Agent-based simulation of trust games for communication and information

A trust game is a two-player game in extended form. The sub game perfect equilibrium of the game is that a player called an investor does not invest the wealth. Because, this behavior is the best response of the investor according to the best response of the another player, called an investor, "not invest". Based on the experimental results of Blacht and Feltovitch(2009), many human subjects choose the equilibrium strategies. However, who are allowed to communicate with the opponent by cheap talk or to observe past activities of the opponent sometimes choose cooperative strategies not strategies. In this study, an agent-based simulation experiments are conducted to analyze the effect of the communication and information effects in the trust games. The experimental result indicates that the effect of information of the past behavior of the opponent to the cooperative behavior is larger than the effect of communication.

[1]  W. Marsden.  I and J , 2012 .

[2]  Phillip Ein-Dor,et al.  Neural nets in a group decision process , 2003, Int. J. Game Theory.

[3]  Claudia Pahl-Wostl,et al.  Understanding Climate Policy Using Participatory Agent-Based Social Simulation , 2000, MABS.

[4]  A. Roth,et al.  Learning in Extensive-Form Games: Experimental Data and Simple Dynamic Models in the Intermediate Term* , 1995 .

[5]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[6]  A. Roth,et al.  Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria , 1998 .

[7]  Luke Hunsberger,et al.  Whatever You Say , 2006, JELIA.

[8]  Jaime Simão Sichman,et al.  Multi-Agent-Based Simulation , 2002, Lecture Notes in Computer Science.

[9]  A. Rapoport,et al.  Coordination, “Magic,” and Reinforcement Learning in a Market Entry Game , 1998 .

[10]  R. Hegselmann,et al.  Simulating Social Phenomena , 1997 .

[11]  John H. Miller,et al.  Auctions with Artificial Adaptive Agents , 1995 .

[12]  Nick Feltovich,et al.  Whatever you say, your reputation precedes you: Observation and cheap talk in the trust game , 2009 .

[13]  Amnon Rapoport,et al.  Coordination in market entry games with symmetric players , 1995 .

[14]  Jane Sell,et al.  “Liar, Liar... ” , 1997 .

[15]  David B. Fogel,et al.  Evolving neural networks to play checkers without relying on expert knowledge , 1999, IEEE Trans. Neural Networks.

[16]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[17]  Eyal Winter,et al.  Coordination and Learning Behavior in Large Groups with Asymmetric Players , 2002, Games Econ. Behav..

[18]  S. Parsons,et al.  Game Theory and Decision Theory in Agent-Based Systems , 2002, Multiagent Systems, Artificial Societies, and Simulated Organizations.

[19]  E. Rowland.  Theory of Games and Economic Behavior , 1946, Nature.

[20]  Jaime Simão Sichman,et al.  Multi-Agent-Based Simulation II , 2003, Lecture Notes in Computer Science.

[21]  Sander van der Hoog,et al.  On Multi-Agent Based Simulation , 2004 .

[22]  Isaac Meilijson,et al.  Evolution of reinforcement learning in foraging bees: a simple explanation for risk averse behavior , 2002, Neurocomputing.