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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 the 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 game-theoretic techniques that we learn in this course are:

  • non-cooperative games to analyze competition, and
  • cooperative games to analyze cooperation

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 outline.

This video (credit: ICTS, TIFR) also gives a good idea of what this course is about.

Lecture plan:

  • Lecture 1: Ecosystem thinking
  • Lecture 2: Single-player decision making
  • Lecture 3: Multi-player interactive decision making
  • Lecture 4: Evolutionary games and complex systems
  • Lecture 5: Cases – Warner Bros. and Paramount Pictures
  • Lecture 6: Complexity of competition, Case – Judo
  • Lecture 7: Complexity of cooperation — I
  • Lecture 8: Complexity of cooperation — II
  • Lecture 9: Simultaneous competition and cooperation, Case – Amazon
  • Lecture 10: Cooperation by forming coalitions, Quiz 1
  • Lecture 11: Fair and equitable distribution of cooperative gains, Case – Bankruptcy
  • Lecture 12: Power of a player in cooperative decision-making, Case – UNSC
  • Lecture 13: Matching markets
  • Lecture 14: Cooperation in sharing economy, Case – BlaBlaCar
  • Lecture 15: Sequential decision-making in competition
  • Lecture 16: Simultaneous decision-making in competition, Case – Judiciary
  • Lecture 17: Case: Lesser Antilles Lines
  • Lecture 18: War of attrition and repeated games, Quiz 2
  • Lecture 19: End-term presentations
  • Lecture 20: End-term presentations

Video illustrations:

Games and experiments:

  • Auctioning a Rs.100 note (Lec 18)
  • Evolution of cooperation (Lec 8)

Introduction to Game Theory — PhD Core

Game theory is the study of interactions. A game is played whenever people interact with each other. Technically, game theory is a term given to the normative study of mathematical models of strategic interactions among rational players. The objective is to expose students to some popular game theoretic techniques that can be used to model and analyze various managerial scenarios. The techniques that we will learn in this course are categorized into (i) cooperative game theory, and (ii) non-cooperative game theory. Cooperative games are pertinent to any strategic situations where scarce resources are to be allocated among a group of players who can credibly communicate with each other and take joint actions. In non-cooperative games, no credible communication is allowed and hence the focus is on the strategy and action profiles of individual players. While the course is primarily designed as a methodology course for doctoral students in the POM Area, it can also be useful for students in other areas interested in game-theoretic modeling.

Further details are in the course outline.

Lecture plan:

  • Lecture 1: Preferences and utility
  • Lecture 2: Basics of cooperative games
  • Lecture 3: Basics of cooperative games (continued)
  • Lecture 4: Characteristics function form games
  • Lecture 5: The Core
  • Lecture 6: The Core (continued)
  • Lecture 7: The Shapley value
  • Lecture 8: Power indices
  • Lecture 9: The Nash bargaining solution
  • Lecture 10: The nucleolus
  • Lecture 11: The nucleolus (continued)
  • Lecture 12: Matching theory
  • Lecture 13: Two-person zero-sum game in matrix form
  • Lecture 14: Two-person zero-sum game in matrix form (continued)
  • Lecture 15: Two-person non-zero-sum game in bi-matrix form
  • Lecture 16: Two-person non-zero-sum game in bi-matrix form (continued)
  • Lecture 17: Extensive form games
  • Lecture 18: Some applications of Nash equilibrium
  • Lecture 19: War of attrition and repeated games
  • Lecture 20: Basics of evolutionary games

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 the supply chain, queuing system, communication networks, spectrum auction and bankruptcy problem among others. Further details are in the Course outline

*Last offered in 2018

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. Further details are in the Course outline.

*Last offered in 2018