Short run dynamics in an artificial futures market with human subjects

Takashi Yamada, Yusuke Koyama, Takao Terano

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents the computational results obtained in the strategy experiments in an artificial futures market with human subjects. Participants submit their own strategy files and they receive the performances of all the market participants in order to improve for the next round. After two-round experiments, simulations with only machine agents are run. We find that the time series data support so-called stylized facts in some regards and that experiments of human subjects seem to make the prices be closer to a theoretical value.

Original languageEnglish
Title of host publicationIntelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings
PublisherSpringer Verlag
Pages1092-1101
Number of pages10
ISBN (Print)9783540772255
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007 - Birmingham, United Kingdom
Duration: 2007 Dec 162007 Dec 19

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4881 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007
CountryUnited Kingdom
CityBirmingham
Period07/12/1607/12/19

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Yamada, T., Koyama, Y., & Terano, T. (2007). Short run dynamics in an artificial futures market with human subjects. In Intelligent Data Engineering and Automated Learning - IDEAL 2007 - 8th International Conference, Proceedings (pp. 1092-1101). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4881 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-540-77226-2_109