This course is a rigorous investigation of the evolutionary and epistemic foundations of solution concepts, such as rationalizability and Nash equilibrium. It covers classical topics, such as repeated games, bargaining, and supermodular games as well as new topics such as global games, heterogeneous priors, psychological games, and games without expected utility maximization. Applications are provided when available.
• To understand the importance of competitive and cooperative factors in a variety of decision problems
• To learn how to structure and analyze these problems from a quantitative perspective
Course Learning Outcomes
The essence of game theory is not a set of results - though that surely lies at its foundations - but rather a process - the way in which an argument is constructed, how a puzzle about human behavior is solved. To learn game theory means learning the logical argument that produces a solution, a conclusion, a resolution of a mystery. Therefore the primary objective of this course is to teach how to analyze situations of strategic interaction between agents. Of course in doing so the students will become familiar with the terminology and basic definitions of game theory as well as solution concepts employed in game theory to predict what the outcome of a specific game will be.
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Book Title : Algorithmic Game Theory
Author : Noam Nisan
Publisher : Cambridge University Press
Book Title : A Course in Game Theory
Author : Martin J. Osborne and Ariel Rubinstein
Publisher : MIT
Book Title : Publicly available solutions for an introduction to game theory
Author : Martin J. Osborene