Lectures

01/19 Lecture 1. Introduction to games and societal systems

01/24 Lecture 2. Games and rationalizability

01/26 Lecture 3. Equilibrium concepts

01/31 Lecture 4. Supermodular games and comparative statics

02/02 Lecture 5. Zero sum games and equilibrium computation

02/07 Lecture 6. Potential games, applications of congestion games

02/09 Guest lecture. Network security and flow interdiction, Mathieu Dahan (Georgia Tech)

02/14 Lecture 7. Discrete time learning dynamics: Belief-based learning

02/16 Lecture 8. Continuous time learning dynamics: Imitation and replicator dynamics

02/23 Lecture 9. Extensive form games and subgame perfect equilibrium

02/28 Lecture 10. Repeated games and folk theorem

03/02 Research session. Multi-agent reinforcement learning

  • Introduction of markov games

  • Zhang et. al. "Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms"

  • Sayin, Muhammed, et al. "Decentralized Q-learning in zero-sum Markov games." Advances in Neural Information Processing Systems 34 (2021).

  • Jin, Chi, et al. "V-Learning--A Simple, Efficient, Decentralized Algorithm for Multiagent RL."

  • Haotian, Gu, et al. "Mean-Field Controls with Q-Learning for Cooperative MARL: Convergence and Complexity Analysis"

03/07 Lecture 11. Bayesian games, value of information

03/09 Research session. Human-play and algorithm-play

  • Lanctot M, Zambaldi V, Gruslys A, Lazaridou A, Tuyls K, PĂ©rolat J, Silver D, Graepel T. A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning;

  • Vinyals, Oriol, et al. "Grandmaster level in StarCraft II using multi-agent reinforcement learning." Nature 575.7782 (2019): 350-354.

  • Drew Fudenberg and Annie Liang. Predicting and Understanding Initial Play. The American Economic Review, 2019.

  • Jason Hartford, James R. Wright, Kevin Leyton-Brown. Deep Learning for Predicting Human Strategic Behavior. NeuRIPS 2016.

  • Donahue, Kate, and Jon Kleinberg. "Optimality and Stability in Federated Learning: A Game-theoretic Approach." Advances in Neural Information Processing Systems 34 (2021).

  • Donahue, Kate, and Jon Kleinberg. "Optimality and Stability in Federated Learning: A Game-theoretic Approach." Advances in Neural Information Processing Systems 34 (2021)

03/14 Guest lecture. Learning for decision making, Prof. Nika Haghtalab (UC Berkeley)

03/16 Lecture 12. VCG mechanism design

03/28 Lecture 13. Mechanism design: from for public to for private

03/30 Lecture 14. Payoff equivalence theorem and optimal auction design

04/04 Guest lecture. Reimagining the Machine Learning Life Cycle in Education and Beyond, Prof. Rediet Abebe and Serena Wang (UC Berkeley)

04/06 Research session. Mechanism design I

  • Acemoglu, Daron, Asuman Ozdaglar, and Alireza Tahbaz-Salehi. "Systemic risk and stability in financial networks." American Economic Review 105.2 (2015): 564-608.

  • Acemoglu, Daron, Asuman Ozdaglar, and Alireza Tahbaz-Salehi. Networks, shocks, and systemic risk. No. w20931. National Bureau of Economic Research, 2015.

  • Abebe, Rediet, Jon Kleinberg, and S. Matthew Weinberg. "Subsidy allocations in the presence of income shocks." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 34. No. 05. 2020.

  • Optimal mechanism design and money burning, Jason D. Hartline, Tim Roughgarden

  • Sheffi, Yossi. "Combinatorial auctions in the procurement of transportation services." Interfaces 34.4 (2004): 245-252.

  • Cramton, Peter. "Spectrum auction design." Review of industrial organization 42.2 (2013): 161-190.

04/11 Research session. Mechanism design II

  • Ma, Hongyao, Fei Fang, and David C. Parkes. "Spatio-temporal pricing for ridesharing platforms." Operations Research (2021).

  • Castillo, Juan Camilo, Dan Knoepfle, and Glen Weyl. "Surge pricing solves the wild goose chase." Proceedings of the 2017 ACM Conference on Economics and Computation. 2017.

  • Ellison, Glenn, and Parag A. Pathak. "The Efficiency of Race-Neutral Alternatives to Race-Based Affirmative Action: Evidence from Chicago's Exam Schools." American Economic Review 111.3 (2021): 943-75.

  • Pathak, Parag A. "The mechanism design approach to student assignment." Annu. Rev. Econ. 3.1 (2011): 513-536.

04/13 Research session. Information design: theory and applications

  • Kamenica, Emir. "Bayesian persuasion and information design." Annual Review of Economics 11 (2019): 249-272

  • Bergemann, Dirk, and Stephen Morris. "Information design: A unified perspective." Journal of Economic Literature 57.1 (2019): 44-95.

  • Candogan, Ozan, and Kimon Drakopoulos. "Optimal signaling of content accuracy: Engagement vs. misinformation." Operations Research 68.2 (2020): 497-515.

  • Lingenbrink, David, and Krishnamurthy Iyer. "Optimal signaling mechanisms in unobservable queues." Operations research 67.5 (2019): 1397-1416.

04/18 Lecture 15. Screening and adverse selection

04/20 Guest lecture. Incentive design in the medicare shared savings program, Prof. Anil Aswani (UC Berkeley)

04/25 Research session. Principal-agent problems and data market

  • Carroll, Gabriel. "Robustness and linear contracts." American Economic Review 105.2 (2015): 536-63.

  • Sannikov, Yuliy. "A continuous-time version of the principal-agent problem." The Review of Economic Studies 75.3 (2008): 957-984.

  • Bergemann, Dirk, and Alessandro Bonatti. "Markets for information: An introduction." Annual Review of Economics 11 (2019): 85-107.

  • Fallah, Alireza, et al. "Optimal and Differentially Private Data Acquisition: Central and Local Mechanisms." arXiv preprint arXiv:2201.03968 (2022).

04/27 Market design for power systems, Prof. Dileep Kalathil (Texas A&M)

05/02 Strategic interaction in multi-agent systems, Prof. David Fridovich-Keil (UT Austin), and Prof. Forest Laine (University of Vanderbilt)

05/04 Research project discussion, course summary