating when the room is heated up to the desired temperature. Heating is switched off, but for some time after shutdown is still hot. The room air is heated to higher temperature than the desired one, and this principle is repeated over and over again. Room temperature oscillates around a desired temperature, and it is never stabilized on the desired temperature level.<br />
<br />
== Indecision ==<br />
Main character is an oscillation which is created by combining of two balancing loops with delays. One loop reaches the desired state, and the target state of the second loop moves and that in turn influences the first loop. Both loops never reach their goals together and constantly oscillate around their target states.<br />
<br />
[[File:Archetype-indecision.jpg|thumb|centre|upright=0.5|Indecision <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
Examples may be currency market. Currency prices on the stock exchange is determined by supply and demand. When the offer price is lower than the price that buyers are willing to pay, then the purchasers buy larger amount of the currency. The seller realizes that the price is lower than it might be, and thus they increase the price, it will discourage potential buyers and the price will again gradually decrease below what buyers are willing to pay. The whole process is continually repeated and price oscillates around an equilibrium market price.<br />
<br />
== Drifting Goals ==<br />
It is relatively simple archetype, in which two balancing loops are connected, but in this case there are no delays. Loops stand in opposite of each other and the effort made by one loop cause impossibility to balance the second loop and vice versa.<br />
<br />
[[File:Archetype-drifting-goals.jpg|thumb|centre|upright=0.5|Drifting Goals <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a company that offers trips to the sea. The company decided to offer an additional service to its customers. Due to additional service costs of tours increase and the final price of trips will be higher than before. Next season customers begin to migrate to the competition, and the company decides to reconsider its initial plans and choose a compromise solution. The original goal has not been met and there has been erosion of targets.<br />
<br />
== Escalation ==<br />
This archetype is built on two or more interconnected loops. Increasing the target value in one loop tends to increase the second loop, and possibly other loops. The loops have a negative feedback. Due to the relatively higher value of one loop the other loops values grows. In the system as a whole, the target values continuously increasing in a cycle. Growth of the values can only be stopped by external constraints such as economic constraint, time constraint or space constraint.<br />
<br />
[[File:Archetype-escalation.jpg|thumb|centre|upright=0.5|Escalation <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A typical example is the arms race. One country considers that it is threatened by another country. The first country raises its arms capacity and it forces the other country to also expand its weapons facilities. This again creates concerns among the first earth and once again raises his arms capacity, and the whole cycle repeats. Stop the escalation of the situation usually only lack of resources on one or both sides.<br />
<br />
== Fixes That Fail ==<br />
Archetype is a combination of stabilizing and reinforcing loops. Stabilizing loop trying to reduce the gap between current and desired state. The difference between these states is solved in a way which does not eliminate the problems. This solution only delays the inevitable result and situation as a whole is even worse. The more often undesirable states are solved by an intervention which does not remove the problem, the faster the actual solution gets more difficult.<br />
<br />
[[File:Archetype-fixes-that-fail.jpg|thumb|centre|upright=0.5|Fixes That Fail <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example is the so-called debt trap. Situation where one loan is repaid by another. The first loan is repaid by another loan but under worse conditions, which making the whole situation worse. The situation is temporarily solved, but in the long term, the overall situation deteriorates and more loans always brings a deterioration of the situation and the only apparent solution of the problem.<br />
<br />
== Shifting the Burden ==<br />
Archetype captures the situation when dealing with a problem is solved only by temporarily solution. This short-term also affects the fundamental solution. Attention is given only to short-term solution or a solution which solves only side effects.<br />
<br />
[[File:Archetype-shifting-burden.jpg|thumb|centre|upright=0.5|Shifting the Burden <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example may be the treatment of serious illness. The patient's doctor prescribes for example medication which only relieve pain caused by serious illness. Patient for a short time period feels better, but subsequently his condition gets. Disease gets to an advanced stage and it is significantly more difficult to cure it than at the beginning.<br />
<br />
== Addiction ==<br />
It is based on the archetype Fixes That Fail and it appear where short-term solutions are applied. Short-term solutions gradually lose their effect.<br />
<br />
[[File:Archetype-addiction.jpg|thumb|centre|upright=0.5|Addiction <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example is drug addiction. Drug addict person after a long abstinence feels unwell and he trying to improve his condition by taking another dose of drugs. Condition temporarily improved, but on the other hand, the addiction is than stronger. Drug addict person needs another dose but this dose must be stronger to trigger a similar denial of the original problem of addiction.<br />
<br />
== Reinforcing Loop ==<br />
Reinforcing Loop is the second of two basic archetypes. It serves as a building element, which appears in the other system archetypes. Reinforcing Loop describes a situation where there is a persistent increase or decrease. This loop always leads to a unidirectional development. It is basically the exact opposite of the Balancing Loop.<br />
<br />
[[File:Archetype-reinforcing.jpg|thumb|centre|upright=0.5|Reinforcing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a savings account, which is initially deposited by an amount. The account offers a constant interest rate. After each crediting of interest, the originally deposit increases. In the next period the interest is calculated based on higher amount of money. This situation, however, abstracts from the inflation.<br />
<br />
== Limits to Growth ==<br />
This archetype is a combination of two basic loops - Balancing Loop and Reinforcing Loop. Reinforcing Loop represents steady growth, which is limited by Balancing Loop. Because of Balancing Loop system cannot grow infinitely and always has its limitations.<br />
<br />
[[File:Archetype-limits-to-growth.jpg|thumb|centre|upright=0.5|Limits to Growth <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example might be global population growth. The population is bigger, it grows faster, but the faster it grows, the more resources it needs for its development. Demands on resources disproportionately increases in comparison with available resources on the planet. Growth is limited and cannot be infinite.<br />
<br />
== Accidental Adversaries ==<br />
This archetype is composed of two Reinforcing Loops and around them is the Balancing Loop. Archetype describes the situation where two or more parties try to work, but also trying to increase their own benefit. Efforts to increase their own benefit leads to a reduction in the benefit of the other party and thus of cooperating parties become party rival.<br />
<br />
[[File:Archetype-accidental-adversaries.jpg|thumb|centre|upright=0.5|Accidental Adversaries <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example might be a company represented by its owners and top managers. Owners and managers have a common interest, which is the prosperity of the company. Even though they have a common interest, the interest of each party can be dispersed by other factors. Managers, for example, want to gain bonuses for their performance so they try to artificially inflate the growth and performance of the company. Owners may actually want to realize their short-term gains and thus can choose to pay disproportionately large dividends. This behaviour lead to the fact that the teammates become opponents and their behaviour does not lead to a single common goal.<br />
<br />
== Success to the Successful ==<br />
This model consists of the two Reinforcing Loops which has mutually opposite tendency. One loop is growing and the other declining. The more one loop grows, the more the second loop decreases and vice versa.<br />
<br />
[[File:Archetype-success-to-the-successful.jpg|thumb|centre|upright=0.5|Success to the Successful <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example is Self-fulfilling prophecy. Imagine two people, a confident, successful, another vice versa lacking confidence and success. These two people are constantly reinforcing that there are those who they think they are, and the gulf between them constantly increase. First self-confidence person is going better and better which gives him the courage to push further, the second person on the contrary, confirms that he fails and his self-esteem drops.<br />
<br />
== Tragedy of the Commons ==<br />
Tragedy of the Commons is a situation where two or more parties fighting for a common and limited resource. The fewer the resources left, the more they try to get the share.<br />
<br />
[[File:Archetype-tragedy-of-the-commons.jpg|thumb|centre|upright=0.5|Tragedy of the Commons <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be global fight between various states and corporations about limited oil resources - oilfields. The less new oilfields appear and the faster the current are depleted, the more resources and effort is devoted to extraction. Due to limited resources and shrinking the whole situation escalates further, the situation is tense and there is more conflict and these are more serious than before.<br />
<br />
== Attractiveness Principle ==<br />
Archetype is derived from the Limits to Growth archetype. It is extended by the fact that it is listed more than one threshold. Whereby the limits may not be as big and does not have to limit the same parameter. The basis in this model are the Reinforcing Loops, which is identical with that appearing in the Limits to Growth archetype. Loop strengthens and accelerates time to grow. In the model, there are also Stabilizing Loops that will bring the current state of the coasts. Balancing act with a delay, thus it is possible to establish a short-term imbalance that is gradually stabilized.<br />
<br />
[[File:Archetype-attractiveness-principle.jpg|thumb|centre|upright=0.5|Attractiveness Principle <ref name="sherrer"></ref>]]<br />
<br />
== Growth and Underinvestment ==<br />
Archetype based on the Limits to Growth archetype. The difference here is that the limiting factor or factors dynamically develops along with the development of the entire system. Limitations are therefore not constant, but varies in time. The model shows the system which not enough to invests to itself and create its own limitations in future growth. Archetype is comprised of three loops, two Balancing Loops and one Reinforcing Loop.<br />
<br />
[[File:Archetype-growth-and-underinvestment.jpg|thumb|centre|upright=0.5|Growth and Underinvestment <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example from the life may be preparation of high school student for study at university. If the student does not pay sufficient attention to current high school studies, he can thus create restrictions of his future potential. For example, he will not be accepted to university which he wants to or will be dismissed because does not manage to keep pace with other students. Neglected the study of lower grade may bring future constraints in a higher degree.<br />
<br />
== Growth and Underinvestment with Drifting Standard ==<br />
Archetype based on previous archetype, which is enriched with fourth loop. The fourth loop represents a certain standard, which change over time and reduces the need for future changes. Overall, this tendency leads to the overall growth of the system decreases.<br />
<br />
[[File:Archetype-growth-and-underinvestment-with-drifting-standard.jpg|thumb|centre|upright=0.5|Growth and Underinvestment with Drifting Standard <ref name="sherrer"></ref>]]<br />
<br />
== Quiz ==<br />
Try to match each real example with correct system archetype. Quiz correct answers see below.<br />
<br />
'''Real examples:'''<br />
#Dam (with a constant water level)<br />
#Arms race<br />
#Word of mouth<br />
#Humanitarian assistance (sending food)<br />
#Burning rain forests for palm trees (palm oil)<br />
<br />
'''System archetypes:'''<br />
<ol type="a"><br />
<li>Shifting the Burden</li><br />
<li>Reinforcing Loop</li><br />
<li>Balancing Loop</li><br />
<li>Tragedy of Common</li><br />
<li>Escalation</li><br />
</ol><br />
<br />
== See Also ==<br />
*[[System Dynamics]]<br />
*[https://en.wikipedia.org/wiki/The_Fifth_Discipline The Fifth Discipline]<br />
== References ==<br />
<references/><br />
*Šalamon, Tomáš. (2011). [http://www.designofagentbasedmodels.info ''Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes'']. Řepín, Czech Republic: Bruckner Publishing<br />
*Taborga, Jorge. Systems Archetypes and Their Application.2016 [seen 23. 1. 2016]. Available at https://www.saybrook.edu/rethinkingcomplexity/posts/08-15-11/systems-archetypes-and-their-application<br />
*Continuous Improvement Associates. Systems Thinking Archetypes (Generic Structures). 2003 [seen 22. 1. 2016]. Available at http://www.exponentialimprovement.com/cms/uploads/ArchetypesGeneric02.pdf<br />
*Bellinger, Gene. Archetypes: Interaction Structures of the Universe. 2004 [seen 20. 1. 2016]. Available at http://www.systems-thinking.org/arch/arch.htm#archdg<br />
<br />
== External Links ==<br />
*[https://insightmaker.com/tag/Systems-Archetypes Insight Maker] - A free dynamic modelling and simulation web application.<br />
<br />
== Quiz Correct Answers ==<br />
There are correct answers from quiz hereinabove.<br />
<br />
'''Correct Answers:'''<br />
#c.<br />
#e.<br />
#b.<br />
#a.<br />
#d.<br />
<br />
<br />
[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 11:20, 24 January 2016 (CET)</div>Xkrep33http://www.simulace.info/index.php?title=System_Archetypes&diff=10809System Archetypes2016-01-24T10:18:07Z<p>Xkrep33: </p>
<hr />
<div>== Introduction ==<br />
'''System archetypes''' are the '''patterns''' of behaviour that we can find in complex '''systems'''. Patterns help us to get an idea of how a particular system works. Even though each system is different, and at first look it may seem unique, it is possible to find some similarities - repetitive structures and principles of how systems work. System archetypes help to find and identify these similarities. If we know the principle how the system works, we can better understand it and explore its weaknesses.<br />
<br />
System archetypes, that are part of '''systems thinking''' and [[System Dynamics]], is used to detect patterns of behaviour of '''social systems''' (businesses, communities, states, economies, etc.). They can be used in two areas, namely as a tool to determines the current state - '''a diagnostic tool''' or as a tool to look at the future development of the system - '''a prospective tool'''. As a diagnostic tool helps managers to obtain the perspective of the internal structure of the system, its functioning and help them to get a better idea of the current system state. As a prospective tool it is mainly used for planning. Managers can formulate their future goals and with the knowledge of the functioning of their organization, they can better determine the procedure by which this goal can be reached <ref name="braun">Braun, William. The System Archetypes [online]. 27. 2. 2002 [seen 21. 1. 2016]. Available at http://www.albany.edu/faculty/gpr/PAD724/724WebArticles/sys_archetypes.pdf</ref>.<br />
<br />
== Definition ==<br />
=== System ===<br />
The system consists of a '''set of elements''' and '''relationships''' between them. The individual elements and connections between them together form a larger value than the individual components alone. Each system can have inputs and outputs which form the interface between the system itself and its surroundings <ref name="palan">Palán, Zdeněk. Systém [online].[seen 21. 1. 2016]. Available at http://www.andromedia.cz/andragogicky-slovnik/system</ref>.<br />
<br />
=== Archetype ===<br />
Also called prototype represents a typical example, ideal type or perfect example. Archetype can also be a symbol or a recurrent motif occurring for example in literature <ref name="slovnik">Slovník cizích slov ABZ. Pojem archetyp [online]. 2016 [seen 22. 1. 2016]. Available at http://slovnik-cizich-slov.abz.cz/web.php/slovo/archetyp</ref>.<br />
<br />
== Overview of System Archetypes ==<br />
[[File:Family_Tree.gif|thumb|Family Tree <ref name="isee">isee systems. Archetype Family Tree [online]. 2006 [seen 20. 1. 2016]. Available at http://www.iseesystems.com/Online_training/course/module6/6-03-0-0-tree.htm</ref>]]<br />
There are '''16 unique system archetypes''' in total. These 16 archetypes differ from each other and describe different kind of systems, but all have a common foundation. There are '''two basic archetypes''', from which the remaining 14 archetypes is derived. Respectively, there are two archetypes, which can be combined to form the remaining 14 archetypes. Two basic archetypes are called Balancing Loop and Reinforcing Loop. Archetypes can be also divided into two groups the first is '''Fixing a Problem group''' and second one is '''Influencing Change group''' <ref name="sherrer">Sherrer, J. Alex. A Project Manager's Guide to Systems Thinking: Part 2 [online]. Project Smart. 24. 7. 2010 [seen 21. 1. 2016]. Available at https://www.projectsmart.co.uk/project-managers-guide-to-systems-thinking-part-2.php</ref>.<br />
<br />
=== Fixing a Problem ===<br />
*Balancing Loop<br />
*Balancing Structure with Delay<br />
*Indecision<br />
*Drifting Goals<br />
*Escalation<br />
*Fixes That Fail<br />
*Shifting the Burden<br />
*Addiction<br />
<br />
=== Influencing Change ===<br />
*Reinforcing Loop<br />
*Limits to Growth<br />
*Accidental Adversaries<br />
*Success to the Successful<br />
*Tragedy of the Commons<br />
*Attractiveness Principle<br />
*Growth and Underinvestment<br />
*Growth and Underinvestment with Drifting Standard<br />
<br />
== System Archetype Family Tree ==<br />
To recognize the fact that the archetype is suitable for such a situation can be helpful the Family tree of system archetypes. Family tree is an ordered tree that based on simple questions is capable of directing the user to select the correct system archetype for concrete application.<br />
<br />
== Balancing Loop ==<br />
Balancing Loop contains a negative feedback, which is helping the current state to be closer to the target state. Loop is stabilizing due to the negative feedback. When current state is deflected from the target state it helps to return to a desired state. The greater the difference between the target state and current state cause the faster convergence to final state.<br />
<br />
[[File:Archetype-balancing-structure.jpg|thumb|centre|upright=0.5|Balancing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be filling of an empty glass of water. Initially, the glass is empty, and thus the difference between the actual water level (glass is empty) and the required water level (glass is full) is the maximum. Glass is filled with water and a difference of the current water level and the required water level continues to decrease. At the beginning the glass is filled quickly, and it is obvious that much water is missing, then the difference which lowers the intensity of the water flow. At the moment of achieving the required water level there is no longer any motivation to continue with glass filling. Desired state becomes state achieved –the process is terminated.<br />
<br />
== Balancing Loop with Delay ==<br />
This loop is based on the previous one, except there is a delay, which plays a role in overall system responsiveness. The combination of the negative feedback and the delay in the system creates oscillations. The system is trying to achieve the target state, but because of delay, the system detects the information later. The system thus exceeds the target state.<br />
<br />
[[File:Archetype-balancing-structure-with-delay.jpg|thumb|centre|upright=0.5|Balancing Loop with Delay <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a room heated by a heater equipped with a thermostat. The thermostat switches on the heating when the room temperature is lower than desired. Heating begins to heat up and the thermostat switches off the heating when the room is heated up to the desired temperature. Heating is switched off, but for some time after shutdown is still hot. The room air is heated to higher temperature than the desired one, and this principle is repeated over and over again. Room temperature oscillates around a desired temperature, and it is never stabilized on the desired temperature level.<br />
<br />
== Indecision ==<br />
Main character is an oscillation which is created by combining of two balancing loops with delays. One loop reaches the desired state, and the target state of the second loop moves and that in turn influences the first loop. Both loops never reach their goals together and constantly oscillate around their target states.<br />
<br />
[[File:Archetype-indecision.jpg|thumb|centre|upright=0.5|Indecision <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
Examples may be currency market. Currency prices on the stock exchange is determined by supply and demand. When the offer price is lower than the price that buyers are willing to pay, then the purchasers buy larger amount of the currency. The seller realizes that the price is lower than it might be, and thus they increase the price, it will discourages potential buyers and the price will again gradually decrease below what buyers are willing to pay. The whole process is continually repeated and price oscillates around an equilibrium market price.<br />
<br />
== Drifting Goals ==<br />
It is relatively simple archetype, in which two balancing loops are connected, but in this case there are no delays. Loops stand in opposite of each other and the effort made by one loop cause impossibility to balance the second loop and vice versa.<br />
<br />
[[File:Archetype-drifting-goals.jpg|thumb|centre|upright=0.5|Drifting Goals <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a company that offers trips to the sea. The company decided to offer an additional service to its customers. Due to additional service costs of tours increase and the final price of trips will be higher than before. Next season customers begin to migrate to the competition, and the company decides to reconsider its initial plans and choose a compromise solution. The original goal has not been met and there has been erosion of targets.<br />
<br />
== Escalation ==<br />
This archetype is built on two or more interconnected loops. Increasing the target value in one loop tends to increase the second loop, and possibly other loops. The loops have a negative feedback. Due to the relatively higher value of one loop the other loops values grows. In the system as a whole, the target values continuously increasing in a cycle. Growth of the values can only be stopped by external constraints such as economic constraint, time constraint or space constraint.<br />
<br />
[[File:Archetype-escalation.jpg|thumb|centre|upright=0.5|Escalation <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A typical example is the arms race. One country considers that it is threatened by another country. The first country raises its arms capacity and it forces the other country to also expand its weapons facilities. This again creates concerns among the first earth and once again raises his arms capacity, and the whole cycle repeats. Stop the escalation of the situation usually only lack of resources on one or both sides.<br />
<br />
== Fixes That Fail ==<br />
Archetype is a combination of stabilizing and reinforcing loops. Stabilizing loop trying to reduce the gap between current and desired state. The difference between these states is solved in a way which does not eliminate the problems. This solution only delays the inevitable result and situation as a whole is even worse. The more often undesirable states are solved by an intervention which does not remove the problem, the faster the actual solution gets more difficult.<br />
<br />
[[File:Archetype-fixes-that-fail.jpg|thumb|centre|upright=0.5|Fixes That Fail <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example is the so-called debt trap. Situation where one loan is repaid by another. The first loan is repaid by another loan but under worse conditions, which making the whole situation worse. The situation is temporarily solved, but in the long term, the overall situation deteriorates and more loans always brings a deterioration of the situation and the only apparent solution of the problem.<br />
<br />
== Shifting the Burden ==<br />
Archetype captures the situation when dealing with a problem is solved only by temporarily solution. This short-term also affects the fundamental solution. Attention is given only to short-term solution or a solution which solves only side effects.<br />
<br />
[[File:Archetype-shifting-burden.jpg|thumb|centre|upright=0.5|Shifting the Burden <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example may be the treatment of serious illness. The patient's doctor prescribes for example medication which only relieve pain caused by serious illness. Patient for a short time period feels better, but subsequently his condition gets. Disease gets to an advanced stage and it is significantly more difficult to cure it than at the beginning.<br />
<br />
== Addiction ==<br />
It is based on the archetype Fixes That Fail and it appear where short-term solutions are applied. Short-term solutions gradually lose their effect.<br />
<br />
[[File:Archetype-addiction.jpg|thumb|centre|upright=0.5|Addiction <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example is drug addiction. Drug addict person after a long abstinence feels unwell and he trying to improve his condition by taking another dose of drugs. Condition temporarily improved, but on the other hand, the addiction is than stronger. Drug addict person needs another dose but this dose must be stronger to trigger a similar denial of the original problem of addiction.<br />
<br />
== Reinforcing Loop ==<br />
Reinforcing Loop is the second of two basic archetypes. It serves as a building element, which appears in the other system archetypes. Reinforcing Loop describes a situation where there is a persistent increase or decrease. This loop always leads to a unidirectional development. It is basically the exact opposite of the Balancing Loop.<br />
<br />
[[File:Archetype-reinforcing.jpg|thumb|centre|upright=0.5|Reinforcing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a savings account, which is initially deposited by an amount. The account offers a constant interest rate. After each crediting of interest, the originally deposit increases. In the next period the interest is calculated based on higher amount of money. This situation, however, abstracts from the inflation.<br />
<br />
== Limits to Growth ==<br />
This archetype is a combination of two basic loops - Balancing Loop and Reinforcing Loop. Reinforcing Loop represents steady growth, which is limited by Balancing Loop. Because of Balancing Loop system cannot grow infinitely and always has its limitations.<br />
<br />
[[File:Archetype-limits-to-growth.jpg|thumb|centre|upright=0.5|Limits to Growth <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example might be global population growth. The population is bigger, it grows faster, but the faster it grows, the more resources it needs for its development. Demands on resources disproportionately increases in comparison with available resources on the planet. Growth is limited and cannot be infinite.<br />
<br />
== Accidental Adversaries ==<br />
This archetype is composed of two Reinforcing Loops and around them is the Balancing Loop. Archetype describes the situation where two or more parties try to work, but also trying to increase their own benefit. Efforts to increase their own benefit leads to a reduction in the benefit of the other party and thus of cooperating parties become party rival.<br />
<br />
[[File:Archetype-accidental-adversaries.jpg|thumb|centre|upright=0.5|Accidental Adversaries <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example might be a company represented by its owners and top managers. Owners and managers have a common interest, which is the prosperity of the company. Even though they have a common interest, the interest of each party can be dispersed by other factors. Managers, for example, want to gain bonuses for their performance so they try to artificially inflate the growth and performance of the company. Owners may actually want to realize their short-term gains and thus can choose to pay disproportionately large dividends. This behaviour lead to the fact that the teammates become opponents and their behaviour does not lead to a single common goal.<br />
<br />
== Success to the Successful ==<br />
This model consists of the two Reinforcing Loops which has mutually opposite tendency. One loop is growing and the other declining. The more one loop grows, the more the second loop decreases and vice versa.<br />
<br />
[[File:Archetype-success-to-the-successful.jpg|thumb|centre|upright=0.5|Success to the Successful <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example is Self-fulfilling prophecy. Imagine two people, a confident, successful, another vice versa lacking confidence and success. These two people are constantly reinforcing that there are those who they think they are, and the gulf between them constantly increase. First self-confidence person is going better and better which gives him the courage to push further, the second person on the contrary, confirms that he fails and his self-esteem drops.<br />
<br />
== Tragedy of the Commons ==<br />
Tragedy of the Commons is a situation where two or more parties fighting for a common and limited resource. The fewer the resources left, the more they try to get the share.<br />
<br />
[[File:Archetype-tragedy-of-the-commons.jpg|thumb|centre|upright=0.5|Tragedy of the Commons <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be global fight between various states and corporations about limited oil resources - oilfields. The less new oilfields appear and the faster the current are depleted, the more resources and effort is devoted to extraction. Due to limited resources and shrinking the whole situation escalates further, the situation is tense and there is more conflict and these are more serious than before.<br />
<br />
== Attractiveness Principle ==<br />
Archetype is derived from the Limits to Growth archetype. It is extended by the fact that it is listed more than one threshold. Whereby the limits may not be as big and does not have to limit the same parameter. The basis in this model are the Reinforcing Loops, which is identical with that appearing in the Limits to Growth archetype. Loop strengthens and accelerates time to grow. In the model, there are also Stabilizing Loops that will bring the current state of the coasts. Balancing act with a delay, thus it is possible to establish a short-term imbalance that is gradually stabilized.<br />
<br />
[[File:Archetype-attractiveness-principle.jpg|thumb|centre|upright=0.5|Attractiveness Principle <ref name="sherrer"></ref>]]<br />
<br />
== Growth and Underinvestment ==<br />
Archetype based on the Limits to Growth archetype. The difference here is that the limiting factor or factors dynamically develops along with the development of the entire system. Limitations are therefore not constant, but varies in time. The model shows the system which not enough to invests to itself and create its own limitations in future growth. Archetype is comprised of three loops, two Balancing Loops and one Reinforcing Loop.<br />
<br />
[[File:Archetype-growth-and-underinvestment.jpg|thumb|centre|upright=0.5|Growth and Underinvestment <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example from the life may be preparation of high school student for study at university. If the student does not pay sufficient attention to current high school studies, he can thus create restrictions of his future potential. For example, he will not be accepted to university which he wants to or will be dismissed because does not manage to keep pace with other students. Neglected the study of lower grade may bring future constraints in a higher degree.<br />
