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
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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - A Bayesian approach to continuous type principal-agent problems
AU - Assaf, A. George
AU - Bu, Ruijun
AU - Tsionas, Mike G.
N1 - 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
PY - 2020/2/1
Y1 - 2020/2/1
N2 - 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.
AB - 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.
KW - Pricing
KW - Principal-agent models
KW - Bayesian analysis
KW - Markov chain Monte Carlo
U2 - 10.1016/j.ejor.2019.07.058
DO - 10.1016/j.ejor.2019.07.058
M3 - Journal article
VL - 280
SP - 1188
EP - 1192
JO - European Journal of Operational Research
JF - European Journal of Operational Research
SN - 0377-2217
IS - 3
ER -