Prisoner Dilemma: A Model Taking into Account Expectancies

Natale S. Bonfiglio and Eliano Pessa
Additional contact information
Natale S. Bonfiglio: Università di Pavia
Eliano Pessa: Università di Pavia

A chapter in Systemics of Emergence: Research and Development, 2006, pp 707-714 from Springer

Abstract: Abstract This paper introduces a new neural network model of players’ behavior in iterated Prisoner Dilemma Game. Differently from other models of this kind, but in accordance with theoretical framework of evolutionary game theory, it takes into account players’ expectancies in computation of individual moves at every game step. Such a circumstance, however, led to an increase of the number of model free parameters. It was therefore necessary, to search for optimal parameter values granting for a satisfactory fitting of data obtained in an experiment performed on human subjects, to resort to a genetic algorithm.

Keywords: prisoner dilemma; evolutionary game theory; neural network; genetic algorithm (search for similar items in EconPapers)
Date: 2006
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

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:sprchp:978-0-387-28898-7_50

Ordering information: This item can be ordered from
http://www.springer.com/9780387288987

DOI: 10.1007/0-387-28898-8_50

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().