Evolutionary Development and Learning: Two Facets of Strategy Generation

Ilan Fischer ()

Journal of Artificial Societies and Social Simulation, 2003, vol. 6, issue 1, 7

Abstract: The study examines two approaches to the development of behavioral strategies: i) the evolutionary approach manifested in a Genetic Algorithm, which accounts for gradual development and simultaneous refinement of an entire population; and ii) the behavioral learning approach, which focuses on reinforcements at the individual's level. The current work is part from an ongoing project dealing with the development of strategic behavior. The reported study evaluates the potential of differential reinforcements to provide probabilistic noisy Tit-For-Tat strategies with the motivation to adopt a pure Tit-For-Tat strategy. Results show that provocability and forgiveness, the traits that account for Tit-For-Tat's successes, also prevent it from gaining relative fitness and become an attractor for noisy (non-perfect) Tit-For-Tat strategies.

Keywords: Strategy; Evolution; Learning; Genetic-algorithm; Tit-For-Tat; Noise; Errors (search for similar items in EconPapers)
Date: 2003-01-31
References: Add references at CitEc
Citations:

Downloads: (external link)
http://jasss.soc.surrey.ac.uk/6/1/7.html (text/html)

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:jas:jasssj:2002-41-2

Access Statistics for this article

More articles in Journal of Artificial Societies and Social Simulation from Journal of Artificial Societies and Social Simulation
Bibliographic data for series maintained by Francesco Renzini ().