Course description

This is a research-oriented course that focuses on the design methods for societal systems which are being transformed by big data analytics, Internet-of-Things, and machine learning. Of special interest is the theory and applications of games, information and incentive design for achieving desirable societal outcomes in systems, where the performance is affected by strategic user behavior and dynamics of the physical infrastructure. The goal of this course is to give students an in-depth understanding of (i) fundamentals of game theory, information and incentive design; (ii) modeling techniques of strategic human behavior and equilibrium analysis in networks under technological and physical constraints; (iii) current research applying game theory and incentive design for improving efficiency, resiliency and equity in societal systems.

Course Information

Instructor: Manxi Wu (manxiwu at berkeley.edu)

Co-Instrucor: Shankar Sastry

Teaching assistant: Chinmay Maheshwari

Class time and location: Monday / Wednesday 10:00 - 11:30 am in Soda 310.

Office hours: Friday 12:00 - 1:30 pm at Cory 337A. Office hours in the first two weeks will be held on zoom using the same link as the lectures.

Contact: Sign-up for Piazza (only for registered Berkeley students, see link on bcourse)

Prerequisite: Knowledge in probability, optimization, and machine learning. The course will be fast-paced, but self-included.

Outline

The course includes 15 lectures, 6 literature and research presentation sessions, and 6 guest lectures.

Lecture topics:

1. Basic concepts and static games: rationalizability, pure and mixed Nash equilibrium, correlated equilibrium, supermodular games and comparative statics, potential games, and zero-sum games

2. Extensive-form games: extensive-form representation, subgame perfect equilibrium, finite and infinite repeated games, folk theorem, system dynamics and stochastic games

3. Learning and evolutionary dynamics: best response dynamics and fictitious play, stochastic approximation, convergence analysis, replicator dynamics

4. Mechanism design: Bayesian games, Vickery-Clark-Grove mechanism, Optimal auction design

5. Contract theory (in brief): screening, adverse selection and moral hazard

Literature and research presentation:

1. Multi-agent reinforcement learning

2. Algorithm play and human play in strategic environment

3. Mechanism design with network and integer constraints

4. Societal implications in mechanisms and markets

5. Information design: theory and applications

6. Applications of machine learning in principal-agent problem

Guest lectures:

1. Prof. Mathieu Dahan (Georgia Tech): Games and incentives in cyber security.

2. Prof. Nika Haghtalab (UC Berkeley): Learning for decision making.

3. Prof. Rediet Abebe, and Serena Wang (UC Berkeley): Mechanism design for social good

4. Prof. Anil Aswani (UC Berkeley): Data-driven contract design in healthcare systems

5. Prof. Dileep Kalathil (Texas A&M): Market design for energy systems

6. Prof. David Fridovich-Keil (UT Austin) and Prof. Forrest Laine (Vanderbilt): Strategic behavior in multi-agent dynamical systems

Grading policy:

• Homework (25%): 3 problem sets. Late submission is discounted 10% every 24 hours.

• Research presentation (25%): Sign up for one research session, complete required readings, give a presentation. Write a brief summary.

• Guest speaker session participation (10%)

• Final project (40%): 10% proposal + 15% presentation + 15% final report (team size up to 3).