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Algorithmic Decision Processes

Author

Listed:
  • Carlo Baldassi
  • Fabio Maccheroni
  • Massimo Marinacci
  • Marco Pirazzini

Abstract

We develop a full-fledged analysis of an algorithmic decision process that, in a multialternative choice problem, produces computable choice probabilities and expected decision times.

Suggested Citation

  • Carlo Baldassi & Fabio Maccheroni & Massimo Marinacci & Marco Pirazzini, 2023. "Algorithmic Decision Processes," Papers 2305.03645, arXiv.org.
    • Handle: RePEc:arx:papers:2305.03645
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    References listed on IDEAS

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    1. Elena Reutskaja & Rosemarie Nagel & Colin F. Camerer & Antonio Rangel, 2011. "Search Dynamics in Consumer Choice under Time Pressure: An Eye-Tracking Study," American Economic Review, American Economic Association, vol. 101(2), pages 900-926, April.
    2. 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.
    3. Simon P. Anderson & Jacob K. Goeree & Charles A. Holt, 2004. "Noisy Directional Learning and the Logit Equilibrium," Scandinavian Journal of Economics, Wiley Blackwell, vol. 106(3), pages 581-602, October.
    4. Milica Milosavljevic & Jonathan Malmaud & Alexander Huth & Christof Koch & Antonio Rangel, 2010. "The Drift Diffusion Model can account for the accuracy and reaction time of value-based choices under high and low time pressure," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(6), pages 437-449, October.
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