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

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

Lessons

List of lectures

2. Human decision-making

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

3. Bayesian rationality

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

4. Representations of a game

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

6. 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.

7. Epistemic games

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

11. Backward induction

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