A Bayesian approach to continuous type principal-agent problems

Assaf, A. George and Bu, Ruijun and Tsionas, Mike G. (2020) A Bayesian approach to continuous type principal-agent problems. European Journal of Operational Research, 280 (3). pp. 1188-1192. ISSN 0377-2217

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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.

Item Type:
Journal Article
Journal or Publication Title:
European Journal of Operational Research
Additional Information:
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
Uncontrolled Keywords:
?? pricingprincipal-agent modelsbayesian analysismarkov chain monte carlomodelling and simulationmanagement science and operations researchinformation systems and management ??
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Deposited On:
08 Dec 2020 15:48
Last Modified:
09 Jan 2024 00:26