Date of Degree

2-2016

Document Type

Dissertation

Degree Name

Ph.D.

Program

Computer Science

Advisor(s)

Sergei Artemov

Committee Members

Rohit Parikh

Melvin Fitting

Eric Pacuit

Subject Categories

Logic and Foundations | Other Computer Sciences | Other Economics

Keywords

Epistemic Game Theory, Extensive-Form Games, Tolerance Analysis, Backward Induction, Choice Functions, Knowledge Manipulation

Abstract

In this thesis, we study several topics in extensive-form games. First, we consider perfect information games with belief revision with players who are tolerant of each other’s hypothetical errors. We bound the number of hypothetical non-rational moves of a player that will be tolerated by other players without revising the belief on that player’s rationality on future moves, and investigate which games yield the backward induction solution.

Second, we consider players who have no way of assigning probabilities to various possible outcomes, and define players as conservative, moderate and aggressive depending on the way they choose, and show that all such players could be considered rational.

We then concentrate on games with imperfect and incomplete information and study how conservative, moderate and aggressive players might play such games. We provide models for the behavior of a (truthful) knowledge manipulator whose motives are not known to the active players, and look into how she can bring about a certain knowledge situation about a game, and change the way the game will be played.

 
 

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