Ultimatum Game - Revision history

OtakarTrunda at 20:00, 17 January 2016

2016-01-17T20:00:57Z

OtakarTrunda: /* Conclusion and future work */

2016-01-17T19:59:20Z

‎Conclusion and future work

OtakarTrunda: /* Conclusion and future work */

2016-01-17T19:58:49Z

‎Conclusion and future work

OtakarTrunda: /* Conclusion and future work */

2016-01-17T19:57:28Z

‎Conclusion and future work

OtakarTrunda: /* Software tool */

2016-01-17T19:55:19Z

‎Software tool

OtakarTrunda at 19:53, 17 January 2016

2016-01-17T19:53:54Z

OtakarTrunda: /* Experiments with evolution */

2016-01-17T19:53:02Z

‎Experiments with evolution

OtakarTrunda: /* Experiments with evolution */

2016-01-17T19:50:34Z

‎Experiments with evolution

OtakarTrunda: /* Rules of the game */

2016-01-17T19:47:44Z

‎Rules of the game

OtakarTrunda at 19:44, 17 January 2016

2016-01-17T19:44:05Z

← Older revision		Revision as of 20:00, 17 January 2016
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	The following [http://www.ms.mff.cuni.cz/~truno7am/simulace/UltimatumGame.zip archive] contains all C# source codes of the software tool used to conduct the simulations as well as executable files and results of simulations presented in this report.		The following [http://www.ms.mff.cuni.cz/~truno7am/simulace/UltimatumGame.zip archive] contains all C# source codes of the software tool used to conduct the simulations as well as executable files and results of simulations presented in this report.

−		+	[[User:OtakarTrunda\|OtakarTrunda]] ([[User talk:OtakarTrunda\|talk]]) 21:00, 17 January 2016 (CET)
−	[[User:OtakarTrunda\|OtakarTrunda]] ([[User talk:OtakarTrunda\|talk]]) 20:53, 17 January 2016 (CET)

@@ Line 274: / Line 274: @@
 The greedy strategy, which seems to be an attractor, is definitely not optimal in all environments - for example suppose that a group of 100 agents is using the greedy strategy of P = 0.2, A = 0.1.
-Then if we add another group of 100 agents to the system which would use a "moral" strategy of P = 0.4, A = 0.3, this second group would be more profitable in long run (without evolution). Proof: lets denote the "greedy"
+Then if we add another group of 100 agents to the system which would use a "moral" strategy of P = 0.4, A = 0.3, this second group would be more profitable in long run (without evolution). Proof: lets denote the "greedy" group as A and the "moral" group as B. Each single game can run according to these four scenarios:
 # the proposer and the responder are from A: in this case the game results in "accept" and the money goes to group A
 # the proposer and the responder are from B: similarly, the game will result in "accept" and the money goes to group B

@@ Line 274: / Line 274: @@
 The greedy strategy, which seems to be an attractor, is definitely not optimal in all environments - for example suppose that a group of 100 agents is using the greedy strategy of P = 0.2, A = 0.1.
-Then if we add another group of 100 agents to the system which would use a "moral" strategy of P = 0.4, A = 0.3, this second group would be more profitable in long run. Proof: lets denote the "greedy"
+Then if we add another group of 100 agents to the system which would use a "moral" strategy of P = 0.4, A = 0.3, this second group would be more profitable in long run (without evolution). Proof: lets denote the "greedy"
 group A and the "moral" group B. Each single game can run according to these four scenarios:
 # the proposer and the responder are from A: in this case the game results in "accept" and the money goes to group A

@@ Line 270: / Line 270: @@
 # For every strategy there is an environment where this strategy doesn't work
 # Whenever most players use the same strategy, a single individual can take advantage on them by using different strategy
-#: This is the reason why our evolutionary process didn't lead to any interesting results
+#: This is the reason why our evolutionary process didn't lead to any interesting results and converges toward the upper-left corner
 # When using our evolutionary process as a transition rule, the point (0,0) - upper left corner - is a strong attractor whenever all players are using similar strategy

