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AI and Digital Platforms

Economics of Artificial Intelligence

Specialize in Artificial Intelligence and its impact on the economy.

10h (5 days)

€1,260

Face-to-face

English

Next edition: June 28-July 1, 2027

Early bird deadline: April 15, 2027

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AI and Digital Platforms

Economics of Artificial Intelligence

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Who is this Course for?

AI and Digital Platforms

Economics of Artificial Intelligence

Course Overview

Artificial Intelligence (AI) algorithms are increasingly crowding our markets and, more broadly, shaping our societies. The impact of algorithms is an issue of great importance in policy circles and increasingly studied in academic work.

This short course will bring students to the frontier of research on the Economics of Artificial Intelligence. We will focus on some of the topics that will pose imminent and relevant challenges in this rapidly evolving area for economic research and policymakers. This course brings participants to the research frontier on the economics of AI, covering the industrial organisation of foundation models, AI’s effects on jobs and growth, agentic markets, and AI in finance and banking, with particular emphasis on competition and regulatory policy.

This course is particularly suitable for advanced graduate students looking for a dissertation topic as well as practitioners and legal scholars seeking to catch up with the state-of-the-art academic research on digital markets.

Faculty

Who is this course for?

This course would be ideal for:

Advanced graduate students looking for a dissertation topic
Practitioners seeking to catch up with state-of-the-art academic research on digital markets

Learning outcomes

By the end of this course, participants will have:

Become acquainted with state-of-the-art research on the impact of AI on key economic outcomes
Assessed the evidence on AI’s effects on tasks, jobs, productivity, and adoption
Analyzed competition in foundation-model markets: the vertical stack, AI partnerships, and foreclosure
Understood what makes AI agents economically distinct from human decision-makers and from traditional algorithms, and why this calls for new research
Assessed how AI is reshaping finance and banking: adoption, algorithmic trading, credit markets, herding, and financial stability
Understood reinforcement learning and applied it to the theory and evidence on algorithmic collusion
Analyzed how recommender systems and choice manipulation shape consumer choice and competition
Critically evaluated policy responses: the DMA, DSA, and AI Act; algorithmic bias and collusion; data-driven incumbency

Key topics for Economics of AI course

Take a look at the course outline below.

What is AI?

Definitions and views
Machine learning
Reinforcement learning
Foundation models (a teaser, developed fully in Section 2)
Key facts and trends
Economics in AI
Economics of AI

Foundation models

What is a foundation model? Capabilities
Competition in the vertical stack
What’s new: industry features (proliferation, entry barriers, cost-per-performance, converging performance, winner-take-all vs. commoditization)
AI partnerships
Partnerships: theory (foreclosure, technology and talent pooling)

AI, jobs, and growth

AI as a general-purpose technology
Jobs and tasks before LLMs: exposure and evidence
Jobs after LLMs: exposure
Systematic evidence (RCTs: customer support, software development)
Macroeconomics: task-based model and labor demand
Macroeconomics: productivity and growth
Adoption

Reinforcement learning

The Markov decision process framework
Policies, values, and the Bellman equation
Model-free methods and Q-learning

Agentic markets: An overview

Agentic sellers: pricing algorithms

Introduction: algorithms in markets
Pricing theory: commitment and competition
Algorithmic collusion (Calvano, Calzolari, Denicolò, Pastorello)
Empirical evidence (German retail gasoline)
Field experiment on Amazon

Agentic platforms: recommender systems

Introduction and sources of power
Quantifying the power of Spotify
Recommender systems: definition and economics
Recommender systems and competition

Agentic platforms: Choice manipulation

Framework: manipulating choices
Inflated recommendations (Peitz and Sobolev)
Self-preferencing (Aridor and Gonçalves)
Playing it safe (Calvano and Jullien)

Agentic buyers

AI in finance and banking

AI-driven algorithmic trading: correlated trading, algorithmic collusion, manipulation, market concentration
Credit markets and banking: machine learning in credit scoring and lending, AI disruption risk and debt-financed AI investment
Herding and financial stability: do AI agents herd more or less than humans? LLM lab experiment (Hansen and Lee); algorithmic herding (Dou, Goldstein, Li and Yang)

Algorithms and public policy

Broad policy challenges
Why is AI different?
The policy landscape: DMA, DSA, AI Act
Application: tackling algorithmic bias
Application: tackling algorithmic collusion
Data-driven incumbency advantage
Looking ahead

Why join our Summer School?

