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Searching and Learning by Trial and Error

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  • Steven Callander

Abstract

I study a dynamic model of trial-and-error search in which agents do not have complete knowledge of how choices are mapped into outcomes. Agents learn about the mapping by observing the choices of earlier agents and the outcomes that are realized. The key novelty is that the mapping is represented as the realized path of a Brownian motion. I characterize for this environment the optimal behavior each period as well as the trajectory of experimentation and learning through time. Applied to new product development, the model shares features of the data with the well-known Product Life Cycle. (JEL D81, D83, D92, L26)

Suggested Citation

  • Steven Callander, 2011. "Searching and Learning by Trial and Error," American Economic Review, American Economic Association, vol. 101(6), pages 2277-2308, October.
    • Handle: RePEc:aea:aecrev:v:101:y:2011:i:6:p:2277-2308
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    References listed on IDEAS

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    1. Philippe Aghion & Patrick Bolton & Christopher Harris & Bruno Jullien, 1991. "Optimal Learning by Experimentation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(4), pages 621-654.
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    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D92 - Microeconomics - - Micro-Based Behavioral Economics - - - Intertemporal Firm Choice, Investment, Capacity, and Financing
    • L26 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Entrepreneurship

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