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
Link To Document :
بازگشت