All BSE Summer courses are taught to the same high standard as our Master’s programs. Join us to:

1

Network with like-minded peers

2

Study in vibrant Barcelona

3

Learn from world-renowned faculty

List of References

Below is a list of references that may help you prepare for the course, or if you’d like to do some further reading.

Books and Articles

Acemoglu, D. (2024). The Simple Macroeconomics of AI. Economic Policy, 40(121), 13–58 (2025); NBER Working Paper 32487.
Acemoglu, D., & Restrepo, P. (2019). Automation and New Tasks: How Technology Displaces and Reinstates Labor. Journal of Economic Perspectives, 33(2), 3–30.
Aggarwal, C. C. (2016). Recommender Systems: The Textbook. Springer.
Agrawal, A., Gans, J., & Goldfarb, A. (2018). Prediction Machines: The Simple Economics of Artificial Intelligence. Harvard Business Review Press.
Agrawal, A., Gans, J., & Goldfarb, A. (2019). Economic Policy for Artificial Intelligence. Innovation Policy and the Economy, 19, 139–159. University of Chicago Press.
Agrawal, A., Gans, J., & Goldfarb, A. (2022). Power and Prediction: The Disruptive Economics of Artificial Intelligence. Harvard Business Review Press.
Aguiar, L., & Waldfogel, J. (2021). Platforms, Power, and Promotion: Evidence from Spotify Playlists. Journal of Industrial Economics, 69(3), 653–691.
Aldasoro, I., Gambacorta, L., Korinek, A., Shreeti, V., & Stein, M. (2024). Intelligent Financial System: How AI Is Transforming Finance. BIS Working Paper 1194, Bank for International Settlements.
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 (BigMine).
Aridor, G., & Gonçalves, D. (2022). Self-preferencing by a vertically-integrated intermediary. International Journal of Industrial Organization, 83, 102850.
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).
Athey, S. (2019). The Impact of Machine Learning on Economics. In The Economics of Artificial Intelligence: An Agenda (pp. 507–552). University of Chicago Press.
Autor, D., Levy, F., & Murnane, R. (2003). The Skill Content of Recent Technological Change: An Empirical Exploration. Quarterly Journal of Economics, 118(4), 1279–1333.
Bajari, P., Chernozhukov, V., Hortaçsu, A., & Suzuki, J. (2019). The Impact of Big Data on Firm Performance: An Empirical Investigation. AEA Papers and Proceedings, 109, 33–37.
Berg, T., Burg, V., Gombović, A., & Puri, M. (2020). On the Rise of FinTechs: Credit Scoring Using Digital Footprints. Review of Financial Studies, 33(7), 2845–2897.
Bommasani, R., et al. (2021). On the Opportunities and Risks of Foundation Models. arXiv:2108.07258.
Bramante, R., Calvano, E., Calzolari, G., & Schäfer, M. (2022). A field experiment on algorithmic pricing on Amazon (“Algorithms in the Wild”). Working paper.
Brown, Z., & MacKay, A. (2023). Competition in Pricing Algorithms. American Economic Journal: Microeconomics, 15(2), 109–156 (working paper 2021).
Brynjolfsson, E., Li, D., & Raymond, L. (2025). Generative AI at Work. Quarterly Journal of Economics, 140(2), 889–942.
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.
Brynjolfsson, E., Rock, D., & Syverson, C. (2019). Artificial Intelligence and the Modern Productivity Paradox. In The Economics of Artificial Intelligence: An Agenda (pp. 23–60). University of Chicago Press.
Budish, E., Cramton, P., & Shim, J. (2015). The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response. Quarterly Journal of Economics, 130(4), 1547–1621.
Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2020). Artificial Intelligence, Algorithmic Pricing, and Collusion. American Economic Review, 110(10), 3267–3297.
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.
Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2021). Algorithmic collusion with imperfect monitoring. International Journal of Industrial Organization, 79, 102712.
Calvano, E., Calzolari, G., Denicolò, V., & Pastorello, S. (2025). Artificial Intelligence, Algorithmic Recommendations and Competition. SSRN 4448010.
Calvano, E., & Jullien, B. (2022). Playing it Safe: Reputation and Cautious Recommendations. Working paper.
Cartea, Á., Chang, P., & Penalva, J. (2022). Algorithms and Supracompetitive Prices in Electronic Markets: The Impact of Tick Size. Working paper, SSRN 4105954.
Cartea, Á., Colliard, J.-E., Foucault, T., Hoffmann, P., Graumans, R., & Sigaux, J.-D. (2026). Artificial Intelligence and Financial Markets. ECB Discussion Paper Series, No. 32, European Central Bank.
Chen, L., Mislove, A., & Wilson, C. (2016). An Empirical Analysis of Algorithmic Pricing on Amazon Marketplace. Proceedings of WWW 2016, 1339–1349.
Colliard, J.-E., Foucault, T., & Lovo, S. (2026). Algorithmic Pricing and Liquidity in Securities Markets. Review of Financial Studies, forthcoming.
Cook, L. D. (2026). The Opportunities and Risks AI Presents for the Economy and Financial System. Speech at the Stanford Institute for Economic Policy Research, May 27. Board of Governors of the Federal Reserve System.
