Dynamic Theories of Price and Investment in Markets with Imperfect Competition

ECS561 Dynamic Theories of Price and Investment in Markets with Imperfect Competition

Autumn 2022

  • Topics

    This course investigates dynamic firm conduct with respect to price and investment in imperfectly competitive markets. Students will be exposed to analytical and numerical methods within two game-theoretic frameworks: equilibria in repeated games and Markov Perfect Equilibria in dynamic games with state variables. Pricing issues to be explored include penetration pricing (in the presence of learning-by-doing), predatory pricing, and collusive pricing. Our examination of collusive pricing is motivated by two empirical phenomena:

    1) collusive practices by intermediate goods cartels in the face of private monitoring for compliance;

    2) pricing dynamics that may be driven by cartel members desire to avoid detection.

    A topic of recent and growing interest among competition authorities is algorithmic collusion whereby learning algorithms, rather than human managers, collude. Recent research shows how artificial agents using Q-learning can coordinate on collusive strategies. Introducing the possibility of predation, we then explore when a market leader invites competitors to collude and when it instead seeks to drive competitors out of the market. Investment is studied in the context of capacity expansion and product quality improvement. Competition in capacity investment offers an explanation for persistent differences in market shares not tied to cost or product heterogeneity. Investment in product quality will be examined while endogenizing whether firms compete or collude in prices. A welfare trade-off emerges in that consumers can be better off under price collusion due to intensified competition in product quality.

  • Learning outcome


    At the end of the course students will:

    • Have a sound knowledge on how to solve equilibria in repeated games
    • Have a sound knowledge on Markov Perfect Equilibria in dynamic games with state variables


    At the end of the course students will:

    • Be able to use analytical and numerical methods within two game-theoretic frameworks

    General Competence

    At the end of the course students will:

    • Understand how important pricing issues affect the market outcomes in the economy: In particular on penetration pricing (in the presence of learning-by-doing), predatory pricing, and collusive pricing.

  • Teaching

    Plenary lectures.

    Intensive course: May 20-23 (Monday, Tuesday, Wednesday, Thursday), four hours each day: 1015-1200 and 1315-1500.

  • Recommended prerequisites

    Good knowledge withinin IO, ie., game theory and microeconomic theory.

  • Compulsory Activity


  • Assessment

    Individual written project.

    The project need not be fully executed, but it must have five parts:

    i) statement of the research question(s);

    ii) brief survey of the literature addressing that question;

    iii) description of the theoretical model;

    iv) how you plan to use that model to address the research question (that is, the types of results you plan to derive, e.g., comparative statics);

    v) some progress in executing the project.

    If it were successfully executed, the project must be an original contribution which means either that you've proposed an original research question or you've proposed a new approach to tackling an existing research question.

    The length of the project should be 10-15 pages.

    The project is due 2-3 weeks after course completion and is anticipated to require around two weeks of work (70 hours).

  • Grading Scale


  • Literature

    Competition in price and investment

    • General
      • Doraszelski, Ulrich and Ariel Pakes, "A Framework for Applied Dynamic Analysis in IO," in Handbook of Industrial Organization, Volume 3, Mark Armstrong and Robert Porter (eds.), 2007
    • Dynamic pricing
      • Learning curve: Cabral, Lúis and Michael Riordan, "The Learning Curve, Market Dominance, and Predatory Pricing," Econometrica, 62 (1994), 1115-1140.
      • Private information: Sweeting, Andrew, Xuezhen Tao, and Xinlu Yao, "Dynamic Oligopoly Pricing with Asymmetric Information: Implications for Mergers," University of Maryland, April 2018.
    • Capacity investment
      • Besanko, David and Ulrich Doraszelski, "Capacity Dynamics and Endogenous Asymmetries in Firm Size," RAND Journal of Economics,35 (2004), 23-49.


    • Semi-collusion: price collusion and investment competition
      • Fersthman, Chaim and Ariel Pakes, "A Dynamic Oligopoly with Collusion and Price Wars," RAND Journal of Economics, 31 (2000), 207-236.
    • Collusive and predatory pricing
      • Wiseman, Thomas, "When Does Predation Dominate Collusion?," Econometrica, 85 (2017), 555-584.
    • Collusive pricing and detection avoidance
      • Harrington, Joseph E., Jr., "Cartel Pricing Dynamics in the Presence of an Antitrust Authority," RAND Journal of Economics, 35 (2004), 651-673.
    • Collusive pricing with private monitoring
      • Harrington, Joseph E. Jr. and Andrzej Skrzypacz, "Private Monitoring and Communication in Cartels: Explaining Recent Cartel Practices," American Economic Review, 101 (2011), 2425-2449.
      • Sugaya, Takuo and Alexander Wolitzky, .Maintaining Privacy in Cartels,.Stanford University, May 2017 (Journal of Political Economy, forthcoming)
    • Algorithmic collusion
      • Calvano, Emilo, Giacomo Calzolari, Vincenzo Denicolo, and Sergio Pastorello, "Artifcial Intelligence, Algorithmic Pricing and Collusion," University of Bologna, December 2018.


ECTS Credits
Teaching language


Course responsible

Professor Frode Steen and Professor II. Joseph Harrington, Department of Economics.