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Adaptive Proposer for Ultimatum Game

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Artificial Neural Networks and Machine Learning – ICANN 2016 (ICANN 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9886))

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Abstract

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.

This research was supported by Grant Agency of the Czech Republic, No 13-13502S.

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Notes

  1. 1.

    All functions with time-dependent arguments generally depend on time.

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Author information

Authors and Affiliations

  1. Department of Adaptive Systems, Institute of Information Theory and Automation, Czech Academy of Sciences, POB 18, 182 08, Prague 8, Czech Republic

    František Hůla, Marko Ruman & Miroslav Kárný

Authors
  1. František Hůla
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  2. Marko Ruman
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  3. Miroslav Kárný
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Correspondence to František Hůla .

Editor information

Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

  2. University of Lausanne, Lausanne, Switzerland

    Paolo Masulli

  3. Universitat Politécnica de Catalunya, Terrrassa, Spain

    Antonio Javier Pons Rivero

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Hůla, F., Ruman, M., Kárný, M. (2016). Adaptive Proposer for Ultimatum Game. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_39

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  • DOI: https://doi.org/10.1007/978-3-319-44778-0_39

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44777-3

  • Online ISBN: 978-3-319-44778-0

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