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

Author

Listed:
  • 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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    6. Shen Li & Yuyang Zhang & Zhaolin Ren & Claire Liang & Na Li & Julie A. Shah, 2024. "Enhancing Preference-based Linear Bandits via Human Response Time," Papers 2409.05798, arXiv.org, revised Oct 2024.
    7. Carlo Baldassi & Fabio Maccheroni & Massimo Marinacci & Marco Pirazzini, 2023. "Algorithmic Decision Processes," Papers 2305.03645, arXiv.org.
    8. Carlos Alós-Ferrer & Maximilian Mihm, 2021. "Updating stochastic choice," ECON - Working Papers 381, Department of Economics - University of Zurich.
    9. Zhen Yan & Xiao Zhou & Rong Du, 2024. "An enhanced SIR dynamic model: the timing and changes in public opinion in the process of information diffusion," Electronic Commerce Research, Springer, vol. 24(3), pages 2021-2044, September.
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