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Thinking Ahead: The Decision Problem

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  • Patrick Bolton
  • Antoine Faure-Grimaud

Abstract

We propose a model of costly decision making based on time-costs of deliberating current and future decisions. We model an individual decision-maker's thinking process as a thought-experiment that takes time, and lets the decision maker 'think ahead' about future decision problems in yet unrealized states of nature. By formulating an intertemporal, state-contingent, planning problem which may involve costly deliberation in every state of nature, and by letting the decision maker deliberate ahead of the realization of a state, we attempt to capture the basic observation that individuals generally do not think through a complete action plan. Instead, individuals prioritize their thinking and leave deliberations on less important decisions to the time or event when they arise. Copyright 2009, Wiley-Blackwell.

Suggested Citation

  • Patrick Bolton & Antoine Faure-Grimaud, 2009. "Thinking Ahead: The Decision Problem," Review of Economic Studies, Oxford University Press, vol. 76(4), pages 1205-1238.
  • Handle: RePEc:oup:restud:v:76:y:2009:i:4:p:1205-1238
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    File URL: http://hdl.handle.net/10.1111/j.1467-937X.2009.00554.x
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    Cited by:

    1. Vincent Anesi & Mikhail Safronov, 2021. "Cloturing Deliberation," DEM Discussion Paper Series 21-03, Department of Economics at the University of Luxembourg.
    2. N. Gregory Mankiw & Ricardo Reis, 2002. "Sticky Information versus Sticky Prices: A Proposal to Replace the New Keynesian Phillips Curve," The Quarterly Journal of Economics, Oxford University Press, vol. 117(4), pages 1295-1328.
    3. Iossa, Elisabetta & Martimort, David, 2016. "Corruption in PPPs, incentives and contract incompleteness," International Journal of Industrial Organization, Elsevier, vol. 44(C), pages 85-100.
    4. Andrew Caplin & Daniel Martin, 2017. "Defaults and Attention: The Drop Out Effect," Revue économique, Presses de Sciences-Po, vol. 68(5), pages 747-755.
    5. Lewis A. Kornhauser & W. Bentley MacLeod, 2012. "Contracts between Legal Persons," Introductory Chapters, in: Robert Gibbons & John Roberts (ed.),The Handbook of Organizational Economics, Princeton University Press.
    6. Maćkowiak, Bartosz & Wiederholt, Mirko, 2018. "Lack of preparation for rare events," Journal of Monetary Economics, Elsevier, vol. 100(C), pages 35-47.
    7. Donaldson, Jason Roderick & Gromb, Denis & Piacentino, Giorgia, 2020. "The paradox of pledgeability," Journal of Financial Economics, Elsevier, vol. 137(3), pages 591-605.
    8. Philippe Aghion & Richard Holden, 2011. "Incomplete Contracts and the Theory of the Firm: What Have We Learned over the Past 25 Years?," Journal of Economic Perspectives, American Economic Association, vol. 25(2), pages 181-197, Spring.
    9. Jean Beuve & Claudine Desrieux, 2016. "Repeated interactions and endogenous contractual incompleteness," Theory and Decision, Springer, vol. 80(1), pages 125-158, January.
    10. Alessandro De Chiara, 2018. "Courts' Decisions, Cooperative Investments, and Incomplete Contracts," CEU Working Papers 2018_5, Department of Economics, Central European University.
    11. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2018. "Prediction, Judgment, and Complexity: A Theory of Decision-Making and Artificial Intelligence," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 89-110, National Bureau of Economic Research, Inc.
    12. Gomes, Orlando, 2012. "Thought experimentation and the Phillips curve," Research in Economics, Elsevier, vol. 66(1), pages 45-64.
    13. Binswanger, Johannes, 2011. "Dynamic decision making with feasibility goals: A procedural-rationality approach," Journal of Economic Behavior & Organization, Elsevier, vol. 78(3), pages 219-228, May.

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    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis

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