A Bayesian approach to continuous type principal-agent problems

A. George Assaf, Ruijun Bu and Mike Tsionas

European Journal of Operational Research, 2020, vol. 280, issue 3, 1188-1192

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.

Keywords: Pricing; Principal-agent models; Bayesian analysis; Markov chain Monte Carlo (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:280:y:2020:i:3:p:1188-1192

DOI: 10.1016/j.ejor.2019.07.058

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