<br />
== Growth and Underinvestment with Drifting Standard ==<br />
Archetype based on previous archetype, which is enriched with fourth loop. The fourth loop represents a certain standard, which change over time and reduces the need for future changes. Overall, this tendency leads to the overall growth of the system decreases.<br />
<br />
[[File:Archetype-growth-and-underinvestment-with-drifting-standard.jpg|thumb|centre|upright=0.5|Growth and Underinvestment with Drifting Standard <ref name="sherrer"></ref>]]<br />
<br />
== Quiz ==<br />
Try to match each real example with correct system archetype. Quiz correct answers see below.<br />
<br />
'''Real examples:'''<br />
#Dam (with a constant water level)<br />
#Arms race<br />
#Word of mouth<br />
#Humanitarian assistance (sending food)<br />
#Burning rain forests for palm trees (palm oil)<br />
<br />
'''System archetypes:'''<br />
<ol type="a"><br />
<li>Shifting the Burden</li><br />
<li>Reinforcing Loop</li><br />
<li>Balancing Loop</li><br />
<li>Tragedy of Common</li><br />
<li>Escalation</li><br />
</ol><br />
<br />
== See Also ==<br />
*[[System Dynamics]]<br />
*[https://en.wikipedia.org/wiki/The_Fifth_Discipline The Fifth Discipline]<br />
== References ==<br />
<references/><br />
*Šalamon, Tomáš. (2011). [http://www.designofagentbasedmodels.info ''Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes'']. Řepín, Czech Republic: Bruckner Publishing<br />
*Taborga, Jorge. Systems Archetypes and Their Application.2016 [seen 23. 1. 2016]. Available at https://www.saybrook.edu/rethinkingcomplexity/posts/08-15-11/systems-archetypes-and-their-application<br />
*Continuous Improvement Associates. Systems Thinking Archetypes (Generic Structures). 2003 [seen 22. 1. 2016]. Available at http://www.exponentialimprovement.com/cms/uploads/ArchetypesGeneric02.pdf<br />
*Bellinger, Gene. Archetypes: Interaction Structures of the Universe. 2004 [seen 20. 1. 2016]. Available at http://www.systems-thinking.org/arch/arch.htm#archdg<br />
<br />
== External Links ==<br />
*[https://insightmaker.com/tag/Systems-Archetypes Insight Maker] - A free dynamic modeling and simulation web application.<br />
<br />
== Quiz Correct Answers ==<br />
There are correct answers from quiz hereinabove.<br />
<br />
'''Correct Answers:'''<br />
#c.<br />
#e.<br />
#b.<br />
#a.<br />
#d.<br />
<br />
<br />
[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 11:18, 24 January 2016 (CET)</div>Xkrep33http://www.simulace.info/index.php?title=System_Archetypes&diff=10808System Archetypes2016-01-24T10:17:45Z<p>Xkrep33: </p>
<hr />
<div>== Introduction ==<br />
'''System archetypes''' are the '''patterns''' of behaviour that we can find in complex '''systems'''. Patterns help us to get an idea of how a particular system works. Even though each system is different, and at first look it may seem unique, it is possible to find some similarities - repetitive structures and principles of how systems work. System archetypes help to find and identify these similarities. If we know the principle how the system works, we can better understand it and explore its weaknesses.<br />
<br />
System archetypes, that are part of '''systems thinking''' and [[System Dynamics]], is used to detect patterns of behaviour of '''social systems''' (businesses, communities, states, economies, etc.). They can be used in two areas, namely as a tool to determines the current state - '''a diagnostic tool''' or as a tool to look at the future development of the system - '''a prospective tool'''. As a diagnostic tool helps managers to obtain the perspective of the internal structure of the system, its functioning and help them to get a better idea of the current system state. As a prospective tool it is mainly used for planning. Managers can formulate their future goals and with the knowledge of the functioning of their organization, they can better determine the procedure by which this goal can be reached <ref name="braun">Braun, William. The System Archetypes [online]. 27. 2. 2002 [seen 21. 1. 2016]. Available at http://www.albany.edu/faculty/gpr/PAD724/724WebArticles/sys_archetypes.pdf</ref>.<br />
<br />
== Definition ==<br />
=== System ===<br />
The system consists of a '''set of elements''' and '''relationships''' between them. The individual elements and connections between them together form a larger value than the individual components alone. Each system can have inputs and outputs which form the interface between the system itself and its surroundings <ref name="palan">Palán, Zdeněk. Systém [online].[seen 21. 1. 2016]. Available at http://www.andromedia.cz/andragogicky-slovnik/system</ref>.<br />
<br />
=== Archetype ===<br />
Also called prototype represents a typical example, ideal type or perfect example. Archetype can also be a symbol or a recurrent motif occurring for example in literature <ref name="slovnik">Slovník cizích slov ABZ. Pojem archetyp [online]. 2016 [seen 22. 1. 2016]. Available at http://slovnik-cizich-slov.abz.cz/web.php/slovo/archetyp</ref>.<br />
<br />
== Overview of System Archetypes ==<br />
[[File:Family_Tree.gif|thumb|Family Tree <ref name="isee">isee systems. Archetype Family Tree [online]. 2006 [seen 20. 1. 2016]. Available at http://www.iseesystems.com/Online_training/course/module6/6-03-0-0-tree.htm</ref>]]<br />
There are '''16 unique system archetypes''' in total. These 16 archetypes differ from each other and describe different kind of systems, but all have a common foundation. There are '''two basic archetypes''', from which the remaining 14 archetypes is derived. Respectively, there are two archetypes, which can be combined to form the remaining 14 archetypes. Two basic archetypes are called Balancing Loop and Reinforcing Loop. Archetypes can be also divided into two groups the first is '''Fixing a Problem group''' and second one is '''Influencing Change group''' <ref name="sherrer">Sherrer, J. Alex. A Project Manager's Guide to Systems Thinking: Part 2 [online]. Project Smart. 24. 7. 2010 [seen 21. 1. 2016]. Available at https://www.projectsmart.co.uk/project-managers-guide-to-systems-thinking-part-2.php</ref>.<br />
<br />
=== Fixing a Problem ===<br />
*Balancing Loop<br />
*Balancing Structure with Delay<br />
*Indecision<br />
*Drifting Goals<br />
*Escalation<br />
*Fixes That Fail<br />
*Shifting the Burden<br />
*Addiction<br />
<br />
=== Influencing Change ===<br />
*Reinforcing Loop<br />
*Limits to Growth<br />
*Accidental Adversaries<br />
*Success to the Successful<br />
*Tragedy of the Commons<br />
*Attractiveness Principle<br />
*Growth and Underinvestment<br />
*Growth and Underinvestment with Drifting Standard<br />
<br />
== System Archetype Family Tree ==<br />
To recognize the fact that the archetype is suitable for such a situation can be helpful the Family tree of system archetypes. Family tree is an ordered tree that based on simple questions is capable of directing the user to select the correct system archetype for concrete application.<br />
<br />
== Balancing Loop ==<br />
Balancing Loop contains a negative feedback, which is helping the current state to be closer to the target state. Loop is stabilizing due to the negative feedback. When current state is deflected from the target state it helps to return to a desired state. The greater the difference between the target state and current state cause the faster convergence to final state.<br />
<br />
[[File:Archetype-balancing-structure.jpg|thumb|centre|upright=0.5|Balancing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be filling of an empty glass of water. Initially, the glass is empty, and thus the difference between the actual water level (glass is empty) and the required water level (glass is full) is the maximum. Glass is filled with water and a difference of the current water level and the required water level continues to decrease. At the beginning the glass is filled quickly, and it is obvious that much water is missing, then the difference which lowers the intensity of the water flow. At the moment of achieving the required water level there is no longer any motivation to continue with glass filling. Desired state becomes state achieved –the process is terminated.<br />
<br />
== Balancing Loop with Delay ==<br />
This loop is based on the previous one, except there is a delay, which plays a role in overall system responsiveness. The combination of the negative feedback and the delay in the system creates oscillations. The system is trying to achieve the target state, but because of delay, the system detects the information later. The system thus exceeds the target state.<br />
<br />
[[File:Archetype-balancing-structure-with-delay.jpg|thumb|centre|upright=0.5|Balancing Loop with Delay <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a room heated by a heater equipped with a thermostat. The thermostat switches on the heating when the room temperature is lower than desired. Heating begins to heat up and the thermostat switches off the heating when the room is heated up to the desired temperature. Heating is switched off, but for some time after shutdown is still hot. The room air is heated to higher temperature than the desired one, and this principle is repeated over and over again. Room temperature oscillates around a desired temperature, and it is never stabilized on the desired temperature level.<br />
<br />
== Indecision ==<br />
Main character is an oscillation which is created by combining of two balancing loops with delays. One loop reaches the desired state, and the target state of the second loop moves and that in turn influences the first loop. Both loops never reach their goals together and constantly oscillate around their target states.<br />
<br />
[[File:Archetype-indecision.jpg|thumb|centre|upright=0.5|Indecision <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
Examples may be currency market. Currency prices on the stock exchange is determined by supply and demand. When the offer price is lower than the price that buyers are willing to pay, then the purchasers buy larger amount of the currency. The seller realizes that the price is lower than it might be, and thus they increase the price, it will discourages potential buyers and the price will again gradually decrease below what buyers are willing to pay. The whole process is continually repeated and price oscillates around an equilibrium market price.<br />
<br />
== Drifting Goals ==<br />
It is relatively simple archetype, in which two balancing loops are connected, but in this case there are no delays. Loops stand in opposite of each other and the effort made by one loop cause impossibility to balance the second loop and vice versa.<br />
<br />
[[File:Archetype-drifting-goals.jpg|thumb|centre|upright=0.5|Drifting Goals <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a company that offers trips to the sea. The company decided to offer an additional service to its customers. Due to additional service costs of tours increase and the final price of trips will be higher than before. Next season customers begin to migrate to the competition, and the company decides to reconsider its initial plans and choose a compromise solution. The original goal has not been met and there has been erosion of targets.<br />
<br />
== Escalation ==<br />
This archetype is built on two or more interconnected loops. Increasing the target value in one loop tends to increase the second loop, and possibly other loops. The loops have a negative feedback. Due to the relatively higher value of one loop the other loops values grows. In the system as a whole, the target values continuously increasing in a cycle. Growth of the values can only be stopped by external constraints such as economic constraint, time constraint or space constraint.<br />
<br />
[[File:Archetype-escalation.jpg|thumb|centre|upright=0.5|Escalation <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A typical example is the arms race. One country considers that it is threatened by another country. The first country raises its arms capacity and it forces the other country to also expand its weapons facilities. This again creates concerns among the first earth and once again raises his arms capacity, and the whole cycle repeats. Stop the escalation of the situation usually only lack of resources on one or both sides.<br />
<br />
== Fixes That Fail ==<br />
Archetype is a combination of stabilizing and reinforcing loops. Stabilizing loop trying to reduce the gap between current and desired state. The difference between these states is solved in a way which does not eliminate the problems. This solution only delays the inevitable result and situation as a whole is even worse. The more often undesirable states are solved by an intervention which does not remove the problem, the faster the actual solution gets more difficult.<br />
<br />
[[File:Archetype-fixes-that-fail.jpg|thumb|centre|upright=0.5|Fixes That Fail <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example is the so-called debt trap. Situation where one loan is repaid by another. The first loan is repaid by another loan but under worse conditions, which making the whole situation worse. The situation is temporarily solved, but in the long term, the overall situation deteriorates and more loans always brings a deterioration of the situation and the only apparent solution of the problem.<br />
<br />
== Shifting the Burden ==<br />
Archetype captures the situation when dealing with a problem is solved only by temporarily solution. This short-term also affects the fundamental solution. Attention is given only to short-term solution or a solution which solves only side effects.<br />
<br />
[[File:Archetype-shifting-burden.jpg|thumb|centre|upright=0.5|Shifting the Burden <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example may be the treatment of serious illness. The patient's doctor prescribes for example medication which only relieve pain caused by serious illness. Patient for a short time period feels better, but subsequently his condition gets. Disease gets to an advanced stage and it is significantly more difficult to cure it than at the beginning.<br />
<br />
== Addiction ==<br />
It is based on the archetype Fixes That Fail and it appear where short-term solutions are applied. Short-term solutions gradually lose their effect.<br />
<br />
[[File:Archetype-addiction.jpg|thumb|centre|upright=0.5|Addiction <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example is drug addiction. Drug addict person after a long abstinence feels unwell and he trying to improve his condition by taking another dose of drugs. Condition temporarily improved, but on the other hand, the addiction is than stronger. Drug addict person needs another dose but this dose must be stronger to trigger a similar denial of the original problem of addiction.<br />
<br />
== Reinforcing Loop ==<br />
Reinforcing Loop is the second of two basic archetypes. It serves as a building element, which appears in the other system archetypes. Reinforcing Loop describes a situation where there is a persistent increase or decrease. This loop always leads to a unidirectional development. It is basically the exact opposite of the Balancing Loop.<br />
<br />
[[File:Archetype-reinforcing.jpg|thumb|centre|upright=0.5|Reinforcing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a savings account, which is initially deposited by an amount. The account offers a constant interest rate. After each crediting of interest, the originally deposit increases. In the next period the interest is calculated based on higher amount of money. This situation, however, abstracts from the inflation.<br />
<br />
== Limits to Growth ==<br />
This archetype is a combination of two basic loops - Balancing Loop and Reinforcing Loop. Reinforcing Loop represents steady growth, which is limited by Balancing Loop. Because of Balancing Loop system cannot grow infinitely and always has its limitations.<br />
<br />
[[File:Archetype-limits-to-growth.jpg|thumb|centre|upright=0.5|Limits to Growth <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example might be global population growth. The population is bigger, it grows faster, but the faster it grows, the more resources it needs for its development. Demands on resources disproportionately increases in comparison with available resources on the planet. Growth is limited and cannot be infinite.<br />
<br />
== Accidental Adversaries ==<br />
This archetype is composed of two Reinforcing Loops and around them is the Balancing Loop. Archetype describes the situation where two or more parties try to work, but also trying to increase their own benefit. Efforts to increase their own benefit leads to a reduction in the benefit of the other party and thus of cooperating parties become party rival.<br />
<br />
[[File:Archetype-accidental-adversaries.jpg|thumb|centre|upright=0.5|Accidental Adversaries <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example might be a company represented by its owners and top managers. Owners and managers have a common interest, which is the prosperity of the company. Even though they have a common interest, the interest of each party can be dispersed by other factors. Managers, for example, want to gain bonuses for their performance so they try to artificially inflate the growth and performance of the company. Owners may actually want to realize their short-term gains and thus can choose to pay disproportionately large dividends. This behaviour lead to the fact that the teammates become opponents and their behaviour does not lead to a single common goal.<br />
<br />
== Success to the Successful ==<br />
This model consists of the two Reinforcing Loops which has mutually opposite tendency. One loop is growing and the other declining. The more one loop grows, the more the second loop decreases and vice versa.<br />
<br />
[[File:Archetype-success-to-the-successful.jpg|thumb|centre|upright=0.5|Success to the Successful <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example is Self-fulfilling prophecy. Imagine two people, a confident, successful, another vice versa lacking confidence and success. These two people are constantly reinforcing that there are those who they think they are, and the gulf between them constantly increase. First self-confidence person is going better and better which gives him the courage to push further, the second person on the contrary, confirms that he fails and his self-esteem drops.<br />
<br />
== Tragedy of the Commons ==<br />
Tragedy of the Commons is a situation where two or more parties fighting for a common and limited resource. The fewer the resources left, the more they try to get the share.<br />
<br />
[[File:Archetype-tragedy-of-the-commons.jpg|thumb|centre|upright=0.5|Tragedy of the Commons <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be global fight between various states and corporations about limited oil resources - oilfields. The less new oilfields appear and the faster the current are depleted, the more resources and effort is devoted to extraction. Due to limited resources and shrinking the whole situation escalates further, the situation is tense and there is more conflict and these are more serious than before.<br />
<br />
== Attractiveness Principle ==<br />
Archetype is derived from the Limits to Growth archetype. It is extended by the fact that it is listed more than one threshold. Whereby the limits may not be as big and does not have to limit the same parameter. The basis in this model are the Reinforcing Loops, which is identical with that appearing in the Limits to Growth archetype. Loop strengthens and accelerates time to grow. In the model, there are also Stabilizing Loops that will bring the current state of the coasts. Balancing act with a delay, thus it is possible to establish a short-term imbalance that is gradually stabilized.<br />
<br />
[[File:Archetype-attractiveness-principle.jpg|thumb|centre|upright=0.5|Attractiveness Principle <ref name="sherrer"></ref>]]<br />
<br />
== Growth and Underinvestment ==<br />
Archetype based on the Limits to Growth archetype. The difference here is that the limiting factor or factors dynamically develops along with the development of the entire system. Limitations are therefore not constant, but varies in time. The model shows the system which not enough to invests to itself and create its own limitations in future growth. Archetype is comprised of three loops, two Balancing Loops and one Reinforcing Loop.<br />
<br />
[[File:Archetype-growth-and-underinvestment.jpg|thumb|centre|upright=0.5|Growth and Underinvestment <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example from the life may be preparation of high school student for study at university. If the student does not pay sufficient attention to current high school studies, he can thus create restrictions of his future potential. For example, he will not be accepted to university which he wants to or will be dismissed because does not manage to keep pace with other students. Neglected the study of lower grade may bring future constraints in a higher degree.<br />
<br />
== Growth and Underinvestment with Drifting Standard ==<br />
Archetype based on previous archetype, which is enriched with fourth loop. The fourth loop represents a certain standard, which change over time and reduces the need for future changes. Overall, this tendency leads to the overall growth of the system decreases.<br />
<br />
[[File:Archetype-growth-and-underinvestment-with-drifting-standard.jpg|thumb|centre|upright=0.5|Growth and Underinvestment with Drifting Standard <ref name="sherrer"></ref>]]<br />
<br />
== Quiz ==<br />
Try to match each real example with correct system archetype. Quiz correct answers see below.<br />
<br />
'''Real examples:'''<br />
#Dam (with a constant water level)<br />
#Arms race<br />
#Word of mouth<br />
#Humanitarian assistance (sending food)<br />
#Burning rain forests for palm trees (palm oil)<br />
<br />
'''System archetypes:'''<br />
<ol type="a"><br />
<li>Shifting the Burden</li><br />
<li>Reinforcing Loop</li><br />
<li>Balancing Loop</li><br />
<li>Tragedy of Common</li><br />
<li>Escalation</li><br />
</ol><br />
<br />
== See Also ==<br />
*[[System Dynamics]]<br />
*[https://en.wikipedia.org/wiki/The_Fifth_Discipline The Fifth Discipline]<br />
== References ==<br />
<references/><br />
*Šalamon, Tomáš. (2011). [http://www.designofagentbasedmodels.info ''Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes'']. Řepín, Czech Republic: Bruckner Publishing<br />
*Taborga, Jorge. Systems Archetypes and Their Application.2016 [seen 23. 1. 2016]. Available at https://www.saybrook.edu/rethinkingcomplexity/posts/08-15-11/systems-archetypes-and-their-application<br />
*Continuous Improvement Associates. Systems Thinking Archetypes (Generic Structures). 2003 [seen 22. 1. 2016]. Available at http://www.exponentialimprovement.com/cms/uploads/ArchetypesGeneric02.pdf<br />
*Bellinger, Gene. Archetypes: Interaction Structures of the Universe. 2004 [seen 20. 1. 2016]. Available at http://www.systems-thinking.org/arch/arch.htm#archdg<br />
<br />
== External Links ==<br />
*[https://insightmaker.com/tag/Systems-Archetypes Insight Maker] - A free dynamic modeling and simulation web application.<br />
<br />
== Quiz Correct Answers ==<br />
There are correct answers from quiz hereinabove.<br />
<br />
'''Correct Answers:'''<br />
#c.<br />
#e.<br />
#b.<br />
#a.<br />
#d.<br />
<br />
<br />
[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 17:27, 23 January 2016 (CET)</div>Xkrep33http://www.simulace.info/index.php?title=System_Archetypes&diff=10807System Archetypes2016-01-23T16:39:11Z<p>Xkrep33: </p>
<hr />
<div>== Introduction ==<br />
'''System archetypes''' are the '''patterns''' of behaviour that we can find in complex '''systems'''. Patterns help us to get an idea of how a particular system works. Even though each system is different, and at first look it may seem unique, it is possible to find some similarities - repetitive structures and principles of how systems work. System archetypes help to find and identify these similarities. If we know the principle how the system works, we can better understand it and explore its weaknesses.<br />
<br />
System archetypes, that are part of '''systems thinking''' and [[System Dynamics]], is used to detect patterns of behaviour of '''social systems''' (businesses, communities, states, economies, etc.). They can be used in two areas, namely as a tool to determines the current state - '''a diagnostic tool''' or as a tool to look at the future development of the system - '''a prospective tool'''. As a diagnostic tool helps managers to obtain the perspective of the internal structure of the system, its functioning and help them to get a better idea of the current system state. As a prospective tool it is mainly used for planning. Managers can formulate their future goals and with the knowledge of the functioning of their organization, they can better determine the procedure by which this goal can be reached <ref name="braun">Braun, William. The System Archetypes [online]. 27. 2. 2002 [seen 21. 1. 2016]. Available at http://www.albany.edu/faculty/gpr/PAD724/724WebArticles/sys_archetypes.pdf</ref>.<br />
<br />
== Definition ==<br />
=== System ===<br />
The system consists of a '''set of elements''' and '''relationships''' between them. The individual elements and connections between them together form a larger value than the individual components alone. Each system can have inputs and outputs which form the interface between the system itself and its surroundings <ref name="palan">Palán, Zdeněk. Systém [online].[seen 21. 1. 2016]. Available at http://www.andromedia.cz/andragogicky-slovnik/system</ref>.<br />
<br />
=== Archetype ===<br />
Also called prototype represents a typical example, ideal type or perfect example. Archetype can also be a symbol or a recurrent motif occurring for example in literature <ref name="slovnik">Slovník cizích slov ABZ. Pojem archetyp [online]. 2016 [seen 22. 1. 2016]. Available at http://slovnik-cizich-slov.abz.cz/web.php/slovo/archetyp</ref>.<br />
<br />
== Overview of System Archetypes ==<br />
[[File:Family_Tree.gif|thumb|Family Tree <ref name="isee">isee systems. Archetype Family Tree [online]. 2006 [seen 20. 1. 2016]. Available at http://www.iseesystems.com/Online_training/course/module6/6-03-0-0-tree.htm</ref>]]<br />
There are '''16 unique system archetypes''' in total. These 16 archetypes differ from each other and describe different kind of systems, but all have a common foundation. There are '''two basic archetypes''', from which the remaining 14 archetypes is derived. Respectively, there are two archetypes, which can be combined to form the remaining 14 archetypes. Two basic archetypes are called Balancing Loop and Reinforcing Loop. Archetypes can be also divided into two groups the first is '''Fixing a Problem group''' and second one is '''Influencing Change group''' <ref name="sherrer">Sherrer, J. Alex. A Project Manager's Guide to Systems Thinking: Part 2 [online]. Project Smart. 24. 7. 2010 [seen 21. 1. 2016]. Available at https://www.projectsmart.co.uk/project-managers-guide-to-systems-thinking-part-2.php</ref>.<br />
<br />
=== Fixing a Problem ===<br />
*Balancing Loop<br />
*Balancing Structure with Delay<br />
*Indecision<br />
*Drifting Goals<br />
*Escalation<br />
*Fixes That Fail<br />
*Shifting the Burden<br />
*Addiction<br />
<br />
=== Influencing Change ===<br />
*Reinforcing Loop<br />
*Limits to Growth<br />
*Accidental Adversaries<br />
*Success to the Successful<br />
*Tragedy of the Commons<br />
*Attractiveness Principle<br />
*Growth and Underinvestment<br />
*Growth and Underinvestment with Drifting Standard<br />
<br />
== System Archetype Family Tree ==<br />
To recognize the fact that the archetype is suitable for such a situation can be helpful the Family tree of system archetypes. Family tree is an ordered tree that based on simple questions is capable of directing the user to select the correct system archetype for concrete application.<br />
<br />
== Balancing Loop ==<br />
Balancing Loop contains a negative feedback, which is helping the current state to be closer to the target state. Loop is stabilizing due to the negative feedback. When current state is deflected from the target state it helps to return to a desired state. The greater the difference between the target state and current state cause the faster convergence to final state.<br />
<br />
[[File:Archetype-balancing-structure.jpg|thumb|centre|upright=0.5|Balancing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be filling of an empty glass of water. Initially, the glass is empty, and thus the difference between the actual water level (glass is empty) and the required water level (glass is full) is the maximum. Glass is filled with water and a difference of the current water level and the required water level continues to decrease. At the beginning the glass is filled quickly, and it is obvious that much water is missing, then the difference which lowers the intensity of the water flow. At the moment of achieving the required water level there is no longer any motivation to continue with glass filling. Desired state becomes state achieved –the process is terminated.<br />
<br />
== Balancing Loop with Delay ==<br />
This loop is based on the previous one, except there is a delay, which plays a role in overall system responsiveness. The combination of the negative feedback and the delay in the system creates oscillations. The system is trying to achieve the target state, but because of delay, the system detects the information later. The system thus exceeds the target state.<br />
<br />
[[File:Archetype-balancing-structure-with-delay.jpg|thumb|centre|upright=0.5|Balancing Loop with Delay <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a room heated by a heater equipped with a thermostat. The thermostat switches on the heating when the room temperature is lower than desired. Heating begins to heat up and the thermostat switches off the heating when the room is heated up to the desired temperature. Heating is switched off, but for some time after shutdown is still hot. The room air is heated to higher temperature than the desired one, and this principle is repeated over and over again. Room temperature oscillates around a desired temperature, and it is never stabilized on the desired temperature level.<br />
<br />
== Indecision ==<br />
Main character is an oscillation which is created by combining of two balancing loops with delays. One loop reaches the desired state, and the target state of the second loop moves and that in turn influences the first loop. Both loops never reach their goals together and constantly oscillate around their target states.<br />
<br />
[[File:Archetype-indecision.jpg|thumb|centre|upright=0.5|Indecision <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
Examples may be currency market. Currency prices on the stock exchange is determined by supply and demand. When the offer price is lower than the price that buyers are willing to pay, then the purchasers buy larger amount of the currency. The seller realizes that the price is lower than it might be, and thus they increase the price, it will discourages potential buyers and the price will again gradually decrease below what buyers are willing to pay. The whole process is continually repeated and price oscillates around an equilibrium market price.<br />
<br />
== Drifting Goals ==<br />
It is relatively simple archetype, in which two balancing loops are connected, but in this case there are no delays. Loops stand in opposite of each other and the effort made by one loop cause impossibility to balance the second loop and vice versa.<br />
<br />
[[File:Archetype-drifting-goals.jpg|thumb|centre|upright=0.5|Drifting Goals <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a company that offers trips to the sea. The company decided to offer an additional service to its customers. Due to additional service costs of tours increase and the final price of trips will be higher than before. Next season customers begin to migrate to the competition, and the company decides to reconsider its initial plans and choose a compromise solution. The original goal has not been met and there has been erosion of targets.<br />
<br />
== Escalation ==<br />
This archetype is built on two or more interconnected loops. Increasing the target value in one loop tends to increase the second loop, and possibly other loops. The loops have a negative feedback. Due to the relatively higher value of one loop the other loops values grows. In the system as a whole, the target values continuously increasing in a cycle. Growth of the values can only be stopped by external constraints such as economic constraint, time constraint or space constraint.<br />
