Mutual Rationalizability in Vector-Payoff Games

  • Erella Eisenstadt-Matalon
  • Amiram MoshaiovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11411)


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.


Game theory Non-cooperative games Set-based optimization Set domination Multi-criteria decision-analysis 



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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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Tel-Aviv UniversityTel-AvivIsrael
  2. 2.ORT Braude College of EngineeringKarmielIsrael

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