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Hypotheses about Typical General Human Strategic Behavior in a Concrete Case

  • Rustam Tagiew
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5883)

Abstract

This work is about typical human behavior in general strategic interactions called also games. This work is not about modelling best human performance in well-known games like chess or poker. It tries to answer the question ’How can we exactly describe what we typically do, if we interact strategically in games we have not much experience with?’ in a concrete case. This concrete case is a very basic scenario - a repeated zero sum game with imperfect information. Rational behavior in games is best studied by game theory. But, numerous experiments with untrained subjects showed that human behavior in strategic interactions deviates from predictions of game theory. However, people do not behave in a fully indescribable way. Subjects deviate from game theoretic predictions in the concrete scenario presented here. As results, a couple of regularities in the data is presented first which are also reported in related work. Then, the way of using machine learning for describing human behavior is given. Machine learning algorithms provide automatically generated hypotheses about human behavior. Finally, designing a formalism for representing human behavior is discussed.

Keywords

Bayesian Network Strategic Interaction Board Game Hypothesis Space Sequential Minimal Optimization 
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.

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References

  1. 1.
    Ariely, D. (ed.): Predictably irrational. HarperCollinsPublishers (2008)Google Scholar
  2. 2.
    Nash, J.: Non-cooperative games. Annals of Mathematics (54), 286–295 (1951)Google Scholar
  3. 3.
    Gobet, F., de Voogt, A., Retschitzki, J.: The Psychology of Board Games. Psy. Press (2004)Google Scholar
  4. 4.
    Li, D.H.: Kriegspiel: Chess Under Uncertainty. Premier (1994)Google Scholar
  5. 5.
    Camerer, C.F.: Behavioral Game Theory. Princeton University Press, New Jersey (2003)zbMATHGoogle Scholar
  6. 6.
    Eysenck, M.W., Keane, M.T.: Cognitive Psychology. Psychology Press (2005)Google Scholar
  7. 7.
    Gal, Y., Pfeffer, A.: A language for modeling agents’ decision making processes in games. In: AAMAS, pp. 265–272. ACM Press, New York (2003)Google Scholar
  8. 8.
    Fagin, R., Halpern, J., Moses, Y., Vardi, M.: Reasoning about Knowledge. MIT Press, Cambridge (1995)zbMATHGoogle Scholar
  9. 9.
    Gal, Y., Pfeffer, A.: Modeling reciprocal behavior in human bilateral negotiation. In: AAAI, pp. 815–820. AAAI Press, Menlo Park (2007)Google Scholar
  10. 10.
    Marchiori, D., Warglien, M.: Predicting human interactive learning by regret-driven neural networks. Science 319, 1111–1113 (2008)CrossRefGoogle Scholar
  11. 11.
    Erev, I., Roth, A.: Predicting how people play games. American Economic Review 88 (1998)Google Scholar
  12. 12.
    Erev, I., Roth, A., Slonim, L., Barron, G.: Predictive value and the usefulness of game theoretic models. International Journal of Forecasting 18, 359–368 (2002)CrossRefGoogle Scholar
  13. 13.
    Ho, T.-H., Camerer, C., Chong, J.-K.: Self-tuning expirience-weighted attraction learning in games. Journal of Economic Theory 133, 177–198 (2007)CrossRefMathSciNetzbMATHGoogle Scholar
  14. 14.
    Ert, E., Erev, I.: Replicated alternatives and the role of confusion, chasing and regret in decisions from experience. Journal of Behavioral Decision Making 20, 305–322 (2007)CrossRefGoogle Scholar
  15. 15.
    Tagiew, R.: Towards a framework for management of strategic interaction. In: ICAART, pp. 587–590. INSTICC Press (2009)Google Scholar
  16. 16.
    Harary, F.: The number of oriented graphs. Michigan Mathematical J. 4, 221–224 (1957)CrossRefGoogle Scholar
  17. 17.
    Heumer, G., Amor, H.B., Jung, B.: Grasp recognition for uncalibrated data gloves: A machine learning approach. Presence - Teleoperators and Virtual Environ 17, 121–142 (2008)CrossRefGoogle Scholar
  18. 18.
    Stahl, D.O., Wilson, P.W.: Experimental evidence on players’ models of other players. Economic Behavior & Organization, 309–327 (1994)Google Scholar
  19. 19.
    Richard, M., Palfrey, T.: Quantal response equilibria for normal form games. Games and Economic Behavior 10, 6–38 (1995)CrossRefMathSciNetzbMATHGoogle Scholar
  20. 20.
    Miller, G.A.: The magical number seven plus or minus two: Some limitations on our capacity for processing information. Psychological Review 63, 81–97 (1956)CrossRefGoogle Scholar
  21. 21.
    Budescu, D.V., Rapoport, A.: Subjective randomization in one- and two-person games. Journal of Behavioral Decission Making 7, 261–278 (1994)CrossRefGoogle Scholar
  22. 22.
    Mitchell, T.M.: Machine Learning. McGraw-Hill, New York (1997)zbMATHGoogle Scholar
  23. 23.
    Witten, I.H., Frank, E.: Data Mining. Morgan Kaufmann, San Francisco (2005)zbMATHGoogle Scholar
  24. 24.
    Platt, J.C.: Fast training of support vector machines using sequential minimal optimization. In: Advances in Kernel Methods - Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
  25. 25.
    Cessie, S., Houwelingen, J.C.: Ridge estimators in logistic regression. Applied Statistics 41, 191–201 (1992)CrossRefzbMATHGoogle Scholar
  26. 26.
    Penrose, R.: The Emperor’s New Mind. Oxford University Press, Oxford (1989)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rustam Tagiew
    • 1
  1. 1.Institute for Computer Science of TU Bergakademie FreibergGermany

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