Economics of AI

Readings

• Poole, D., Mackworth, A., & Goebel, R. (1998). Computational Intelligence: A Logical Approach. Oxford University Press.
• Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Press.

Readings

• Mullainathan, S., & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87–106. doi
• Athey, S. (2019). The Impact of Machine Learning on Economics. In The Economics of Artificial Intelligence (pp. 507–552). University of Chicago Press.

Readings

• Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
• Mnih, V., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529–533. (Atari / Deep Q-learning.)
• Silver, D., et al. (2018). A general reinforcement learning algorithm that masters chess, shogi and Go through self-play. Science, 362(6419), 1140–1144. (AlphaZero.)

Readings

• Bommasani, R., et al. (2021). On the Opportunities and Risks of Foundation Models. arXiv:2108.07258. arXiv (Developed fully in Section 2.)

Readings

• Stanford HAI (2025). Artificial Intelligence Index Report 2025. Stanford University.
• Perrault, R., & Clark, J. (2024). Artificial Intelligence Index Report 2024. Stanford HAI.

Readings

• Hardt, M., Megiddo, N., Papadimitriou, C., & Wootters, M. (2015). Strategic Classification. arXiv:1506.06980. arXiv

Readings

• Agrawal, A., Gans, J., & Goldfarb, A. (2022). Power and Prediction: The Disruptive Economics of Artificial Intelligence. Harvard Business Review Press.
• Acemoglu, D., & Restrepo, P. (2019, 2021). Automation, tasks, and the direction of technological change. (AI as a substitute for cognition / labor.)

Readings

• Bommasani, R., et al. (2021). On the Opportunities and Risks of Foundation Models. arXiv:2108.07258. arXiv

Readings

• Korinek, A., & Vipra, J. (2024). Market Concentration Implications of Foundation Models: The Invisible Hand of ChatGPT. Working paper (NBER; Economic Policy).
• McElheran, K., et al. (2024). AI Adoption in America: Who, What, and Where. Journal of Economics & Management Strategy, 33(2), 375–415.

Readings

• Stanford HAI (2025). Artificial Intelligence Index Report 2025. (Charts: parameters by sector, training cost, cost-per-performance, arena convergence.)

Readings

• U.S. Federal Trade Commission (2024). Partnerships Between Cloud Service Providers and AI Developers. FTC 6(b) staff report.

Readings

• Rey, P., & Tirole, J. (2007). A Primer on Foreclosure. Handbook of Industrial Organization, Vol. 3, 2145–2220.
• Jeon, D.-S., & Lefouili, Y. (2018). Cross-licensing and patent pools. RAND Journal of Economics.

Readings

• Brynjolfsson, E., Rock, D., & Syverson, C. (2019). Artificial Intelligence and the Modern Productivity Paradox. In The Economics of AI: An Agenda. University of Chicago Press. (Solow paradox; productivity J-curve.)

Readings

• Brynjolfsson, E., Mitchell, T., & Rock, D. (2018). What Can Machines Learn, and What Does It Mean for Occupations and the Economy? AEA Papers and Proceedings, 108, 43–47.
• Autor, D., Levy, F., & Murnane, R. (2003). The Skill Content of Recent Technological Change. Quarterly Journal of Economics, 118(4).

Readings

• Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An Early Look at the Labor Market Impact Potential of LLMs. arXiv:2303.10130. arXiv

Readings

• Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at Work. Quarterly Journal of Economics, 140(2), 889–942. (Customer-support field experiment.)
• Cui, K. Z., et al. (2025). The Effects of Generative AI on High-Skilled Work: Evidence from Software Developers. SSRN working paper.

Readings

• Acemoglu, D., & Restrepo, P. (2019). Automation and New Tasks: How Technology Displaces and Reinstates Labor. Journal of Economic Perspectives, 33(2), 3–30.

Readings

• Acemoglu, D. (2024). The Simple Macroeconomics of AI. Economic Policy; NBER WP 32487.

Readings

• McElheran, K., et al. (2024). AI Adoption in America: Who, What, and Where. Journal of Economics & Management Strategy, 33(2), 375–415.

Readings

• Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press. (Chapter 1.)

Readings

• Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press. (Chapters 3–4; Bellman equations.)

Readings

• Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press. (Chapter 6.)
• Watkins, C., & Dayan, P. (1992). Q-learning. Machine Learning, 8, 279–292. (Convergence theorem.)

Readings

• Chen, L., Mislove, A., & Wilson, C. (2016). An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace. Proceedings of WWW 2016, 1339–1349.
• Ezrachi, A., & Stucke, M. (2016). Virtual Competition. Harvard University Press.
• Harrington, J. (2018). Developing Competition Law for Collusion by Autonomous Artificial Agents. Journal of Competition Law & Economics.

