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Mutual Rationalizability in Vector-Payoff Games

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Evolutionary Multi-Criterion Optimization (EMO 2019)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11411))

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Abstract

This paper deals with vector-payoff games, which are also known as Multi-Objective Games (MOGs), multi-payoff games and multi-criteria games. Such game models assume that each of the players does not necessarily consider only a scalar payoff, but rather takes into account the possibility of self-conflicting objectives. In particular, this paper focusses on static non-cooperative zero-sum MOGs in which each of the players is undecided about the objective preferences, but wishes to reveal tradeoff information to support strategy selection. The main contribution of this paper is the introduction of a novel solution concept to MOGs, which is termed here as Multi-Payoff Mutual-Rationalizability (MPMR). In addition, this paper provides a discussion on the development of co-evolutionary algorithms for solving real-life MOGs using the proposed solution concept.

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Acknowledgment

The authors would like to thank E. Solan for referring them to the work of Bernheim [7] and of Pearce [8], and to also thank the anonymous reviewers of this paper.

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Authors and Affiliations

  1. Tel-Aviv University, 69978, Tel-Aviv, Israel

    Erella Eisenstadt-Matalon & Amiram Moshaiov

  2. ORT Braude College of Engineering, Karmiel, Israel

    Erella Eisenstadt-Matalon

Authors
  1. Erella Eisenstadt-Matalon
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  2. Amiram Moshaiov
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Corresponding author

Correspondence to Amiram Moshaiov .

Editor information

Editors and Affiliations

  1. Michigan State University, East Lansing, MI, USA

    Kalyanmoy Deb

  2. Michigan State University, East Lansing, MI, USA

    Erik Goodman

  3. CINVESTAV-IPN, Mexico, Mexico

    Carlos A. Coello Coello

  4. University of Wuppertal, Wuppertal, Germany

    Kathrin Klamroth

  5. University of Jyvaskyla, Jyväskylä, Finland

    Kaisa Miettinen

  6. Otto von Guericke University Magdeburg, Magdeburg, Germany

    Sanaz Mostaghim

  7. Cornell University, Ithaca, NY, USA

    Patrick Reed

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Eisenstadt-Matalon, E., Moshaiov, A. (2019). Mutual Rationalizability in Vector-Payoff Games. In: Deb, K., et al. Evolutionary Multi-Criterion Optimization. EMO 2019. Lecture Notes in Computer Science(), vol 11411. Springer, Cham. https://doi.org/10.1007/978-3-030-12598-1_47

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  • DOI: https://doi.org/10.1007/978-3-030-12598-1_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12597-4

  • Online ISBN: 978-3-030-12598-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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