Evolutionary Iterated Prisoner’s Dilemma Game

1. Evolutionary Computation, 2009 Evolutionary Iterated Prisoner’s Dilemma Game H.-T. Kim

2. Outline • Evolutionary Prisoner's Dilemma Game • Prisoner's Dilemma Game • Iterated Prisoner's Dilemma Game • N-person Iterated Prisoner's Dilemma Game • Robert Axelrod’s nIPD game • Evolution of Iterated Prisoner's Dilemma Game Strategies in Structured Demes Under Random Pairing in Game Playing • Simulation on Worksite Interactions between Laborers and Firms by using Multi-Agent based Evolutionary Computation

3. Prisoner's Dilemma Game <죄수의 딜레마 게임의 예> 어느 날 SC랩 공용통장에서 거액의 연구지원금이 사라진 사건이 발생, 교수님이 범인으로 승현이와 주원이를 지목했다. 하지만 물증은 없는 상황. 그래서 교수님은 그 두 명을 각각 따로 방으로 불러서 다음과 같이 말했다. ‘만약 범행을 순순히 시인한다면 정직한 너한테만 특별히 이번 달 연구비를 2배로 주고 졸업도 일찍 시켜주겠다. 하지만 괜히 입다물고 있다가 다른 사람이 자백하면 그 사람만 혜택을 주고, 너는 석사졸업 후 6년짜리 박사과정으로 보내겠다.’ 하지만 교수님은 만약 둘 다 자백하면 혜택은 전혀 주지 않을 계획이다. 이 상황에서, 어떻게 하는 것이 승현과 주원의 가장 합리적인 선택일까? 내쉬 균형!

4. Prisoner's Dilemma Game • 게임의 특징 • 1950 년대에 Merrill Flood 와 Melvin Dresher 에 의해서 고안 • 죄수 2명이 형사에게 취조 당하는 상황을 가정한 모델 • 2명의 player는 서로 의사소통 불가능 • 많은 사회현상이 이러한 형태를 닮아 있다는 점에서 중요한 모델 • 게임이론, 경제학, 그리고 정치학에서 깊이 연구 • Ex) 군비경쟁, 가격경쟁… • 게임의 조건 • R : 상호협력시의 payoff T : 나만 배반했을시의payoff • P : 상호배반시의 payoff S : 나만 협력했을시의payoff

5. Iterated Prisoner's Dilemma Game • IPD 게임 • 2명의 player가 Prisoner Dilemma 게임을여러 번 반복 • 상대방이 배반했을시 벌칙을 가하는 것이 가능 • IPD 게임의 결과 • 게임을 반복할수록 서로 협력하는 양상을 보임 • 게임의 player가 학습능력이 있어야 함  반복을 통해, 협력하는 것이 궁극적으로 더 많은 이득을 가져온다는 것을 학습

6. N-person Iterated Prisoner's Dilemma Game • nIPD게임의 특징 • 2명  n명으로 player가 증가 • Real-world problem을 보다 폭 넓게 반영 • 문제를 모델링하기 위해 ‘진화연산’이 주로 사용 • Robert Axelrod의 nIPD게임 실험 • nIPD게임 상황에서, 각 개체는 어떻게 행동하는 것이 가장 합리적인가? • 실험 과정 • step1) 각 분야의 전문가가 수동으로 작성한 전략을 서로 경쟁 • step2) 진화연산을 이용해 각 개체의 전략을 진화  실험결과, 가장 우세 전략은?

7. Robert Axelrod’s nIPD game – Step1 • 각 학문 분야의 전문가들에게 IPD 게임에서 특정 행동 전략을 수행하는 프로그램 요청 • 각 프로그램은 이전의 3번의 게임에서 자신과 상대방의 행동(배신, 협력)을 기억 • 자신의 행동전략은 이 기억에 기반 • ex) 상대방이 2번 배신했으면 나도 배신, 무조건 배신, 2회 협력 후 1회 배신 • 각 프로그램을 서로 경쟁시켜 가장 우세한 전략을 선정 • 방식 : round-robin tournament • 총 63개의 프로그램이 경쟁 • 어떤 프로그램은 마르코프 모델이나 베이즈 추론 같은 복잡한 기법을 사용 • 게임의 최종승자 • 제출된 전략중 가장 간단한 ‘TIT FOR TAT’ • TIT FOR TAT: 처음은 일단 협력, 이후부터는 상대방의 행동을 따라하기

8. Robert Axelrod’s nIPD game – Step2 • 진화연산이 전략을 성공적으로 진화시킬 수 있는지 실험 • Encoding • C : Cooperation D : Defect • 이전 1번의 게임에 대해, • 행동전략 : 각 경우에 대해 어떻게 행동(협력, 배반)할지 규정 • ex)TIT FOR TAT CC CD DC DD 총 4가지 경우가 존재! case 2 case 3 case 4 case 1 CC CD DC DD 협력 배신 배신 협력

