Solving matrix game with rough payoffs using genetic algorithm

Sankar Kumar Roy () and Prasanta Mula ()
Additional contact information
Sankar Kumar Roy: Vidyasagar University
Prasanta Mula: ISRO Satellite Centre

Operational Research, 2016, vol. 16, issue 1, No 6, 117-130

Abstract: Abstract In this paper, we analyze a matrix game using a rough programming approach. The combination of a matrix game and a rough programming approach represents a new class defined as a rough matrix game. The pay-off elements are characterized by rough variables, and the uncertainties of the rough variables are measured using a measure known as trust. Based on this trust measure, we defined trust equilibrium strategies and a rough expected value. We derived a series of optimal solutions to a rough matrix game using a genetic algorithm. We present a numerical example that illustrates the effectiveness of our rough matrix game.

Keywords: Two-person zero-sum game; Equilibrium strategies; Rough set; Rough programming; Trust measure; Genetic algorithm (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s12351-015-0189-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:operea:v:16:y:2016:i:1:d:10.1007_s12351-015-0189-6

Ordering information: This journal article can be ordered from
https://www.springer ... search/journal/12351

DOI: 10.1007/s12351-015-0189-6

Access Statistics for this article

Operational Research is currently edited by Nikolaos F. Matsatsinis, John Psarras and Constantin Zopounidis

More articles in Operational Research from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().