Decision field theory extensions for behavior modeling in dynamic environment using Bayesian belief network

Seungho Lee, Young Jun Son, Judy Jin

Research output: Contribution to journalArticle

36 Scopus citations

Abstract

Decision field theory (DFT), widely known in the field of mathematical psychology, provides a mathematical model for the evolution of the preferences among options of a human decision-maker. The evolution is based on the subjective evaluation for the options and his/her attention on an attribute (interest). In this paper, we extend DFT to cope with the dynamically changing environment. The proposed extended DFT (EDFT) updates the subjective evaluation for the options and the attention on the attribute, where Bayesian belief network (BBN) is employed to infer these updates under the dynamic environment. Four important theorems are derived regarding the extension, which enhance the usability of EDFT by providing the minimum time steps required to obtain the stabilized results before running the simulation (under certain assumptions). A human-in-the-loop experiment is conducted for the virtual stock market to illustrate and validate the proposed EDFT. The preliminary result is quite promising.

Original languageEnglish (US)
Pages (from-to)2297-2314
Number of pages18
JournalInformation Sciences
Volume178
Issue number10
DOIs
StatePublished - May 15 2008

Keywords

  • Bayesian belief network
  • Decision field theory
  • Human decision-making
  • Preference

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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