Dynamic oligopolistic games under uncertainty: A stochastic programming approach

Talat S. Genc, Stanley S. Reynolds, Suvrajeet Sen

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


This paper studies several stochastic programming formulations of dynamic oligopolistic games under uncertainty. We argue that one of the models, namely games with probabilistic scenarios (GPS), provides an appropriate formulation. For such games, we show that symmetric players earn greater expected profits as demand volatility increases. This result suggests that even in an increasingly volatile market, players may have an incentive to participate in the market. The key to our approach is the so-called scenario formulation of stochastic programming. In addition to several modeling insights, we also discuss the application of GPS to the electricity market in Ontario, Canada. The examples presented in this paper illustrate that this approach can address dynamic games that are clearly out of reach for dynamic programming, a common approach in the literature on dynamic games.

Original languageEnglish (US)
Pages (from-to)55-80
Number of pages26
JournalJournal of Economic Dynamics and Control
Issue number1
StatePublished - Jan 2007


  • Dynamic games
  • Electricity markets
  • S-adapted open-loop equilibrium
  • Stochastic programming

ASJC Scopus subject areas

  • Economics and Econometrics
  • Control and Optimization
  • Applied Mathematics


Dive into the research topics of 'Dynamic oligopolistic games under uncertainty: A stochastic programming approach'. Together they form a unique fingerprint.

Cite this