User:Holp11 - Revision history

Holp11: /* Possible Model Extensions */

2025-01-10T21:38:39Z

‎Possible Model Extensions

Holp11: /* References */

2025-01-10T21:36:39Z

‎References

Holp11: /* Conclusion */

2025-01-10T21:36:02Z

‎Conclusion

Holp11: /* Second Large World */

2025-01-10T21:35:39Z

‎Second Large World

Holp11: /* Large World */

2025-01-10T21:35:17Z

‎Large World

Holp11: /* 3 Guards and 10 Customers */

2025-01-10T21:34:44Z

‎3 Guards and 10 Customers

Holp11: /* 3 Guards */

2025-01-10T21:34:13Z

‎3 Guards

Holp11: /* Small World, Guard Speed 2, and 2 Guards */

2025-01-10T21:33:22Z

‎Small World, Guard Speed 2, and 2 Guards

Holp11: /* Simulation User Interface */

2025-01-10T21:31:25Z

‎Simulation User Interface

Holp11: /* Tracked Indicators */

2025-01-10T21:29:47Z

‎Tracked Indicators

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 This simulation revealed valuable insights into improving store security by analysing how guards, thieves, customers, and store layouts interact. Preventing theft isn’t just about hiring more guards—it’s about making smarter, targeted changes to store operations. For example, in a small store with two guards, only 40% of thieves were caught while 60% escaped, leading to a guard efficiency of just 1.8%. Increasing the guard count to three improved capture rates to 60% and raised efficiency to 3.8% but escapes still occurred. Additionally, thieves spent an average of 22 to 44 ticks in the store, depending on factors like steal delay. Shorter delays allowed thieves to escape faster, while longer ones gave guards more chances.
 Store layout and customer traffic were also crucial factors. Narrow shelves with minimal spacing made it harder for guards to see, while wider shelves created blind spots thieves could exploit. Balancing shelf spacing is key to ensuring guards can patrol effectively without disrupting the shopping experience. Similarly, high customer traffic, with spawn thresholds between 0.30 and 0.50, made it more difficult for guards to distinguish thieves from shoppers, often increasing escape rates. While the exact numbers and percentages may vary depending on specific scenarios, this simulation proved to be a useful tool for testing different security strategies. For Mr. Ha Son Do, it emphasizes the importance of placing guards strategically, optimizing store layouts, and focusing on guard training to improve theft prevention in a cost-effective and adaptable way.
 == References ==

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 * [https://www.geeksforgeeks.org/bresenhams-line-generation-algorithm/ Bresenham's Algorithm]
 * [https://www.geeksforgeeks.org/euclidean-distance/ Euclidean Distance]
-* [http://www.robotomie.cz/pocitadlo/pridej_zaznam.php?nazev=obsah/netlogo/netlogo.zip Simulation File: NetLogo for Store Security Simulation]
+* [http://www.robotomie.cz/pocitadlo/pridej_zaznam.php?nazev=obsah/netlogo/netlogo.zip
 == Downloadable Files ==
 * [http://www.robotomie.cz/pocitadlo/pridej_zaznam.php?nazev=obsah/netlogo/netlogo.zip Simulation File: NetLogo for Store Security Simulation]

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 == Conclusion ==
-This simulation revealed valuable insights into improving store security by analyzing how guards, thieves, customers, and store layouts interact. Preventing theft isn’t just about hiring more guards—it’s about making smarter, targeted changes to store operations. For example:
+This simulation revealed valuable insights into improving store security by analysing how guards, thieves, customers, and store layouts interact. Preventing theft isn’t just about hiring more guards—it’s about making smarter, targeted changes to store operations. For example, in a small store with two guards, only 40% of thieves were caught while 60% escaped, leading to a guard efficiency of just 1.8%. Increasing the guard count to three improved capture rates to 60% and raised efficiency to 3.8% but escapes still occurred. Additionally, thieves spent an average of 22 to 44 ticks in the store, depending on factors like steal delay. Shorter delays allowed thieves to escape faster, while longer ones gave guards more chances.
+Store layout and customer traffic were also crucial factors. Narrow shelves with minimal spacing made it harder for guards to see, while wider shelves created blind spots thieves could exploit. Balancing shelf spacing is key to ensuring guards can patrol effectively without disrupting the shopping experience. Similarly, high customer traffic, with spawn thresholds between 0.30 and 0.50, made it more difficult for guards to distinguish thieves from shoppers, often increasing escape rates. While the exact numbers and percentages may vary depending on specific scenarios, this simulation proved to be a useful tool for testing different security strategies. For Mr. Ha Son Do, it emphasizes the importance of placing guards strategically, optimizing store layouts, and focusing on guard training to improve theft prevention in a cost-effective and adaptable way.
 == Possible Model Extensions ==

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 === Second Large World ===
-* '''Captured Thieves''': 2–4
+In the first screenshot, 2 thieves were caught, while 4 escaped. In the second screenshot, guards performed significantly better, capturing 4 thieves with only 2 escaping. This improvement suggests better guard effectiveness, potentially due to better positioning .
-* '''Escaped Thieves''': 2–4
+Conflicts more than doubled, rising from 10 in the first screenshot to 21 in the second screenshot. This indicates that guards were more actively engaging with thieves, which contributed to the higher capture rate.
-* '''Conflicts''': 10–21 interactions
+The average time thieves spent in the shop decreased from 44 ticks in the first screenshot to 31.33 ticks in the second screenshot. This shows that guards were quicker at detecting and responding to thieves in the second scenario.
-* '''Average Time in Shop''': 31.33–44 ticks
+Despite the improvements, guard efficiency was very low in both screenshots—1.8% in the first screenshot and 3.8% in the second screenshot
 == Conclusion ==

