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  • paper_rev4_July_2019

    Rights statement: This is the author’s version of a work that was accepted for publication in European journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 280, 3, 2020 DOI: 10.1016/j.ejor.2019.07.058

    Accepted author manuscript, 580 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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A Bayesian approach to continuous type principal-agent problems

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<mark>Journal publication date</mark>1/02/2020
<mark>Journal</mark>European Journal of Operational Research
Issue number3
Volume280
Number of pages5
Pages (from-to)1188-1192
Publication StatusPublished
Early online date30/07/19
<mark>Original language</mark>English

Abstract

Singham (2019) proposed an important advance in the numerical solution of continuous type principal-agent problems using Monte Carlo simulations from the distribution of agent “types” followed by bootstrapping. In this paper, we propose a Bayesian approach to the problem which produces nearly the same results without the need to rely on optimization or lower and upper bounds for the optimal value of the objective function. Specifically, we cast the problem in terms of maximizing the posterior expectation with respect to a suitable posterior measure. In turn, we use efficient Markov Chain Monte Carlo techniques to perform the computations.

Bibliographic note

This is the author’s version of a work that was accepted for publication in European journal of Operational Research. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in European Journal of Operational Research, 280, 3, 2020 DOI: 10.1016/j.ejor.2019.07.058