Risk consideration and cooperation in the iterated prisoner’s dilemma

This paper investigates the cooperative behavior in the two-player iterated prisoner’s dilemma (IPD) game with the consideration of income stream risk. The standard deviation of one-move payoffs for players is defined for measuring the income stream risk, and thus the risk effect on the cooperation in the two-player IPD game is examined. A two-population coevolutionary learning model, embedded with a niching technique, is developed to search optimal strategies for two players to play the IPD game. As experimental results illustrate, risk-averse players perform better than risk-seeking players in cooperating with opponents. In particular, in the case with short game encounters, in which cooperation has been demonstrated to be difficult to achieve in previous work, a high level of cooperation can be obtained in the IPD if both players are risk-averse. The reason is that risk consideration induces players to negotiate for stable gains, which lead to steady mutual cooperation in the IPD. This cooperative pattern is found to be quite robust against low levels of noise. However, with increasingly higher levels of noise, only intermediate levels of cooperation can be achieved in games between two risk-averse players. Games with risk-seeking players get to even lower cooperation levels. By comparing the players’ strategies coevolved with and without a high level of noise, the main reason for the reduction in the extent of cooperation can be explained as the lack of contrition and forgiveness of players in the high-noise interactions. Moreover, although increasing encounter length is helpful in improving cooperation in the noiseless and low-noise IPD, we find that it may enforce the absence of contrition and forgiveness, and thus make cooperation even more difficult in the high-noise games.

[1]  Graham Kendall,et al.  A Strategy with Novel Evolutionary Features for the Iterated Prisoner's Dilemma , 2009, Evolutionary Computation.

[2]  M. Nowak,et al.  Evolution of indirect reciprocity by image scoring , 1998, Nature.

[3]  Kalyanmoy Deb,et al.  Optimal Strategies of the Iterated Prisoner's Dilemma Problem for Multiple Conflicting Objectives , 2009, IEEE Trans. Evol. Comput..

[4]  Ludo Waltman,et al.  A mathematical analysis of the long-run behavior of genetic algorithms for social modeling , 2009, Soft Computing.

[5]  Christian Grimme,et al.  Connecting Community-Grids by supporting job negotiation with coevolutionary Fuzzy-Systems , 2011, Soft Comput..

[6]  Huw David Dixon,et al.  Keeping Up With the Joneses: Competition and the Evolution of Collusion in an Oligopolistic Economy , 1998 .

[7]  Uzay Kaymak,et al.  Economic modeling using evolutionary algorithms: the effect of a binary encoding of strategies , 2009 .

[8]  M. Ochea,et al.  Evolution of repeated prisoner's dilemma play under logit dynamics , 2013 .

[9]  D. Fudenberg,et al.  Emergence of cooperation and evolutionary stability in finite populations , 2004, Nature.

[10]  Drew Fudenberg,et al.  The Folk Theorem in Repeated Games with Discounting or with Incomplete Information , 1986 .

[11]  Xin Yao,et al.  Multiple Choices and Reputation in Multiagent Interactions , 2007, IEEE Transactions on Evolutionary Computation.

[12]  Minqiang Li,et al.  An investigation on niching multiple species based on population replacement strategies for multimodal functions optimization , 2009, Soft Comput..

[13]  Andries Petrus Engelbrecht,et al.  Particle swarm optimization approaches to coevolve strategies for the iterated prisoner's dilemma , 2005, IEEE Transactions on Evolutionary Computation.

[14]  M. Nowak,et al.  A strategy of win-stay, lose-shift that outperforms tit-for-tat in the Prisoner's Dilemma game , 1993, Nature.

[15]  Juan C. Burguillo,et al.  Using self-organizing maps with complex network topologies and coalitions for time series prediction , 2014, Soft Comput..

[16]  G. S. van Doorn,et al.  Coaction versus reciprocity in continuous-time models of cooperation. , 2014, Journal of theoretical biology.

[17]  David B. Fogel,et al.  Evolving Behaviors in the Iterated Prisoner's Dilemma , 1993, Evolutionary Computation.

[18]  R. Axelrod,et al.  How to Cope with Noise in the Iterated Prisoner's Dilemma , 1995 .

[19]  Peter Tiño,et al.  Measuring Generalization Performance in Coevolutionary Learning , 2008, IEEE Transactions on Evolutionary Computation.

[20]  Jason Barr,et al.  Organization, learning and cooperation , 2004 .

[21]  Daniel A. Ashlock,et al.  Fingerprint analysis of the noisy prisoner’s dilemma , 2009, 2007 IEEE Congress on Evolutionary Computation.

[22]  Hisao Ishibuchi,et al.  Evolution of iterated prisoner's dilemma game strategies in structured demes under random pairing in game playing , 2005, IEEE Transactions on Evolutionary Computation.

[23]  H. Ohtsuki,et al.  A simple rule for the evolution of cooperation on graphs and social networks , 2006, Nature.

[24]  Seth Bullock,et al.  Combating Coevolutionary Disengagement by Reducing Parasite Virulence , 2004, Evolutionary Computation.

[25]  Colin Camerer Behavioral Game Theory: Experiments in Strategic Interaction , 2003 .

[26]  K. Lindgren,et al.  Evolutionary dynamics of spatial games , 1994 .

