Mutual rationalizability in vector-payoff games

Erella Eisenstadt-Matalon; Amiram Moshaiov

doi:10.1007/978-3-030-12598-1_47

Mutual rationalizability in vector-payoff games

Erella Eisenstadt-Matalon, Amiram Moshaiov^*

^*Corresponding author for this work

School of Mechanical Engineering

ORT Braude - College of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

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.

Original language	English
Title of host publication	Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings
Editors	Sanaz Mostaghim, Carlos A. Coello Coello, Kalyanmoy Deb, Erik Goodman, Patrick Reed, Kathrin Klamroth, Kaisa Miettinen
Publisher	Springer Verlag
Pages	593-604
Number of pages	12
ISBN (Print)	9783030125974
DOIs	https://doi.org/10.1007/978-3-030-12598-1_47
State	Published - 2019
Event	10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019 - East Lansing, United States Duration: 10 Mar 2019 → 13 Mar 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11411 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019
Country/Territory	United States
City	East Lansing
Period	10/03/19 → 13/03/19

Keywords

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

Access to Document

10.1007/978-3-030-12598-1_47

Cite this

Eisenstadt-Matalon, E., & Moshaiov, A. (2019). Mutual rationalizability in vector-payoff games. In S. Mostaghim, C. A. Coello Coello, K. Deb, E. Goodman, P. Reed, K. Klamroth, & K. Miettinen (Eds.), Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings (pp. 593-604). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11411 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-12598-1_47

Eisenstadt-Matalon, Erella ; Moshaiov, Amiram. / Mutual rationalizability in vector-payoff games. Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings. editor / Sanaz Mostaghim ; Carlos A. Coello Coello ; Kalyanmoy Deb ; Erik Goodman ; Patrick Reed ; Kathrin Klamroth ; Kaisa Miettinen. Springer Verlag, 2019. pp. 593-604 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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author = "Erella Eisenstadt-Matalon and Amiram Moshaiov",

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Eisenstadt-Matalon, E & Moshaiov, A 2019, Mutual rationalizability in vector-payoff games. in S Mostaghim, CA Coello Coello, K Deb, E Goodman, P Reed, K Klamroth & K Miettinen (eds), Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11411 LNCS, Springer Verlag, pp. 593-604, 10th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2019, East Lansing, United States, 10/03/19. https://doi.org/10.1007/978-3-030-12598-1_47

Mutual rationalizability in vector-payoff games. / Eisenstadt-Matalon, Erella; Moshaiov, Amiram.
Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings. ed. / Sanaz Mostaghim; Carlos A. Coello Coello; Kalyanmoy Deb; Erik Goodman; Patrick Reed; Kathrin Klamroth; Kaisa Miettinen. Springer Verlag, 2019. p. 593-604 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11411 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - 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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Eisenstadt-Matalon E, Moshaiov A. Mutual rationalizability in vector-payoff games. In Mostaghim S, Coello Coello CA, Deb K, Goodman E, Reed P, Klamroth K, Miettinen K, editors, Evolutionary Multi-Criterion Optimization - 10th International Conference, EMO 2019, Proceedings. Springer Verlag. 2019. p. 593-604. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-12598-1_47

Mutual rationalizability in vector-payoff games

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