Home > Research > Publications & Outputs > A Bayesian approach to continuous type principa...

Electronic data

  • paper_rev4_July_2019

    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

    Accepted author manuscript, 580 KB, PDF document

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

Links

Text available via DOI:

View graph of relations

A Bayesian approach to continuous type principal-agent problems

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Published

Standard

A Bayesian approach to continuous type principal-agent problems. / Assaf, A. George; Bu, Ruijun; Tsionas, Mike G.
In: European Journal of Operational Research, Vol. 280, No. 3, 01.02.2020, p. 1188-1192.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Assaf, AG, Bu, R & Tsionas, MG 2020, 'A Bayesian approach to continuous type principal-agent problems', European Journal of Operational Research, vol. 280, no. 3, pp. 1188-1192. https://doi.org/10.1016/j.ejor.2019.07.058

APA

Assaf, A. G., Bu, R., & Tsionas, M. G. (2020). A Bayesian approach to continuous type principal-agent problems. European Journal of Operational Research, 280(3), 1188-1192. https://doi.org/10.1016/j.ejor.2019.07.058

Vancouver

Assaf AG, Bu R, Tsionas MG. A Bayesian approach to continuous type principal-agent problems. European Journal of Operational Research. 2020 Feb 1;280(3):1188-1192. Epub 2019 Jul 30. doi: 10.1016/j.ejor.2019.07.058

Author

Assaf, A. George ; Bu, Ruijun ; Tsionas, Mike G. / A Bayesian approach to continuous type principal-agent problems. In: European Journal of Operational Research. 2020 ; Vol. 280, No. 3. pp. 1188-1192.

Bibtex

@article{9f08ec1bbaf840dfa65ea3fdf46b3431,
title = "A Bayesian approach to continuous type principal-agent problems",
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",
author = "Assaf, {A. George} and Ruijun Bu and Tsionas, {Mike G.}",
note = "This is the author{\textquoteright}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",
year = "2020",
month = feb,
day = "1",
doi = "10.1016/j.ejor.2019.07.058",
language = "English",
volume = "280",
pages = "1188--1192",
journal = "European Journal of Operational Research",
issn = "0377-2217",
publisher = "Elsevier Science B.V.",
number = "3",

}

RIS

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 -