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Acquisition of Project-Specific Assets with Bayesian Updating

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  • H. Dharma Kwon

    (Department of Business Administration, University of Illinois at Urbana--Champaign, Champaign, Illinois 61820)

  • Steven A. Lippman

    (UCLA Anderson School of Management, Los Angeles, California 90095)

Abstract

We study the impact of learning on the optimal policy and the time-to-decision in an infinite-horizon Bayesian sequential decision model with two irreversible alternatives: exit and expansion. In our model, a firm undertakes a small-scale pilot project to learn, via Bayesian updating, about the project's profitability, which is known to be in one of two possible states. The firm continuously observes the project's cumulative profit, but the true state of the profitability is not immediately revealed because of the inherent noise in the profit stream. The firm bases its exit or expansion decision on the posterior probability distribution of the profitability. The optimal policy is characterized by a pair of thresholds for the posterior probability. We find that the time-to-decision does not necessarily have a monotonic relation with the arrival rate of new information.

Suggested Citation

  • H. Dharma Kwon & Steven A. Lippman, 2011. "Acquisition of Project-Specific Assets with Bayesian Updating," Operations Research, INFORMS, vol. 59(5), pages 1119-1130, October.
    • Handle: RePEc:inm:oropre:v:59:y:2011:i:5:p:1119-1130
    DOI: 10.1287/opre.1110.0949
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Katolnik, Svetlana & Schöndube, Jens Robert, 2014. "Don't Kill the Goose that Lays the Golden Eggs: Strategic Delay in Project Completion," Hannover Economic Papers (HEP) dp-533, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
    3. Anyan Qi & Hyun-Soo Ahn & Amitabh Sinha, 2017. "Capacity Investment with Demand Learning," Operations Research, INFORMS, vol. 65(1), pages 145-164, February.
    4. J. Michael Harrison & Nur Sunar, 2015. "Investment Timing with Incomplete Information and Multiple Means of Learning," Operations Research, INFORMS, vol. 63(2), pages 442-457, April.
    5. Anyan Qi & Hyun-Soo Ahn & Amitabh Sinha, 2017. "Capacity Investment with Demand Learning," Operations Research, INFORMS, vol. 65(1), pages 145-164, February.
    6. Onesun Steve Yoo & Tingliang Huang & Kenan Arifoğlu, 2021. "A Theoretical Analysis of the Lean Start-up Method," Marketing Science, INFORMS, vol. 40(3), pages 395-412, May.
    7. H. Dharma Kwon & Wenxin Xu & Anupam Agrawal & Suresh Muthulingam, 2016. "Impact of Bayesian Learning and Externalities on Strategic Investment," Management Science, INFORMS, vol. 62(2), pages 550-570, February.
    8. Zhenyu Hu & Wenjie Tang, 2021. "Size Matters, So Does Duration: The Interplay Between Offer Size and Offer Deadline," Management Science, INFORMS, vol. 67(8), pages 4935-4960, August.
    9. Sebastian Sund & Lars H. Sendstad & Jacco J. J. Thijssen, 2022. "Kalman filter approach to real options with active learning," Computational Management Science, Springer, vol. 19(3), pages 457-490, July.
    10. Rutger-Jan Lange & Coen Teulings, 2018. "The option value of vacant land and the optimal timing of city extensions," Tinbergen Institute Discussion Papers 18-033/III, Tinbergen Institute.
    11. Thijssen, Jacco J.J. & Bregantini, Daniele, 2017. "Costly sequential experimentation and project valuation with an application to health technology assessment," Journal of Economic Dynamics and Control, Elsevier, vol. 77(C), pages 202-229.
    12. Hao Zhang, 2022. "Analytical Solution to a Discrete-Time Model for Dynamic Learning and Decision Making," Management Science, INFORMS, vol. 68(8), pages 5924-5957, August.
    13. Dalby, Peder A.O. & Gillerhaugen, Gisle R. & Hagspiel, Verena & Leth-Olsen, Tord & Thijssen, Jacco J.J., 2018. "Green investment under policy uncertainty and Bayesian learning," Energy, Elsevier, vol. 161(C), pages 1262-1281.

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