DocumentCode
1540711
Title
Evolution of unplanned coordination in a market selection game
Author
Ishibuchi, Hisao ; Sakamoto, Ryoji ; Nakashima, Tomoharu
Author_Institution
Dept. of Ind. Eng., Osaka Prefecture Univ., Japan
Volume
5
Issue
5
fYear
2001
fDate
10/1/2001 12:00:00 AM
Firstpage
524
Lastpage
534
Abstract
This paper examines the evolution of unplanned coordination among independent agents in a market selection game, which is a noncooperative repeated game with many agents and several markets. Every agent is supposed to simultaneously choose a single market for maximizing its own payoff obtained by selling its product at the selected market. It is assumed that the market price is determined by the total supply of products. For example, if many agents choose a particular market, the market price at that market is low. The point of the market selection is to choose a market that is not chosen by many other agents. In this paper, game strategies are genetically updated by localized selection and mutation. A new strategy of an agent is probabilistically selected from its neighbors´ strategies by the selection operation or randomly updated by the mutation operation. It is shown that the maximization of each agent´s payoff leads to the unplanned coordination of the market selection where the undesired concentration of agents is avoided. The unplanned coordination is compared with the planned global coordination obtained by the maximization of the total payoff over all agents
Keywords
economic cybernetics; game theory; genetic algorithms; multi-agent systems; optimisation; agent concentration; agent payoff maximization; game strategies; genetic updating; global coordination; independent agents; localized mutation; localized selection; market price; market selection game; mutation operation; noncooperative repeated game; probabilistic selection; selection operation; total payoff maximization; unplanned coordination; unplanned coordination evolution; Cost function; Evolutionary computation; Game theory; Genetic algorithms; Genetic mutations; Industrial engineering; Machine learning; Supervised learning; Transportation;
fLanguage
English
Journal_Title
Evolutionary Computation, IEEE Transactions on
Publisher
ieee
ISSN
1089-778X
Type
jour
DOI
10.1109/4235.956715
Filename
956715
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