Advertisement

Formalizing Risk Strategies and Risk Strategy Equilibrium in Agent Interactions Modeled as Infinitely Repeated Games

  • Ka-man Lam
  • Ho-fung Leung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4088)

Abstract

To design intelligent agents for multi-agent applications, like auctions and negotiations, we need to first analyze how agents should interact in these applications. Game theory is a tool, which can be used. In game theory, decision-making often depends on probability and expected utility. However, decision makers usually violate the expected utility theory when there is risk in the choices. Instead, decision makers make decisions according to their attitudes towards risk. Also, reputations of other agents in making certain actions also affect decision-making. In this paper, we make use of risk attitude, reputation and utility for making decisions. We define the concepts of risk strategies, risk strategy equilibrium, and a formalized way to find the risk strategy equilibrium in infinitely repeated games. Simulations show that players get higher payoff by using risk strategies than using other game theoretic strategies.

Keywords

Nash Equilibrium Game Theory Mixed Strategy Pure Strategy Intelligent Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gagne, R.: The Conditions of Learning. Holt, Rinehart and Winston, New York (1985)Google Scholar
  2. 2.
    He, M., Leung, H.F., Jennings, N.: A fuzzy-logic based bidding strategy for autonomous agents in continuous double auctions. IEEE Transactions on Knowledge and Data Engineering 15(6), 985–1003 (2003)Google Scholar
  3. 3.
    Amosweb economic gloss, http://www.amosweb.com/gls
  4. 4.
    Kahneman, D., Tversky, A.: Prospect theory: An analysis of decision under risk. Econometrica 47(2), 263–291 (1979)MATHCrossRefGoogle Scholar
  5. 5.
    Lam, K.M., Leung, H.F.: Risk strategies and risk strategy equilibrium in agent interactions modeled as normal repeated 2 ×2 risk games. In: The Eighth Pacific Rim International Workshop on Multi-Agents (Paper received the Best Paper Award) (2005)Google Scholar
  6. 6.
    Lam, K.M., Leung, H.F.: A trust/honesty model with adaptive strategy for multiagent semi-competitive environments. Autonomous Agents and Multi-Agent Systems (to appear)Google Scholar
  7. 7.
    Liu, Y., Goodwin, R., Keonig, S.: Risk-averse auction agents. In: Proceedings of Autonomous Agents and Multi-Agent Systems, pp. 353–360 (2003)Google Scholar
  8. 8.
    Luce, R.D., Raiffa, H.: Games and Decisions. John Wiley and Sons, New York (1957)MATHGoogle Scholar
  9. 9.
    Mui, L., Mohtashemi, M., Halberstadt, A.: A computational model of trust and reputation. In: Proceedings of 35th Hawaii International Conference on System Science (2002)Google Scholar
  10. 10.
    Nash, J.F.: Equilibrium points in n-person games. In: Proceedings of the National Academy of Science of the United States of America, pp. 48–49 (1950)Google Scholar
  11. 11.
    Osborne, M.J., Rubinstein, A.: A Course in Game Theory. MIT Press, Cambridge (1994)MATHGoogle Scholar
  12. 12.
    Rubiera, J.C., Lopez, J.M.M., Muro, J.D.: A fuzzy model of reputation in multi-agent systems. In: Proceedings of the Fifth International Conference on Autonomous Agents, pp. 25–26 (2001)Google Scholar
  13. 13.
    Sabater, J., Sierra, C.: Regret: A reputation model for gregarious societies. In: Proceedings of Fourth International Workshop on Deception, Fraud and Trust in Agent Societies (2001)Google Scholar
  14. 14.
    Sartain, A.Q., North, A.J., Strange, J.R., Chapman, H.M.: Psychology | Understanding Human Behavior. McGraw-Hill, New York (1962)Google Scholar
  15. 15.
    Staw, B.M., Sandelands, L.E., Dutton, J.E.: Threat-rigidity effects in organizational behavior: A multilevel analysis. Administrative Science Quarterly 26, 501–524 (1981)CrossRefGoogle Scholar
  16. 16.
    von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, Princeton (1944)MATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Ka-man Lam
    • 1
  • Ho-fung Leung
    • 1
  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong Kong 

Personalised recommendations