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Establishing the laws of preferential choice behavior

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  • Sudeep Bhatia
  • Graham Loomes
  • Daniel Read

Abstract

Mathematical and computational decision models are powerful tools for studying choice behavior, and hundreds of distinct decision models have been proposed over the long interdisciplinary history of decision making research. The existence of so many models has led to theoretical fragmentation and redundancy, obscuring key insights into choice behavior, and preventing consensus about the essential properties of preferential choice. We provide a synthesis of formal models of risky, multiattribute, and intertemporal choice, three important domains in decision making. We identify recurring insights discovered by scholars of different generations and different disciplines across these three domains, and use these insights to classify over 150 existing models as involving various combinations of eight key mathematical and computational properties. These properties capture the main avenues of theoretical development in decision making research and can be used to understand the similarities and differences between decision models, aiding both theoretical analyses and empirical tests.

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  • Sudeep Bhatia & Graham Loomes & Daniel Read, 2021. "Establishing the laws of preferential choice behavior," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 16(6), pages 1324-1369, November.
  • Handle: RePEc:jdm:journl:v:16:y:2021:i:6:p:1324-1369
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