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A Behavioral Characterization of the Drift Diffusion Model and Its Multialternative Extension for Choice Under Time Pressure

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  • Carlo Baldassi

    (Department of Decision Sciences, Bocconi University, 20136 Milan, Italy; Bocconi Institute for Data Science and Analytics (BIDSA), Bocconi University, Milan, Italy 20136; Artificial Intelligence Lab (ARTLAB), Bocconi University, Milan, Italy 20136;)

  • Simone Cerreia-Vioglio

    (Department of Decision Sciences, Bocconi University, 20136 Milan, Italy; Artificial Intelligence Lab (ARTLAB), Bocconi University, Milan, Italy 20136; Innocenzo Gasparini Institute for Economic Research (IGIER), Bocconi University, Milan, Italy 20136)

  • Fabio Maccheroni

    (Department of Decision Sciences, Bocconi University, 20136 Milan, Italy; Artificial Intelligence Lab (ARTLAB), Bocconi University, Milan, Italy 20136; Innocenzo Gasparini Institute for Economic Research (IGIER), Bocconi University, Milan, Italy 20136)

  • Massimo Marinacci

    (Department of Decision Sciences, Bocconi University, 20136 Milan, Italy; Artificial Intelligence Lab (ARTLAB), Bocconi University, Milan, Italy 20136; Innocenzo Gasparini Institute for Economic Research (IGIER), Bocconi University, Milan, Italy 20136)

  • Marco Pirazzini

    (Department of Decision Sciences, Bocconi University, 20136 Milan, Italy; Artificial Intelligence Lab (ARTLAB), Bocconi University, Milan, Italy 20136;)

Abstract

In this paper, we provide an axiomatic foundation for the value-based version of the drift diffusion model (DDM) of Ratcliff, a successful model that describes two-alternative speeded decisions between consumer goods. Our axioms present a test for model misspecification and connect the externally observable properties of choice with an important neurophysiologic account of how choice is internally implemented. We then extend our axiomatic analysis to multialternative choice under time pressure. In a nutshell, we show that binary DDM comparisons of the alternatives, paired with Markovian exploration of the consideration set, approximately lead to softmaximization.

Suggested Citation

  • Carlo Baldassi & Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci & Marco Pirazzini, 2020. "A Behavioral Characterization of the Drift Diffusion Model and Its Multialternative Extension for Choice Under Time Pressure," Management Science, INFORMS, vol. 66(11), pages 5075-5093, November.
    • Handle: RePEc:inm:ormnsc:v:66:y:2020:i:11:p:5075-5093
    DOI: 10.1287/mnsc.2019.3475
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    References listed on IDEAS

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    3. Carlo Baldassi & Fabio Maccheroni & Massimo Marinacci & Marco Pirazzini, 2023. "Algorithmic Decision Processes," Papers 2305.03645, arXiv.org.
    4. Simone Cerreia-Vioglio & Fabio Maccheroni & Massimo Marinacci & Aldo Rustichini, 2023. "Multinomial Logit Processes and Preference Discovery: Inside and Outside the Black Box," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(3), pages 1155-1194.
    5. Carlos Alós-Ferrer & Maximilian Mihm, 2021. "Updating stochastic choice," ECON - Working Papers 381, Department of Economics - University of Zurich.
    6. Pëllumb Reshidi & Alessandro Lizzeri & Leeat Yariv & Jimmy Chan & Wing Suen, 2022. "Individual and Collective Information Acquisition: An Experimental Study," Working Papers 312, Princeton University, Department of Economics, Center for Economic Policy Studies..
    7. Gerelt Tserenjigmid, 2021. "The Order-Dependent Luce Model," Management Science, INFORMS, vol. 67(11), pages 6915-6933, November.

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