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

34 Citations (Scopus)

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

Fingerprint

Bayesian Belief Networks
Behavior Modeling
Decision Theory
Bayesian networks
Dynamic Environment
Discrete Fourier transforms
Field Theory
Subjective Evaluation
Update
Attribute
Mathematical models
Stock Market
Usability
Mathematical Model
Experiments
Theorem
Experiment
Modeling
Bayesian belief networks
Field theory

Keywords

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

ASJC Scopus subject areas

  • Statistics and Probability
  • Electrical and Electronic Engineering
  • Statistics, Probability and Uncertainty
  • Information Systems and Management
  • Information Systems
  • Computer Science Applications
  • Artificial Intelligence

Cite this

Decision field theory extensions for behavior modeling in dynamic environment using Bayesian belief network. / Lee, Seungho; Son, Young-Jun; Jin, Judy.

In: Information Sciences, Vol. 178, No. 10, 15.05.2008, p. 2297-2314.

Research output: Contribution to journalArticle

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