A framework of oligopolistic market simulation with coevolutionary computation

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

The paper presents a new framework of oligopolistic market simulation based on coevolutionary computation. The coevolutionary computation architecture can be regarded as a special model of the agent-based computational economics (ACE), which is a computational study of economies modeled as dynamic systems of interacting agents. The supply function equilibrium (SFE) model of an oligopolistic market is used in simulation. The piece-wise affine and continuous supply functions which have a large number of pieces are used to numerically estimate the equilibrium supply functions of any shapes. An example based on the cost data from the real-world electricity industry is used to validate the approach presented in this paper. Simulation results show that the coevolutionary approach robustly converges to SFE in different cases. The approach is robust and flexible and has the potential to be used to solve the complicated equilibrium problems in real-world oligopolistic markets.

Original languageEnglish
Title of host publicationAdvances in Natural Computation - Second International Conference, ICNC 2006, Proceedings,
PublisherSpringer Verlag
Pages860-869
Number of pages10
ISBN (Print)3540459014, 9783540459019
DOIs
Publication statusPublished - Sept 2006
Event2nd International Conference on Natural Computation, ICNC 2006 - Xi'an, China
Duration: 24 Sept 200628 Sept 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4221 LNCS - I
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Natural Computation, ICNC 2006
Country/TerritoryChina
CityXi'an
Period24/09/0628/09/06

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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