9. Robert Axelrod’s nIPD game – Step2 • Encoding - 이전 3번의 게임을 기억해야 하는 경우 CC CCCC(case 1) CC CCCD (case 2) CC CCDC (case 3) … DD DDDC (case 63) DD DDDD(case 64)  따라서 총 64bit + 6bit 로 전략 encoding 가능 • 64bit : 각 경우와 행동을 1대1 맵핑 • 6bit : 이전 3번의 행동을 기억 • EX) CCDCDDDC … DC CCDDCD • 가능한 전략의 수 = 270 64가지 경우

10. Robert Axelrod’s nIPD game – Step2 • 기타 변수들 • Fitness : Payoff의 합 • Population : 20 • Generation : 50 • 실험결과 • 진화된 대부분의 전략은 협력에 보답하고 배신에는 보복하는 양상 • TIT FOR TAT과 유사!! • TIT FOR TAT 보다 더 높은 점수를 얻는 전략도 발견 • 실험 양상 • 초기 세대에는 협조적인 개체들이 다른 개체에 보답을 받지 못하고 소멸 • 약 10~20세대 이후에는 협조에 보답하고 배신에 보복하는 전략이 등장 • 이후 위와 같은 전략이 population에 다수 분포

11. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL. 9, NO. 6, DECEMBER 2005 Evolution of Iterated Prisoner's Dilemma Game Strategies in Structured Demes Under Random Pairing in Game PlayingHisaoIshibuchi, Member, IEEE, and Naoki Namikawa, Student Member, IEEE 김 희택

12. Outline • Introduction • Two neighbor structure • IPD game structure • Mating strategy • Simulation – Standard Pairing Scheme • Random Pairing Scheme • Simulation – Random Pairing Scheme • Conclusion

13. Introduction • Spatial IPD game • Framework of structured demes • Cells of two-dimensional grid-world • Two neighborhood structure • Interaction among players through the IPD game • Interaction among players for mating strategies • Similar to world of territorial animals or plant • Random pairing scheme • Plays game with a randomly chosen neighbor at every round • Demonstrate evolution of cooperation behavior (in random pairing)

14. Basic structure – Payoff Matrix • Payoff Matrix of the game

15. Basic structure – Strategy Encoding • Single player has a single strategy • Every Strategy is represented by 5 bit binary sequence • Example of strategy (TIT-FOR-TAT)

16. IPD game structure – World and Neighborhood • Use 31 * 31 grid-world • All player locate on one cell • 961 player exist • Examples of neighborhood structure

17. IPD game structure - Game play and Fitness • NIPD(i) • The set of Player i and its neighbors • Game play • The game is iterated for a pre-specified number of rounds (e.g, 100 rounds) • Each playerplays game against only its neighbors • Randomly select opponents • Fitness • Average payoff obtained from each round of the game

18. Mating strategy – formulation • NGA(i) • Set of player i and its neighbors • NIPD(i) = NGA(i) is not always hold • Parents is selected from NGA(i) • Using roulette wheel selection • Selection probability of strategy j • f(si) : fitness of player i with strategy si • Fmin(NGA(i)) : minimum fitness among the NGA(i)

19. Mating strategy – crossover and mutation • One point crossover • Bitmap mutation

20. Simulation • Two kinds of simulation • Simulate two neighborhood structure with standard pairing scheme • Verify the effect of two neighborhood structure on evolution of cooperative behavior • Simulate two neighborhood structure with random pairing scheme • Examine the effect random pairing scheme on evolution of cooperative behavior • 961 spatially fixed player (31 * 31 grid-world) • Mistake (noisy IPD model) • A player chooses an action different from its strategy

21. Standard Pairing Simulation – Parameter Setting • Case of two neighborhood structure • Parameter value

22. Standard Pairing Simulation – Result • NIPD has a significant effect on the evolution of cooperative behavior • NGA has a much smaller effect than NIPD • Small NIPD facilitate the evolution of cooperative behavior <Mistake probability 0.1>

23. Standard Pairing Simulation – Result (2) • Better results were obtained from smaller mistake probabilities • Cooperative behavior were evolved independently from the two neighborhood structures <Mistake probability 0.01> <Mistake probability 0.001>

24. Random Pairing Scheme • Every player chooses its opponent randomly from NIPD at every round of the game • The memory about the interaction with a neighbor may influence an player’s future action against another neighbor