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 === Large World ===
-* '''Captured Thieves''': 60%
+In both simulations, 60% of thieves were captured, while 40% escaped, even with six guards patrolling the shop. This shows that the guards were effective to some extent. The number of conflicts changed. In Screenshot 1, there were 19 conflicts, while Screenshot 2 had 28 conflicts. This suggests that guards were more engaged in the second simulation, but it didn’t result in a better capture rate. This could indicate inefficiencies in how guards respond after spotting thieves. Thieves spent more time in the shop in the second simulation, with an average of 33.6 ticks compared to 24.6 ticks in the first.
-* '''Escaped Thieves''': 40%
+The guard's efficiency also showed a notable difference. In Screenshot 1, efficiency was 5.9%, while in Screenshot 2, it improved to 18.3%. Having up to 20 customers in the store added complexity to the guards’ task. With so many people, guards struggled to differentiate between thieves and customers. This could explain why the escape rate remained consistent despite the increased guard activity in the second scenario.
 === Second Large World ===

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 === 3 Guards and 10 Customers ===
-* '''Captured Thieves''': 40%
+In both screenshots, only 40% of thieves were caught, while 60% escaped. Despite having three guards, the thieves still managed to evade capture more often than not, highlighting how challenging it is to achieve full coverage of the shop.
-* '''Escaped Thieves''': 60%
+The number of conflicts (instances where guards spotted thieves) were higher in Screenshot 2 (15) compared to Screenshot 1 (11). This could be due to differences in how the guards and thieves moved or interacted.
-* '''Conflicts''': 15 interactions
+On average, thieves spent about 25-26 ticks in the shop before either being captured or escaping. This suggests that guards were relatively quick to respond but could benefit from slight improvements in speed or strategy.
-* '''Average Time in Shop''': 25–26 ticks
+The guard's efficiency was low in both scenarios, with values of 5.8% and 4.2%. This indicates that while guards were active, they weren't being utilized to their full potential, possibly due to their positioning or the number of customers in the shop.
 === Large World ===

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 === 3 Guards ===
-* '''Captured Thieves''': 60%
+Analysis:
-* '''Escaped Thieves''': 40%
+In Screenshot 1, the caught 60% of the thieves. However, in Screenshot 2, they only managed to capture 20%, which shows that their performance wasn’t consistent. This difference might be because of how the guards were positioned or the random movements of the thieves.
-* '''Conflicts''': 26 interactions
+The guards were clearly more active in Screenshot 1, with 26 conflicts recorded. In Screenshot 2, there were only 5 conflicts, which means the guards either didn’t spot the thieves as often or weren’t as engaged.
-* '''Average Time in Shop''': 20.2 ticks
+On average, thieves spent less time in the shop in Screenshot 1 (20.2 ticks) compared to Screenshot 2 (25.6 ticks). This shows that guards in Screenshot 1 were better at interrupting the thieves’ activities and making them leave sooner.
 === 3 Guards and 10 Customers ===

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 == Simulation Results with Analysis ==
 === Small World, Guard Speed 2, and 2 Guards ===
-* '''Captured Thieves''': 40%
+Analysis:
-* '''Escaped Thieves''': 60%
+In the first screenshot, 2 thieves were captured (40%), while 3 thieves escaped (60%). This suggests improved guard effectiveness in the second scenario.
-* '''Conflicts''': 9 interactions
+Conflicts, representing guard-thief interactions, increased slightly from 8 in the first screenshot to 9 in the second screenshot. This indicates a marginally more active engagement between guards and thieves.
-* '''Average Time in Shop''': 22 ticks
+Thieves spent an average of 28 ticks in the shop in the first screenshot, but this dropped to 22 ticks in the second.
 === 3 Guards ===

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 === Simulation User Interface ===
-* '''Sliders''': Control parameters such as the number of guards, thieves, and customers, as well as properties like vision range, guard speed, and shelf dimensions.
+The user interface provides sliders for adjusting parameters, monitors for real-time metrics, and plots to visualize dynamics over time.
-* '''Monitors''': Display real-time metrics such as the number of captured thieves, escaped thieves, and conflicts.
+* '''Sliders''': Users can control key parameters such as the number of guards, thieves, and customers, as well as properties like vision range, guard speed, and shelf dimensions.
-* '''Graphs''': Visualize the number of guards, thieves, and customers in the shop over time using color-coded lines.
+* '''Monitors''': Displays real-time metrics such as the number of captured thieves, escaped thieves, and conflicts.
 == Simulation Results with Analysis ==

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 === Tracked Indicators ===
-* '''Captured Thieves''': Total number of thieves caught by guards.
+* '''Captured Thieves''': Definition: The total number of thieves caught by guards during the simulation.
-* '''Captured Thieves Percentage''': Percentage of thieves caught.
+Displayed Value: The number is shown in the box labelled "Captured thieves".
-* '''Escaped Thieves''': Total number of thieves who successfully left the store after stealing.
+Usage: Tracks guard efficiency in stopping thefts.
-* '''Escaped Thieves Percentage''': Percentage of thieves who escaped.
-* '''People in Shop (Graph)''': Real-time graph showing the number of thieves, customers, and guards in the shop over time.
+* '''Captured Thieves Percentage''': Definition: The percentage of thieves caught out of the total number of thieves in the simulation.
-* '''Thieves Average Time in Shop''': Average time thieves spend in the shop before being captured or escaping.
+Displayed Value: Shown in "Captured thieves percentage"
-* '''Use of Guards Percentage''': Represents how effectively guards are being utilized.
+Calculation: Captured Thieves Percentage = (Captured Thieves / Total Thieves) × 100
 === Simulation User Interface ===