A Bayesian approach to continuous type principal-agent problems

Assaf, AG, Bu, R ORCID: 0000-0002-3947-3038 and Tsionas, MG
(2020) A Bayesian approach to continuous type principal-agent problems. European Journal of Operational Research, 280 (3). 1188 - 1192.

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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: Article
Uncontrolled Keywords: Pricing, Principal-agent models, Bayesian analysis, Markov chain Monte Carlo
Depositing User: Symplectic Admin
Date Deposited: 21 Aug 2019 08:02
Last Modified: 20 Jun 2022 14:10
DOI: 10.1016/j.ejor.2019.07.058
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3052237