[27]  Graham Kendall,et al.  Evolutionary Stability of Discriminating Behaviors With the Presence of Kin Cheaters , 2013, IEEE Transactions on Cybernetics.

[28]  W. Raub,et al.  REVOLUTION AND RISK , 1998 .

[29]  Xiao-Jun Zeng,et al.  A Stackelberg game-theoretic approach to optimal real-time pricing for the smart grid , 2013, Soft Comput..

[30]  Paul Charbonneau,et al.  Crossover and Evolutionary Stability in the Prisoner's Dilemma , 2007, Evolutionary Computation.

[31]  Peter Tiño,et al.  Relationship Between Generalization and Diversity in Coevolutionary Learning , 2009, IEEE Transactions on Computational Intelligence and AI in Games.

[32]  Simon M. Lucas,et al.  Coevolving Game-Playing Agents: Measuring Performance and Intransitivities , 2013, IEEE Transactions on Evolutionary Computation.

[33]  Xin Yao,et al.  Speciation as automatic categorical modularization , 1997, IEEE Trans. Evol. Comput..

[34]  Kent D. Miller,et al.  Strategic Risk and Corporate Performance: an Analysis of Alternative Risk Measures , 1990 .

[35]  Siang Yew Chong,et al.  Improving Generalization Performance in Co-Evolutionary Learning , 2012, IEEE Transactions on Evolutionary Computation.

[36]  David B. Fogel,et al.  On the Relationship between the Duration of an Encounter and the Evolution of Cooperation in the Iterated Prisoner's Dilemma , 1995, Evolutionary Computation.

[37]  W. Press,et al.  Iterated Prisoner’s Dilemma contains strategies that dominate any evolutionary opponent , 2012, Proceedings of the National Academy of Sciences.

[38]  T. Furusawa The negotiation of sustainable tariffs , 1999 .

[39]  Alasdair I. Houston,et al.  Variation in behaviour promotes cooperation in the Prisoner's Dilemma game , 2004, Nature.

[40]  Xin Yao,et al.  On Evolving Robust Strategies for Iterated Prisoner's Dilemma , 1993, Evo Workshops.

[41]  T. Friedman The World Is Flat [Updated and Expanded]: A Brief History of the Twenty-first Century , 2006 .

[42]  Jerker Denrell,et al.  Strategic responsiveness and Bowman's risk–return paradox , 2007 .

[43]  M. V. van Assen,et al.  The effect of nonlinear utility on behaviour in repeated prisoner’s dilemmas , 2010 .

[44]  Hussein A. Abbass,et al.  Evolution and Incremental Learning in the Iterated Prisoner's Dilemma , 2009, IEEE Transactions on Evolutionary Computation.

[45]  Ramakrishnan Pakath,et al.  The Iterated Prisoner's Dilemma: early experiences with Learning Classifier System-based simple agents , 2001, Decis. Support Syst..

[46]  R. Riolo,et al.  Evolution of cooperation without reciprocity , 2001, Nature.

[47]  Han La Poutré,et al.  Stabilization of tag-mediated interaction by sexual reproduction in an evolutionary agent system , 2005, Inf. Sci..

[48]  Hisao Ishibuchi,et al.  Evolution of Strategies With Different Representation Schemes in a Spatial Iterated Prisoner's Dilemma Game , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[49]  Graham Kendall,et al.  Engineering Design of Strategies for Winning Iterated Prisoner's Dilemma Competitions , 2011, IEEE Transactions on Computational Intelligence and AI in Games.

[50]  Asim Karim,et al.  Automatic Personalized Spam Filtering through Significant Word Modeling , 2007 .

[51]  Xin Yao,et al.  Behavioral diversity, choices and noise in the iterated prisoner's dilemma , 2005, IEEE Transactions on Evolutionary Computation.

[52]  Paul Belleflamme,et al.  Sustainable Collusion on Separate Markets , 2006 .

[53]  Christos A. Ioannou Coevolution of finite automata with errors , 2014 .

[54]  W. Hamilton,et al.  The Evolution of Cooperation , 1984 .

[55]  Don Tapping,et al.  Value Stream Management: Eight Steps to Planning, Mapping, and Sustaining Lean Improvements , 2002 .

[56]  Hui Zhang,et al.  Evolutionary prisoner's dilemma game on graphs and social networks with external constraint. , 2014, Journal of theoretical biology.

[57]  Xin Yao,et al.  Progress in Evolutionary Computation , 1995, Lecture Notes in Computer Science.

[58]  John H. Miller,et al.  The coevolution of automata in the repeated Prisoner's Dilemma , 1996 .

[59]  Ho-fung Leung,et al.  Incorporating Risk Attitude and Reputation into Infinitely Repeated Games and an Analysis on the Iterated Prisoner's Dilemma , 2007 .

[60]  Jinwu Gao,et al.  Credibilistic extensive game with fuzzy payoffs , 2013, Soft Comput..

[61]  Raymond Chiong,et al.  Effects of Iterated Interactions in Multiplayer Spatial Evolutionary Games , 2012, IEEE Transactions on Evolutionary Computation.

[62]  Xin Yao,et al.  Co-Evolution in Iterated Prisoner's Dilemma with Intermediate Levels of Cooperation: Application to Missile Defense , 2002, Int. J. Comput. Intell. Appl..