Jerome Busemeyer (Indiana University)
Forging together Cognitive and Decision Science Principles to Build Decision Field Theory

Decision theorists are becoming increasingly frustrated by the ephemeral nature of measured utilities. The theorist searches for coherence in the utilities of a decision maker across tasks and contexts, but the behavioral data force the theorist to think otherwise. In particular, choices among gambles can reverse with irrelevant changes in descriptions of events; preferences measured by choices can reverse when these preferences are measured instead by certainty equivalents; choices among options can reverse by adding irrelevant options to change the context of the choice set; choices between actions can reverse when these choices are made under different time constraints. The purpose of this paper is to show that by building stochastic models that treat utility as a latent parameter of a dynamic choice process, it is possible to recover the coherence that decision theorists seek. In particular, decision field theory is presented, which is a dynamic and stochastic theory that forges together principles from both cognitive and decision sciences. The paper concludes with the point that a theoretical trade off must be accepted – more complex models of behavior are required to recover simpler representations of utility.

Busemeyer, J. R., & Townsend, J. T. (1993) Decision Field Theory: A dynamic cognition approach to decision making. Psychological Review, 100, 432-459. http://mypage.iu.edu/~jbusemey/psy_rev_1993.pdf

Roe, R. M., Busemeyer, J. R., & Townsend, J. T. (2001) Multi-alternative decision field theory: A dynamic connectionist model of decision making. Psychological Review, 108, 370-392. http://mypage.iu.edu/~jbusemey/Roe_Psy_Rev.pdf

Johnson, J. G. & Busemeyer, J. R. (2005) A dynamic, computational model of preference reversal phenomena. Psychological Review, 112(4), 841-861. http://mypage.iu.edu/~jbusemey/pref_rev.pdf