25. Random Pairing Simulation – Result (1) • The same parameter specifications were used as in the previous • Evolution of cooperative behavior is very difficult to achieve • Increase number of opponents  Decreased the probability to play against the same opponent  Decrease in average payoff <Mistake probability 0>

26. Random Pairing Simulation – Result (2) • Strategy characterized by the genetic form “1***1”

27. Random Pairing Simulation – Result (3) • Strategy characterized by the genetic form “****0” • The existence of strategies of this type prevents the consecutive occurrence of mutual cooperation

28. Random Pairing Simulation – Result (4) • Strategy characterized by the genetic form “11**1” • Those strategies have the ability to recover from mutual defection (D, D) • This ability seems to be important under a noisy situation

29. Random Pairing Simulation – Result (5) • The TFT strategy “10011” increased its percentage to almost 100% • Higher average payoff was obtained from strategies of the form “11**1,” rather than the TFT strategy “10011.”

30. Other Simulations

31. Conclusion • Formulated a spatial IPD game using the concept of two neighborhood structures • Interaction among players through the IPD game • Mating strategies • Computer Simulation • Use of a small interaction neighborhood facilitated the evolution of cooperative behavior • Introduced a random pairing scheme with the two neighborhood structures • Computer Simulation • Cooperative behavior was evolved when we smallest interaction neighborhood is used • Future Work • Explain the results of random pairing scheme simulation • Use a stochastic strategy represented by a string of real numbers between 0 and 1 • Evolution of cooperative behavior under the random pairing scheme in a large interaction neighborhood

32. Social Simulation Workshop at the International Joint Conference on Artificial Intelligence Simulation on Worksite Interactions between Laborers and Firms by usingMulti-Agent based Evolutionary Computation Soft Computing Laboratory, Yonsei University Hee-Taek Kim and Sung-Bae Cho elsein@sclab.yonsei.ac.kr , sbcho@cs.yonsei.ac.kr

33. Motivation • Laborers and firms formulate strategic relationship • What is rational strategy in position of laborer or firm • Can we drive mutual benefits relation between Laborers and firm? • General economic belief • laborer tends to cooperate with cooperative firms • Firm tends to cooperate with cooperative laborers <Laborer> <Firm> Low wage, but high productivity High wage... Wage Labor

34. Introduction of the Simulation Model • Construct computational work-site interaction model • Multi-agent based approach • Consist of worker agent and firm agent • Implement adaptive agent by using evolutionary computation • Simulate interaction between workers and firms • Workers and firms are mutually interact each other • Make collaborative or competitive relationship

35. Evolutionary Computation • Based on Darwinism • “Survivals of the fittest” • Apply evolutionism to computation • Widely used to modeling social phenomena • Individual population, behavioral rule, selection and reproduction • Each individual can adapt to dynamic environment • Basic evolution process Reproduction (Crossover and mutation) Population Calculate Fitness Selection

36. Simulation Process – Laborer’s Phase • The interaction protocol between workers and firms can be divided into two phase • Laborer’s phase and firm’s phase <Laborers Phase> • Laborers have to decide whether to resign from firm or not • Laborers have to decide whether to cooperate or defect with his employer

37. Simulation Process – Firm’s Phase • Firm’s phase • Firms have to decide whether to cooperate or defect with his opponent laborers

38. Overall Process of Simulation

39. Simulation framework

40. Internal Attributes – Laborer

41. Internal Attributes– Firm

42. Action of Agent • Cooperation and defection • Laborer • Cooperation : High Productivity (ProdH) • Defection : Low Productivity (ProdL) • Resign : resign from opponent firm • Firm • Cooperation : High wage (WageH) • Defection : Low wage (WageL)

43. Behavioral Strategy of Agent • Behavioral strategy determine current action of the agent • All individuals has its own strategy • All strategies evolve as the simulation being progressed

44. Evolutionary Engine • Fitness evaluation • Firm • The capital attribute is treated as fitness of the firm • Laborer • The asset attribute is treated as fitness of the laborer • Selection • Used roulette wheel selection • Possibility of selection

45. Evolutionary Engine • Crossover and mutation • One point cross over • One point bit mutation • Elimination • Eliminate incapable agents from simulation

46. Experimental Design

47. Experimental Result

48. Experimental Result (2)

49. Conclusion – Second Experiment • Forbid resignation of laborers • Laborers cannot escape from vicious firm • Firms just want to extort faithful laborer • Results in breakdown of all agents because of selfish behavior of the firms

50. Current Works • Extend 2*2 interaction model  Continuous model based on linear algebra • Asset/livingCost X1 + RecentGivenPay X2 + Continuous X3… • Beside previous activity of opponent agent, many other factors can affect current action of the agent • Environmental information, my current state, opponent state, and so on… • Test various policies to simulation model and analysis it’s effect