<br />
[[File:Archetype-escalation.jpg|thumb|centre|upright=0.5|Escalation <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A typical example is the arms race. One country considers that it is threatened by another country. The first country raises its arms capacity and it forces the other country to also expand its weapons facilities. This again creates concerns among the first earth and once again raises his arms capacity, and the whole cycle repeats. Stop the escalation of the situation usually only lack of resources on one or both sides.<br />
<br />
== Fixes That Fail ==<br />
Archetype is a combination of stabilizing and reinforcing loops. Stabilizing loop trying to reduce the gap between current and desired state. The difference between these states is solved in a way which does not eliminate the problems. This solution only delays the inevitable result and situation as a whole is even worse. The more often undesirable states are solved by an intervention which does not remove the problem, the faster the actual solution gets more difficult.<br />
<br />
[[File:Archetype-fixes-that-fail.jpg|thumb|centre|upright=0.5|Fixes That Fail <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example is the so-called debt trap. Situation where one loan is repaid by another. The first loan is repaid by another loan but under worse conditions, which making the whole situation worse. The situation is temporarily solved, but in the long term, the overall situation deteriorates and more loans always brings a deterioration of the situation and the only apparent solution of the problem.<br />
<br />
== Shifting the Burden ==<br />
Archetype captures the situation when dealing with a problem is solved only by temporarily solution. This short-term also affects the fundamental solution. Attention is given only to short-term solution or a solution which solves only side effects.<br />
<br />
[[File:Archetype-shifting-burden.jpg|thumb|centre|upright=0.5|Shifting the Burden <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example may be the treatment of serious illness. The patient's doctor prescribes for example medication which only relieve pain caused by serious illness. Patient for a short time period feels better, but subsequently his condition gets. Disease gets to an advanced stage and it is significantly more difficult to cure it than at the beginning.<br />
<br />
== Addiction ==<br />
It is based on the archetype Fixes That Fail and it appear where short-term solutions are applied. Short-term solutions gradually lose their effect.<br />
<br />
[[File:Archetype-addiction.jpg|thumb|centre|upright=0.5|Addiction <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example is drug addiction. Drug addict person after a long abstinence feels unwell and he trying to improve his condition by taking another dose of drugs. Condition temporarily improved, but on the other hand, the addiction is than stronger. Drug addict person needs another dose but this dose must be stronger to trigger a similar denial of the original problem of addiction.<br />
<br />
== Reinforcing Loop ==<br />
Reinforcing Loop is the second of two basic archetypes. It serves as a building element, which appears in the other system archetypes. Reinforcing Loop describes a situation where there is a persistent increase or decrease. This loop always leads to a unidirectional development. It is basically the exact opposite of the Balancing Loop.<br />
<br />
[[File:Archetype-reinforcing.jpg|thumb|centre|upright=0.5|Reinforcing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a savings account, which is initially deposited by an amount. The account offers a constant interest rate. After each crediting of interest, the originally deposit increases. In the next period the interest is calculated based on higher amount of money. This situation, however, abstracts from the inflation.<br />
<br />
== Limits to Growth ==<br />
This archetype is a combination of two basic loops - Balancing Loop and Reinforcing Loop. Reinforcing Loop represents steady growth, which is limited by Balancing Loop. Because of Balancing Loop system cannot grow infinitely and always has its limitations.<br />
<br />
[[File:Archetype-limits-to-growth.jpg|thumb|centre|upright=0.5|Limits to Growth <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example might be global population growth. The population is bigger, it grows faster, but the faster it grows, the more resources it needs for its development. Demands on resources disproportionately increases in comparison with available resources on the planet. Growth is limited and cannot be infinite.<br />
<br />
== Accidental Adversaries ==<br />
This archetype is composed of two Reinforcing Loops and around them is the Balancing Loop. Archetype describes the situation where two or more parties try to work, but also trying to increase their own benefit. Efforts to increase their own benefit leads to a reduction in the benefit of the other party and thus of cooperating parties become party rival.<br />
<br />
[[File:Archetype-accidental-adversaries.jpg|thumb|centre|upright=0.5|Accidental Adversaries <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example might be a company represented by its owners and top managers. Owners and managers have a common interest, which is the prosperity of the company. Even though they have a common interest, the interest of each party can be dispersed by other factors. Managers, for example, want to gain bonuses for their performance so they try to artificially inflate the growth and performance of the company. Owners may actually want to realize their short-term gains and thus can choose to pay disproportionately large dividends. This behaviour lead to the fact that the teammates become opponents and their behaviour does not lead to a single common goal.<br />
<br />
== Success to the Successful ==<br />
This model consists of the two Reinforcing Loops which has mutually opposite tendency. One loop is growing and the other declining. The more one loop grows, the more the second loop decreases and vice versa.<br />
<br />
[[File:Archetype-success-to-the-successful.jpg|thumb|centre|upright=0.5|Success to the Successful <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example is Self-fulfilling prophecy. Imagine two people, a confident, successful, another vice versa lacking confidence and success. These two people are constantly reinforcing that there are those who they think they are, and the gulf between them constantly increase. First self-confidence person is going better and better which gives him the courage to push further, the second person on the contrary, confirms that he fails and his self-esteem drops.<br />
<br />
== Tragedy of the Commons ==<br />
Tragedy of the Commons is a situation where two or more parties fighting for a common and limited resource. The fewer the resources left, the more they try to get the share.<br />
<br />
[[File:Archetype-tragedy-of-the-commons.jpg|thumb|centre|upright=0.5|Tragedy of the Commons <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be global fight between various states and corporations about limited oil resources - oilfields. The less new oilfields appear and the faster the current are depleted, the more resources and effort is devoted to extraction. Due to limited resources and shrinking the whole situation escalates further, the situation is tense and there is more conflict and these are more serious than before.<br />
<br />
== Attractiveness Principle ==<br />
Archetype is derived from the Limits to Growth archetype. It is extended by the fact that it is listed more than one threshold. Whereby the limits may not be as big and does not have to limit the same parameter. The basis in this model are the Reinforcing Loops, which is identical with that appearing in the Limits to Growth archetype. Loop strengthens and accelerates time to grow. In the model, there are also Stabilizing Loops that will bring the current state of the coasts. Balancing act with a delay, thus it is possible to establish a short-term imbalance that is gradually stabilized.<br />
<br />
[[File:Archetype-attractiveness-principle.jpg|thumb|centre|upright=0.5|Attractiveness Principle <ref name="sherrer"></ref>]]<br />
<br />
== Growth and Underinvestment ==<br />
Archetype based on the Limits to Growth archetype. The difference here is that the limiting factor or factors dynamically develops along with the development of the entire system. Limitations are therefore not constant, but varies in time. The model shows the system which not enough to invests to itself and create its own limitations in future growth. Archetype is comprised of three loops, two Balancing Loops and one Reinforcing Loop.<br />
<br />
[[File:Archetype-growth-and-underinvestment.jpg|thumb|centre|upright=0.5|Growth and Underinvestment <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example from the life may be preparation of high school student for study at university. If the student does not pay sufficient attention to current high school studies, he can thus create restrictions of his future potential. For example, he will not be accepted to university which he wants to or will be dismissed because does not manage to keep pace with other students. Neglected the study of lower grade may bring future constraints in a higher degree.<br />
<br />
== Growth and Underinvestment with Drifting Standard ==<br />
Archetype based on previous archetype, which is enriched with fourth loop. The fourth loop represents a certain standard, which change over time and reduces the need for future changes. Overall, this tendency leads to the overall growth of the system decreases.<br />
<br />
[[File:Archetype-growth-and-underinvestment-with-drifting-standard.jpg|thumb|centre|upright=0.5|Growth and Underinvestment with Drifting Standard <ref name="sherrer"></ref>]]<br />
<br />
== See also ==<br />
*[[System Dynamics]]<br />
*[https://en.wikipedia.org/wiki/The_Fifth_Discipline The Fifth Discipline]<br />
== References ==<br />
<references/><br />
*Šalamon, Tomáš. (2011). [http://www.designofagentbasedmodels.info ''Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes'']. Řepín, Czech Republic: Bruckner Publishing<br />
*Taborga, Jorge. Systems Archetypes and Their Application.2016 [seen 23. 1. 2016]. Available at https://www.saybrook.edu/rethinkingcomplexity/posts/08-15-11/systems-archetypes-and-their-application<br />
*Continuous Improvement Associates. Systems Thinking Archetypes (Generic Structures). 2003 [seen 22. 1. 2016]. Available at http://www.exponentialimprovement.com/cms/uploads/ArchetypesGeneric02.pdf<br />
*Bellinger, Gene. Archetypes: Interaction Structures of the Universe. 2004 [seen 20. 1. 2016]. Available at http://www.systems-thinking.org/arch/arch.htm#archdg<br />
<br />
== External links ==<br />
*[https://insightmaker.com/tag/Systems-Archetypes Insight Maker] - A free dynamic modeling and simulation web application.<br />
<br />
[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 17:27, 23 January 2016 (CET)</div>Xkrep33http://www.simulace.info/index.php?title=System_Archetypes&diff=10806System Archetypes2016-01-23T16:33:40Z<p>Xkrep33: </p>
<hr />
<div>== Introduction ==<br />
System archetypes are the patterns of behaviour that we can find in complex systems. Patterns help us to get an idea of how a particular system works. Even though each system is different, and at first look it may seem unique, it is possible to find some similarities - repetitive structures and principles of how systems work. System archetypes help to find and identify these similarities. If we know the principle how the system works, we can better understand it and explore its weaknesses.<br />
<br />
System archetypes, that are part of systems thinking and system dynamics, is used to detect patterns of behaviour of social systems (businesses, communities, states, economies, etc.). They can be used in two areas, namely as a tool to determines the current state - a diagnostic tool or as a tool to look at the future development of the system - a prospective tool. As a diagnostic tool helps managers to obtain the perspective of the internal structure of the system, its functioning and help them to get a better idea of the current system state. As a prospective tool it is mainly used for planning. Managers can formulate their future goals and with the knowledge of the functioning of their organization, they can better determine the procedure by which this goal can be reached <ref name="braun">Braun, William. The System Archetypes [online]. 27. 2. 2002 [seen 21. 1. 2016]. Available at http://www.albany.edu/faculty/gpr/PAD724/724WebArticles/sys_archetypes.pdf</ref>.<br />
<br />
== Definition ==<br />
=== System ===<br />
The system consists of a set of elements and relationships between them. The individual elements and connections between them together form a larger value than the individual components alone. Each system can have inputs and outputs which form the interface between the system itself and its surroundings <ref name="palan">Palán, Zdeněk. Systém [online].[seen 21. 1. 2016]. Available at http://www.andromedia.cz/andragogicky-slovnik/system</ref>.<br />
<br />
=== Archetype ===<br />
Also called prototype represents a typical example, ideal type or perfect example. Archetype can also be a symbol or a recurrent motif occurring for example in literature <ref name="slovnik">Slovník cizích slov ABZ. Pojem archetyp [online]. 2016 [seen 22. 1. 2016]. Available at http://slovnik-cizich-slov.abz.cz/web.php/slovo/archetyp</ref>.<br />
<br />
== Overview of System Archetypes ==<br />
[[File:Family_Tree.gif|thumb|Family Tree <ref name="isee">isee systems. Archetype Family Tree [online]. 2006 [seen 20. 1. 2016]. Available at http://www.iseesystems.com/Online_training/course/module6/6-03-0-0-tree.htm</ref>]]<br />
There are 16 unique system archetypes in total. These 16 archetypes differ from each other and describe different kind of systems, but all have a common foundation. There are two basic archetypes, from which the remaining 14 archetypes is derived. Respectively, there are two archetypes, which can be combined to form the remaining 14 archetypes. Two basic archetypes are called Balancing Loop and Reinforcing Loop. Archetypes can be also divided into two groups the first is Fixing a Problem group and second one is Influencing Change group <ref name="sherrer">Sherrer, J. Alex. A Project Manager's Guide to Systems Thinking: Part 2 [online]. Project Smart. 24. 7. 2010 [seen 21. 1. 2016]. Available at https://www.projectsmart.co.uk/project-managers-guide-to-systems-thinking-part-2.php</ref>.<br />
<br />
=== Fixing a Problem ===<br />
*Balancing Loop<br />
*Balancing Structure with Delay<br />
*Indecision<br />
*Drifting Goals<br />
*Escalation<br />
*Fixes That Fail<br />
*Shifting the Burden<br />
*Addiction<br />
<br />
=== Influencing Change ===<br />
*Reinforcing Loop<br />
*Limits to Growth<br />
*Accidental Adversaries<br />
*Success to the Successful<br />
*Tragedy of the Commons<br />
*Attractiveness Principle<br />
*Growth and Underinvestment<br />
*Growth and Underinvestment with Drifting Standard<br />
<br />
== System Archetype Family Tree ==<br />
To recognize the fact that the archetype is suitable for such a situation can be helpful the Family tree of system archetypes. Family tree is an ordered tree that based on simple questions is capable of directing the user to select the correct system archetype for concrete application.<br />
<br />
== Balancing Loop ==<br />
Balancing Loop contains a negative feedback, which is helping the current state to be closer to the target state. Loop is stabilizing due to the negative feedback. When current state is deflected from the target state it helps to return to a desired state. The greater the difference between the target state and current state cause the faster convergence to final state.<br />
<br />
[[File:Archetype-balancing-structure.jpg|thumb|centre|upright=0.5|Balancing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be filling of an empty glass of water. Initially, the glass is empty, and thus the difference between the actual water level (glass is empty) and the required water level (glass is full) is the maximum. Glass is filled with water and a difference of the current water level and the required water level continues to decrease. At the beginning the glass is filled quickly, and it is obvious that much water is missing, then the difference which lowers the intensity of the water flow. At the moment of achieving the required water level there is no longer any motivation to continue with glass filling. Desired state becomes state achieved –the process is terminated.<br />
<br />
== Balancing Loop with Delay ==<br />
This loop is based on the previous one, except there is a delay, which plays a role in overall system responsiveness. The combination of the negative feedback and the delay in the system creates oscillations. The system is trying to achieve the target state, but because of delay, the system detects the information later. The system thus exceeds the target state.<br />
<br />
[[File:Archetype-balancing-structure-with-delay.jpg|thumb|centre|upright=0.5|Balancing Loop with Delay <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a room heated by a heater equipped with a thermostat. The thermostat switches on the heating when the room temperature is lower than desired. Heating begins to heat up and the thermostat switches off the heating when the room is heated up to the desired temperature. Heating is switched off, but for some time after shutdown is still hot. The room air is heated to higher temperature than the desired one, and this principle is repeated over and over again. Room temperature oscillates around a desired temperature, and it is never stabilized on the desired temperature level.<br />
<br />
== Indecision ==<br />
Main character is an oscillation which is created by combining of two balancing loops with delays. One loop reaches the desired state, and the target state of the second loop moves and that in turn influences the first loop. Both loops never reach their goals together and constantly oscillate around their target states.<br />
<br />
[[File:Archetype-indecision.jpg|thumb|centre|upright=0.5|Indecision <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
Examples may be currency market. Currency prices on the stock exchange is determined by supply and demand. When the offer price is lower than the price that buyers are willing to pay, then the purchasers buy larger amount of the currency. The seller realizes that the price is lower than it might be, and thus they increase the price, it will discourages potential buyers and the price will again gradually decrease below what buyers are willing to pay. The whole process is continually repeated and price oscillates around an equilibrium market price.<br />
<br />
== Drifting Goals ==<br />
It is relatively simple archetype, in which two balancing loops are connected, but in this case there are no delays. Loops stand in opposite of each other and the effort made by one loop cause impossibility to balance the second loop and vice versa.<br />
<br />
[[File:Archetype-drifting-goals.jpg|thumb|centre|upright=0.5|Drifting Goals <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a company that offers trips to the sea. The company decided to offer an additional service to its customers. Due to additional service costs of tours increase and the final price of trips will be higher than before. Next season customers begin to migrate to the competition, and the company decides to reconsider its initial plans and choose a compromise solution. The original goal has not been met and there has been erosion of targets.<br />
<br />
== Escalation ==<br />
This archetype is built on two or more interconnected loops. Increasing the target value in one loop tends to increase the second loop, and possibly other loops. The loops have a negative feedback. Due to the relatively higher value of one loop the other loops values grows. In the system as a whole, the target values continuously increasing in a cycle. Growth of the values can only be stopped by external constraints such as economic constraint, time constraint or space constraint.<br />
<br />
[[File:Archetype-escalation.jpg|thumb|centre|upright=0.5|Escalation <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A typical example is the arms race. One country considers that it is threatened by another country. The first country raises its arms capacity and it forces the other country to also expand its weapons facilities. This again creates concerns among the first earth and once again raises his arms capacity, and the whole cycle repeats. Stop the escalation of the situation usually only lack of resources on one or both sides.<br />
<br />
== Fixes That Fail ==<br />
Archetype is a combination of stabilizing and reinforcing loops. Stabilizing loop trying to reduce the gap between current and desired state. The difference between these states is solved in a way which does not eliminate the problems. This solution only delays the inevitable result and situation as a whole is even worse. The more often undesirable states are solved by an intervention which does not remove the problem, the faster the actual solution gets more difficult.<br />
<br />
[[File:Archetype-fixes-that-fail.jpg|thumb|centre|upright=0.5|Fixes That Fail <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example is the so-called debt trap. Situation where one loan is repaid by another. The first loan is repaid by another loan but under worse conditions, which making the whole situation worse. The situation is temporarily solved, but in the long term, the overall situation deteriorates and more loans always brings a deterioration of the situation and the only apparent solution of the problem.<br />
<br />
== Shifting the Burden ==<br />
Archetype captures the situation when dealing with a problem is solved only by temporarily solution. This short-term also affects the fundamental solution. Attention is given only to short-term solution or a solution which solves only side effects.<br />
<br />
[[File:Archetype-shifting-burden.jpg|thumb|centre|upright=0.5|Shifting the Burden <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example may be the treatment of serious illness. The patient's doctor prescribes for example medication which only relieve pain caused by serious illness. Patient for a short time period feels better, but subsequently his condition gets. Disease gets to an advanced stage and it is significantly more difficult to cure it than at the beginning.<br />
<br />
== Addiction ==<br />
It is based on the archetype Fixes That Fail and it appear where short-term solutions are applied. Short-term solutions gradually lose their effect.<br />
<br />
[[File:Archetype-addiction.jpg|thumb|centre|upright=0.5|Addiction <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example is drug addiction. Drug addict person after a long abstinence feels unwell and he trying to improve his condition by taking another dose of drugs. Condition temporarily improved, but on the other hand, the addiction is than stronger. Drug addict person needs another dose but this dose must be stronger to trigger a similar denial of the original problem of addiction.<br />
<br />
== Reinforcing Loop ==<br />
Reinforcing Loop is the second of two basic archetypes. It serves as a building element, which appears in the other system archetypes. Reinforcing Loop describes a situation where there is a persistent increase or decrease. This loop always leads to a unidirectional development. It is basically the exact opposite of the Balancing Loop.<br />
<br />
[[File:Archetype-reinforcing.jpg|thumb|centre|upright=0.5|Reinforcing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a savings account, which is initially deposited by an amount. The account offers a constant interest rate. After each crediting of interest, the originally deposit increases. In the next period the interest is calculated based on higher amount of money. This situation, however, abstracts from the inflation.<br />
<br />
== Limits to Growth ==<br />
This archetype is a combination of two basic loops - Balancing Loop and Reinforcing Loop. Reinforcing Loop represents steady growth, which is limited by Balancing Loop. Because of Balancing Loop system cannot grow infinitely and always has its limitations.<br />
<br />
[[File:Archetype-limits-to-growth.jpg|thumb|centre|upright=0.5|Limits to Growth <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example might be global population growth. The population is bigger, it grows faster, but the faster it grows, the more resources it needs for its development. Demands on resources disproportionately increases in comparison with available resources on the planet. Growth is limited and cannot be infinite.<br />
<br />
== Accidental Adversaries ==<br />
This archetype is composed of two Reinforcing Loops and around them is the Balancing Loop. Archetype describes the situation where two or more parties try to work, but also trying to increase their own benefit. Efforts to increase their own benefit leads to a reduction in the benefit of the other party and thus of cooperating parties become party rival.<br />
<br />
[[File:Archetype-accidental-adversaries.jpg|thumb|centre|upright=0.5|Accidental Adversaries <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example might be a company represented by its owners and top managers. Owners and managers have a common interest, which is the prosperity of the company. Even though they have a common interest, the interest of each party can be dispersed by other factors. Managers, for example, want to gain bonuses for their performance so they try to artificially inflate the growth and performance of the company. Owners may actually want to realize their short-term gains and thus can choose to pay disproportionately large dividends. This behaviour lead to the fact that the teammates become opponents and their behaviour does not lead to a single common goal.<br />
<br />
== Success to the Successful ==<br />
This model consists of the two Reinforcing Loops which has mutually opposite tendency. One loop is growing and the other declining. The more one loop grows, the more the second loop decreases and vice versa.<br />
<br />
[[File:Archetype-success-to-the-successful.jpg|thumb|centre|upright=0.5|Success to the Successful <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example is Self-fulfilling prophecy. Imagine two people, a confident, successful, another vice versa lacking confidence and success. These two people are constantly reinforcing that there are those who they think they are, and the gulf between them constantly increase. First self-confidence person is going better and better which gives him the courage to push further, the second person on the contrary, confirms that he fails and his self-esteem drops.<br />
<br />
== Tragedy of the Commons ==<br />
Tragedy of the Commons is a situation where two or more parties fighting for a common and limited resource. The fewer the resources left, the more they try to get the share.<br />
<br />
[[File:Archetype-tragedy-of-the-commons.jpg|thumb|centre|upright=0.5|Tragedy of the Commons <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be global fight between various states and corporations about limited oil resources - oilfields. The less new oilfields appear and the faster the current are depleted, the more resources and effort is devoted to extraction. Due to limited resources and shrinking the whole situation escalates further, the situation is tense and there is more conflict and these are more serious than before.<br />
<br />
== Attractiveness Principle ==<br />
Archetype is derived from the Limits to Growth archetype. It is extended by the fact that it is listed more than one threshold. Whereby the limits may not be as big and does not have to limit the same parameter. The basis in this model are the Reinforcing Loops, which is identical with that appearing in the Limits to Growth archetype. Loop strengthens and accelerates time to grow. In the model, there are also Stabilizing Loops that will bring the current state of the coasts. Balancing act with a delay, thus it is possible to establish a short-term imbalance that is gradually stabilized.<br />
<br />
[[File:Archetype-attractiveness-principle.jpg|thumb|centre|upright=0.5|Attractiveness Principle <ref name="sherrer"></ref>]]<br />
<br />
== Growth and Underinvestment ==<br />
Archetype based on the Limits to Growth archetype. The difference here is that the limiting factor or factors dynamically develops along with the development of the entire system. Limitations are therefore not constant, but varies in time. The model shows the system which not enough to invests to itself and create its own limitations in future growth. Archetype is comprised of three loops, two Balancing Loops and one Reinforcing Loop.<br />
<br />
[[File:Archetype-growth-and-underinvestment.jpg|thumb|centre|upright=0.5|Growth and Underinvestment <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example from the life may be preparation of high school student for study at university. If the student does not pay sufficient attention to current high school studies, he can thus create restrictions of his future potential. For example, he will not be accepted to university which he wants to or will be dismissed because does not manage to keep pace with other students. Neglected the study of lower grade may bring future constraints in a higher degree.<br />
<br />
== Growth and Underinvestment with Drifting Standard ==<br />
Archetype based on previous archetype, which is enriched with fourth loop. The fourth loop represents a certain standard, which change over time and reduces the need for future changes. Overall, this tendency leads to the overall growth of the system decreases.<br />
<br />
[[File:Archetype-growth-and-underinvestment-with-drifting-standard.jpg|thumb|centre|upright=0.5|Growth and Underinvestment with Drifting Standard <ref name="sherrer"></ref>]]<br />
<br />
== See also ==<br />
*[[System Dynamics]]<br />
*[https://en.wikipedia.org/wiki/The_Fifth_Discipline The Fifth Discipline]<br />
== References ==<br />
<references/><br />
*Šalamon, Tomáš. (2011). [http://www.designofagentbasedmodels.info ''Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes'']. Řepín, Czech Republic: Bruckner Publishing<br />
*Taborga, Jorge. Systems Archetypes and Their Application.2016 [seen 23. 1. 2016]. Available at https://www.saybrook.edu/rethinkingcomplexity/posts/08-15-11/systems-archetypes-and-their-application<br />
*Continuous Improvement Associates. Systems Thinking Archetypes (Generic Structures). 2003 [seen 22. 1. 2016]. Available at http://www.exponentialimprovement.com/cms/uploads/ArchetypesGeneric02.pdf<br />
*Bellinger, Gene. Archetypes: Interaction Structures of the Universe. 2004 [seen 20. 1. 2016]. Available at http://www.systems-thinking.org/arch/arch.htm#archdg<br />
<br />
== External links ==<br />
*[https://insightmaker.com/tag/Systems-Archetypes Insight Maker] - A free dynamic modeling and simulation web application.<br />
<br />
[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 17:27, 23 January 2016 (CET)</div>Xkrep33http://www.simulace.info/index.php?title=System_Archetypes&diff=10805System Archetypes2016-01-23T16:27:38Z<p>Xkrep33: </p>
<hr />
<div>== Introduction ==<br />
System archetypes are the patterns of behaviour that we can find in complex systems. Patterns help us to get an idea of how a particular system works. Even though each system is different, and at first look it may seem unique, it is possible to find some similarities - repetitive structures and principles of how systems work. System archetypes help to find and identify these similarities. If we know the principle how the system works, we can better understand it and explore its weaknesses.<br />
<br />
System archetypes, that are part of systems thinking and system dynamics, is used to detect patterns of behaviour of social systems (businesses, communities, states, economies, etc.). They can be used in two areas, namely as a tool to determines the current state - a diagnostic tool or as a tool to look at the future development of the system - a prospective tool. As a diagnostic tool helps managers to obtain the perspective of the internal structure of the system, its functioning and help them to get a better idea of the current system state. As a prospective tool it is mainly used for planning. Managers can formulate their future goals and with the knowledge of the functioning of their organization, they can better determine the procedure by which this goal can be reached <ref name="braun">Braun, William. The System Archetypes [online]. 27. 2. 2002 [seen 21. 1. 2016]. Available at http://www.albany.edu/faculty/gpr/PAD724/724WebArticles/sys_archetypes.pdf</ref>.<br />
<br />
== Definition ==<br />
=== System ===<br />