Cowgill, B., & Tucker, C. (2020). Algorithmic Fairness and Economics. Working paper (prepared for the Journal of Economic Perspectives).
Cui, K. Z., Demirer, M., Jaffe, S., Musolff, L., Peng, S., & Salz, T. (2025). The Effects of Generative AI on High-Skilled Work: Evidence from Software Developers. SSRN; Management Science.
Danielsson, J., & Uthemann, A. (2025). Artificial Intelligence and Financial Crises. Journal of Financial Stability, 101453.
Dou, W., Goldstein, I., & Ji, Y. (2023). AI-Powered Trading, Algorithmic Collusion, and Price Efficiency. Working paper.
Dou, W., Goldstein, I., Li, Z., & Yang, L. (2025). Human Edge, Machine Limits: Algorithmic Herding and AI-Human Competition. Working paper.
Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models. arXiv:2303.10130.
European Union. Digital Markets Act (Regulation 2022/1925); Digital Services Act (Regulation 2022/2065); Artificial Intelligence Act (Regulation 2024/1689).
Ezrachi, A., & Stucke, M. (2016). Virtual Competition: The Promise and Perils of the Algorithm-Driven Economy. Harvard University Press.
Farronato, C., Fradkin, A., & MacKay, A. (2023). Self-Preferencing at Amazon: Evidence from Search Rankings. NBER Working Paper 30894; AEA Papers and Proceedings, 113.
Financial Stability Board (2024). The Financial Stability Implications of Artificial Intelligence. Report, FSB.
Foucault, T., Gambacorta, L., Jiang, W., & Vives, X. (2025). Artificial Intelligence in Finance. Barcelona Reports, Vol. 7. CEPR Press.
Fuster, A., Goldsmith-Pinkham, P., Ramadorai, T., & Walther, A. (2022). Predictably Unequal? The Effects of Machine Learning on Credit Markets. Journal of Finance, 77(1), 5–47.
Hagiu, A., & Wright, J. (2023). Data-enabled learning, network effects, and competitive advantage. RAND Journal of Economics, 54(4), 638–667.
Hansen, A. L., & Lee, S. J. (2025). Financial Stability Implications of Generative AI: Taming the Animal Spirits. Finance and Economics Discussion Series 2025-090, Board of Governors of the Federal Reserve System.
Hardt, M., Megiddo, N., Papadimitriou, C., & Wootters, M. (2015). Strategic Classification. arXiv:1506.06980; Proc. ACM ITCS 2016, 111–122.
Harrington, J. (2018). Developing Competition Law for Collusion by Autonomous Artificial Agents. Journal of Competition Law & Economics, 14(3), 331–363.
International Monetary Fund (2024). Global Financial Stability Report, Chapter 3: Advances in Artificial Intelligence: Implications for Capital Market Activities. IMF, Washington, DC.
Jeon, D.-S., & Lefouili, Y. (2018). Cross-licensing and competition. RAND Journal of Economics, 49(3), 656–671.
Johnson, J., Rhodes, A., & Wildenbeest, M. (2023). Platform Design When Sellers Use Pricing Algorithms. Econometrica, 91(5), 1841–1879.
Kamenica, E., & Gentzkow, M. (2011). Bayesian Persuasion. American Economic Review, 101(6), 2590–2615.
Korinek, A., & Vipra, J. (2024). Market Concentration Implications of Foundation Models. Working paper (NBER; Economic Policy).
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.
Lee, K. H., & Musolff, L. (2024). Entry into Two-Sided Markets Shaped by Platform-Guided Search. Working paper (R&R, Econometrica).
McElheran, K., et al. (2024). AI Adoption in America: Who, What, and Where. Journal of Economics & Management Strategy, 33(2), 375–415.
Miklós-Thal, J., & Tucker, C. (2019). Collusion by Algorithm: Does Better Demand Prediction Facilitate Coordination Between Sellers? Management Science, 65(4), 1552–1561.
Mnih, V., et al. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529–533.
Mullainathan, S., & Spiess, J. (2017). Machine Learning: An Applied Econometric Approach. Journal of Economic Perspectives, 31(2), 87–106.
O’Connor, J., & Wilson, N. (2021). Reduced demand uncertainty and the sustainability of collusion: How AI could affect competition. Information Economics and Policy, 54, 100882.
Peitz, M., & Sobolev, A. (2022). Inflated Recommendations. RAND Journal of Economics, 54(4) (2025); working paper 2022.
Perrault, R., & Clark, J. (2024). Artificial Intelligence Index Report 2024. Stanford HAI.
Poole, D., Mackworth, A., & Goebel, R. (1998). Computational Intelligence: A Logical Approach. Oxford University Press.
Rambachan, A., Kleinberg, J., Ludwig, J., & Mullainathan, S. (2020). An Economic Approach to Regulating Algorithms. Working paper; NBER Working Paper 27111.
Rey, P., & Tirole, J. (2007). A Primer on Foreclosure. In Handbook of Industrial Organization (Vol. 3, pp. 2145–2220). North-Holland.
Silver, D., et al. (2018). A general reinforcement learning algorithm that masters chess, shogi and Go through self-play. Science, 362(6419), 1140–1144.
Stanford HAI (2025). Artificial Intelligence Index Report 2025. Stanford University.
Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction (2nd ed.). MIT Press.
Watkins, C., & Dayan, P. (1992). Q-learning. Machine Learning, 8, 279–292.

