Dissertations, Theses, and Capstone Projects
Date of Degree
2-2016
Document Type
Dissertation
Degree Name
Ph.D.
Program
Computer Science
Advisor
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.
Recommended Citation
Tasdemir, Cagil, "Epistemic Considerations on Extensive-Form Games" (2016). CUNY Academic Works.
https://academicworks.cuny.edu/gc_etds/781