The system consists of a set of elements and relationships between them. The individual elements and connections between them together form a larger value than the individual components alone. Each system can have inputs and outputs which form the interface between the system itself and its surroundings <ref name="palan">Palán, Zdeněk. Systém [online].[seen 21. 1. 2016]. Available at http://www.andromedia.cz/andragogicky-slovnik/system</ref>.<br />
<br />
=== Archetype ===<br />
Also called prototype represents a typical example, ideal type or perfect example. Archetype can also be a symbol or a recurrent motif occurring for example in literature <ref name="slovnik">Slovník cizích slov ABZ. Pojem archetyp [online]. 2016 [seen 22. 1. 2016]. Available at http://slovnik-cizich-slov.abz.cz/web.php/slovo/archetyp</ref>.<br />
<br />
== Overview of System Archetypes ==<br />
[[File:Family_Tree.gif|thumb|Family Tree <ref name="isee">isee systems. Archetype Family Tree [online]. 2006 [seen 20. 1. 2016]. Available at http://www.iseesystems.com/Online_training/course/module6/6-03-0-0-tree.htm</ref>]]<br />
There are 16 unique system archetypes in total. These 16 archetypes differ from each other and describe different kind of systems, but all have a common foundation. There are two basic archetypes, from which the remaining 14 archetypes is derived. Respectively, there are two archetypes, which can be combined to form the remaining 14 archetypes. Two basic archetypes are called Balancing Loop and Reinforcing Loop. Archetypes can be also divided into two groups the first is Fixing a Problem group and second one is Influencing Change group <ref name="sherrer">Sherrer, J. Alex. A Project Manager's Guide to Systems Thinking: Part 2 [online]. Project Smart. 24. 7. 2010 [seen 21. 1. 2016]. Available at https://www.projectsmart.co.uk/project-managers-guide-to-systems-thinking-part-2.php</ref>.<br />
<br />
=== Fixing a Problem ===<br />
*Balancing Loop<br />
*Balancing Structure with Delay<br />
*Indecision<br />
*Drifting Goals<br />
*Escalation<br />
*Fixes That Fail<br />
*Shifting the Burden<br />
*Addiction<br />
<br />
=== Influencing Change ===<br />
*Reinforcing Loop<br />
*Limits to Growth<br />
*Accidental Adversaries<br />
*Success to the Successful<br />
*Tragedy of the Commons<br />
*Attractiveness Principle<br />
*Growth and Underinvestment<br />
*Growth and Underinvestment with Drifting Standard<br />
<br />
== System Archetype Family Tree ==<br />
To recognize the fact that the archetype is suitable for such a situation can be helpful the Family tree of system archetypes. Family tree is an ordered tree that based on simple questions is capable of directing the user to select the correct system archetype for concrete application.<br />
<br />
== Balancing Loop ==<br />
Balancing Loop contains a negative feedback, which is helping the current state to be closer to the target state. Loop is stabilizing due to the negative feedback. When current state is deflected from the target state it helps to return to a desired state. The greater the difference between the target state and current state cause the faster convergence to final state.<br />
<br />
[[File:Archetype-balancing-structure.jpg|thumb|centre|upright=0.5|Balancing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be filling of an empty glass of water. Initially, the glass is empty, and thus the difference between the actual water level (glass is empty) and the required water level (glass is full) is the maximum. Glass is filled with water and a difference of the current water level and the required water level continues to decrease. At the beginning the glass is filled quickly, and it is obvious that much water is missing, then the difference which lowers the intensity of the water flow. At the moment of achieving the required water level there is no longer any motivation to continue with glass filling. Desired state becomes state achieved –the process is terminated.<br />
<br />
== Balancing Loop with Delay ==<br />
This loop is based on the previous one, except there is a delay, which plays a role in overall system responsiveness. The combination of the negative feedback and the delay in the system creates oscillations. The system is trying to achieve the target state, but because of delay, the system detects the information later. The system thus exceeds the target state.<br />
<br />
[[File:Archetype-balancing-structure-with-delay.jpg|thumb|centre|upright=0.5|Balancing Loop with Delay <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a room heated by a heater equipped with a thermostat. The thermostat switches on the heating when the room temperature is lower than desired. Heating begins to heat up and the thermostat switches off the heating when the room is heated up to the desired temperature. Heating is switched off, but for some time after shutdown is still hot. The room air is heated to higher temperature than the desired one, and this principle is repeated over and over again. Room temperature oscillates around a desired temperature, and it is never stabilized on the desired temperature level.<br />
<br />
== Indecision ==<br />
Main character is an oscillation which is created by combining of two balancing loops with delays. One loop reaches the desired state, and the target state of the second loop moves and that in turn influences the first loop. Both loops never reach their goals together and constantly oscillate around their target states.<br />
<br />
[[File:Archetype-indecision.jpg|thumb|centre|upright=0.5|Indecision <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
Examples may be currency market. Currency prices on the stock exchange is determined by supply and demand. When the offer price is lower than the price that buyers are willing to pay, then the purchasers buy larger amount of the currency. The seller realizes that the price is lower than it might be, and thus they increase the price, it will discourages potential buyers and the price will again gradually decrease below what buyers are willing to pay. The whole process is continually repeated and price oscillates around an equilibrium market price.<br />
<br />
== Drifting Goals ==<br />
It is relatively simple archetype, in which two balancing loops are connected, but in this case there are no delays. Loops stand in opposite of each other and the effort made by one loop cause impossibility to balance the second loop and vice versa.<br />
<br />
[[File:Archetype-drifting-goals.jpg|thumb|centre|upright=0.5|Drifting Goals <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a company that offers trips to the sea. The company decided to offer an additional service to its customers. Due to additional service costs of tours increase and the final price of trips will be higher than before. Next season customers begin to migrate to the competition, and the company decides to reconsider its initial plans and choose a compromise solution. The original goal has not been met and there has been erosion of targets.<br />
<br />
== Escalation ==<br />
This archetype is built on two or more interconnected loops. Increasing the target value in one loop tends to increase the second loop, and possibly other loops. The loops have a negative feedback. Due to the relatively higher value of one loop the other loops values grows. In the system as a whole, the target values continuously increasing in a cycle. Growth of the values can only be stopped by external constraints such as economic constraint, time constraint or space constraint.<br />
<br />
[[File:Archetype-escalation.jpg|thumb|centre|upright=0.5|Escalation <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A typical example is the arms race. One country considers that it is threatened by another country. The first country raises its arms capacity and it forces the other country to also expand its weapons facilities. This again creates concerns among the first earth and once again raises his arms capacity, and the whole cycle repeats. Stop the escalation of the situation usually only lack of resources on one or both sides.<br />
<br />
== Fixes That Fail ==<br />
Archetype is a combination of stabilizing and reinforcing loops. Stabilizing loop trying to reduce the gap between current and desired state. The difference between these states is solved in a way which does not eliminate the problems. This solution only delays the inevitable result and situation as a whole is even worse. The more often undesirable states are solved by an intervention which does not remove the problem, the faster the actual solution gets more difficult.<br />
<br />
[[File:Archetype-fixes-that-fail.jpg|thumb|centre|upright=0.5|Fixes That Fail <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example is the so-called debt trap. Situation where one loan is repaid by another. The first loan is repaid by another loan but under worse conditions, which making the whole situation worse. The situation is temporarily solved, but in the long term, the overall situation deteriorates and more loans always brings a deterioration of the situation and the only apparent solution of the problem.<br />
<br />
== Shifting the Burden ==<br />
Archetype captures the situation when dealing with a problem is solved only by temporarily solution. This short-term also affects the fundamental solution. Attention is given only to short-term solution or a solution which solves only side effects.<br />
<br />
[[File:Archetype-shifting-burden.jpg|thumb|centre|upright=0.5|Shifting the Burden <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example may be the treatment of serious illness. The patient's doctor prescribes for example medication which only relieve pain caused by serious illness. Patient for a short time period feels better, but subsequently his condition gets. Disease gets to an advanced stage and it is significantly more difficult to cure it than at the beginning.<br />
<br />
== Addiction ==<br />
It is based on the archetype Fixes That Fail and it appear where short-term solutions are applied. Short-term solutions gradually lose their effect.<br />
<br />
[[File:Archetype-addiction.jpg|thumb|centre|upright=0.5|Addiction <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example is drug addiction. Drug addict person after a long abstinence feels unwell and he trying to improve his condition by taking another dose of drugs. Condition temporarily improved, but on the other hand, the addiction is than stronger. Drug addict person needs another dose but this dose must be stronger to trigger a similar denial of the original problem of addiction.<br />
<br />
== Reinforcing Loop ==<br />
Reinforcing Loop is the second of two basic archetypes. It serves as a building element, which appears in the other system archetypes. Reinforcing Loop describes a situation where there is a persistent increase or decrease. This loop always leads to a unidirectional development. It is basically the exact opposite of the Balancing Loop.<br />
<br />
[[File:Archetype-reinforcing.jpg|thumb|centre|upright=0.5|Reinforcing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a savings account, which is initially deposited by an amount. The account offers a constant interest rate. After each crediting of interest, the originally deposit increases. In the next period the interest is calculated based on higher amount of money. This situation, however, abstracts from the inflation.<br />
<br />
== Limits to Growth ==<br />
This archetype is a combination of two basic loops - Balancing Loop and Reinforcing Loop. Reinforcing Loop represents steady growth, which is limited by Balancing Loop. Because of Balancing Loop system cannot grow infinitely and always has its limitations.<br />
<br />
[[File:Archetype-limits-to-growth.jpg|thumb|centre|upright=0.5|Limits to Growth <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example might be global population growth. The population is bigger, it grows faster, but the faster it grows, the more resources it needs for its development. Demands on resources disproportionately increases in comparison with available resources on the planet. Growth is limited and cannot be infinite.<br />
<br />
== Accidental Adversaries ==<br />
This archetype is composed of two Reinforcing Loops and around them is the Balancing Loop. Archetype describes the situation where two or more parties try to work, but also trying to increase their own benefit. Efforts to increase their own benefit leads to a reduction in the benefit of the other party and thus of cooperating parties become party rival.<br />
<br />
[[File:Archetype-accidental-adversaries.jpg|thumb|centre|upright=0.5|Accidental Adversaries <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example might be a company represented by its owners and top managers. Owners and managers have a common interest, which is the prosperity of the company. Even though they have a common interest, the interest of each party can be dispersed by other factors. Managers, for example, want to gain bonuses for their performance so they try to artificially inflate the growth and performance of the company. Owners may actually want to realize their short-term gains and thus can choose to pay disproportionately large dividends. This behaviour lead to the fact that the teammates become opponents and their behaviour does not lead to a single common goal.<br />
<br />
== Success to the Successful ==<br />
This model consists of the two Reinforcing Loops which has mutually opposite tendency. One loop is growing and the other declining. The more one loop grows, the more the second loop decreases and vice versa.<br />
<br />
[[File:Archetype-success-to-the-successful.jpg|thumb|centre|upright=0.5|Success to the Successful <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example is Self-fulfilling prophecy. Imagine two people, a confident, successful, another vice versa lacking confidence and success. These two people are constantly reinforcing that there are those who they think they are, and the gulf between them constantly increase. First self-confidence person is going better and better which gives him the courage to push further, the second person on the contrary, confirms that he fails and his self-esteem drops.<br />
<br />
== Tragedy of the Commons ==<br />
Tragedy of the Commons is a situation where two or more parties fighting for a common and limited resource. The fewer the resources left, the more they try to get the share.<br />
<br />
[[File:Archetype-tragedy-of-the-commons.jpg|thumb|centre|upright=0.5|Tragedy of the Commons <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be global fight between various states and corporations about limited oil resources - oilfields. The less new oilfields appear and the faster the current are depleted, the more resources and effort is devoted to extraction. Due to limited resources and shrinking the whole situation escalates further, the situation is tense and there is more conflict and these are more serious than before.<br />
<br />
== Attractiveness Principle ==<br />
Archetype is derived from the Limits to Growth archetype. It is extended by the fact that it is listed more than one threshold. Whereby the limits may not be as big and does not have to limit the same parameter. The basis in this model are the Reinforcing Loops, which is identical with that appearing in the Limits to Growth archetype. Loop strengthens and accelerates time to grow. In the model, there are also Stabilizing Loops that will bring the current state of the coasts. Balancing act with a delay, thus it is possible to establish a short-term imbalance that is gradually stabilized.<br />
<br />
[[File:Archetype-attractiveness-principle.jpg|thumb|centre|upright=0.5|Attractiveness Principle <ref name="sherrer"></ref>]]<br />
<br />
== Growth and Underinvestment ==<br />
Archetype based on the Limits to Growth archetype. The difference here is that the limiting factor or factors dynamically develops along with the development of the entire system. Limitations are therefore not constant, but varies in time. The model shows the system which not enough to invests to itself and create its own limitations in future growth. Archetype is comprised of three loops, two Balancing Loops and one Reinforcing Loop.<br />
<br />
[[File:Archetype-growth-and-underinvestment.jpg|thumb|centre|upright=0.5|Growth and Underinvestment <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example from the life may be preparation of high school student for study at university. If the student does not pay sufficient attention to current high school studies, he can thus create restrictions of his future potential. For example, he will not be accepted to university which he wants to or will be dismissed because does not manage to keep pace with other students. Neglected the study of lower grade may bring future constraints in a higher degree.<br />
<br />
== Growth and Underinvestment with Drifting Standard ==<br />
Archetype based on previous archetype, which is enriched with fourth loop. The fourth loop represents a certain standard, which change over time and reduces the need for future changes. Overall, this tendency leads to the overall growth of the system decreases.<br />
<br />
[[File:Archetype-growth-and-underinvestment-with-drifting-standard.jpg|thumb|centre|upright=0.5|Growth and Underinvestment with Drifting Standard <ref name="sherrer"></ref>]]<br />
<br />
== See also ==<br />
*[[System Dynamics]]<br />
*[https://en.wikipedia.org/wiki/The_Fifth_Discipline The Fifth Discipline]<br />
== References ==<br />
<references/><br />
*Šalamon, Tomáš. (2011). [http://www.designofagentbasedmodels.info ''Design of Agent-Based Models : Developing Computer Simulations for a Better Understanding of Social Processes'']. Řepín, Czech Republic: Bruckner Publishing<br />
*Taborga, Jorge. Systems Archetypes and Their Application.2016 [seen 23. 1. 2016]. Available at https://www.saybrook.edu/rethinkingcomplexity/posts/08-15-11/systems-archetypes-and-their-application<br />
<br />
== External links ==<br />
*[https://insightmaker.com/tag/Systems-Archetypes Insight Maker] - A free dynamic modeling and simulation web application.<br />
<br />
[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 17:27, 23 January 2016 (CET)</div>Xkrep33http://www.simulace.info/index.php?title=System_Archetypes&diff=10804System Archetypes2016-01-23T16:22:10Z<p>Xkrep33: Created page with "== Introduction == System archetypes are the patterns of behaviour that we can find in complex systems. Patterns help us to get an idea of how a particular system works. Even..."</p>
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<div>== Introduction ==<br />
System archetypes are the patterns of behaviour that we can find in complex systems. Patterns help us to get an idea of how a particular system works. Even though each system is different, and at first look it may seem unique, it is possible to find some similarities - repetitive structures and principles of how systems work. System archetypes help to find and identify these similarities. If we know the principle how the system works, we can better understand it and explore its weaknesses.<br />
<br />
System archetypes, that are part of systems thinking and system dynamics, is used to detect patterns of behaviour of social systems (businesses, communities, states, economies, etc.). They can be used in two areas, namely as a tool to determines the current state - a diagnostic tool or as a tool to look at the future development of the system - a prospective tool. As a diagnostic tool helps managers to obtain the perspective of the internal structure of the system, its functioning and help them to get a better idea of the current system state. As a prospective tool it is mainly used for planning. Managers can formulate their future goals and with the knowledge of the functioning of their organization, they can better determine the procedure by which this goal can be reached <ref name="braun">Braun, William. The System Archetypes [online]. 27. 2. 2002 [seen 21. 1. 2016]. Available at http://www.albany.edu/faculty/gpr/PAD724/724WebArticles/sys_archetypes.pdf</ref>.<br />
<br />
== Definition ==<br />
=== System ===<br />
The system consists of a set of elements and relationships between them. The individual elements and connections between them together form a larger value than the individual components alone. Each system can have inputs and outputs which form the interface between the system itself and its surroundings <ref name="palan">Palán, Zdeněk. Systém [online].[seen 21. 1. 2016]. Available at http://www.andromedia.cz/andragogicky-slovnik/system</ref>.<br />
<br />
=== Archetype ===<br />
Also called prototype represents a typical example, ideal type or perfect example. Archetype can also be a symbol or a recurrent motif occurring for example in literature <ref name="slovnik">Slovník cizích slov ABZ. Pojem archetyp [online]. 2016 [seen 22. 1. 2016]. Available at http://slovnik-cizich-slov.abz.cz/web.php/slovo/archetyp</ref>.<br />
<br />
== Overview of System Archetypes ==<br />
[[File:Family_Tree.gif|thumb|Family Tree <ref name="isee">isee systems. Archetype Family Tree [online]. 2006 [seen 20. 1. 2016]. Available at http://www.iseesystems.com/Online_training/course/module6/6-03-0-0-tree.htm</ref>]]<br />
There are 16 unique system archetypes in total. These 16 archetypes differ from each other and describe different kind of systems, but all have a common foundation. There are two basic archetypes, from which the remaining 14 archetypes is derived. Respectively, there are two archetypes, which can be combined to form the remaining 14 archetypes. Two basic archetypes are called Balancing Loop and Reinforcing Loop. Archetypes can be also divided into two groups the first is Fixing a Problem group and second one is Influencing Change group <ref name="sherrer">Sherrer, J. Alex. A Project Manager's Guide to Systems Thinking: Part 2 [online]. Project Smart. 24. 7. 2010 [seen 21. 1. 2016]. Available at https://www.projectsmart.co.uk/project-managers-guide-to-systems-thinking-part-2.php</ref>.<br />
<br />
=== Fixing a Problem ===<br />
*Balancing Loop<br />
*Balancing Structure with Delay<br />
*Indecision<br />
*Drifting Goals<br />
*Escalation<br />
*Fixes That Fail<br />
*Shifting the Burden<br />
*Addiction<br />
<br />
=== Influencing Change ===<br />
*Reinforcing Loop<br />
*Limits to Growth<br />
*Accidental Adversaries<br />
*Success to the Successful<br />
*Tragedy of the Commons<br />
*Attractiveness Principle<br />
*Growth and Underinvestment<br />
*Growth and Underinvestment with Drifting Standard<br />
<br />
== System Archetype Family Tree ==<br />
To recognize the fact that the archetype is suitable for such a situation can be helpful the Family tree of system archetypes. Family tree is an ordered tree that based on simple questions is capable of directing the user to select the correct system archetype for concrete application.<br />
<br />
== Balancing Loop ==<br />
Balancing Loop contains a negative feedback, which is helping the current state to be closer to the target state. Loop is stabilizing due to the negative feedback. When current state is deflected from the target state it helps to return to a desired state. The greater the difference between the target state and current state cause the faster convergence to final state.<br />
<br />
[[File:Archetype-balancing-structure.jpg|thumb|centre|upright=0.5|Balancing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be filling of an empty glass of water. Initially, the glass is empty, and thus the difference between the actual water level (glass is empty) and the required water level (glass is full) is the maximum. Glass is filled with water and a difference of the current water level and the required water level continues to decrease. At the beginning the glass is filled quickly, and it is obvious that much water is missing, then the difference which lowers the intensity of the water flow. At the moment of achieving the required water level there is no longer any motivation to continue with glass filling. Desired state becomes state achieved –the process is terminated.<br />
<br />
== Balancing Loop with Delay ==<br />
This loop is based on the previous one, except there is a delay, which plays a role in overall system responsiveness. The combination of the negative feedback and the delay in the system creates oscillations. The system is trying to achieve the target state, but because of delay, the system detects the information later. The system thus exceeds the target state.<br />
<br />
[[File:Archetype-balancing-structure-with-delay.jpg|thumb|centre|upright=0.5|Balancing Loop with Delay <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a room heated by a heater equipped with a thermostat. The thermostat switches on the heating when the room temperature is lower than desired. Heating begins to heat up and the thermostat switches off the heating when the room is heated up to the desired temperature. Heating is switched off, but for some time after shutdown is still hot. The room air is heated to higher temperature than the desired one, and this principle is repeated over and over again. Room temperature oscillates around a desired temperature, and it is never stabilized on the desired temperature level.<br />
<br />
== Indecision ==<br />
Main character is an oscillation which is created by combining of two balancing loops with delays. One loop reaches the desired state, and the target state of the second loop moves and that in turn influences the first loop. Both loops never reach their goals together and constantly oscillate around their target states.<br />
<br />
[[File:Archetype-indecision.jpg|thumb|centre|upright=0.5|Indecision <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
Examples may be currency market. Currency prices on the stock exchange is determined by supply and demand. When the offer price is lower than the price that buyers are willing to pay, then the purchasers buy larger amount of the currency. The seller realizes that the price is lower than it might be, and thus they increase the price, it will discourages potential buyers and the price will again gradually decrease below what buyers are willing to pay. The whole process is continually repeated and price oscillates around an equilibrium market price.<br />
<br />
== Drifting Goals ==<br />
It is relatively simple archetype, in which two balancing loops are connected, but in this case there are no delays. Loops stand in opposite of each other and the effort made by one loop cause impossibility to balance the second loop and vice versa.<br />
<br />
[[File:Archetype-drifting-goals.jpg|thumb|centre|upright=0.5|Drifting Goals <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a company that offers trips to the sea. The company decided to offer an additional service to its customers. Due to additional service costs of tours increase and the final price of trips will be higher than before. Next season customers begin to migrate to the competition, and the company decides to reconsider its initial plans and choose a compromise solution. The original goal has not been met and there has been erosion of targets.<br />
<br />
== Escalation ==<br />
This archetype is built on two or more interconnected loops. Increasing the target value in one loop tends to increase the second loop, and possibly other loops. The loops have a negative feedback. Due to the relatively higher value of one loop the other loops values grows. In the system as a whole, the target values continuously increasing in a cycle. Growth of the values can only be stopped by external constraints such as economic constraint, time constraint or space constraint.<br />
<br />
[[File:Archetype-escalation.jpg|thumb|centre|upright=0.5|Escalation <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A typical example is the arms race. One country considers that it is threatened by another country. The first country raises its arms capacity and it forces the other country to also expand its weapons facilities. This again creates concerns among the first earth and once again raises his arms capacity, and the whole cycle repeats. Stop the escalation of the situation usually only lack of resources on one or both sides.<br />
<br />
== Fixes That Fail ==<br />
Archetype is a combination of stabilizing and reinforcing loops. Stabilizing loop trying to reduce the gap between current and desired state. The difference between these states is solved in a way which does not eliminate the problems. This solution only delays the inevitable result and situation as a whole is even worse. The more often undesirable states are solved by an intervention which does not remove the problem, the faster the actual solution gets more difficult.<br />
<br />
[[File:Archetype-fixes-that-fail.jpg|thumb|centre|upright=0.5|Fixes That Fail <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example is the so-called debt trap. Situation where one loan is repaid by another. The first loan is repaid by another loan but under worse conditions, which making the whole situation worse. The situation is temporarily solved, but in the long term, the overall situation deteriorates and more loans always brings a deterioration of the situation and the only apparent solution of the problem.<br />
<br />
== Shifting the Burden ==<br />
Archetype captures the situation when dealing with a problem is solved only by temporarily solution. This short-term also affects the fundamental solution. Attention is given only to short-term solution or a solution which solves only side effects.<br />
<br />
[[File:Archetype-shifting-burden.jpg|thumb|centre|upright=0.5|Shifting the Burden <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example may be the treatment of serious illness. The patient's doctor prescribes for example medication which only relieve pain caused by serious illness. Patient for a short time period feels better, but subsequently his condition gets. Disease gets to an advanced stage and it is significantly more difficult to cure it than at the beginning.<br />
<br />
== Addiction ==<br />
It is based on the archetype Fixes That Fail and it appear where short-term solutions are applied. Short-term solutions gradually lose their effect.<br />
<br />
[[File:Archetype-addiction.jpg|thumb|centre|upright=0.5|Addiction <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example is drug addiction. Drug addict person after a long abstinence feels unwell and he trying to improve his condition by taking another dose of drugs. Condition temporarily improved, but on the other hand, the addiction is than stronger. Drug addict person needs another dose but this dose must be stronger to trigger a similar denial of the original problem of addiction.<br />
<br />
== Reinforcing Loop ==<br />
Reinforcing Loop is the second of two basic archetypes. It serves as a building element, which appears in the other system archetypes. Reinforcing Loop describes a situation where there is a persistent increase or decrease. This loop always leads to a unidirectional development. It is basically the exact opposite of the Balancing Loop.<br />
<br />
[[File:Archetype-reinforcing.jpg|thumb|centre|upright=0.5|Reinforcing Loop <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be a savings account, which is initially deposited by an amount. The account offers a constant interest rate. After each crediting of interest, the originally deposit increases. In the next period the interest is calculated based on higher amount of money. This situation, however, abstracts from the inflation.<br />
<br />
== Limits to Growth ==<br />
This archetype is a combination of two basic loops - Balancing Loop and Reinforcing Loop. Reinforcing Loop represents steady growth, which is limited by Balancing Loop. Because of Balancing Loop system cannot grow infinitely and always has its limitations.<br />
<br />
[[File:Archetype-limits-to-growth.jpg|thumb|centre|upright=0.5|Limits to Growth <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example might be global population growth. The population is bigger, it grows faster, but the faster it grows, the more resources it needs for its development. Demands on resources disproportionately increases in comparison with available resources on the planet. Growth is limited and cannot be infinite.<br />
<br />
== Accidental Adversaries ==<br />
This archetype is composed of two Reinforcing Loops and around them is the Balancing Loop. Archetype describes the situation where two or more parties try to work, but also trying to increase their own benefit. Efforts to increase their own benefit leads to a reduction in the benefit of the other party and thus of cooperating parties become party rival.<br />
<br />
