-

 

 

 




Optimization Online





 

Games with distributionally robust joint chance constraints

Shen Peng(shenp***at***kth.se)
Abdel Lisser(abdel.lisser***at***lri.fr)
Vikas Vikram Singh(vikassingh***at***maths.iitd.ac.in)

Abstract: This paper studies an n-player non-cooperative game with strategy sets defined by stochastic linear constraints. The stochastic constraints of each player are jointly satisfied with a probability exceeding a given threshold. We consider the case where the row vectors defining the constraints are independent random vectors whose probability distributions are not completely known and belong to a certain distributional uncertainty set. The random constraints of each player are formulated as a distributionally robust joint chance constraint. We consider one density based uncertainty set and four two-moments based uncertainty sets. One of the considered uncertainty set is based on a nonnegative support. Under standard assumptions on players' payoff functions, we show that there exists a Nash equilibrium of a distributionally robust chance-constrained game for each uncertainty set.

Keywords: Chance-constrained game, Nash equilibrium, Distributionally robust optimization, Nonnegative support.

Category 1: Stochastic Programming

Category 2: Other Topics (Game Theory )

Citation:

Download: [PDF]

Entry Submitted: 01/16/2020
Entry Accepted: 01/16/2020
Entry Last Modified: 01/16/2020

Modify/Update this entry


  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository

 

Submit
Update
Policies
Coordinator's Board
Classification Scheme
Credits
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society