Behavioral game theory

This lecture focuses on mathematical descriptions of strategic situations in which payoffs to agents depend on the behavior of the other agents (applied to the analysis of conflict, cooperation, and corruption).

The best for the group comes when everyone in the group does what’s best for himself and the groupJohn Nash

Instructor: Jorge Finke

Office hours: TBD

Level: graduate

TA: TBD

OVERVIEW

WeekLectures
1Lec 1 - Introduction
2Lec 2 - Human decision-making
3Lec 3 - Bayesian rationality
4Lec 4 - Representations of a game
5Lec 5 - Existence of solutions
6Lec 6 - The ultimatum game
7Lec 7 - Epistemic games
8Lec 8 - Battle of the sexes
9Lec 9 - Rationalizable strategies
10Lec 10 - Common knowledge
11Lec 11 - Backward induction
12Lec 12 - Extensive form rationalizability
13Lec 13 - Extensive form CKM
14Lec 14 - Models of corruption
15Lec 15 - Unification of behavioral sciences

Lessons

Human decision-making

Decision theory and human decision-making; beliefs, preferences, and constraints; consistent preference orderings; utility functions.

Bayesian rationality

Bayesian rationality; Savage’s Axioms; expected utility principle (Savage’s Theorem); biases and heuristics; Prospect theory.

Representations of a game

Representing a game; extensive form game; normal form game; solution concepts; strict dominance; iterated dominance; Nash equilibria.

Existence of solutions

Mixed strategies, Nash equilibria (formal definition), the fundamental theorem; paradigmatic games.

The ultimatum game

Game theory an human behavior; conditions for altruism; the ultimatum game; norms of cooperation; the public goods game; implications for policy-making.

Epistemic games

Epistemic games; how to incorporate beliefs into games? An simple epistemic game.

Backward induction

Backwards induction (extensive form game); subgame perfect Nash equilibria; perfect Bayesian Nash equilibria

Extensive form CKM

Modal logic of knowledge; formalize extensive form CKR; CKR → backward induction.