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Competition and Cooperation in Business Ecosystems – MBA Elective

There are no permanent enemies or friends in business. “Business is cooperation when it comes to creating a pie and competition when it comes to dividing it up”. Businesses are part of complex ecosystems crossing a variety of industries in which they make interdependent strategic decisions to compete and cooperate. An ecosystem view of businesses is becoming more relevant today due to technological advancements. For example, a linear thinking supply chain is transitioning into a networked thinking “supply ecosystem”. Game theory, a tool for analysis of interactive decision-making, provides a systematic way to analyze competition and cooperation in strategic ecosystems. This course is about analysis of competition and cooperation using game theory. Game theory deals with situations when rational players interact strategically. Players are said to be rational if they have their own description of which states of the world they like, and they consistently attempt to bring these states of the world. They are strategic if they consider their knowledge or expectation of other players’ actions or responses while pursuing their objectives. Their interactions could be competitive, cooperative or something in between. The interactive effects identified by game theoretic analysis often formalize pre-existing intuitions, but they can also be unanticipated or counter-intuitive

The course is structured around the following themes:

  • At micro level – various games that businesses play in an ecosystem
  • At macro level – overall functioning, evolution and survival of an ecosystem

The game theoretic techniques that we learn in this course are:

  • non-cooperative games to analyze competition
  • cooperative games to analyze cooperation, and
  • evolutionary games to understand evolution

My goal in this course is to provide you a deeper understanding of the basic ideas behind these techniques through real-life examples, and not to teach the abstract mathematics behind them. Further details are in the course introduction (slides / video) and course outline.

  • Lecture 1: An ecosystem view of businesses
  • Lecture 2: A general background to decision theory
  • Lecture 3: A general background to game theory
  • Lecture 4: Experiments 1A & 1B
  • Lecture 5: Coopetition, Amazon case
  • Lecture 6: Judo strategy, Judo case
  • Lecture 7: Analysis of cooperation – characteristic function games and the core
  • Lecture 8: Analysis of cooperation – the Shapley value
  • Lecture 9: Equitable allocation of payoffs, Bankruptcy case
  • Lecture 10: Measuring voting power, UNSC case
  • Lecture 11: Analysis of competition – simultaneous decision making
  • Lecture 12: Analysis of competition – sequential decision making, Judiciary case
  • Lecture 13: War of attrition, BSB vs Sky case
  • Lecture 14: Iterated Prisoners’ Dilemma, Experiment 2
  • Lecture 15: Sharing economy and two-sided markets
  • Lecture 16: Trust and cooperation in sharing economy, BlaBlaCar case
  • Lecture 17: Matching markets
  • Lecture 18: Evolutionary games and complex systems
  • Lecture 19: Student presentations
  • Lecture 20: Student presentations

Cooperative Game Theory and Applications – PhD Elective

This is an introductory course on cooperative game theory for doctoral students. The course has an equal mix of theory and application. Students will find this course to be a comprehensive overview of basic concepts in cooperative games and a few related topics such as matching theory and core-selecting auctions. The course will also enable students to learn how to apply some of these concepts to model business problems, particularly the problems arising in operations management. To give a broader perspective the topics for application are selected from supply chain, queuing system, communication networks, spectrum auction and bankruptcy problem among others. Course outline

Optimization Models in Operations Management – PhD Core

This is an optimization course for doctoral students in the production and operations management area. In the course, we first study Nonlinear Programming, wherein a nonlinear objective function is optimized subject to nonlinear constraints. We next study Convexity and the properties of convex functions. For convex functions, all we have to do is to identify a local optimum and we are assured that it is a global optimum. We next study Dynamic Programming, a collection of mathematical tools used to analyze sequential decision processes. We see how Dynamic Programming is used to solve the shortest route problem, resource allocation problems, production control and network flow problems, etc.