Marketing Letters

, 19:255

Sequential sampling models of choice: Some recent advances

Authors

    • J. W. Goethe Universität (Marketing)
  • Joe Johnson
    • Miami University (Psychology)
  • Jörg Rieskamp
    • University Basel (Psychology)
  • Greg M. Allenby
    • Ohio State University (Marketing)
  • Jeff D. Brazell
    • The Modellers, LLC (Marketing)
  • Adele Diederich
    • Jacobs University Bremen (Psychology)
  • J. Wesley Hutchinson
    • University of Pennsylvania (Marketing)
  • Steven MacEachern
    • Ohio State University (Statistics)
  • Shiling Ruan
    • Ohio State University (Statistics)
  • Jim Townsend
    • Indiana University (Psychology)
Article

DOI: 10.1007/s11002-008-9039-0

Cite this article as:
Otter, T., Johnson, J., Rieskamp, J. et al. Mark Lett (2008) 19: 255. doi:10.1007/s11002-008-9039-0

Abstract

Choice models in marketing and economics are generally derived without specifying the underlying cognitive process of decision making. This approach has been successfully used to predict choice behavior. However, it has not much to say about such aspects of decision making as deliberation, attention, conflict, and cognitive limitations and how these influence choices. In contrast, sequential sampling models developed in cognitive psychology explain observed choices based on assumptions about cognitive processes that return the observed choice as the terminal state. We illustrate three advantages of this perspective. First, making explicit assumptions about underlying cognitive processes results in measures of deliberation, attention, conflict, and cognitive limitation. Second, the mathematical representations of underlying cognitive processes imply well documented departures from Luce’s Choice Axiom such as the similarity, compromise, and attraction effects. Third, the process perspective predicts response time and thus allows for inference based on observed choices and response times. Finally, we briefly discuss the relationship between these cognitive models and rules for statistically optimal decisions in sequential designs.

Keywords

Luce’s Axiom Choice models Diffusion models Race models Human information processing Response time Optimal decision making Likelihood based inference

Copyright information

© Springer Science+Business Media, LLC 2008