A predictive model for self-motivated decision-making behavior

Nisheeth Srivastava, Paul R. Schrater

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

Abstract

Traditional models of decision-making make simplistic assumptions about the motivations of agents and consistently fail to predict realistic behavior patterns. Since most modern economic and social theories heavily use computational modeling and simulation to quantify the effects of various policy options, the failure of classical decision theories to adequately capture realistic agential behavior is a considerable problem. We present a computational model of decision-making that adapts the mathematical machinery of reinforcement learning to take the intrinsic motivations of agents into account. Our model is uniquely principled in theories of embodied cognition and bounded rationality and predicts previously unexplained 'irrational' behavior of human subjects on real experimental tasks.

Original languageEnglish (US)
Title of host publication20th Annual Conference on Behavior Representation in Modeling and Simulation 2011, BRiMS 2011
Pages82-89
Number of pages8
StatePublished - Dec 1 2011
Event20th Annual Conference on Behavior Representation in Modeling and Simulation 2011, BRiMS 2011 - Sundance, UT, United States
Duration: Mar 21 2011Mar 24 2011

Publication series

Name20th Annual Conference on Behavior Representation in Modeling and Simulation 2011, BRiMS 2011

Other

Other20th Annual Conference on Behavior Representation in Modeling and Simulation 2011, BRiMS 2011
CountryUnited States
CitySundance, UT
Period3/21/113/24/11

Keywords

  • Behavioral biases
  • Cognitive models
  • Decision theory

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