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Multinomial logit processes and preference discovery: inside and outside the black box

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  • S. Cerreia-Vioglio
  • F. Maccheroni
  • M. Marinacci
  • A. Rustichini

Abstract

We provide both an axiomatic and a neuropsychological characterization of the dependence of choice probabilities on time in the softmax (or Multinomial Logit Process) form: MLP is the most widely used model of preference discovery in all fields of decision making, from Quantal Response Equilibrium to Discrete Choice Analysis, from Psychophysics and Neuroscience to Combinatorial Optimization. Our axiomatic characterization of softmax permits to empirically test its descriptive validity and to better understand its conceptual underpinnings as a theory of agents'rationality. Our neuropsychological foundation provides a computational model that may explain softmax emergence in human behavior and that naturally extends to multialternative choice the classical Diffusion Model paradigm of binary choice. These complementary approaches provide a complete perspective on softmaximization as a model of preference discovery, both in terms of internal (neuropsychological) causes and external (behavioral) effect. Keywords: Discrete Choice Analysis, Drift Diffusion Model, Luce Model, Metropolis Algorithm, Multinomial Logit Model, Quantal Response Equilibrium

Suggested Citation

  • S. Cerreia-Vioglio & F. Maccheroni & M. Marinacci & A. Rustichini, 2017. "Multinomial logit processes and preference discovery: inside and outside the black box," Working Papers 615, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    • Handle: RePEc:igi:igierp:615
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    Cited by:

    1. Mira Frick & Ryota Iijima & Tomasz Strzalecki, 2019. "Dynamic Random Utility," Econometrica, Econometric Society, vol. 87(6), pages 1941-2002, November.
    2. Emerson Melo, 2021. "Learning In Random Utility Models Via Online Decision Problems," CAEPR Working Papers 2022-003 Classification-D, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    3. Pennesi, Daniele, 2021. "Intertemporal discrete choice," Journal of Economic Behavior & Organization, Elsevier, vol. 186(C), pages 690-706.
    4. Emerson Melo, 2021. "Learning in Random Utility Models Via Online Decision Problems," Papers 2112.10993, arXiv.org, revised Aug 2022.
    5. Federico Echenique & Kota Saito, 2019. "General Luce model," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 68(4), pages 811-826, November.
    6. Doğan, Serhat & Yıldız, Kemal, 2021. "Odds supermodularity and the Luce rule," Games and Economic Behavior, Elsevier, vol. 126(C), pages 443-452.
    7. 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.

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    Keywords

    discrete choice analysis; drift diffusion model; luce model; metropolis algorithm; multinomial logit model; quantal response equilibrium;
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