A comparative study of game theoretic and evolutionary models for software agents
OA Location
Author(s)
Fatima, S
Wooldridge, M
Jennings, NR
Type
Journal Article
Abstract
Most of the existing work in the study of bargaining behaviour uses techniques from game theory. Game theoretic models for bargaining assume that players are perfectly rational and that this rationality in common knowledge. However, the perfect rationality assumption dows not hold for real-life bargaining scenarios with humans as players, since results from experimental economics show that humans find their way to the best strategy through trial and error, and not typically by means of rational deliberation. Such players are said to be boundedly rational. In playing a game against an opponent with bounded rationality, the most effective strategy of a player is not the equilibrium strategy but the one that is the best reply to the opponent’s strategy. The evolutionary model provides a means for studying the bargaining behaviour of boundedly rational players. This paper provides a comprehensive comparison of the gane theoretic and evolutionary approaches to bargaining by examing their assumptions, goals, and limitations. We then study the implications of these differences from the perspective of the software agent developer.
Date Issued
2005-04-01
Date Acceptance
2004-04-01
Citation
Artificial Intelligence Review, 2005, 23, pp.187-205
ISSN
1573-7462
Publisher
Springer Verlag (Germany)
Start Page
187
End Page
205
Journal / Book Title
Artificial Intelligence Review
Volume
23
Identifier
http://eprints.soton.ac.uk/261146/
Subjects
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
bargaining
e-commerce
evolutionary algorithms
game theory
software agents
ARTIFICIAL ADAPTIVE AGENTS
INCOMPLETE INFORMATION
AUTOMATED NEGOTIATION
Artificial Intelligence & Image Processing
0801 Artificial Intelligence And Image Processing
1702 Cognitive Science
Notes
keywords: bargaining, game theory, evolutionary algorithms, software agents, e-commerce