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A Bayesian approach to continuous type principal-agent problems
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DOI：10.1016/j.ejor.2019.07.058.png)
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Abstract
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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 boot-strapping. 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. (C) 2019 Elsevier B.V. All rights reserved.
Keywords:
Pricing
Principal-agent models
Bayesian analysis
Markov chain Monte Carlo
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