CS 785: Multiagent Systems: Games, Algorithms, Evolution


PG standing or CS365, CS345

Course Contents:

We will explore decision making behaviour in communities of agents and in particular how information is used, produced and transmitted in such situations. We will study game theoretic and evolutionary approaches (with an algorithmic emphasis) to model such behaviour and try to understand what implications the results have for problems in biology and in human and non-human communities. (The course will not be concerned with distributed AI or cooperative problem solving by software agents.)


  1. Biological and human communities - structure and properties.
  2. Competition, cooperation, evolution.
  3. What is a game? Non evolutionary and evolutionary games. Numerous examples from biology, economics, decision theory, social choice etc.
  4. Payoffs, strategies - pure, mixed, behavioural. Stability and equilibrium.
  5. Information production, transmission and use in games. Rationality, common knowledge.
  6. Competition, non-cooperation and the corresponding games.
  7. Cooperation and related games.
  8. Dynamic models and evolutionary games.
  9. Fairness, trust, reputation in decision making and games.
  10. Public goods, the commons and related problems.
  11. Brief introduction to mechanism design.

Books and References:

  1. Yoav Shoham, Kevin Leyton-Brown, Multiagent Systems: Algorithmic, Game Theoretic and Logical Foundations, Cambridge University Press, 2009.
  2. Hans Peters, Game Theory: A Multi-leveled Approach, Springer, 2008.
  3. Noam Nisan, Tim Roughgarden, Vijay Vazirani, Algorithmic Game Theory, Cambridge University Press, 2007.
  4. Martin Nowak, Evolutionary Dynamics: Exploring the Equations of Life, Belnap Press, 2006.
  5. Karl Sigmund, The Calculus of Selfishness, Princeton University Press, 2010.
  6. Research papers.