A Stochastic Multiple-Leader Stackelberg Model: Analysis, Computation, and Application

Victor DeMiguel () and Huifu Xu ()
Additional contact information
Victor DeMiguel: Department of Management Science and Operations, London Business School, Regent's Park, London NW1 4SA, United Kingdom
Huifu Xu: School of Mathematics, University of Southampton, Highfield, Southampton SO17 1BJ, United Kingdom

Operations Research, 2009, vol. 57, issue 5, 1220-1235

Abstract: We study an oligopoly consisting of M leaders and N followers that supply a homogeneous product (or service) noncooperatively. Leaders choose their supply levels first, knowing the demand function only in distribution. Followers make their decisions after observing the leader supply levels and the realized demand function. We term the resulting equilibrium a stochastic multiple-leader Stackelberg-Nash-Cournot (SMS) equilibrium. We show the existence and uniqueness of SMS equilibrium under mild assumptions. We also propose a computational approach to find the equilibrium based on the sample average approximation method and analyze its rate of convergence. Finally, we apply this framework to model competition in the telecommunication industry.

Keywords: programming; noncooperative games/group decisions; Stackelberg game; equilibrium existence; uniqueness; sample average approximation (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (51)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.1080.0686 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:57:y:2009:i:5:p:1220-1235

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().