[[File:Archetype-accidental-adversaries.jpg|thumb|centre|upright=0.5|Accidental Adversaries <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A good example might be a company represented by its owners and top managers. Owners and managers have a common interest, which is the prosperity of the company. Even though they have a common interest, the interest of each party can be dispersed by other factors. Managers, for example, want to gain bonuses for their performance so they try to artificially inflate the growth and performance of the company. Owners may actually want to realize their short-term gains and thus can choose to pay disproportionately large dividends. This behaviour lead to the fact that the teammates become opponents and their behaviour does not lead to a single common goal.<br />
<br />
== Success to the Successful ==<br />
This model consists of the two Reinforcing Loops which has mutually opposite tendency. One loop is growing and the other declining. The more one loop grows, the more the second loop decreases and vice versa.<br />
<br />
[[File:Archetype-success-to-the-successful.jpg|thumb|centre|upright=0.5|Success to the Successful <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
A practical example is Self-fulfilling prophecy. Imagine two people, a confident, successful, another vice versa lacking confidence and success. These two people are constantly reinforcing that there are those who they think they are, and the gulf between them constantly increase. First self-confidence person is going better and better which gives him the courage to push further, the second person on the contrary, confirms that he fails and his self-esteem drops.<br />
<br />
== Tragedy of the Commons ==<br />
Tragedy of the Commons is a situation where two or more parties fighting for a common and limited resource. The fewer the resources left, the more they try to get the share.<br />
<br />
[[File:Archetype-tragedy-of-the-commons.jpg|thumb|centre|upright=0.5|Tragedy of the Commons <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example might be global fight between various states and corporations about limited oil resources - oilfields. The less new oilfields appear and the faster the current are depleted, the more resources and effort is devoted to extraction. Due to limited resources and shrinking the whole situation escalates further, the situation is tense and there is more conflict and these are more serious than before.<br />
<br />
== Attractiveness Principle ==<br />
Archetype is derived from the Limits to Growth archetype. It is extended by the fact that it is listed more than one threshold. Whereby the limits may not be as big and does not have to limit the same parameter. The basis in this model are the Reinforcing Loops, which is identical with that appearing in the Limits to Growth archetype. Loop strengthens and accelerates time to grow. In the model, there are also Stabilizing Loops that will bring the current state of the coasts. Balancing act with a delay, thus it is possible to establish a short-term imbalance that is gradually stabilized.<br />
<br />
[[File:Archetype-attractiveness-principle.jpg|thumb|centre|upright=0.5|Attractiveness Principle <ref name="sherrer"></ref>]]<br />
<br />
== Growth and Underinvestment ==<br />
Archetype based on the Limits to Growth archetype. The difference here is that the limiting factor or factors dynamically develops along with the development of the entire system. Limitations are therefore not constant, but varies in time. The model shows the system which not enough to invests to itself and create its own limitations in future growth. Archetype is comprised of three loops, two Balancing Loops and one Reinforcing Loop.<br />
<br />
[[File:Archetype-growth-and-underinvestment.jpg|thumb|centre|upright=0.5|Growth and Underinvestment <ref name="sherrer"></ref>]]<br />
<br />
'''Example:'''<br />
An example from the life may be preparation of high school student for study at university. If the student does not pay sufficient attention to current high school studies, he can thus create restrictions of his future potential. For example, he will not be accepted to university which he wants to or will be dismissed because does not manage to keep pace with other students. Neglected the study of lower grade may bring future constraints in a higher degree.<br />
<br />
== Growth and Underinvestment with Drifting Standard ==<br />
Archetype based on previous archetype, which is enriched with fourth loop. The fourth loop represents a certain standard, which change over time and reduces the need for future changes. Overall, this tendency leads to the overall growth of the system decreases.<br />
<br />
[[File:Archetype-growth-and-underinvestment-with-drifting-standard.jpg|thumb|centre|upright=0.5|Growth and Underinvestment with Drifting Standard <ref name="sherrer"></ref>]]<br />
<br />
== See also ==<br />
*[[System Dynamics]]<br />
*[https://en.wikipedia.org/wiki/The_Fifth_Discipline The Fifth Discipline]<br />
== References ==<br />
<references/><br />
*Taborga, Jorge. Systems Archetypes and Their Application.2016 [seen 23. 1. 2016]. Available at https://www.saybrook.edu/rethinkingcomplexity/posts/08-15-11/systems-archetypes-and-their-application<br />
<br />
== External links ==<br />
*[https://insightmaker.com/tag/Systems-Archetypes Insight Maker] - A free dynamic modeling and simulation web application.<br />
<br />
[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 17:21, 23 January 2016 (CET)</div>Xkrep33http://www.simulace.info/index.php?title=File:Family_Tree.gif&diff=10803File:Family Tree.gif2016-01-23T14:37:08Z<p>Xkrep33: </p>
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<div>Semestral papers from winter term 2015/2016. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2015/2016|assignment approved]].<br />
<br />
==Simulations==<br />
--[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 15:35, 15 January 2016 (CET) [[Maze Solving Robot Simulation]]<br />
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--[[User:Dinara|Dinara]] ([[User talk:Dinara|talk]]) 22:30, 15 January 2016 (CET) [[Aircraft boarding methods]]<br />
<br />
--[[User:OtakarTrunda|OtakarTrunda]] ([[User talk:OtakarTrunda|talk]]) 11:48, 16 January 2016 (CET) [[Ultimatum Game]]<br />
<br />
--[[User:Xpokl18|Xpokl18]] ([[User talk:Xpokl18|talk]]) 06:51, 17 January 2016 (CET) [[RetrieverBreeder]]<br />
<br />
--[[User:Martin.zima|Martin.zima]] ([[User talk:Martin.zima|talk]]) 10:28, 17 January 2016 (CET) [[Lane-merge optimization]]<br />
<br />
--[[User:Xhudj17|Xhudj17]] ([[User talk:Xhudj17|talk]]) 11:25, 17 January 2016 (CET) [[Crossroad simulation]]<br />
<br />
--[[User:xtomp36|xtomp36]] ([[User talk:xtomp36|talk]]) 15:33, 17 January 2016 (CET) [[Load-balancing]]<br />
<br />
==Papers==<br />
--[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 18:14, 22 January 2016 (CET) [[System Archetypes]]</div>Xkrep33http://www.simulace.info/index.php?title=Pseudorandom_number_generators&diff=10708Pseudorandom number generators2016-01-19T10:31:20Z<p>Xkrep33: </p>
<hr />
<div>Random numbers are useful for a variety of purposes, such as generating data encryption keys, simulating and modeling complex phenomena and for selecting random samples from larger data sets. They have also been used aesthetically, for example in literature and music, and are of course ever popular for games and gambling. When discussing single numbers, a random number is one that is drawn from a set of possible values, each of which is equally probable, i.e., a uniform distribution. When discussing a sequence of random numbers, each number drawn must be statistically independent of the others.<ref name="intro">[http://www.random.org/randomness/ Introduction to Randomness and Random Numbers, 2012, Random.org]</ref><br />
<br />
This chapter aims at explaining why it's hard and very important (besides interesting) to understand how to get a computer to generate proper random numbers. Since most of currently used security and encryption standards depend on random numbers, it is easy to imagine that by selecting not secured algorithms causes many aspects of our digital lives to become exposed to clever programmers and companies interested in data analysis or less legal practices (identity theft, surveillance, bank fraud and so on). One must realize that the privacy of for example all their banking activities, email communication, social networking and so on heavily depends on randomness in applied security mechanisms.<br />
<br />
In cryptography, a pseudorandom generator (or PSG) is procedure that outputs a sequence computationally indistinguishable from truly random sequence with uniformly distributed random sequence. The prefix pseudo (from Greek ψευδής "lying, false") is used to mark something as false, fraudulent, or pretending to be something it is not. Pseudo random generators find application in many fields besides cryptography such as applied mathematics, physics and simulations.<br />
Simulations often require mechanisms producing sequences of random values. These procedures are certainly non-trivial and often require significant amounts of computational time.<br />
<br />
<br />
__TOC__<br />
<br />
== Definition ==<br />
A pseudorandom generator (or PRG) is a (deterministic) map <math>{{G:\{0, 1\}^l \rightarrow \{0, 1\}^n}}</math>, where <math>{{n \geq l}}</math>. Here <math>{{l}}</math> is the 'seed length' and <math>{{n - l \geq 0}}</math> is the 'stretch'. We typically think that <math>{{n \gg l}}</math> and that <math>{{G}}</math> is efficiently computable in some model. If <math>{{f:\{0, 1\}^n \rightarrow \{0, 1\}}}</math> is any 'statistical test', we say that G '<math>{{\epsilon}}</math>-fools' <math>{{f}}</math> is<br />
<br />
<br />
<math>{{|Pr[f({U_n} = 1)] - Pr[f(G({U_l})) = 1)]| \leq \epsilon}}</math><br />
<br />
<br />
where <math>{U_m}</math> denotes a uniformly random string in <math>{{\{0, 1\}^m}}</math>. Here the string <math>{{U_l}}</math> is called the 'seed'. If <math>{{C}}</math> is a class of tests, we say that G '<math>{{\epsilon}}</math>-fools <math>{{C}}</math>' or is an '<math>{{\epsilon}}</math>-PRG against <math>{{C}}</math>' if <math>{{G}}</math> <math>{{\epsilon}}</math>-fools <math>{{f}}</math> for every <math>{{f \in C}}</math>.<ref name="cmu">[http://www.cs.cmu.edu/~odonnell/complexity/docs/lecture16.pdf Lecture 16: Nisan's PRG for small space, 15-855: Intensive Intro to Complexity Theory. Spring 2009, Carnegie Mellon University, USA.]: page 1</ref><br />
<br />
<br />
In other words we are trying to convince any outside party (let's call them an adversary) that sequences returned from PRG are being produced chosen at random. Adversary may use statistical test algorithms to check simultaneously outputs from PRG and uniformly random sequnces. PRGs ensure that both outputs look the same to the adversary..<br />
<br />
== Required properties ==<br />
Reliable PRG should have all these properties<ref name="props">[http://www.cs.iit.edu/~cs549/lectures/CNS-6-2-handout.pdf Xiang-Yang Li, Pseudo-random Number - Cryptography and Network Security. Illinois Institute of Technology, USA]</ref>:<br />
<br />
'''Unbiased - Uniform distribution'''<br/><br />
By definition of the word unbiased this property states that PRG is showing no prejudice for or against something. In PRG language this means that all values of whatever sample size is collected are equiprobable. This property ensures the independence of the generator and its stability against certain types of attacks.<br />
<br />
<br />
'''Unpredictable - Independence'''<br/><br />
It is impossible to predict what the next output will be, given all the previous outputs, but not the internal state. If this is not guaranteed then basically anyone can pose as a generator. This is used for example in the man in the middle attack.<br />
<br />
<br />
'''Unreproducible'''<br/><br />
Two of the same generators, given the same starting conditions, will produce different outputs. Certain types of spoofing attacks try to reproduce subset of real production environment in order to exploit the lack of security in respect with this condition.<br />
<br />
<br />
'''Long period'''<br/><br />
The generator should be of long period because this property directly influences the randomness of generated outputs. This is crucial for example for simulations since they are conducted in order to simulate dynamic behavior and states of an environment not only the cyclic stages of an environment.<br />
<br />
<br />
'''Fast computation'''<br/><br />
The generator should be reasonably fast. It is always a good idea to take care about the users of the generator since they might be in a quite constrained environment either by hardware specifications or by expected time performance.<br />
<br />
<br />
'''Security'''<br/><br />
The generator should be secured. Basically, security of the generator ensures that no one can break the generator in reasonable time either by the brute-force approach or by more clever ones. If there is no polynomial-time algorithm that on the first <math>m</math> output sequence can predict the <math>{m + 1}^{th}</math> bit with probability greater than 0.5 we consider the generator to be secured.<br />
<br />
== Construction of simple PRG ==<br />
There are many ways how to construct PRGs and one of the simplest ones is to use pseudorandom functions and expand the key. A pseudorandom function (or PRF) is any function defined over <math>{{\textnormal(K, X, Y)}}</math> :<br />
<br />
<math>{{F:K \times X \rightarrow Y}}</math><br />
<br />
where:<br />
<br />
# <math>{{K}}</math> is key space<br />
# <math>{{X}}</math> is input space<br />
# <math>{{Y}}</math> is output space<br />
<br />
such that exists efficient algorithm to evaluate <math>{{\textnormal F(k,x)}}</math>.<ref name=crypto>[https://class.coursera.org/crypto/class/index Boneh, D. (2012); Lecture 3 - Block ciphers, Introduction to Cryptography. Stanford, USA.]</ref><br />
<br />
<br />
On the other hand pseudorandom permutation is any function defined over <math>{{\textnormal(K, X)}}</math>:<br />
<br />
<math>{{E:K \times X \rightarrow X}}</math><br />
<br />
such that<ref name=crypto/>:<br />
<br />
# Exists efficient deterministic algorithm to evaluate <math>{{\textnormal E(k,x)}}</math><br />
# The function <math>{{\textnormal F(k,\cdot)}}</math> is one-to-one<br />
# Exists efficient inversion algorithm <math>{{\textnormal D(k,y)}}</math><br />
<br />
<br />
Any pseudorandom permutation (or PRP) is also pseudorandom function given<br />
# <math>{{X=Y}}</math><br />
# Function is efficiently invertible<br />
<br />
<br />
So let <math>{{F: K \times \{0, 1\}^n \rightarrow \{0, 1\}^n}}</math> be a PRF<br />
<br />
<br />
<math>{{<br />
\begin{cases}<br />
Functions[X, Y]: \text{ all functions from X to Y}\\<br />
S_F = {F(k, \cdot) st. k \in K} \subseteq Functions[X, Y]<br />
\end{cases}<br />
</math><br />
<br />
<br />
For PRF to be suitable for use in PRG it must be secure and therefore computationally indistinguishable from random function <math>{{f(\cdot)}}</math>. This situation is depicted below:<br />
<br />
<br />
[[File:secure_prf.png|x125px]]<br />
<br />
<br />
The adversary can not distinguish whether output came from <math>{{S_F}}</math> or some random <math>{{f(\cdot)}}</math>.<br />
<br />
<br />
If <math>{{F}}</math> is secure PRF we can use key expantion to construct secure PRG defined as<br />
<br />
<br />
<math>{{G: K \times \{0, 1\}^{nt}}}</math><br />
where:<br />
# <math>{{n}}</math> number of bits in each block<br />
# <math>{{t}}</math> number of generated blocks<br />
<br />
<br />
We get return value by using key expansion. Great advantage of using this approach is ability to employ multiple CPU cores and take advantage of parallelization (for example odd values are computed by core 1; even values are computed by core 2). Security of PRG is provided by fact that <math>{{F(k, \cdot)}}</math> is indistinguishable from random <math>{{f(\cdot)}}</math>.<br />
<br />
<math>{{G(k) = F(k, 0) || F(k, 1) || ... || F(k, t-1)}}</math><br />
<br />
==Linear methods==<br />
<br />
=== Linear Congruential Generator ===<br />
{| class=infobox width=250px<br />
|style="background:#33CC33;color:#FFFFFF"|<center>'''Advantages'''</center><br />
|-<br />
|<ul><br />
<li>fast computation time</li><br />
<li>small memery requirements</li><br />
<li>suitable for embedded systems</li><br />
<li>suitable for gaming consoles (high order bits)</li><br />
|</ul><br />
|}<br />
{| class=infobox width=250px<br />
|style="background:#EB1405;color:#FFFFFF"|<center>'''Disadvantages'''</center><br />
|-<br />
|<ul><br />
<li>low quality of randomness</li><br />
<li>selection from <math>n</math>-dimensional space produces points positioned on lines</li><br />
<li>weak against the spectral test</li><br />
<li>big differences in the length of the period for low and high order bits</li><br />
|</ul><br />
|}<br />
Linear Congruential Generator (or LCG) is one of the best known PRGs in the world. This generator is defined as follows<br />
<br />
: <math>X_{n+1} \equiv \left( a X_n + c \right)~~\pmod{m}</math><br />
<br />
where:<br />
# <math>X_{n}</math> is the sequence of pseudorandom values<br />
# <math> X_0,\,0 \le X_0 < m</math> is the seed<br />
# <math> a,\,0 < a < m</math> is the multiplier<br />
# <math> c,\,0 \le c < m</math> is the increment<br />
# <math> m,\, 0<m </math> is the modulo<br />
<br />
are integer constants that specify the generator.<ref name="Knuth-1997">Knuth, D. E. (1997). The Art of Computer Programming, volume 2. Addison Wesley, third edition.</ref><br />
<br />
The range of output values is restricted since after at most <math>m</math> values the period starts to repeat itself (in the terms of repeating the same pattern). The most significant element in terms of the length of the period is the multiplier. Best outputs from generator with regard to the length of the period are provided given values:<br />
<br />
*<math>a - 1</math> is divisable by all the primes that divide the multiplier<br />
*<math>a - 1</math> is multiple of number 4, when the multiplier is multiple of number 4<br />
*the multiplier and the increment do not have common divisor (except from 1)<br />
<br />
An example of output exported from WolframAplha application<ref name="LCG">[http://demonstrations.wolfram.com/LinearCongruentialGenerators/ WolframAlpha - Linear Congruential Generators (2012)]</ref>:<br />
<br />
[[File:LCG_example.png]]<br />
<br />
'''Example:'''<br/><br />
Following example shows how to compute first five output values for specified LCG:<br />
<br />
* <math>m = 7902</math><br />
* <math>a = 4331</math><br />
* <math>c = 3492</math><br />
* <math>x_0 = 1477</math>.<br />
<br />
{| class="wikitable"<br />
|+<br />
! <math>X_1</math> || <math>\equiv</math> || <math>4331 * X_0 + 3492 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || <math>4331 * 1477 + 3492 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || 7661<br />
|-<br />
! <math>X_2</math> || <math>\equiv</math> || <math>4331 * X_1 + 3492 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || <math>4331 * 7661 + 3492 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || 2785<br />
|-<br />
! <math>X_3</math> || <math>\equiv</math> || <math>4331 * X_2 + 3492 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || <math>4331 * 2785 + 3492 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || 6875<br />
|-<br />
! <math>X_4</math> || <math>\equiv</math> || <math>4331 * X_3 + 3492 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || <math>4331 * 6875 + 3492 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || 4381<br />
|-<br />
! <math>X_5</math> || <math>\equiv</math> || <math>4331 * X_4 + 3492 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || <math>4331 * 4381 + 3492 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || 4901<br />
|}<br />
<br />
=== Multiplicative Congruential Generator ===<br />
{| class=infobox width=250px<br />
|style="background:#003366;color:#FFFFFF"|<center>'''Choice of m:'''</center><br />
|-<br />
|Most natural choice for <math>m</math> is one that equals to the capacity of a computer word.<br/><br/><math>m = 2^b</math> (binary machine), where <math>b</math> is the number of bits in the computer word.<br/><br/><math>m = 10^d</math> (decimal machine), where <math>d</math> is the number of digits in the computer word.<br />
|}<br />
Multiplicative Congruent Generator (or MCG) is simplified version of LCG since if ''c'' = 0 in LCG we get the MCG<ref name="Knuth-1997"/>. This generator is defined as follows:<br />
<br />
<math>X_{n+1} \equiv a X_n ~~\pmod{m}</math><br />
<br />
where:<br />
# <math>X_{n}</math> is the sequence of pseudorandom values<br />
# <math> X_0,\,0 \le X_0 < m</math> is the seed<br />
# <math> a,\,0 < a < m</math> is the multiplier<br />
# <math> m,\, 0<m </math> is the modulo<br />
<br />
An example of output (of 59-bit multiplicative congruential generator) exported from WolframAplha application<ref name="MTaF">[http://demonstrations.wolfram.com/MersenneTwisterAndFriends/ WolframAlpha - Mersenne Twister and Friends (2012)]</ref>:<br />
<br />
[[File:MCG59_example.png]]<br />
<br />
'''Example:'''<br/><br />
Following example shows how to compute first five output values for specified MCG:<br />
<br />
* <math>m = 5037</math><br />
* <math>a = 3414</math><br />
* <math>x_0 = 1739</math>.<br />
<br />
{| class="wikitable"<br />
|+<br />
! <math>X_1</math> || <math>\equiv</math> || <math>X_0 * 3414 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || <math>1739 * 3414{\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || 3360<br />
|-<br />
! <math>X_2</math> || <math>\equiv</math> || <math>X_1 * 3414 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || <math>3360 * 3414{\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || 1791<br />
|-<br />
! <math>X_3</math> || <math>\equiv</math> || <math>X_2 * 3414 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || <math>1791 * 3414{\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || 4593<br />
|-<br />
! <math>X_4</math> || <math>\equiv</math> || <math>X_3 * 3414 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || <math>4593 * 3414{\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || 0321<br />
|-<br />
! <math>X_5</math> || <math>\equiv</math> || <math>X_4 * 3414 {\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || <math>0321 * 3414{\hskip 0.5cm} (mod{\hskip 0.15cm}7902)</math> || <math>\equiv</math> || 2865<br />
|}<br />
<br />
=== Lagged Fibonacci Generator ===<br />
{| class=infobox width=250px<br />
|style="background:#33CC33;color:#FFFFFF"|<center>'''Advantages'''</center><br />
|-<br />
|<ul><br />
<li>fast computation time</li><br />
<li>efficient implementation</li><br />
<li>long period</li><br />
<li>acceptable performance on standard statistical tests for randomness</li><br />
<li>a large number of independent streams of numbers can be generated from the same initial values</li><br />
|</ul><br />
|}<br />
{| class=infobox width=250px<br />
|style="background:#EB1405;color:#FFFFFF"|<center>'''Disadvantages'''</center><br />
|-<br />
|<ul><br />
<li>poor behavior with R(24,55) and smaller generators</li><br />
<li>weak against birthday spacing and generalized triple tests</li><br />
<li>complexity of the initialization of LFGs</li><br />
<li>outputs are very sensitive to initial conditions</li><br />
|</ul><br />
|}<br />
Lagged Fibonacci generator (or LFG) is one of the fastest PRGs providing long period. LFG is defined as follows<br />
<br />
<math>LFG(p, q, \bigoplus)</math><br />
<br />
where:<br />
# <math>p</math> is coeficient of lag, <math>p > q</math><br />
# <math>q</math> is coeficient of lag, <math>p > q</math><br />
# <math>\bigoplus</math> binary operation such as adding or subtracting in modulo <math>m</math>, multiplication in modulo <math>m</math> or bitwise exclusive <math>OR</math> (<math>XOR</math>)<br />
<br />
and the sequence is defined by<br />
<br />
<math>x_n = x_{n-p} \bigoplus x_{n-q}</math><br />
<br />
<br />
For generator to work, <math>p</math> and <math>q</math> must be odd numbers. Generator stores used <math>p</math> values in a lag table. In order to achieve the maximum length of the period and fair degree of randomness parameters need to be set in the following way:<br />
# <math>m = 2^b</math> <br />
# <math>p</math> and <math>q</math> have values of powers of primitive polynomials<br />
<br />
producing the length of the period for <math>XOR</math>:<br />
<br />
<math>P = 2^p - 1</math><ref name=LFG>[http://www.vutbr.cz/www_base/zav_prace_soubor_verejne.php?file_id=9489 Mikulka Z., (2008); Random Number Generators - Bachelor’s thesis. University of technology - Faculty of electrical engeneering and communication, Department of telecomunications, Brno.]: page 14</ref><br />
<br />
<br />
An example of an output<ref name=LFG-output>[http://sprng.cs.fsu.edu/Version2.0/test-results.html SPRNG Libraries Documentation]</ref>:<br />
<br />
[[File:LFG_example.gif]]<br />
<br />
=== Mersenne Twister ===<br />
{| class=infobox width=250px<br />
|style="background:#33CC33;color:#FFFFFF"|<center>'''Advantages'''</center><br />
|-<br />
|<ul><br />
<li>long period</li><br />
<li>passes number of tests for statistical randomness</li><br />
<li>passes most of the strict tests from TestU01 crush randomness tests</li><br />
|</ul><br />
|}<br />
{| class=infobox width=250px<br />
|style="background:#EB1405;color:#FFFFFF"|<center>'''Disadvantages'''</center><br />
|-<br />
|<ul><br />
<li>not suitable for cryptography (in its native form)</li><br />
<li>after certain number of observations one is able to predict outputs of the future iterations</li><br />
<li>it takes quite long time to turn non-random initial state into sufficiently random output that passes randomness tests</li><br />
<li>may require LFG or LCG to do the initial seeding</li><br />
|</ul><br />
|}<br />
Mersenne twister (or MT) is one of the modern implementations of PRGs and it provides very high quality of peudorandom numbers. It was developed by Makoto Matsumoto and Takuji Nishimura in 1997 with the aim of replacement of some known faults of older PRGs. MT is founded on a matrix linear recurrence over a finite binary field. Nowadays, the most used versions are MT with 32 and 64 bit word length. Due to optimizations applied to MT it is optimized to be used in [[Monte_Carlo_method|Monte Carlo method]] simulaions in many fields of science.<br />
<br />
Since the description of algorithm is quite , I would like to point the readers to [http://cryptography.gmu.edu/~jkaps/download.php?docid=1083 this paper] from George Mason University where the internal mechanics of MT are explained in detail.<br />
<br />
An example of output (of Mersenne twister shift register generator) exported from WolframAplha application<ref name="MTaF"/>:<br />
<br />
[[File:MT_example.png]]<br />
<br />
==Nonlinear methods==<br />
=== Blum Blum Shub ===<br />
{| class=infobox width=250px<br />
|style="background:#33CC33;color:#FFFFFF"|<center>'''Advantages'''</center><br />
|-<br />
|<ul><br />
<li>unpredictable (passes next-bit test)</li><br />
<li>security is based on difficulty of factoring <math>N</math></li><br />
<li>suitable for key generation</li><br />
|</ul><br />
|}<br />
{| class=infobox width=250px<br />
|style="background:#EB1405;color:#FFFFFF"|<center>'''Disadvantages'''</center><br />
|-<br />
|<ul><br />
<li>slow computation times</li><br />
<li>uses very large numbers</li><br />
<li>not suitable for cipher use</li><br />
|</ul><br />
|}<br />
Blum Blum Shub (or BBS) is PRG developed by Lenore Blum, Manuel Blum and Michael Shub in 1986. It is defined as follows:<br />
<br />
<math>x_{n+1} = x_n^2 \hspace{0.5cm} mod M</math><br />
<br />
where:<br />
# <math>p</math> random large prime<br />
# <math>q</math> random large prime<br />
# <math>M</math> is th eproduct of <math>p</math> and <math>q</math><br />
# <math>x_0</math> is the seed; usually an integer that is co-prime to <math>M</math><ref>Lenore Blum, Manuel Blum, and Michael Shub. "A Simple Unpredictable Pseudo-Random Number Generator", SIAM Journal on Computing, volume 15, pages 364–383, May 1986.</ref><br />
<br />
<br />
BBS does not find application in simulations due to its running time. On the other hand due to it's security properties it is appropriate for use in cryptography.<br />
<br />
An example of output constructed in Maple 14 (seed=10, range=1000):<br />
<br />
[[File:BBS_example.gif]]<br />
<br />
==Testing==<br />
Very important concept to ensure reliability and determine possible areas of use is testing PRGs. There are many tests for PRGs. First of all let's take a look at bacis catogories of tests.<br />
<br />
=== Theoretic tests ===<br />
These tests aim at detailed study of internal structure of a PRG, its parameters and inner workings. They are typically used when theoretical concepts of PRG are publicly known. From cryptography we know that best way to test any security concept is to assume that adversary already knows the structure and inner workings of tested concept so we rely on complexity of the math backing this concept.<br />
<br />
<br />
These tests aim at:<br />
* finding logical gaps in proposed solutions<br />
* short comings in the ways parameters are processed<br />
* detail analysis and possible test covarage (in terms of the software development testing)<br />
<br />
<br />
Examples:<br />
* Autocorrelation test<br />
** Analysis serial correlation of the members of the sequence. Very nice example and tutorial how to approach this problem is described [http://www.aritzhaupt.com/resource/autocorrelation/ here] with simple applet for computing the autocorrelation test<br />
* Spectral test<br />
** This test detects periodical aspects of produced sequences. Nice application of these test on LCGs is demonstrated [http://random.mat.sbg.ac.at/tests/theory/spectral/ here]<br />
<br />
=== Blackbox testing ===<br />
Sometimes adversary does not have an access to the used PRG or simply cannot determine what kind of PRG (if any) is used. In these cases adversary uses another approach and tries to determine the PRG by supplying certain groups of parameters and analysing and testing provided result sequences. Result analysis is based on finding similarities and patterns in result sets. In case of less secured PRG the adversary is able to determine just by doing that, whether they are interacting with PRG or truly random function. An adversary might be able to determine a kind of PRG or even the concrete implementation based on the randomness of gathered outputs.<br />
<br />
<br />
These tests aim at:<br />
* faults in PRG inner workings<br />
* repeating sequences and patterns in result sequences<br />
* outputs of certain combination of entry parameters<br />
<br />
==References==<br />
<references /><br />
==Extarnal links==<br />
# [http://demonstrations.wolfram.com/LinearCongruentialGenerators/ WolframAlpha - Linear Congruential Generator]<br />
# [http://demonstrations.wolfram.com/MersenneTwisterAndFriends/ WolframAlpha - Mersenne Twister simulator]<br />
# [http://cryptography.gmu.edu/~jkaps/download.php?docid=1083 Mersenne Twister – A Pseudo Random Number Generator and its Variants]<br />
# [http://www.aritzhaupt.com/resource/autocorrelation/ Autocorrelation test]<br />
# [http://random.mat.sbg.ac.at/tests/theory/spectral/ Spectral test]<br />
# [http://www.random.org Web dedicated to the 'randomness']<br />
<br />
==Self test==<br />
1. What is the plaintext for cipher text "33 63 66 15 41 79 85 15 65 58 85" with following encryption properties:<br />
<br />
Lets assume that a secret message has been prepared by converting the letters into digits following the rule:<br />
<br />
{| class="wikitable" style="text-align:center;"<br />
|+<br />
! || A || B || C || D || E || F || G || H || I || J || K || L || M || N || O || P || Q || R || S || T || U || V || W || X || Y || Z<br />
|-<br />
! Letter code || 01 || 02 || 03 || 04 || 05 || 06 || 07 || 08 || 09 || 10 || 11 || 12 || 13 || 14 || 15 || 16 || 17 || 18 || 19 || 20 || 21 || 22 || 23 || 24 || 25 || 26<br />
|}<br />
<br />
Then the successive digits are added, modulo 10 to the successive digits of the output of a LCG with following properties: <math>m = 8397</math>, <math>a = 4381</math>, <math>c = 7364</math> and <math>x_0 = 2134</math>.<br />
<br />
If <math>x_1 = 1234</math>, then cipher for plaintext AB is:<br />
{| class="wikitable"<br />
|+<br />
! Plaintext || A || B<br />
|-<br />
! Plaintext code || 01 || 02<br />
|-<br />
! Key digits || 12 || 34<br />
|-<br />
! Ciphertext || 13 || 36<br />
|}<br />
----<br />
2. Let <math>F:K \times X \rightarrow \{0, 1\}^{128}</math> be a secure PRF. I following generator G a secure PRF?<br />
<br />
<math>G(k, x) = {<br />
\begin{cases}<br />
0^{128} {\hskip 1cm} \text{if } x = 0\\<br />
F(k, x) {\hskip 0.5cm} \text{otherwise}\\<br />
\end{cases}<br />
</math><br />
<br />
* a) No, it is easy to distinguish G from a random function<br />
* b) Yes, an attack on G would also break F<br />
* c) It depends on F<br />
----<br />
3. Which required property of PRG is not fulfilled based on the following output from a generator:<br />
{| class="wikitable"<br />
|+<br />
! Input || 235 || 803 || 186 || 597 || 931 || 235 || 274 || 727<br />
|-<br />
