Adaptive Proposer for Ultimatum Game

详细信息    查看全文

• 关键词：Games ; Markov decision process ; Bayesian learning
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9886
• 期：1
• 页码：330-338
• 全文大小：222 KB
• 参考文献：1.Abramowitz, M., Stegun, I.A.: Handbook of Mathematical Functions. Dover Publications, New York (1972)MATH
  2.Avanesyan, G.: Decision making in ultimatum game. Master’s thesis. University of Economics, Prague (2014)
  3.Bellman, R.E.: Adaptive Control Processes. Princeton University Press, Princeton (1961)CrossRef MATH
  4.Berger, J.O.: Statistical Decision Theory and Bayesian Analysis. Springer, New York (1985)CrossRef MATH
  5.Boyd, R.: Cross-cultural Ultimatum Game Research Group - Rob Boyd, Joe Henrich problem. Unpublished article
  6.Feldbaum, A.A.: Theory of dual control. Autom. Remote Control 21(9), 874–880 (1960)MathSciNet
  7.Fiori, M., Lintas, A., Mesrobian, S., Villa, A.E.P.: Effect of emotion and personality on deviation from purely rational decision-making. In: Guy, T.V., Kárný, M., Wolpert, D.H. (eds.) Decision Making and Imperfection, vol. 474, pp. 129–169. Springer, Berlin (2013)CrossRef
  8.Güth, W.: On ultimatum bargaining experiments: a personal review. J. Econ. Behav. Org. 27(3), 329–344 (1995)CrossRef
  9.Guy, T.V., Kárný, M., Lintas, A., Villa, A.E.P.: Theoretical models of decision-making in the ultimatum game: fairness vs. reason. In: Wang, R., Pan, X. (eds.) Advances in Cognitive Neurodynamics (V). Advances in Cognitive Neurodynamics. Springer, Singapore (2015)
  10.Harsanyi, J.C.: Games with incomplete information played by Bayesian players I-III. Manage. Sci. 50(12), 1804–1817 (2004). SupplementCrossRef
  11.Kárný, M.: Recursive estimation of high-order Markov chains: approximation by finite mixtures. Inf. Sci. 326, 188–201 (2016)CrossRef
  12.Kárný, M., Böhm, J., Guy, T.V., Jirsa, L., Nagy, I., Nedoma, P., Tesař, L.: Optimized Bayesian Dynamic Advising: Theory and Algorithms. Springer, London (2006)
  13.Kárný, M., Kroupa, T.: Axiomatisation of fully probabilistic design. Inf. Sci. 186(1), 105–113 (2012)MathSciNet CrossRef MATH
  14.Knejflová, Z., Avanesyan, G., Guy, T.V., Kárný, M.: What lies beneath players’ non-rationality in ultimatum game? In: Guy, T.V., Kárný, M. (eds.) Proceedings of the 3rd International Workshop on Scalable Decision Making, ECML/PKDD 2013 (2013)
  15.Kumar, P.R.: A survey on some results in stochastic adaptive control. SIAM J. Control Appl. 23, 399–409 (1985)MathSciNet
  16.Peterka, V.: Bayesian approach to system identification. In: Eykhoff, P. (ed.) Trends and Progress in System Identification, pp. 239–304. Pergamon Press, Oxford (1981)
  17.Puterman, M.L.: Markov Decision Processes. Wiley, New York (1994)CrossRef MATH
  18.Rubinstein, A.: Perfect equilibrium in a bargaining model. Econometrica 50(1), 97–109 (1982)MathSciNet CrossRef MATH
  19.Ruman, M., H\(\mathring{\rm u}\) la, F., Kárný, M., Guy, T.V.: Deliberation-aware responder in multi-proposer ultimatum game. In: Proceedings of ICANN 2016 (2016)
  20.Sanfey, A.G., Rilling, J.K., Aronson, J.A., Nystrom, L.E., Cohen, J.D.: The neural basis of economic decision-making in the ultimatum game. Science 300(5626), 1755–1758 (2003)CrossRef
  21.Si, J., Barto, A.G., Powell, W.B., Wunsch, D. (eds.): Handbook of Learning and Approximate Dynamic Programming. Wiley-IEEE Press, Danvers (2004)
  22.von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton University Press, New York (1944)MATH
  
• 作者单位：František Hůla (16)
  Marko Ruman (16)
  Miroslav Kárný (16)
  
  16. Department of Adaptive Systems, Institute of Information Theory and Automation, Czech Academy of Sciences, POB 18, 182 08, Prague 8, Czech Republic
  
• 丛书名：Artificial Neural Networks and Machine Learning – ICANN 2016
• ISBN：978-3-319-44778-0
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9886

文摘

Ultimate Game serves for extensive studies of various aspects of human decision making. The current paper contribute to them by designing proposer optimising its policy using Markov-decision-process (MDP) framework combined with recursive Bayesian learning of responder’s model. Its foreseen use: (i) standardises experimental conditions for studying rationality and emotion-influenced decision making of human responders; (ii) replaces the classical game-theoretical design of the players’ policies by an adaptive MDP, which is more realistic with respect to the knowledge available to individual players and decreases player’s deliberation effort; (iii) reveals the need for approximate learning and dynamic programming inevitable for coping with the curse of dimensionality; (iv) demonstrates the influence of the fairness attitude of the proposer on the game course; (v) prepares the test case for inspecting exploration-exploitation dichotomy.