@@ Line 56: / Line 56: @@
 [[file:RandomDistribution.png]]
-With low granularity, the number of categories is low and they can be concisesly depicted, but the numbers are imprecise since we group together large ranges of values (e.g. in the previous example, agents with P = 2.1 and P = 3.9 cannot by distinguished. High granularity on the other hand provides precise numbers but the diagram is more complex.
+With low granularity, the number of categories is low and they can be concisely depicted, but the numbers are imprecise since we group together large ranges of values (e.g. in the previous example, agents with P = 2.1 and P = 3.9 cannot by distinguished. High granularity on the other hand provides precise numbers but the diagram is more complex.
 The number of agents in the simulation is set to granularity x granularity such that there is at least as much agents as groups.

← Older revision		Revision as of 19:53, 17 January 2016
Line 290:		Line 290:

	The following [http://www.ms.mff.cuni.cz/~truno7am/simulace/UltimatumGame.zip archive] contains all C# source codes of the software tool used to conduct the simulations as well as executable files and results of simulations presented in this report.		The following [http://www.ms.mff.cuni.cz/~truno7am/simulace/UltimatumGame.zip archive] contains all C# source codes of the software tool used to conduct the simulations as well as executable files and results of simulations presented in this report.
		+
		+
		+	[[User:OtakarTrunda\|OtakarTrunda]] ([[User talk:OtakarTrunda\|talk]]) 20:53, 17 January 2016 (CET)

← Older revision		Revision as of 19:53, 17 January 2016
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	[[file:EvoUniformDistribution1M.png]][[file:EvoUniformSkore1M.png]]		[[file:EvoUniformDistribution1M.png]][[file:EvoUniformSkore1M.png]]
		+
		+	Distribution and score after 5 million steps:
		+
		+	[[file:EvoUniformDistribution5M.png]][[file:EvoUniformSkore5M.png]]

	Distribution and score after 10 million steps:		Distribution and score after 10 million steps:

@@ Line 232: / Line 232: @@
 We only present the results of an experiment with uniform environment here. We conducted experiments with evolution in each of the previously mentioned environments, but the results were always very
 similar - the whole population moved close to the central point of the strategy space and from there it slowly converged towards the upper-left corner, to the strategy "Offer very little, accept anything".
 Distribution and score after 10 million steps:

@@ Line 8: / Line 8: @@
 A few additional information about the game should be pointed out
--- the players are anonymous - they don't know each other, never communicated before and never will after the game
+* the players are anonymous - they don't know each other, never communicated before and never will after the game
--- the players can't communicate during the game except of the proposal. There is only one proposal (no second chance in case of rejection)
+* the players can't communicate during the game except of the proposal. There is only one proposal (no second chance in case of rejection)
--- the responder knows the total amount of money. E.g. if the proposal is $10, he knows weather it is 10 out of 20 or 10 out of 1000.
+* the responder knows the total amount of money. E.g. if the proposal is $10, he knows weather it is 10 out of 20 or 10 out of 1000.
--- goal of all players is to maximize their own profit
+* goal of all players is to maximize their own profit
 =Method=

← Older revision		Revision as of 19:44, 17 January 2016
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−	Aim of this project is to analyze the socio-economical game known as the Ultimatum game. We study different strategies for the game ~~and~~ how successful those strategies are in various scenarios. We also study how the strategies of the players will develop through time if we use an evolutionary algorithm to adjust them.	+	Aim of this project is to analyze the socio-economical game known as the Ultimatum game. We study different strategies for the game in order to determine how successful those strategies are in various scenarios. We also study how the strategies of the players will develop through time if we use an evolutionary algorithm to adjust them.

	We study the game using a multi-agent system where the agents play the Ultimatum game against each other using different strategies.		We study the game using a multi-agent system where the agents play the Ultimatum game against each other using different strategies.