! Output || 345812 || 971486 || 207319 || 349183 || 729460 || 345812 || 367428 || 319708<br />
|-<br />
|}<br />
<br />
* a) unbiased<br />
* b) unpredictable<br />
* c) unreproducible<br />
* d) none of the above<br />
----<br />
4. What is value of <math>X_8</math> for MCG defined as follows: <math>m = 6478</math>, <math>a = 5620</math>, and <math>x_0 = 3671</math>.<br />
<br />
* a) 4572<br />
* b) 3649<br />
* c) 2892<br />
* d) 6217<br />
----<br />
5. Categorize test based on its description.<br />
<br />
Birthday spacings: Choose random points on a large interval. The spacings between the points should be asymptotically exponentially distributed. The name is based on the birthday paradox.<br />
<br />
* a) theoretic test<br />
* b) blackbox testing<br />
----<br />
=== Solution ===<br />
* '''1. Answer:''' LCGINACTION<br />
** First determine first 6 outputs of the generator. Get letter codes by reversed modulo 10 addition and translate the ciphertext.<br />
* '''2. Answer:''' a<br />
** When the adversary queries G at x = 0 they always get 0 and they know they are interacting with PRF and not truly random function.<br />
* '''3. Answer:''' c<br />
** Generator produced same output for two identical inputs<br />
* '''4. Answer:''' c<br />
* '''5. Answer:''' a</div>Xkrep33http://www.simulace.info/index.php?title=Maze_Solving_Robot_Simulation&diff=10431Maze Solving Robot Simulation2016-01-16T21:18:45Z<p>Xkrep33: </p>
<hr />
<div>One of the typical tasks of the basics of robotics is to build a robot that is able to find his way out of the maze. For this task are used robots that can move along a flat surface, turn and scan the area beneath them at the same time. Maze is represented by a black line on a white surface. The robot moves along the black line, and his aim is to find the end of the maze, which is usually represented by large black rectangle. Task respectively robot is commonly called Line Maze Solving Robot.<br />
<br />
The robot is driven by preloaded algorithm. There are many different algorithms and their modifications. One of the basic algorithms is Left Hand Rule or Right Hand Rule. The algorithm works as follows, the robot prefers left move before moving forward. If this rule cannot be applied, then prefers forward move instead of right move. The algorithm works for all mazes that cannot contain infinite loop, which the robot is not able to solve.<br />
<br />
Algorithm Left Hand Rule (Right Hand Rule) is used for example for the initial exploration of the maze (for more information see for example this presentation: https://www.pololu.com/file/0J195/line-maze-algorithm.pdf). Robot passes through the maze and finds its end. During a trip through the maze memorizes all the steps. Stored step data is then robot able to optimize and find the shortest route to the destination. During the second stage the robot can avoid the blind paths and solve the maze using shorter way. As part of this simulation is implemented only the first part - finding way out of the maze. This video shows how the process works: https://www.youtube.com/watch?v=Z0LIO0tEZG4.<br />
<br />
==Problem Definition==<br />
The primary goal is to create an autonomous robot which is Able to find way out of the maze. The robot is programmed and his movements cannot be interfered after he starts. The robot must be driven by a non-trivial algorithm that includes randomly generated numbers (solution for endless loop situations). The robot must be able to find a way out of maze in a finite time. It means he should not get into an endless cycle of no return.<br />
<br />
Secondary objective is to determine what is the difference between a primitive robot and the robot uses a smarter algorithm. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left.<br />
==Method== <br />
===Software===<br />
For simulation is used NetLogo software (2D version). NetLogo was chosen as the most appropriate tool for the simulation because it makes it easy to program the robot's behaviour while allowing visualization of his behaviour. NetLogo also allows to customize the user interface. User can test how a robot algorithm works in real-time and simultaneously monitor other indicators.<br />
<br />
===Autonomous Robot===<br />
Robot is able to move up, down, left and right (viewed from above). He is able to find the way out without any intervention. Robot is able to move in any environment (maze) where exists at least one possible way out.<br />
<br />
===Intelligent vs Primitive Robot===<br />
So called intelligent robot uses an algorithm that allows him to find way out of any maze, where such a path exists. Primitive robot on the other hand, is a robot which uses a very simple set of movement instructions. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left. Primitive robot is added to the simulation as a reference. Simulation of intelligent robot are shown in comparison with a primitive robot.<br />
<br />
===Environment (Maze)===<br />
The environment is adapted to the possibilities of the NetLogo. Environment is represented by the World in NetLogo. The World consists of black, grey and green patches. Black patches represent the path where robot can move. Grey patches represent walls, the robot cannot enter them and finally the green patches are the door out of the maze.<br />
<br />
The attached archive contains seven different mazes. Environments vary to demonstrate that the robot is able to avoid infinite loop situations. Maze was created using the Pac-Man Level Editor, which is part of the Library Models in the NetLogo program. Editor has been adapted for the needs of this simulation in accordance to the Creative Commons license. The revised editor served only as a tool for quick creating of a various environments, for the simulation itself is not necessary, therefore the editor is not part of the archive. Size of all seven mazes is 31 x 31 patches.<br />
<br />
==Model==<br />
===Intelligent Robot===<br />
As outlined in the introduction above, intelligent robot (red in NetLogo) uses Left Hand Rule algorithm. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. If any of situations mentioned is not possible, the robot will remain in place and only turn left. Later, when another tick occurs, robot again apply all the previous rules.<br />
<br />
Left Hand Rule algorithm is functional for all mazes that cannot contain endless loop. Those situation is not possible to solve by using this algorithm. The purpose of this simulation is to create the robot which is be able to avoid cases with endless loops. For this purpose, algorithm of the robot enriched an element of chance. In one percent of cases when ticks occur robot determine his rotation by a random number generator. For this purpose, is used function for generating random integers.<br />
<br />
===Primitive Robot===<br />
Primitive robot (blue in NetLogo) uses an algorithm that allows him to movement straight and if he encounters an obstacle turns left. This simple algorithm has the result that the robot is quite often stuck in an endless loop. Primitive robot is created only as a reference to intelligent robot.<br />
<br />
===User Interface===<br />
The user interface is divided into three areas. The first area Settings (left) is used to select one of the seven mazes, setting robot type, number of robots in a maze and to run a simulation. The second area Visualization (centre) displays the maze and motion of robots in real time. In the third part Statistics (right) are shown statistics of current simulation.<br />
<br />
====Settings====<br />
Settings consists of five control elements. Three of the five elements are the chooser type and allow to set up one of the seven mazes, setting robot type and number of robots. Mazes are stored in separated files and they need to be stored in the same folder as the file with nlogo extension. There are three combination of robot type. The maze can display separately each of the two types (intelligent and primitive) robots, or both types simultaneously. Selecting the number of robots adjusts how many instances of each type of robot will be displayed in the visualization. In the case that is shown more than one instance of robot, robots are not interact each other. Displaying multiple robots simultaneously serve only for better understanding of the behaviour of intelligent and primitive robot in shorter time. The remaining two buttons is used to apply the selected settings and run the entire simulation.<br />
<br />
[[File:Xkrep33 Settings.PNG]]<br />
<br />
====Visualization====<br />
The middle section shows the maze and selected type and number of robots.<br />
<br />
[[File:Xkrep33 Vizualization.PNG]]<br />
<br />
====Statistics====<br />
Statistics show three main indicators. The first indicates the number of robots who remain in the maze. This indicator captures all the robots who have not found their way out of the maze yet. The second indicator is a graphical representation of the number of robots remaining in maze during the time elapsed. The third indicator is the average number of steps that robots needed to solve the maze. All indicators are shown, after running a simulation.<br />
<br />
[[File:Xkrep33 Statistics.PNG]]<br />
<br />
==Results==<br />
The results are shown in the table. The table contains two figures for both types of robots. Indicator Number of robots remained means how many robots remained in maze when one type of robot solved the maze. If there were more than one robot at the beginning, so it means that all robots of one type solved the maze. Second indicator Average number of steps shows how many steps all robots of one type made on average when they solved the maze. If robots of some type were stuck in infinite loop situation, then numbers are provided in brackets.<br />
<br />
There were one thousand of robots of each type at the beginning of each simulation.<br />
<br />
{| class="wikitable"<br />
|+ Results of seven different mazes simulation<br />
! style="font-weight: bold;" | <br />
! colspan="2" style="font-weight: bold;" | Intelligent robots<br />
! colspan="2" style="font-weight: bold;" | Primitive robots<br />
|-<br />
| style="font-weight: bold;" | Maze No.<br />
| style="font-weight: bold;" | Number of robots remained<br />
| style="font-weight: bold;" | Average number of steps<br />
| style="font-weight: bold;" | Number of robots remained<br />
| style="font-weight: bold;" | Average number of steps<br />
|-<br />
| style="font-weight: bold;" | 1<br />
| 0<br />
| 587<br />
| 1,000<br />
| (24,804)<br />
|-<br />
| style="font-weight: bold;" | 2<br />
| 0<br />
| 528<br />
| 1,000<br />
| (4,536)<br />
|-<br />
| style="font-weight: bold;" | 3<br />
| 0<br />
| 812<br />
| 1,000<br />
| (24,043)<br />
|-<br />
| style="font-weight: bold;" | 4<br />
| 0<br />
| 80<br />
| 1,000<br />
| (744)<br />
|-<br />
| style="font-weight: bold;" | 5<br />
| 0<br />
| 519<br />
| 1,000<br />
| (12,626)<br />
|-<br />
| style="font-weight: bold;" | 6<br />
| 138<br />
| 160<br />
| 0<br />
| 117<br />
|-<br />
| style="font-weight: bold;" | 7<br />
| 0<br />
| 747,177<br />
| 1,000<br />
| (70,286)<br />
|}<br />
<br />
==Conclusion==<br />
Simulation was done in all seven mazes. Simulation was always done with a sample of thousand robots of each type (two thousand robots at once). The proposed algorithm of intelligent robots was proved. Robots using a smart algorithm base on Left Hand Rule algorithm and generating random numbers, have always been able to solve the maze. Only in one case (maze No. 6) were primitive robots faster than intelligent one. Even in this case, however, all intelligent robots solved the maze. The primary objective of the simulation, which was to create autonomous robot that does not fall into an infinite loop, was achieved.<br />
<br />
The results table clearly shows that primitive algorithm was not very effective. In six of the seven cases, the robots who used this algorithm, get into an endless loop with no return. Only in one case, all primitive robots get out of the maze, and even faster than so called intelligent. It was rather a coincidence, because the maze No. 6 is the only one that could be resolved only by moving straight with combination of left turning in case of an obstacle.<br />
<br />
It can be said that the algorithm used for intelligent robots was relatively effective and above all its results were very stable. The only exception was a maze No. 7. Maze No. 7 is essentially infinite space with one exit in the middle. But this maze was also solvable for intelligent robots.<br />
<br />
==Code==<br />
Downloadable archive with nlogo file and seven csv files:<br />
<br />
[[File:MazeSolvingRobotSimulation.zip]]<br />
<br />
[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 22:17, 16 January 2016 (CET)</div>Xkrep33http://www.simulace.info/index.php?title=Maze_Solving_Robot_Simulation&diff=10430Maze Solving Robot Simulation2016-01-16T18:13:53Z<p>Xkrep33: </p>
<hr />
<div>One of the typical tasks of the basics of robotics is to build a robot that is able to find his way out of the maze. For this task are used robots that can move along a flat surface, turn and scan the area beneath them at the same time. Maze is represented by a black line on a white surface. The robot moves along the black line, and his aim is to find the end of the maze, which is usually represented by large black rectangle. Task respectively robot is commonly called Line Maze Solving Robot.<br />
<br />
The robot is driven by preloaded algorithm. There are many different algorithms and their modifications. One of the basic algorithms is Left Hand Rule or Right Hand Rule. The algorithm works as follows, the robot prefers left move before moving forward. If this rule cannot be applied, then prefers forward move instead of right move. The algorithm works for all mazes that cannot contain infinite loop, which the robot is not able to solve.<br />
<br />
Algorithm Left Hand Rule (Right Hand Rule) is used for example for the initial exploration of the maze (for more information see for example this presentation: https://www.pololu.com/file/0J195/line-maze-algorithm.pdf). Robot passes through the maze and finds its end. During a trip through the maze memorizes all the steps. Stored step data is then robot able to optimize and find the shortest route to the destination. During the second stage the robot can avoid the blind paths and solve the maze using shorter way. As part of this simulation is implemented only the first part - finding way out of the maze. This video shows how the process works: https://www.youtube.com/watch?v=Z0LIO0tEZG4.<br />
<br />
==Problem Definition==<br />
The primary goal is to create an autonomous robot which is Able to find way out of the maze. The robot is programmed and his movements cannot be interfered after he starts. The robot must be driven by a non-trivial algorithm that includes randomly generated numbers (solution for endless loop situations). The robot must be able to find a way out of maze in a finite time. It means he should not get into an endless cycle of no return.<br />
<br />
Secondary objective is to determine what is the difference between a primitive robot and the robot uses a smarter algorithm. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left.<br />
==Method== <br />
===Software===<br />
For simulation is used NetLogo software (2D version). NetLogo was chosen as the most appropriate tool for the simulation because it makes it easy to program the robot's behaviour while allowing visualization of his behaviour. NetLogo also allows to customize the user interface. User can test how a robot algorithm works in real-time and simultaneously monitor other indicators.<br />
<br />
===Auto Robot===<br />
Robot is able to move up, down, left and right (viewed from above). He is able to find the way out without any intervention. Robot is able to move in any environment (maze) where exists at least one possible way out.<br />
<br />
===Intelligent vs Primitive Robot===<br />
So called intelligent robot uses an algorithm that allows him to find way out of any maze, where such a path exists. Primitive robot on the other hand, is a robot which uses a very simple set of movement instructions. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left. Primitive robot is added to the simulation as a reference. Simulation of intelligent robot are shown in comparison with a primitive robot.<br />
<br />
===Environment (Maze)===<br />
The environment is adapted to the possibilities of the NetLogo. Environment is represented by the World in NetLogo. The World consists of black, grey and green patches. Black patches represent the path where robot can move. Grey patches represent walls, the robot cannot enter them and finally the green patches are the door out of the maze.<br />
<br />
The attached archive contains seven different mazes. Environments vary to demonstrate that the robot is able to avoid infinite loop situations. Maze was created using the Pac-Man Level Editor, which is part of the Library Models in the NetLogo program. Editor has been adapted for the needs of this simulation in accordance to the Creative Commons license. The revised editor served only as a tool for quick creating of a various environments, for the simulation itself is not necessary, therefore the editor is not part of the archive. Size of all seven mazes is 31 x 31 patches.<br />
<br />
==Model==<br />
===Intelligent Robot===<br />
As outlined in the introduction above, intelligent robot (red in NetLogo) uses Left Hand Rule algorithm. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. If any of situations mentioned is not possible, the robot will remain in place and only turn left. Later, when another tick occurs, robot again apply all the previous rules.<br />
<br />
Left Hand Rule algorithm is functional for all mazes that cannot contain endless loop. Those situation is not possible to solve by using this algorithm. The purpose of this simulation is to create the robot which is be able to avoid cases with endless loops. For this purpose, algorithm of the robot enriched an element of chance. In one percent of cases when ticks occur robot determine his rotation by a random number generator. For this purpose, is used function for generating random integers.<br />
<br />
===Primitive Robot===<br />
Primitive robot (blue in NetLogo) uses an algorithm that allows him to movement straight and if he encounters an obstacle turns left. This simple algorithm has the result that the robot is quite often stuck in an endless loop. Primitive robot is created only as a reference to intelligent robot.<br />
<br />
===User Interface===<br />
The user interface is divided into three areas. The first area Settings (left) is used to select one of the seven mazes, setting robot type, number of robots in a maze and to run a simulation. The second area Visualization (centre) displays the maze and motion of robots in real time. In the third part Statistics (right) are shown statistics of current simulation.<br />
<br />
====Settings====<br />
Settings consists of five control elements. Three of the five elements are the chooser type and allow to set up one of the seven mazes, setting robot type and number of robots. Mazes are stored in separated files and they need to be stored in the same folder as the file with nlogo extension. There are three combination of robot type. The maze can display separately each of the two types (intelligent and primitive) robots, or both types simultaneously. Selecting the number of robots adjusts how many instances of each type of robot will be displayed in the visualization. In the case that is shown more than one instance of robot, robots are not interact each other. Displaying multiple robots simultaneously serve only for better understanding of the behaviour of intelligent and primitive robot in shorter time. The remaining two buttons is used to apply the selected settings and run the entire simulation.<br />
<br />
[[File:Xkrep33 Settings.PNG]]<br />
<br />
====Visualization====<br />
The middle section shows the maze and selected the type and number of robots.<br />
<br />
[[File:Xkrep33 Vizualization.PNG]]<br />
<br />
====Statistics====<br />
Statistics show three main indicators. The first indicates the number of robots who remain in the maze. This indicator captures all the robots who have not found their way out of the maze yet. The second indicator is a graphical representation of the number of robots remaining in maze during the time elapsed. The third indicator is the average number of steps that robots needed to solve the maze. All indicators are shown, after running a simulation.<br />
<br />
[[File:Xkrep33 Statistics.PNG]]<br />
<br />
==Results==<br />
The results are shown in the table. The table contains two figures for both types of robots. Indicator Number of robots remained means how many robots remained in maze when one type of robot solved the maze. If there were more than one robot at the beginning, so it means that all robots of one type solved the maze. Second indicator Average number of steps shows how many steps all robots of one type made on average when they solved the maze. If robots of some type were stuck in infinite loop situation, then numbers are provided in brackets.<br />
<br />
There were one thousand of robots of each type at the beginning of each simulation.<br />
<br />
{| class="wikitable"<br />
|+ Results of seven different mazes simulation<br />
! style="font-weight: bold;" | <br />
! colspan="2" style="font-weight: bold;" | Intelligent robots<br />
! colspan="2" style="font-weight: bold;" | Primitive robots<br />
|-<br />
| style="font-weight: bold;" | Maze No.<br />
| style="font-weight: bold;" | Number of robots remained<br />
| style="font-weight: bold;" | Average number of steps<br />
| style="font-weight: bold;" | Number of robots remained<br />
| style="font-weight: bold;" | Average number of steps<br />
|-<br />
| style="font-weight: bold;" | 1<br />
| 0<br />
| 587<br />
| 1,000<br />
| (24,804)<br />
|-<br />
| style="font-weight: bold;" | 2<br />
| 0<br />
| 528<br />
| 1,000<br />
| (4,536)<br />
|-<br />
| style="font-weight: bold;" | 3<br />
| 0<br />
| 812<br />
| 1,000<br />
| (24,043)<br />
|-<br />
| style="font-weight: bold;" | 4<br />
| 0<br />
| 80<br />
| 1,000<br />
| (744)<br />
|-<br />
| style="font-weight: bold;" | 5<br />
| 0<br />
| 519<br />
| 1,000<br />
| (12,626)<br />
|-<br />
| style="font-weight: bold;" | 6<br />
| 138<br />
| 160<br />
| 0<br />
| 117<br />
|-<br />
| style="font-weight: bold;" | 7<br />
| 0<br />
| 747,177<br />
| 1,000<br />
| (70,286)<br />
|}<br />
<br />
==Conclusion==<br />
Simulation was done in all seven mazes. Simulation was always done with a sample of thousand robots of each type (two thousand robots at once). The proposed algorithm of intelligent robots was proved. Robots using a smart algorithm base on Left Hand Rule algorithm and generating random numbers, have always been able to solve the maze. Only in one case (maze No. 6) were primitive robots faster than intelligent one. Even in this case, however, all intelligent robots solved the maze. The primary objective of the simulation, which was to create autonomous robot that does not fall into an infinite loop, was achieved.<br />
<br />
The results table clearly shows that primitive algorithm was not very effective. In six of the seven cases, the robots who used this algorithm, get into an endless loop with no return. Only in one case, all primitive robots get out of the maze, and even faster than so called intelligent. It was rather a coincidence, because the maze No. 6 is the only one that could be resolved only by moving straight with combination of left turning in case of an obstacle.<br />
<br />
It can be said that the algorithm used for intelligent robots was relatively effective and above all its results were very stable. The only exception was a maze No. 7. Maze No. 7 is essentially infinite space with one exit in the middle. But this maze was also solvable for intelligent robots.<br />
<br />
==Code==<br />
Downloadable archive with nlogo file and seven csv files:<br />
<br />
[[File:MazeSolvingRobotSimulation.zip]]</div>Xkrep33http://www.simulace.info/index.php?title=Maze_Solving_Robot_Simulation&diff=10429Maze Solving Robot Simulation2016-01-16T17:44:19Z<p>Xkrep33: </p>
<hr />
<div>One of the typical tasks of the basics of robotics is to build a robot that is able to find his way out of the maze. For this task are used robots that can move along a flat surface, turn and scan the area beneath them at the same time. Maze is represented by a black line on a white surface. The robot moves along the black line, and his aim is to find the end of the maze, which is usually represented by large black rectangle. Task respectively robot is commonly called Line Maze Solving Robot.<br />
<br />
The robot is driven by preloaded algorithm. There are many different algorithms and their modifications. One of the basic algorithms is Left Hand Rule or Right Hand Rule. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. The algorithm works for all mazes that cannot contain infinite loop, which the robot is not able to solve.<br />
<br />
Algorithm Left Hand Rule (Right Hand Rule) is used for example for the initial exploration of the maze (for more information see for example this presentation: https://www.pololu.com/file/0J195/line-maze-algorithm.pdf). robot passes through the maze and finds its end. During a trip through the maze memorizes all the steps. Stored step data is then robot able to optimize and find the shortest route to the destination. During the second stage the robot can avoid the blind paths and solve the maze using shorter way. As part of this simulation is implemented only the first part - finding way out of the maze. This video shows how the process works: https://www.youtube.com/watch?v=Z0LIO0tEZG4.<br />
<br />
==Problem Definition==<br />
The primary goal is to create an autonomous robot which is Able to find way out of the maze. The robot is programmed and his movements cannot be interfered after he starts. The robot must be driven by a non-trivial algorithm that includes randomly generated numbers. The robot must be able to find a way out of maze in a finite time. It means he should not get into an endless cycle of no return.<br />
<br />
Secondary objective is to determine what is the difference between a primitive robot and the robot uses a smarter algorithm. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left.<br />
==Method== <br />
===Software===<br />
For simulation is used NetLogo software (2D version). NetLogo was chosen as the most appropriate tool for the simulation because it makes it easy to program the robot's behaviour while allowing visualization of his behaviour. NetLogo also allows to customize the user interface. User can test how a robot algorithm works in real-time and simultaneously monitor other indicators.<br />
<br />
===Auto Robot===<br />
Robot is able to move up, down, left and right (viewed from above). He is able to find the way out without any intervention. Robot is able to move in any environment (maze) where exists at least one possible way out.<br />
<br />
===Intelligent vs Primitive Robot===<br />
So called intelligent robot uses an algorithm that allows him to find way out of any maze, where such a path exists. Primitive robot on the other hand, is a robot which uses a very simple set of movement instructions. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left. Primitive robot is added to the simulation as a reference. Simulation of intelligent robot are shown in comparison with a primitive robot.<br />
<br />
===Environment (Maze)===<br />
The environment is adapted to the possibilities of the NetLogo. Environment is represented by the World in NetLogo. The World consists of black, grey and green patches. Black patches represent the path where robot can move. Grey patches represent walls, the robot cannot enter them and finally the green patches are the door out of the maze.<br />
<br />
The attached archive contains seven different mazes. Environments vary to demonstrate that the robot is able to avoid infinite loop situations. Maze was created using the Pac-Man Level Editor, which is part of the Library Models in the NetLogo program. Editor has been adapted for the needs of this simulation in accordance to the Creative Commons license. The revised editor served only as a tool for quick creating of a various environments, for the simulation itself is not necessary, therefore the editor is not part of the archive. Size of all seven mazes is 31 x 31 patches.<br />
<br />
==Model==<br />
===Intelligent Robot===<br />
As outlined in the introduction above, intelligent robot (red in NetLogo) uses Left Hand Rule algorithm. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. If any of situations mentioned is not possible, the robot will remain in place and only turn left. Later, when another tick occurs, robot again apply all the previous rules.<br />
<br />
Left Hand Rule algorithm is functional for all mazes that cannot contain endless loop. Those situation is not possible to solve by using this algorithm. The purpose of this simulation is to create the robot which is be able to avoid cases with endless loops. For this purpose, algorithm of the robot enriched an element of chance. In one percent of cases when ticks occur robot determine his rotation by a random number generator. For this purpose, is used function for generating random integers.<br />
<br />
===Primitive Robot===<br />
Primitive robot (blue in NetLogo) uses an algorithm that allows him to movement straight and if he encounters an obstacle turns left. This simple algorithm has the result that the robot is quite often stuck in an endless loop. Primitive robot is created only as a reference to intelligent robot.<br />
<br />
===User Interface===<br />
The user interface is divided into three areas. The first area Settings (left) is used to select one of the seven mazes, setting robot type, number of robots in a maze and to run a simulation. The second area Visualization (centre) displays the maze and motion of robots in real time. In the third part Statistics (right) are shown statistics of current simulation.<br />
<br />
====Settings====<br />
Settings consists of five control elements. Three of the five elements are the chooser type and allow to set up one of the seven mazes, setting robot type and number of robots. Mazes are stored in separated files and they need to be stored in the same folder as the file with nlogo extension. There are three combination of robot type. The maze can display separately each of the two types (intelligent and primitive) robots, or both types simultaneously. Selecting the number of robots adjusts how many instances of each type of robot will be displayed in the visualization. In the case that is shown more than one instance of robot, robots are not interact each other. Displaying multiple robots simultaneously serve only for better understanding of the behaviour of intelligent and primitive robot in shorter time. The remaining two buttons is used to apply the selected settings and run the entire simulation.<br />
<br />
[[File:Xkrep33 Settings.PNG]]<br />
<br />
====Visualization====<br />
The middle section shows the maze and selected the type and number of robots.<br />
<br />
[[File:Xkrep33 Vizualization.PNG]]<br />
<br />
====Statistics====<br />
Statistics show three main indicators. The first indicates the number of robots who remain in the maze. This indicator captures all the robots who have not found their way out of the maze yet. The second indicator is a graphical representation of the number of robots remaining in time. The third indicator is the average number of steps that robots needed to solve the maze. All indicators are shown, after running a simulation.<br />
<br />
[[File:Xkrep33 Statistics.PNG]]<br />
<br />
==Results==<br />
The results are shown in the table. The table contains two figures for both types of robots. Indicator Number of robots remained means how many robots remained in maze when one type of robot solved the maze. If there were more than one robot at the beginning, so it means that all robots of one typy solved the maze. Second indicator Average number of steps shows how many steps all robots of one type made when robots of one type solved the maze. If robots of some type were stuck in infinite loop situation then numbers are provided in brackets.<br />
<br />
There were thousand of robots of each type at the beggining of each simulation.<br />
<br />
{| class="wikitable"<br />
|+ Results of seven different mazes simulation<br />
! style="font-weight: bold;" | <br />
! colspan="2" style="font-weight: bold;" | Intelligent robots<br />
! colspan="2" style="font-weight: bold;" | Primitive robots<br />
|-<br />
| style="font-weight: bold;" | Maze No.<br />
| style="font-weight: bold;" | Number of robots remained<br />
| style="font-weight: bold;" | Average number of steps<br />
| style="font-weight: bold;" | Number of robots remained<br />