Readings

• Brown, Z., & MacKay, A. (2023). Competition in Pricing Algorithms. American Economic Journal: Microeconomics, 15(2), 109–156 (working paper 2021).
• O’Connor, J., & Wilson, N. (2021). Reduced demand uncertainty and the sustainability of collusion. Information Economics and Policy, 54, 100882.
• Miklós-Thal, J., & Tucker, C. (2019). Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination? Management Science.

Readings

• Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2020). Artificial Intelligence, Algorithmic Pricing, and Collusion. American Economic Review, 110(10), 3267–3297. doi
• Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2021). Algorithmic collusion with imperfect monitoring. International Journal of Industrial Organization, 79, 102712.

Readings

• Assad, S., Clark, R., Ershov, D., & Xu, L. (2024). Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market. Journal of Political Economy, 132(3), 723–771 (working paper 2021).

Readings

• Bramante, R., Calvano, E., Calzolari, G., & Schäfer, M. (2022). A field experiment on algorithmic pricing on Amazon (“Algorithms in the Wild”). Working paper.

Readings

• Aggarwal, C. C. (2016). Recommender Systems: The Textbook. Springer. (Chapters 2–3.)

Readings

• Aguiar, L., & Waldfogel, J. (2021). Platforms, Power, and Promotion: Evidence from Spotify Playlists. Journal of Industrial Economics, 69(3), 653–691.

Readings

• Amatriain, X. (2013). Big & Personal: data and models behind Netflix recommendations. Proceedings of the 2nd Int. Workshop on Big Data, Streams and Heterogeneous Source Mining.
• Aggarwal, C. C. (2016). Recommender Systems: The Textbook. Springer.

Readings

• Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2025). Artificial Intelligence, Algorithmic Recommendations and Competition. SSRN. doi
• Lee, K. H., & Musolff, L. (2024). Entry into two-sided markets shaped by platform-guided search. R&R Econometrica.

Readings

• Kamenica, E., & Gentzkow, M. (2011). Bayesian Persuasion. American Economic Review, 101(6), 2590–2615. doi

Readings

• Peitz, M., & Sobolev, A. (2022). Inflated Recommendations. RAND Journal of Economics, 54(4) (2025); working paper 2022.

Readings

• Aridor, G., & Gonçalves, D. (2022). Self-preferencing by a vertically-integrated intermediary. International Journal of Industrial Organization, 83, 102850.
• Farronato, C., Fradkin, A., & MacKay, A. (2023). Self-Preferencing at Amazon: Evidence from Search Rankings. NBER Working Paper 30894; AEA Papers and Proceedings, 113.

Readings

• Calvano, E., & Jullien, B. (2022). Playing it Safe: Reputation and Cautious Recommendations. Working paper.

Readings

• Agrawal, A., Gans, J., & Goldfarb, A. (2019). Economic Policy for Artificial Intelligence. Innovation Policy and the Economy, 19. University of Chicago Press.

Readings

Conceptual discussion — draws on the theory-of-harm readings in 10d–10f.

Readings

• European Union. Digital Markets Act (Reg. 2022/1925); Digital Services Act (Reg. 2022/2065); Artificial Intelligence Act (Reg. 2024/1689).

Readings

• Cowgill, B., & Tucker, C. (2020). Algorithmic Fairness and Economics. (Prepared for the Journal of Economic Perspectives.)
• Lambrecht, A., & Tucker, C. (2019). Algorithmic Bias? An Empirical Study of Apparent Gender-Based Discrimination in the Display of STEM Career Ads. Management Science, 65(7), 2966–2981.
• Rambachan, A., Kleinberg, J., Ludwig, J., & Mullainathan, S. (2020). An Economic Approach to Regulating Algorithms. Working paper.

Readings

• Calvano, E., Calzolari, G., Denicolò, V., Harrington, J. E., & Pastorello, S. (2020). Protecting consumers from collusive prices due to AI. Science, 370(6520), 1040–1042.
• Johnson, J., Rhodes, A., & Wildenbeest, M. (2023). Platform Design When Sellers Use Pricing Algorithms. Econometrica, 91(5), 1841–1879. (Buy box / price-directed prominence.)
• Budish, E., Cramton, P., & Shim, J. (2015). The High-Frequency Trading Arms Race: Frequent Batch Auctions. Quarterly Journal of Economics, 130(4).

Readings

• Hagiu, A., & Wright, J. (2023). Data-enabled learning, network effects, and competitive advantage. RAND Journal of Economics, 54(4), 638–667.
• Bajari, P., et al. (2019). The Impact of Big Data on Firm Performance: An Empirical Investigation. AEA Papers and Proceedings, 109.

Readings

• Bramante, R., Calvano, E., Calzolari, G., & Schäfer, M. (2022). A field experiment on algorithmic pricing on Amazon (“Algorithms in the Wild”). Working paper.