Admissions and Requirements

Please make sure you meet the following admissions criteria before you apply.

Next edition: June 28-July 1, 2027

Early bird deadline: April 15, 2027

Requirements

A strong background in Economics or a field closely related to the course topic (Statistics, Law, etc.)
Postgraduate degree or current Master’s/PhD studies related to the course topic
Relevant professional experience

Schedule

Here is your schedule for this edition of BSE AI and Digital Platforms Economics of Artificial Intelligence course.

Time

28

mon

29

tue

30

wed

1

thu

08:30-11:00*

Lecture

This course runs for four days with 2.5 hours of teaching per day. Total teaching time is 10 hours.

Credit Transfers (ECTS)

To be eligible for credit transfer, students must complete a final project.

The proposal should be 3–4 pages long, 1.5 spaced, in font size 11, and include the following sections:

Research Question: Clearly state the main question your paper will address.
Motivation: Explain why this question matters and its policy relevance.
Literature Review: Summarize the key existing studies and describe how your work builds on or differs from them.
Methodology: Outline how you plan to address the research question, including your proposed empirical strategy and/or theoretical model.

Consult the Summer School Admissions page for more information about this option.

Certificate of Attendance

Participants who attend more than 80% of the course will receive a Certificate of Attendance, free of charge.

Course

The Economics of Artificial Intelligence

Digital Markets and AI: Cloud, Platforms, Regulation

Modality

Face-to-face

Total Hours

10

ECTS

1

Regular Fee

€1,260

Reduced Fee*

€740

* Reduced Fee applies for PhD or Master’s students, Alumni of BSE Master’s programs, and participants who are unemployed.

Multiple discounts are available.

For more information on discounts for participants funding their own course, please click here.

For more information on discounts for companies and institutions enrolling their employees, please click here.

Full discount conditions are available here.

FAQ

Need more information? Check out our most commonly asked questions.

Can I see the full Summer School calendar?

You can view the full Summer School calendar here.

Is accommodation included in the course fee?

Unfortunately, accommodation is not included in the course fee. Participants are responsible for finding accommodation.

Are the sessions recorded?

Sessions will NOT be recorded; however, the materials provided by the professor will be available for a month after the course has finished.

How much does each Summer School course cost?

Fees for each course may vary. Please consult each course page for accurate information.

Are there any discounts available?

Yes, BSE offers a variety of discounts on its Summer School courses. See more information about available discounts or request a personalized discount quote by email.

Can I take more than one course?

Yes! you can combine any of the Summer School courses (schedule permitting). See the full course calendar.

Cancelation and Refund Policy

Please consult BSE Summer School policies for more information.

Are there any evening activities during the course?

Yes, a social dinner is held once a week for all participants, and it is free to attend.

Contact our Admissions Team

Mix and match your summer courses!

Remember that you can combine Banking Summer School courses with courses in any of the other BSE Summer School programs (schedule permitting). Maximise your learning this summer and take advantage of our multiple-course discount.

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