| style="font-weight: bold;" | Average number of steps<br />
|-<br />
| style="font-weight: bold;" | 1<br />
| 0<br />
| 587<br />
| 1,000<br />
| (24,804)<br />
|-<br />
| style="font-weight: bold;" | 2<br />
| 0<br />
| 528<br />
| 1,000<br />
| (4,536)<br />
|-<br />
| style="font-weight: bold;" | 3<br />
| 0<br />
| 812<br />
| 1,000<br />
| (24,043)<br />
|-<br />
| style="font-weight: bold;" | 4<br />
| 0<br />
| 80<br />
| 1,000<br />
| (744)<br />
|-<br />
| style="font-weight: bold;" | 5<br />
| 0<br />
| 519<br />
| 1,000<br />
| (12,626)<br />
|-<br />
| style="font-weight: bold;" | 6<br />
| 138<br />
| 160<br />
| 0<br />
| 117<br />
|-<br />
| style="font-weight: bold;" | 7<br />
| 0<br />
| 747,177<br />
| 1,000<br />
| (70,286)<br />
|}<br />
<br />
==Conclusion==<br />
Simulation was done in all seven mazes. Simulation was always done with a sample od thousand robots of each type (two thousand robots at once). The proposed algorithm of intelligent robots were proved. Robots using a smart algorithm base on Left Hand Rule algorithm and generating random numbers, have always been able to solve the maze. Only in one case (maze No. 6) were primitive robots faster than intelligent one. Even in this case, however, all intelligent robots solved the maze. The primary objective of the simulation, which was to create autonomous robot that does not fall into an infinite loop, was achieved.<br />
<br />
The results table clearly shows that primitive algorithm was not very effective. In six of the seven cases, the robots who used this algorithm, get into an endless loop with no return. Only in one case, all primitive robots get out of the maze, and even faster than so called intelligent. It was rather a coincidence, because the maze No. 6 is the only one that could be resolved only by moving straight with combination of left turning in case of an obstacle.<br />
<br />
It can be said that the algorithm used for intelligent robots was relatively effective and above all its results were very stable. The only exception was a maze No. 7. Maze No. 7 is essentially infinite space with one exit in the middle. But this maze were also solvable for intelligent robots.<br />
<br />
==Code==<br />
Downloadable archive with nlogo file and seven csv files:<br />
<br />
[[File:MazeSolvingRobotSimulation.zip]]</div>Xkrep33http://www.simulace.info/index.php?title=Maze_Solving_Robot_Simulation&diff=10428Maze Solving Robot Simulation2016-01-16T17:39:32Z<p>Xkrep33: </p>
<hr />
<div>One of the typical tasks of the basics of robotics is to build a robot that is able to find his way out of the maze. For this task are used robots that can move along a flat surface, turn and scan the area beneath them at the same time. Maze is represented by a black line on a white surface. The robot moves along the black line, and his aim is to find the end of the maze, which is usually represented by large black rectangle. Task respectively robot is commonly called Line Maze Solving Robot.<br />
<br />
The robot is driven by preloaded algorithm. There are many different algorithms and their modifications. One of the basic algorithms is Left Hand Rule or Right Hand Rule. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. The algorithm works for all mazes that cannot contain infinite loop, which the robot is not able to solve.<br />
<br />
Algorithm Left Hand Rule (Right Hand Rule) is used for example for the initial exploration of the maze. The robot passes through the maze and finds its end. During a trip through the maze memorizes all the steps. Stored step data is then robot able to optimize and find the shortest route to the destination. During the second stage the robot can avoid the blind paths and solve the maze using shorter way. As part of this simulation is implemented only the first part - finding way out of the maze. This video shows how the process works: https://www.youtube.com/watch?v=Z0LIO0tEZG4.<br />
<br />
==Problem Definition==<br />
The primary goal is to create an autonomous robot which is Able to find way out of the maze. The robot is programmed and his movements cannot be interfered after he starts. The robot must be driven by a non-trivial algorithm that includes randomly generated numbers. The robot must be able to find a way out of maze in a finite time. It means he should not get into an endless cycle of no return.<br />
<br />
Secondary objective is to determine what is the difference between a primitive robot and the robot uses a smarter algorithm. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left.<br />
==Method== <br />
===Software===<br />
For simulation is used NetLogo software (2D version). NetLogo was chosen as the most appropriate tool for the simulation because it makes it easy to program the robot's behaviour while allowing visualization of his behaviour. NetLogo also allows to customize the user interface. User can test how a robot algorithm works in real-time and simultaneously monitor other indicators.<br />
<br />
===Auto Robot===<br />
Robot is able to move up, down, left and right (viewed from above). He is able to find the way out without any intervention. Robot is able to move in any environment (maze) where exists at least one possible way out.<br />
<br />
===Intelligent vs Primitive Robot===<br />
So called intelligent robot uses an algorithm that allows him to find way out of any maze, where such a path exists. Primitive robot on the other hand, is a robot which uses a very simple set of movement instructions. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left. Primitive robot is added to the simulation as a reference. Simulation of intelligent robot are shown in comparison with a primitive robot.<br />
<br />
===Environment (Maze)===<br />
The environment is adapted to the possibilities of the NetLogo. Environment is represented by the World in NetLogo. The World consists of black, grey and green patches. Black patches represent the path where robot can move. Grey patches represent walls, the robot cannot enter them and finally the green patches are the door out of the maze.<br />
<br />
The attached archive contains seven different mazes. Environments vary to demonstrate that the robot is able to avoid infinite loop situations. Maze was created using the Pac-Man Level Editor, which is part of the Library Models in the NetLogo program. Editor has been adapted for the needs of this simulation in accordance to the Creative Commons license. The revised editor served only as a tool for quick creating of a various environments, for the simulation itself is not necessary, therefore the editor is not part of the archive. Size of all seven mazes is 31 x 31 patches.<br />
<br />
==Model==<br />
===Intelligent Robot===<br />
As outlined in the introduction above, intelligent robot (red in NetLogo) uses Left Hand Rule algorithm. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. If any of situations mentioned is not possible, the robot will remain in place and only turn left. Later, when another tick occurs, robot again apply all the previous rules.<br />
<br />
Left Hand Rule algorithm is functional for all mazes that cannot contain endless loop. Those situation is not possible to solve by using this algorithm. The purpose of this simulation is to create the robot which is be able to avoid cases with endless loops. For this purpose, algorithm of the robot enriched an element of chance. In one percent of cases when ticks occur robot determine his rotation by a random number generator. For this purpose, is used function for generating random integers.<br />
<br />
===Primitive Robot===<br />
Primitive robot (blue in NetLogo) uses an algorithm that allows him to movement straight and if he encounters an obstacle turns left. This simple algorithm has the result that the robot is quite often stuck in an endless loop. Primitive robot is created only as a reference to intelligent robot.<br />
<br />
===User Interface===<br />
The user interface is divided into three areas. The first area Settings (left) is used to select one of the seven mazes, setting robot type, number of robots in a maze and to run a simulation. The second area Visualization (centre) displays the maze and motion of robots in real time. In the third part Statistics (right) are shown statistics of current simulation.<br />
<br />
====Settings====<br />
Settings consists of five control elements. Three of the five elements are the chooser type and allow to set up one of the seven mazes, setting robot type and number of robots. Mazes are stored in separated files and they need to be stored in the same folder as the file with nlogo extension. There are three combination of robot type. The maze can display separately each of the two types (intelligent and primitive) robots, or both types simultaneously. Selecting the number of robots adjusts how many instances of each type of robot will be displayed in the visualization. In the case that is shown more than one instance of robot, robots are not interact each other. Displaying multiple robots simultaneously serve only for better understanding of the behaviour of intelligent and primitive robot in shorter time. The remaining two buttons is used to apply the selected settings and run the entire simulation.<br />
<br />
[[File:Xkrep33 Settings.PNG]]<br />
<br />
====Visualization====<br />
The middle section shows the maze and selected the type and number of robots.<br />
<br />
[[File:Xkrep33 Vizualization.PNG]]<br />
<br />
====Statistics====<br />
Statistics show three main indicators. The first indicates the number of robots who remain in the maze. This indicator captures all the robots who have not found their way out of the maze yet. The second indicator is a graphical representation of the number of robots remaining in time. The third indicator is the average number of steps that robots needed to solve the maze. All indicators are shown, after running a simulation.<br />
<br />
[[File:Xkrep33 Statistics.PNG]]<br />
<br />
==Results==<br />
The results are shown in the table. The table contains two figures for both types of robots. Indicator Number of robots remained means how many robots remained in maze when one type of robot solved the maze. If there were more than one robot at the beginning, so it means that all robots of one typy solved the maze. Second indicator Average number of steps shows how many steps all robots of one type made when robots of one type solved the maze. If robots of some type were stuck in infinite loop situation then numbers are provided in brackets.<br />
<br />
{| class="wikitable"<br />
|+ Results of seven different mazes simulation<br />
! style="font-weight: bold;" | <br />
! colspan="2" style="font-weight: bold;" | Intelligent robots<br />
! colspan="2" style="font-weight: bold;" | Primitive robots<br />
|-<br />
| style="font-weight: bold;" | Maze No.<br />
| style="font-weight: bold;" | Number of robots remained<br />
| style="font-weight: bold;" | Average number of steps<br />
| style="font-weight: bold;" | Number of robots remained<br />
| style="font-weight: bold;" | Average number of steps<br />
|-<br />
| style="font-weight: bold;" | 1<br />
| 0<br />
| 587<br />
| 1,000<br />
| (24,804)<br />
|-<br />
| style="font-weight: bold;" | 2<br />
| 0<br />
| 528<br />
| 1,000<br />
| (4,536)<br />
|-<br />
| style="font-weight: bold;" | 3<br />
| 0<br />
| 812<br />
| 1,000<br />
| (24,043)<br />
|-<br />
| style="font-weight: bold;" | 4<br />
| 0<br />
| 80<br />
| 1,000<br />
| (744)<br />
|-<br />
| style="font-weight: bold;" | 5<br />
| 0<br />
| 519<br />
| 1,000<br />
| (12,626)<br />
|-<br />
| style="font-weight: bold;" | 6<br />
| 138<br />
| 160<br />
| 0<br />
| 117<br />
|-<br />
| style="font-weight: bold;" | 7<br />
| 0<br />
| 747,177<br />
| 1,000<br />
| (70,286)<br />
|}<br />
<br />
==Conclusion==<br />
Simulation was done in all seven mazes. Simulation was always done with a sample od thousand robots of each type (two thousand robots at once). The proposed algorithm of intelligent robots were proved. Robots using a smart algorithm base on Left Hand Rule algorithm and generating random numbers, have always been able to solve the maze. Only in one case (maze No. 6) were primitive robots faster than intelligent one. Even in this case, however, all intelligent robots solved the maze. The primary objective of the simulation, which was to create autonomous robot that does not fall into an infinite loop, was achieved.<br />
<br />
The results table clearly shows that primitive algorithm was not very effective. In six of the seven cases, the robots who used this algorithm, get into an endless loop with no return. Only in one case, all primitive robots get out of the maze, and even faster than so called intelligent. It was rather a coincidence, because the maze No. 6 is the only one that could be resolved only by moving straight with combination of left turning in case of an obstacle.<br />
<br />
It can be said that the algorithm used for intelligent robots was relatively effective and above all its results were very stable. The only exception was a maze No. 7. Maze No. 7 is essentially infinite space with one exit in the middle. But this maze were also solvable for intelligent robots.<br />
<br />
==Code==<br />
Downloadable archive with nlogo file and seven csv files:<br />
<br />
[[File:MazeSolvingRobotSimulation.zip]]</div>Xkrep33http://www.simulace.info/index.php?title=Maze_Solving_Robot_Simulation&diff=10427Maze Solving Robot Simulation2016-01-16T16:52:41Z<p>Xkrep33: </p>
<hr />
<div>One of the typical tasks of the basics of robotics is to build a robot that is able to find his way out of the maze. For this task are used robots that can move along a flat surface, turn and scan the area beneath them at the same time. Maze is represented by a black line on a white surface. The robot moves along the black line, and his aim is to find the end of the maze, which is usually represented by large black rectangle. Task respectively robot is commonly called Line Maze Solving Robot.<br />
<br />
The robot is driven by preloaded algorithm. There are many different algorithms and their modifications. One of the basic algorithms is Left Hand Rule or Right Hand Rule. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. The algorithm works for all mazes that cannot contain infinite loop, which the robot is not able to solve.<br />
<br />
Algorithm Left Hand Rule (Right Hand Rule) is used for example for the initial exploration of the maze. The robot passes through the maze and finds its end. During a trip through the maze memorizes all the steps. Stored step data is then robot able to optimize and find the shortest route to the destination. During the second stage the robot can avoid the blind paths and solve the maze using shorter way. As part of this simulation is implemented only the first part - finding way out of the maze. This video shows how the process works: https://www.youtube.com/watch?v=Z0LIO0tEZG4.<br />
<br />
==Problem Definition==<br />
The primary goal is to create an autonomous robot which is Able to find way out of the maze. The robot is programmed and his movements cannot be interfered after he starts. The robot must be driven by a non-trivial algorithm that includes randomly generated numbers. The robot must be able to find a way out of maze in a finite time. It means he should not get into an endless cycle of no return.<br />
<br />
Secondary objective is to determine what is the difference between a primitive robot and the robot uses a smarter algorithm. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left.<br />
==Method== <br />
===Software===<br />
For simulation is used NetLogo software (2D version). NetLogo was chosen as the most appropriate tool for the simulation because it makes it easy to program the robot's behaviour while allowing visualization of his behaviour. NetLogo also allows to customize the user interface. User can test how a robot algorithm works in real-time and simultaneously monitor other indicators.<br />
<br />
===Auto Robot===<br />
Robot is able to move up, down, left and right (viewed from above). He is able to find the way out without any intervention. Robot is able to move in any environment (maze) where exists at least one possible way out.<br />
<br />
===Intelligent vs Primitive Robot===<br />
So called intelligent robot uses an algorithm that allows him to find way out of any maze, where such a path exists. Primitive robot on the other hand, is a robot which uses a very simple set of movement instructions. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left. Primitive robot is added to the simulation as a reference. Simulation of intelligent robot are shown in comparison with a primitive robot.<br />
<br />
===Environment (Maze)===<br />
The environment is adapted to the possibilities of the NetLogo. Environment is represented by the World in NetLogo. The World consists of black, grey and green patches. Black patches represent the path where robot can move. Grey patches represent walls, the robot cannot enter them and finally the green patches are the door out of the maze.<br />
<br />
The attached archive contains seven different mazes. Environments vary to demonstrate that the robot is able to avoid infinite loop situations. Maze was created using the Pac-Man Level Editor, which is part of the Library Models in the NetLogo program. Editor has been adapted for the needs of this simulation in accordance to the Creative Commons license. The revised editor served only as a tool for quick creating of a various environments, for the simulation itself is not necessary, therefore the editor is not part of the archive.<br />
<br />
==Model==<br />
===Intelligent Robot===<br />
As outlined in the introduction above, intelligent robot (red in NetLogo) uses Left Hand Rule algorithm. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. If any of situations mentioned is not possible, the robot will remain in place and only turn left. Later, when another tick occurs, robot again apply all the previous rules.<br />
<br />
Left Hand Rule algorithm is functional for all mazes that cannot contain endless loop. Those situation is not possible to solve by using this algorithm. The purpose of this simulation is to create the robot which is be able to avoid cases with endless loops. For this purpose, algorithm of the robot enriched an element of chance. In one percent of cases when ticks occur robot determine his rotation by a random number generator. For this purpose, is used function for generating random integers.<br />
<br />
===Primitive Robot===<br />
Primitive robot (blue in NetLogo) uses an algorithm that allows him to movement straight and if he encounters an obstacle turns left. This simple algorithm has the result that the robot is quite often stuck in an endless loop. Primitive robot is created only as a reference to intelligent robot.<br />
<br />
===User Interface===<br />
The user interface is divided into three areas. The first area Settings (left) is used to select one of the seven mazes, setting robot type, number of robots in a maze and to run a simulation. The second area Visualization (centre) displays the maze and motion of robots in real time. In the third part Statistics (right) are shown statistics of current simulation.<br />
<br />
====Settings====<br />
Settings consists of five control elements. Three of the five elements are the chooser type and allow to set up one of the seven mazes, setting robot type and number of robots. Mazes are stored in separated files and they need to be stored in the same folder as the file with nlogo extension. There are three combination of robot type. The maze can display separately each of the two types (intelligent and primitive) robots, or both types simultaneously. Selecting the number of robots adjusts how many instances of each type of robot will be displayed in the visualization. In the case that is shown more than one instance of robot, robots are not interact each other. Displaying multiple robots simultaneously serve only for better understanding of the behaviour of intelligent and primitive robot in shorter time. The remaining two buttons is used to apply the selected settings and run the entire simulation.<br />
<br />
[[File:Xkrep33 Settings.PNG]]<br />
<br />
====Visualization====<br />
The middle section shows the maze and selected the type and number of robots.<br />
<br />
[[File:Xkrep33 Vizualization.PNG]]<br />
<br />
====Statistics====<br />
Statistics show three main indicators. The first indicates the number of robots who remain in the maze. This indicator captures all the robots who have not found their way out of the maze yet. The second indicator is a graphical representation of the number of robots remaining in time. The third indicator is the average number of steps that robots needed to solve the maze. All indicators are shown, after running a simulation.<br />
<br />
[[File:Xkrep33 Statistics.PNG]]<br />
<br />
==Results==<br />
The results are shown in the table. The table contains two figures for both types of robots. Indicator Number of robots remained means how many robots remained in maze when one type of robot solved the maze. If there were more than one robot at the beginning, so it means that all robots of one typy solved the maze. Second indicator Average number of steps shows how many steps all robots of one type made when robots of one type solved the maze. If robots of some type were stuck in infinite loop situation then numbers are provided in brackets.<br />
<br />
{| class="wikitable"<br />
! style="font-weight: bold;" | <br />
! colspan="2" style="font-weight: bold;" | Intelligent robots<br />
! colspan="2" style="font-weight: bold;" | Primitive robots<br />
|-<br />
| style="font-weight: bold;" | Maze no.<br />
| style="font-weight: bold;" | Number of robots remained<br />
| style="font-weight: bold;" | Average number of steps<br />
| style="font-weight: bold;" | Number of robots remained<br />
| style="font-weight: bold;" | Average number of steps<br />
|-<br />
| style="font-weight: bold;" | 1<br />
| 0<br />
| 587<br />
| 1,000<br />
| (24,804)<br />
|-<br />
| style="font-weight: bold;" | 2<br />
| 0<br />
| 528<br />
| 1,000<br />
| (4,536)<br />
|-<br />
| style="font-weight: bold;" | 3<br />
| 0<br />
| 812<br />
| 1,000<br />
| (24,043)<br />
|-<br />
| style="font-weight: bold;" | 4<br />
| 0<br />
| 80<br />
| 1,000<br />
| (744)<br />
|-<br />
| style="font-weight: bold;" | 5<br />
| 0<br />
| 519<br />
| 1,000<br />
| (12,626)<br />
|-<br />
| style="font-weight: bold;" | 6<br />
| 138<br />
| 160<br />
| 0<br />
| 117<br />
|-<br />
| style="font-weight: bold;" | 7<br />
| 0<br />
| 747,177<br />
| 1,000<br />
| (70,286)<br />
|}<br />
<br />
==Conclusion==<br />
<br />
==Code==</div>Xkrep33http://www.simulace.info/index.php?title=Maze_Solving_Robot_Simulation&diff=10419Maze Solving Robot Simulation2016-01-16T16:03:50Z<p>Xkrep33: </p>
<hr />
<div>One of the typical tasks of the basics of robotics is to build a robot that is able to find his way out of the maze. For this task are used robots that can move along a flat surface, turn and scan the area beneath them at the same time. Maze is represented by a black line on a white surface. The robot moves along the black line, and his aim is to find the end of the maze, which is usually represented by large black rectangle. Task respectively robot is commonly called Line Maze Solving Robot.<br />
<br />
The robot is driven by preloaded algorithm. There are many different algorithms and their modifications. One of the basic algorithms is Left Hand Rule or Right Hand Rule. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. The algorithm works for all mazes that cannot contain infinite loop, which the robot is not able to solve.<br />
<br />
Algorithm Left Hand Rule (Right Hand Rule) is used for example for the initial exploration of the maze. The robot passes through the maze and finds its end. During a trip through the maze memorizes all the steps. Stored step data is then robot able to optimize and find the shortest route to the destination. During the second stage the robot can avoid the blind paths and solve the maze using shorter way. As part of this simulation is implemented only the first part - finding way out of the maze. This video shows how the process works: https://www.youtube.com/watch?v=Z0LIO0tEZG4.<br />
<br />
==Problem Definition==<br />
The primary goal is to create an autonomous robot which is Able to find way out of the maze. The robot is programmed and his movements cannot be interfered after he starts. The robot must be driven by a non-trivial algorithm that includes randomly generated numbers. The robot must be able to find a way out of maze in a finite time. It means he should not get into an endless cycle of no return.<br />
<br />
Secondary objective is to determine what is the difference between a primitive robot and the robot uses a smarter algorithm. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left.<br />
==Method== <br />
===Software===<br />
For simulation is used NetLogo software (2D version). NetLogo was chosen as the most appropriate tool for the simulation because it makes it easy to program the robot's behaviour while allowing visualization of his behaviour. NetLogo also allows to customize the user interface. User can test how a robot algorithm works in real-time and simultaneously monitor other indicators.<br />
<br />
===Auto Robot===<br />
Robot is able to move up, down, left and right (viewed from above). He is able to find the way out without any intervention. Robot is able to move in any environment (maze) where exists at least one possible way out.<br />
<br />
===Intelligent vs Primitive Robot===<br />
So called intelligent robot uses an algorithm that allows him to find way out of any maze, where such a path exists. Primitive robot on the other hand, is a robot which uses a very simple set of movement instructions. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left. Primitive robot is added to the simulation as a reference. Simulation of intelligent robot are shown in comparison with a primitive robot.<br />
<br />
===Environment (Maze)===<br />
The environment is adapted to the possibilities of the NetLogo. Environment is represented by the World in NetLogo. The World consists of black, grey and green patches. Black patches represent the path where robot can move. Grey patches represent walls, the robot cannot enter them and finally the green patches are the door out of the maze.<br />
<br />
The attached archive contains seven different mazes. Environments vary to demonstrate that the robot is able to avoid infinite loop situations. Maze was created using the Pac-Man Level Editor, which is part of the Library Models in the NetLogo program. Editor has been adapted for the needs of this simulation in accordance to the Creative Commons license. The revised editor served only as a tool for quick creating of a various environments, for the simulation itself is not necessary, therefore the editor is not part of the archive.<br />
<br />
==Model==<br />
===Intelligent Robot===<br />
As outlined in the introduction above, intelligent robot (red in NetLogo) uses Left Hand Rule algorithm. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. If any of situations mentioned is not possible, the robot will remain in place and only turn left. Later, when another tick occurs, robot again apply all the previous rules.<br />
<br />
Left Hand Rule algorithm is functional for all mazes that cannot contain endless loop. Those situation is not possible to solve by using this algorithm. The purpose of this simulation is to create the robot which is be able to avoid cases with endless loops. For this purpose, algorithm of the robot enriched an element of chance. In one percent of cases when ticks occur robot determine his rotation by a random number generator. For this purpose, is used function for generating random integers.<br />
<br />
===Primitive Robot===<br />
Primitive robot (blue in NetLogo) uses an algorithm that allows him to movement straight and if he encounters an obstacle turns left. This simple algorithm has the result that the robot is quite often stuck in an endless loop. Primitive robot is created only as a reference to intelligent robot.<br />
<br />
===User Interface===<br />
The user interface is divided into three areas. The first area Settings (left) is used to select one of the seven mazes, setting robot type, number of robots in a maze and to run a simulation. The second area Visualization (centre) displays the maze and motion of robots in real time. In the third part Statistics (right) are shown statistics of current simulation.<br />
<br />
====Settings====<br />
Settings consists of five control elements. Three of the five elements are the chooser type and allow to set up one of the seven mazes, setting robot type and number of robots. Mazes are stored in separated files and they need to be stored in the same folder as the file with nlogo extension. There are three combination of robot type. The maze can display separately each of the two types (intelligent and primitive) robots, or both types simultaneously. Selecting the number of robots adjusts how many instances of each type of robot will be displayed in the visualization. In the case that is shown more than one instance of robot, robots are not interact each other. Displaying multiple robots simultaneously serve only for better understanding of the behaviour of intelligent and primitive robot in shorter time. The remaining two buttons is used to apply the selected settings and run the entire simulation.<br />
<br />
[[File:Xkrep33 Settings.PNG]]<br />
<br />
====Visualization====<br />
The middle section shows the maze and selected the type and number of robots.<br />
<br />
[[File:Xkrep33 Vizualization.PNG]]<br />
<br />
====Statistics====<br />
Statistics show three main indicators. The first indicates the number of robots who remain in the maze. This indicator captures all the robots who have not found their way out of the maze yet. The second indicator is a graphical representation of the number of robots remaining in time. The third indicator is the average number of steps that robots needed to solve the maze. All indicators are shown, after running a simulation.<br />
<br />
[[File:Xkrep33 Statistics.PNG]]<br />
<br />
==Results==<br />
<br />
==Conclusion==<br />
<br />
==Code==</div>Xkrep33http://www.simulace.info/index.php?title=Maze_Solving_Robot_Simulation&diff=10418Maze Solving Robot Simulation2016-01-16T16:01:05Z<p>Xkrep33: </p>
<hr />
<div>One of the typical tasks of the basics of robotics is to build a robot that is able to find his way out of the maze. For this task are used robots that can move along a flat surface, turn and scan the area beneath them at the same time. Maze is represented by a black line on a white surface. The robot moves along the black line, and his aim is to find the end of the maze, which is usually represented by large black rectangle. Task respectively robot is commonly called Line Maze Solving Robot.<br />
<br />
The robot is driven by preloaded algorithm. There are many different algorithms and their modifications. One of the basic algorithms is Left Hand Rule or Right Hand Rule. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. The algorithm works for all mazes that cannot contain infinite loop, which the robot is not able to solve.<br />
<br />
Algorithm Left Hand Rule (Right Hand Rule) is used for example for the initial exploration of the maze. The robot passes through the maze and finds its end. During a trip through the maze memorizes all the steps. Stored step data is then robot able to optimize and find the shortest route to the destination. During the second stage the robot can avoid the blind paths and solve the maze using shorter way. As part of this simulation is implemented only the first part - finding way out of the maze. This video shows how the process works: https://www.youtube.com/watch?v=Z0LIO0tEZG4.<br />
<br />
==Problem Definition==<br />
The primary goal is to create an autonomous robot which is Able to find way out of the maze. The robot is programmed and his movements cannot be interfered after he starts. The robot must be driven by a non-trivial algorithm that includes randomly generated numbers. The robot must be able to find a way out of maze in a finite time. It means he should not get into an endless cycle of no return.<br />
<br />
Secondary objective is to determine what is the difference between a primitive robot and the robot uses a smarter algorithm. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left.<br />
==Method== <br />
===Software===<br />
For simulation is used NetLogo software (2D version). NetLogo was chosen as the most appropriate tool for the simulation because it makes it easy to program the robot's behaviour while allowing visualization of his behaviour. NetLogo also allows to customize the user interface. User can test how a robot algorithm works in real-time and simultaneously monitor other indicators.<br />
<br />
===Auto Robot===<br />
