Mutual rationalizability in vector-payoff games

Erella Eisenstadt-Matalon, Amiram Moshaiov*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings
EditorsSanaz Mostaghim, Carlos A. Coello Coello, Kalyanmoy Deb, Erik Goodman, Patrick Reed, Kathrin Klamroth, Kaisa Miettinen
PublisherSpringer Verlag
Number of pages12
ISBN (Print)9783030125974
StatePublished - 2019
Event10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019 - East Lansing, United States
Duration: 10 Mar 201913 Mar 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11411 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019
Country/TerritoryUnited States
CityEast Lansing


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


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