Robot is able to move up, down, left and right (viewed from above). He is able to find the way out without any intervention. Robot is able to move in any environment (maze) where exists at least one possible way out.<br />
<br />
===Intelligent vs Primitive Robot===<br />
So called intelligent robot uses an algorithm that allows him to find way out of any maze, where such a path exists. Primitive robot on the other hand, is a robot which uses a very simple set of movement instructions. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left. Primitive robot is added to the simulation as a reference. Simulation of intelligent robot are shown in comparison with a primitive robot.<br />
<br />
===Environment (Maze)===<br />
The environment is adapted to the possibilities of the NetLogo. Environment is represented by the World in NetLogo. The World consists of black, grey and green patches. Black patches represent the path where robot can move. Grey patches represent walls, the robot cannot enter them and finally the green patches are the door out of the maze.<br />
<br />
The attached archive contains seven different mazes. Environments vary to demonstrate that the robot is able to avoid infinite loop situations. Maze was created using the Pac-Man Level Editor, which is part of the Library Models in the NetLogo program. Editor has been adapted for the needs of this simulation in accordance to the Creative Commons license. The revised editor served only as a tool for quick creating of a various environments, for the simulation itself is not necessary, therefore the editor is not part of the archive.<br />
<br />
==Model==<br />
===Intelligent Robot===<br />
As outlined in the introduction above, intelligent robot (red in NetLogo) uses Left Hand Rule algorithm. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. If any of situations mentioned is not possible, the robot will remain in place and only turn left. Later, when another tick occurs, robot again apply all the previous rules.<br />
<br />
Left Hand Rule algorithm is functional for all mazes that cannot contain endless loop. Those situation is not possible to solve by using this algorithm. The purpose of this simulation is to create the robot which is be able to avoid cases with endless loops. For this purpose, algorithm of the robot enriched an element of chance. In one percent of cases when ticks occur robot determine his rotation by a random number generator. For this purpose, is used function for generating random integers.<br />
<br />
===Primitive Robot===<br />
Primitive robot (blue in NetLogo) uses an algorithm that allows him to movement straight and if he encounters an obstacle turns left. This simple algorithm has the result that the robot is quite often stuck in an endless loop. Primitive robot is created only as a reference to intelligent robot.<br />
<br />
===User Interface===<br />
The user interface is divided into three areas. The first area Settings (left) is used to select one of the seven mazes, setting robot type, number of robots in a maze and to run a simulation. The second area Visualization (centre) displays the maze and motion of robots in real time. In the third part Statistics (right) are shown statistics of current simulation.<br />
<br />
====Settings====<br />
Settings consists of five control elements. Three of the five elements are the chooser type and allow to set up one of the seven mazes, setting robot type and number of robots. Mazes are stored in separated files and they need to be stored in the same folder as the file with nlogo extension. There are three combination of robot type. The maze can display separately each of the two types (intelligent and primitive) robots, or both types simultaneously. Selecting the number of robots adjusts how many instances of each type of robot will be displayed in the visualization. In the case that is shown more than one instance of robot, robots are not interact each other. Displaying multiple robots simultaneously serve only for better understanding of the behaviour of intelligent and primitive robot in shorter time. The remaining two buttons is used to apply the selected settings and run the entire simulation.<br />
[[File:xkrep33_Settings.jpg]]<br />
<br />
====Visualization====<br />
The middle section shows the maze and selected the type and number of robots.<br />
[[File:xkrep33_Visualization.jpg]]<br />
<br />
====Statistics====<br />
Statistics show three main indicators. The first indicates the number of robots who remain in the maze. This indicator captures all the robots who have not found their way out of the maze yet. The second indicator is a graphical representation of the number of robots remaining in time. The third indicator is the average number of steps that robots needed to solve the maze. All indicators are shown, after running a simulation.<br />
[[File:xkrep33_Statistics.jpg]]<br />
<br />
==Results==<br />
<br />
==Conclusion==<br />
<br />
==Code==</div>Xkrep33http://www.simulace.info/index.php?title=File:Xkrep33_Vizualization.PNG&diff=10417File:Xkrep33 Vizualization.PNG2016-01-16T15:58:35Z<p>Xkrep33: </p>
<hr />
<div></div>Xkrep33http://www.simulace.info/index.php?title=File:Xkrep33_Statistics.PNG&diff=10416File:Xkrep33 Statistics.PNG2016-01-16T15:58:25Z<p>Xkrep33: </p>
<hr />
<div></div>Xkrep33http://www.simulace.info/index.php?title=File:Xkrep33_Settings.PNG&diff=10415File:Xkrep33 Settings.PNG2016-01-16T15:58:16Z<p>Xkrep33: </p>
<hr />
<div></div>Xkrep33http://www.simulace.info/index.php?title=File:MazeSolvingRobotSimulation.zip&diff=10414File:MazeSolvingRobotSimulation.zip2016-01-16T15:57:07Z<p>Xkrep33: </p>
<hr />
<div></div>Xkrep33http://www.simulace.info/index.php?title=Maze_Solving_Robot_Simulation&diff=10413Maze Solving Robot Simulation2016-01-16T15:49:37Z<p>Xkrep33: </p>
<hr />
<div>One of the typical tasks of the basics of robotics is to build a robot that is able to find his way out of the maze. For this task are used robots that can move along a flat surface, turn and scan the area beneath them at the same time. Maze is represented by a black line on a white surface. The robot moves along the black line, and his aim is to find the end of the maze, which is usually represented by large black rectangle. Task respectively robot is commonly called Line Maze Solving Robot.<br />
<br />
The robot is driven by preloaded algorithm. There are many different algorithms and their modifications. One of the basic algorithms is Left Hand Rule or Right Hand Rule. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. The algorithm works for all mazes that cannot contain infinite loop, which the robot is not able to solve.<br />
<br />
Algorithm Left Hand Rule (Right Hand Rule) is used for example for the initial exploration of the maze. The robot passes through the maze and finds its end. During a trip through the maze memorizes all the steps. Stored step data is then robot able to optimize and find the shortest route to the destination. During the second stage the robot can avoid the blind paths and solve the maze using shorter way. As part of this simulation is implemented only the first part - finding way out of the maze. This video shows how the process works: https://www.youtube.com/watch?v=Z0LIO0tEZG4.<br />
<br />
==Problem Definition==<br />
The primary goal is to create an autonomous robot which is Able to find way out of the maze. The robot is programmed and his movements cannot be interfered after he starts. The robot must be driven by a non-trivial algorithm that includes randomly generated numbers. The robot must be able to find a way out of maze in a finite time. It means he should not get into an endless cycle of no return.<br />
<br />
Secondary objective is to determine what is the difference between a primitive robot and the robot uses a smarter algorithm. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left.<br />
==Method== <br />
===Software===<br />
For simulation is used NetLogo software (2D version). NetLogo was chosen as the most appropriate tool for the simulation because it makes it easy to program the robot's behaviour while allowing visualization of his behaviour. NetLogo also allows to customize the user interface. User can test how a robot algorithm works in real-time and simultaneously monitor other indicators.<br />
<br />
===Auto Robot===<br />
Robot is able to move up, down, left and right (viewed from above). He is able to find the way out without any intervention. Robot is able to move in any environment (maze) where exists at least one possible way out.<br />
<br />
===Intelligent vs Primitive Robot===<br />
So called intelligent robot uses an algorithm that allows him to find way out of any maze, where such a path exists. Primitive robot on the other hand, is a robot which uses a very simple set of movement instructions. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left. Primitive robot is added to the simulation as a reference. Simulation of intelligent robot are shown in comparison with a primitive robot.<br />
<br />
===Environment (Maze)===<br />
The environment is adapted to the possibilities of the NetLogo. Environment is represented by the World in NetLogo. The World consists of black, grey and green patches. Black patches represent the path where robot can move. Grey patches represent walls, the robot cannot enter them and finally the green patches are the door out of the maze.<br />
<br />
The attached archive contains seven different mazes. Environments vary to demonstrate that the robot is able to avoid infinite loop situations. Maze was created using the Pac-Man Level Editor, which is part of the Library Models in the NetLogo program. Editor has been adapted for the needs of this simulation in accordance to the Creative Commons license. The revised editor served only as a tool for quick creating of a various environments, for the simulation itself is not necessary, therefore the editor is not part of the archive.<br />
<br />
==Model==<br />
===Intelligent Robot===<br />
As outlined in the introduction above, intelligent robot uses Left Hand Rule algorithm. The algorithm works as follows, a robot prefers left movement before moving forward. If this rule cannot be applied, then prefers forward move instead right move. If any of situations mentioned is not possible, the robot will remain in place and only turn left. Later, when another tick occurs, robot again apply all the previous rules.<br />
<br />
Left Hand Rule algorithm is functional for all mazes that cannot contain endless loop. Those situation is not possible to solve by using this algorithm. The purpose of this simulation is to create the robot which is be able to avoid cases with endless loops. For this purpose, algorithm of the robot enriched an element of chance. In one percent of cases when ticks occur robot determine his rotation by a random number generator. For this purpose, is used function for generating random integers.<br />
<br />
===Primitive Robot===<br />
Primitive robot uses an algorithm that allows him to movement straight and if he encounters an obstacle turns left. This simple algorithm has the result that the robot is quite often stuck in an endless loop. Primitive robot is created only as a reference to intelligent robot.<br />
<br />
===User Interface===<br />
The user interface is divided into three areas. The first area Settings (left) is used to select one of the seven mazes, setting robot type, number of robots in a maze and to run a simulation. The second area Visualization (centre) displays the maze and motion of robots in real time. In the third part Statistics (right) are shown statistics of current simulation.<br />
<br />
====Settings====<br />
Settings consists of five control elements. Three of the five elements are the chooser type and allow to set up one of the seven mazes, setting robot type and number of robots. Mazes are stored in separated files and they need to be stored in the same folder as the file with nlogo extension. There are three combination of robot type. The maze can display separately each of the two types (intelligent and primitive) robots, or both types simultaneously. Selecting the number of robots adjusts how many instances of each type of robot will be displayed in the visualization. In the case that is shown more than one instance of robot, robots are not interact each other. Displaying multiple robots simultaneously serve only for better understanding of the behaviour of intelligent and primitive robot in shorter time. The remaining two buttons is used to apply the selected settings and run the entire simulation.<br />
<br />
====Visualization====<br />
The middle section shows the maze and selected the type and number of robots.<br />
<br />
====Statistics====<br />
Statistics show three main indicators. The first indicates the number of robots who remain in the maze. This indicator captures all the robots who have not found their way out of the maze yet. The second indicator is a graphical representation of the number of robots remaining in time. The third indicator is the average number of steps that robots needed to solve the maze. All indicators are shown, after running a simulation.<br />
<br />
==Results==<br />
<br />
==Conclusion==<br />
<br />
==Code==</div>Xkrep33http://www.simulace.info/index.php?title=Maze_Solving_Robot_Simulation&diff=10393Maze Solving Robot Simulation2016-01-15T14:50:15Z<p>Xkrep33: </p>
<hr />
<div>Introduction<br />
<br />
==Problem definition==<br />
<br />
==Method==<br />
<br />
==Model==<br />
<br />
==Results==<br />
<br />
==Conclusion==<br />
<br />
==Code==</div>Xkrep33http://www.simulace.info/index.php?title=Maze_Solving_Robot_Simulation&diff=10392Maze Solving Robot Simulation2016-01-15T14:45:56Z<p>Xkrep33: Created page with "Introduction =Problem definition= =Method= =Model= =Results= =Conclusion= =Code="</p>
<hr />
<div>Introduction<br />
<br />
=Problem definition=<br />
<br />
=Method=<br />
<br />
=Model=<br />
<br />
=Results=<br />
<br />
=Conclusion=<br />
<br />
=Code=</div>Xkrep33http://www.simulace.info/index.php?title=WS_2015/2016&diff=10391WS 2015/20162016-01-15T14:36:39Z<p>Xkrep33: </p>
<hr />
<div>Semestral papers from winter term 2015/2016. Please, put here links to the pages with your paper. First you need to have your [[Assignments WS 2015/2016|assignment approved]].<br />
<br />
==Simulations==<br />
--[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 15:35, 15 January 2016 (CET) [[Maze Solving Robot Simulation]]<br />
<br />
==Papers==</div>Xkrep33http://www.simulace.info/index.php?title=Assignments_WS_2015/2016&diff=10324Assignments WS 2015/20162015-12-14T19:46:49Z<p>Xkrep33: </p>
<hr />
<div>{{DISPLAYTITLE:Assignments WS 2015/2016}}<br />
<br />
{{Ambox<br />
| text = <div><br />
Please, put here your assignments. Do not forget to sign them. You can use <nowiki>~~~~</nowiki> (four tildas) for an automatic signature. Use Show preview in order to check the result before your final sumbition.<br />
</div><br />
}}<br />
<br />
{{Ambox<br />
| text = <div><br />
Please, strive to formulate your assignment carefully. We expect an adequate effort to formulate the assignment as it is your semestral paper. Do not forget that your main goal is a research paper. It means your simulation model must generate the results that are specific, measurable and verifiable. Think twice how you will develop your model, which entities you will use, draw a model diagram, consider what you will measure. No sooner than when you have a good idea about the model, submit your assignment. And of course, read [[How to deal with the simulation assignment/en|How to deal with the simulation assignment]].<br />
</div><br />
}}<br />
<br />
{{Ambox<br />
| type = content<br />
| text = <div><br />
In order to avoid possible confusion, please, check if you have added '''approved''' in bold somewhere in our comment under your submission. If there is no '''approved''', it means the assignment was not approved yet.<br />
</div><br />
}}<br />
<br />
= Assignments =<br />
<br />
[[User:Oleg.Svatos|Oleg.Svatos]] ([[User talk:Oleg.Svatos|talk]]) 15:35, 29 November 2015 (CET) Just to warn you in advance - topics like roulette, lottery and other simulations based purely on uniform randomnes '''will not be accepted''' as Monte Carlo simulation.<br />
<br />
--[[User:Dinara|Dinara]] ([[User talk:Dinara|talk]]) 03:32, 11 December 2015 (CET)<br />
<br />
'''Elevator system simulation'''<br />
<br />
''Goal:'' find the best balance between number of elevators and employees in the building (for instance 10 floor business center), so that the waiting time will be minimum<br />
''Environment:'' NetLogo<br />
:Ok, first, I would really prefer if you created a new article for your assignment. This is confusing a bit.<br />
:Nevertheless, your assignment is too short to be evaluated. It must be elaborated in much much greater detail. Anyway, it seems that such a simulation would be better for discrete event simulation. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:33, 13 December 2015 (CET)<br />
<br />
<br />
[[xpokl18]]<br />
<br />
==knam00:Heating system simulation==<br />
===Intro===<br />
<div><br />
Most households in the Czech Republic have mains gas central heating. This is a so-called ‘wet system’, which means a gas-fired boiler heats water to provide central heating through radiators and hot water through the taps in your home. <br />
<br />
Some houses that aren’t connected to the gas network can use electrical heating or liquid petroleum gas (LPG) or heating oil, which work in a similar way to gas central heating, although LPG and oil are delivered by road and stored in a tank, which you may have to buy or rent from your supplier.<br />
<br />
Gas is a highly efficient fuel, so you get a good return on every unit of energy. Modern condensing boilers, which use hot flue gases that are wasted in a standard boiler, have very high efficiency. Some are now 90% or more efficient.<br />
</div><br />
<div><br />
===Simulation design===<br />
====Layout====<br />
[http://imageshack.com/a/img910/6936/OIUdr1.png Preview Heating system]<br />
====People====<br />
There are 6 people using this flat. Each person periodically comes and leaves. <br />
Pair 1 comes on monday and leaves on wednesday <br />
Pair 2 comes on monday and leaves on friday<br />
There are also 2 boys sharing another room - The first one stays here for the whole week and the second one leaves on saturday and comes on monday.<br />
<br />
====Gas boiler====<br />
We own 25kW condensing boiler with 95% efficiency.<br />
Boiler is controlled with thermostat.<br />
<br />
====Thermostat====<br />
Thermostat is placed in the living room - it is set to 22 degrees Celsius from 7:00 to 23:00 and to 19 degrees Celsius from 23:00 to 7:00.<br />
<br />
====Rooms====<br />
There are 3 separated rooms used privately, one living room, kitchen, bathroom, storage room and toilet.<br />
He have high ceilings, so volume of rooms is really big.<br />
<br />
Private rooms - 84mˆ3 x 3<br />
Living room - 168mˆ3<br />
Kitchen - 84mˆ3<br />
Bathroom - 21mˆ3<br />
Storage room - 21mˆ3<br />
Toilet - 21mˆ3<br />
<br />
====Radiators====<br />
We have old radiators made from cast-iron. Each part of this radiator is 600mm high and 200mm deep, its surface is 0,31mˆ2, its capacity is 1,7L.<br />
Each room (except storage room) has its own radiator.<br />
In total, there are 5 radiators with 30 parts and 2 radiators with 15 parts.<br />
<br />
====Heating system====<br />
Consists of radiators and tubes. Total amount of water inside heating system is 255L (5 big radiators) + 51L (2 smaller radiators) + 61L (Amount of water inside tubes - 108m of tubes in total) = 367L.<br />
<br />
====System dynamics====<br />
Water inside heating system gets heated - it takes 4181J to heat up 1L of water to 1 degree Celsius. Then radiators are heated (416J for 1kg of cast-iron to heat-up to 1 degree Celsius), then air is heated according to Stefan-Boltzman law. Heat is distributed among the flat. According to the people in the flat, radiators are turned on or off. Thermostat is inside the living room a living room gets 30% of heat from rooms connected to it. Thermostat stops heating system when requirement are satisfied. Accumulated heat stays in heating system and slowly degrades. I would like to measure amount of gas used for heating system for the period 4 months. <br />
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The system i describe doesn't count with using hot water for shower.<br />
</div><br />
<div><br />
I would like to ask you for software recommendation - not sure if i should use simprocess or vensim.<br />
</div><br />
<div><br />
Martin Knapovský, knam00@vse.cz<br />
</div><br />
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:This could be an interesting topic, but please, finish the assignment. [[User:Tomáš|Tomáš]] ([[User talk:Tomáš|talk]]) 02:44, 13 December 2015 (CET)<br />
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== Christmas market ==<br />
<div><br />
I´m thinking about simulation of Christmas market, on some square as place for vendors. There will be two kind of vendors – with refreshments and with decorations. There will be two entities – vendors and customers. <br />
In first round I want place there some number of customers (for example twenty) and some number of vendors. <br />
Each customer has to buy at least one decoration as a present for somebody. In next rounds each customer can decide if he want to leave a market, buy other present or buy something to eat/drink. Each customer can buy no more than five presents. <br />
Every minute will come five new customers. <br />
There will be a temperature as global variable. <br />
If the temperature is too low, customer will decide, if he/she wants to go home or buy something to eat/drink to get warm. <br />
If customer is on a market for so long, he/she needs to buy something to eat or drink either.<br />
Because there will be more than one vendor of each kind of goods, customer will preference vendor by the distance. <br />
</div><div><br />
My goal is to find an optimal number of vendors and its structure based on the given temperature. <br />
</div><div><br />
For my simulation I decide to use NetLogo. <br />
</div><br />
<div>--[[User:Xhejk15|Xhejk15]] ([[User talk:Xhejk15|talk]]) 20:58, 13 December 2015 (CET)</div><br />
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== xsedd07: assignment ==<br />
Conference and meeting center layout in '''NetLogo'''<br />
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This simulation would display how the crowds (and individuals) would behave within a conference center. The building would have a set plan, what would be changing could be the exits, the information desks, the buffets, the bars, the exhibitions, the lectures, the meetings, the benches and the free space. The visitors and participants would move around based on a schedule and free will, at this point I am not sure to which extent is it wise to design an AI which would control each person as an individual. Hopefully such AI can be made with NetLogo spending just reasonable effort. The simulation extent (regarding total amount of people, rooms...) will be adjusted so that it can be easily overviewed but not yet too simplified.<br />
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The layout will be transformed multiple times, its efficiency will be judged by density of the crowd and queues. Individual buffets, bars e.t.c. should not be abandoned while the others are flooded. Simulation will include staff and toilets to make the environment look more real. At the moment I am open to implementing a possible terrorist attack, which could also be an alternative for this assignment.<br />
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The simulation should track the number of people in lecture halls, in queues and in any otherwise interesting state such as boredom or stress.<br />
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== xtomp36: assigment ==<br />
===Hosting load-balancing simulation===<br />
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A hosting company with it’s own infrastructure is using so called "load balancing" to distribute the overall load between multiple servers (hardware) and “high-availability” to minimize service down-time.<br />
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'''Software used''' <br /><br />
- SIMPROCESS<br />
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'''Possible hosting services'''<br /><br />
- Web hosting<br /><br />
- VPS<br /><br />
- Communication server (TS 3)<br />
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The simulation should also consider critical situation like off-line server or unavailability (for example due to D-DOS attack) of the entire server location (datacenter). In with case the traffic should be re-routed to another location (there are two hosting locations).<br /><br />
I am going to use real data from my own hosting environment. <br />
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'''Variables'''<br /><br />
- Number of servers<br /><br />
- Probability of hardware malfunction<br /><br />
- Probability of software malfunction<br /><br />
- Probability of entire location unavailability<br /><br />
- Service users (website visitors, VPS users, TS 3 clients)<br />
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'''Other'''<br /><br />
In addition there are other devices necessary to enable LB and HA, like a switch. In case of HA enabled there must be at least two same switches at one time to achieve redundancy.<br />
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== Predator-Prey simulation ==<br />
Few weeks ago a came across [http://www.natureworldnews.com/articles/6296/20140308/deer-overpopulation-threat-forest-growth-researchers.htm an interesting article] about deer overpopulation and how this is slowing down forest succession or natural establishment. 'The study was conducted on Cornell land near Freese Road in Ithaca,' as stated in the article.<br /><br />
I would like to make a simulation of the predator-prey concept on this topic. We would be able to simulate the growth of deer, wolf population, the state of the 2 types of vegetation in the forest and how many deers and wolves would be optimal to keep the original forest vegetation from disappearing.<br /><br />
Basically there will be total of 4 entities - 2 agents and 2 patch types:<br /><br />
Deer - agent, consuming the native forest flora,<br /><br />
Wolves - agent, the predator specie that hunts the deer specie,<br /><br />
Forest vegetation - native forest flora that deer specie prefers to consume,<br /><br />
Foreign vegetation - the more the deers eat the native forest vegetation the more this foreign flora thrives.<br /><br />
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NetLogo will be used for this simulation. I understand it might be a little more complex because this simulation would not be simulating only the deer population but the predator (wolves) as well. It would also simulate the interactions between deer and the native forest flora, wolves hunting deers and the foreign flora taking over the forest until there is little or none of the original forest vegetation. <br /><br />
There will be some global variables such as the initial number of deers and wolves; deer, wolf, forest and foreign flora reproduction rates and rules of interaction for these entities.<br />
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--[[User:Xnovs00|Xnovs00]] ([[User talk:Xnovs00|talk]]) 21:57, 13 December 2015 (CET)<br />
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== xhudj17: assigment ==<br />
===Crossroad simulation===<br />
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This simulation should simulate trafic (cars and trams) and pedestrians on crossroad of roads Sokolovska, Jecna and Legerova located on I.P.Pavlova square in Prague.<br />
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'''Software used''' <br /><br />
- NetLogo<br />
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'''Goal'''<br /><br />
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The simulation should try to optimize the trafic lights to reach lowest waiting times possible and to secure safety and fluency of the trafic.<br />
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'''Variables'''<br /><br />
- Propability of incoming cars from each direction<br /><br />
- Probability of incoming pedesterians<br /><br />
- Probability of incoming trams<br /><br />
- Light intervals<br /><br />
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'''Situation desctription'''<br /><br />
The crossroad is combining 3 main streets and 3 smaller streets with several pedestrians crossing and two tram crossing. There are 2 separate trafic lights to controll the traffic.<br />
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Jan Hudecek, XHUDJ17, 14/12/2015 13:50<br />
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== xzimm00: Smart car traffic optimization ==<br />
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The model presented by this paper is simulating the situation of car multi-lane merging. <br />
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=== Goal ===<br />
<br />
The primary goal of the simulation is to create a plausible real-world model of car traffic jams caused by agents operating in an inefficient way and environment that does not make the best of today's technologies that could help control the traffic flow, and to measure the possible improvement if smarter systems were employed.<br />
The author takes into account the inefficiencies of the agents (human error...) and compares this with a (possibly better) solution using automated driver agents, always utilizing an (ideally) optimal (precomputed) traffic flow.<br />
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=== Variables ===<br />
* Car inflow (cars/minute, distributed across the lanes)<br />
* Number of lanes (starting and final, after the lane merge)<br />
* Car speed (varying across the different road segments - i.e. max speed may be limited after the merge because of a traffic obstruction, for example a car accident or roadworks)<br />
* Agents' merging strategy preference (i.e. late vs early merge)<br />
* Agent inefficiency factor (reactions time, premature slowing down, wrong merging strategy used, varying speed, overcautiousness...)<br />
* Amount of kept safety factor (for both human-based and automatic computer-based agent driven cars, so called 'defensive driving', expecting a failure of the others - i.e. mainly the distance kept between the cars)<br />
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=== Methods used ===<br />
* Software used: NetLogo<br />
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=== Expected results ===<br />
Based on the results of the simulation, it should be possible to measure the possible improvement (%) if automated car-driving agents were employed in place of humans in different critical situations of lane mergers.<br />
<br />
(Martin Zima, xzimm00)<br />
--[[User:Martin.zima|Martin.zima]] ([[User talk:Martin.zima|talk]]) 18:31, 14 December 2015 (CET)<br />
<br />
== Maze Solving Robot Simulation ==<br />
=== Assignment (xkrep33) ===<br />
==== Goal ====<br />
The primary goal is to create an autonomous robot, which is able to find way out of the maze. The robot will be programmed and his movements cannot be interfered after he starts. The robot must be driven by a non-trivial algorithm that will include randomly generated numbers. The robot must be able to find a way out of maze in a finite time. It means he should not get into an endless cycle of no return.<br />
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Secondary objective will be to determine what is the difference between a primitive robot and the robot uses a smarter algorithm. Primitive robot is e.g. such a robot who moves only straight forward and if he encounters an obstacle turns left.<br />
==== Software ====<br />
For simulation will be used NetLogo software (2D version).<br />
==== Autonomous robot ====<br />
Robot will be able to move up, down, left and right (viewed from above). He should be able to find the way out without any intervention. Robot will be able to move in any environment (maze) where exists at least one posible way out.<br />
==== Environment (maze) ====<br />
Environment will be represented by the World in NetLogo. The World will consists of black, grey and green patches. Black patches will represents the path where robot can move. The grey patches will represent walls, robot cannot enter them and finally the green patches will be the door out of the maze.<br />
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More than one enviroment will be provided. Environments will vary to prove that the robot is able to avoid an infinite loop situation.<br />
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--[[User:Xkrep33|Xkrep33]] ([[User talk:Xkrep33|talk]]) 20:46, 14 December 2015 (CET)</div>Xkrep33