COMP 2024

The 4th Summer School of Computational and Experimental Economics

Graduate students and young faculty interested in computational and experimental economics are invited to attend this intensive 7-day summer school (which will include the two-day Workshop on Computational and Experimental Economics on June 2-3, 2026).

The summer school will be held from May 31 to June 6 at Beslab of Universitat Pompeu Fabra, Spain.

• Goal: The goal of the summer school is to develop a foundation for using computational tools, machine learning methods, and large-language models to complement and/or explain results from human subject experiments. In particular, throughout the curriculum, students will learn how to implement a variety of agent-based models that have been successful in capturing regularities observed in the experimental and field data. In addition, the summer school will include a two-day workshop on computational and experimental economics which will include presentations by the leading researchers who are interested in experimental and computational economics.
• Schedule Outline: The summer school coursework will begin with an overview of the literature at the intersection of experimental/behavioral economics and computational economics (as highlighted for example by The Handbook of Computational Economics, Heterogenous Agents, edited by Cars Hommes, Blake LeBaron, Volume 4, 2018) and continue with specific implementations of recent models including reinforcement learning, evolutionary learning, and belief learning. In addition, students will compare the performance of the agent-based approach to computational approaches that do not relax the assumptions of perfect rationality and information (e.g., dynamic programming).

The summer school consists of 5 days of instruction with a typical day including:

• Three 90-minute lectures by the faculty
• 90-minute hands-on group project sessions

The last day will include student-groups presenting proposals for a project and getting comments from the faculty. A tentative schedule is provided below.

• Preparation: In order to ensure productive interaction during the summer school, all students will be required to complete a Python tutoriall and submit a set of exercises prior to arrival. In addition, because the summer school will include completion of a number of hand-on programming activities, students are encouraged to bring their own laptop computers with the necessary software installed.

In addition to the tutorial, students will be provided with a set of example programs that will be discussed during the lectures. The expectation is that students will study and execute these programs prior to arrival.

An example of programming exercise during the summer school.

The deadline for applications is March 7, 2026.

Organizers

• Herbert Dawid (Bielefeld University)
• Mikhail Anufriev (University of Technology Sydney)
• Rosemarie Nagel (ICREA-UPF, and BSE)
• Valentyn Panchenko (University of New South Wales)
• Yaroslav Rosokha (Purdue University)

The 4th Summer School of Computational and Experimental Economics

The 4th Summer School of Computational and Experimental Economics

Guest lecturers will be available soon. The following information describes the guest lecturers from the 2024 edition.

Herbert Dawid

Herbert Dawid is professor for Economic Theory and Computational Economics at Bielefeld University and currently president of the Society for Computational Economics. He earned his doctoral degree at the Vienna University of Technology (1995). His research mainly focuses on economic dynamics, in particular industrial dynamics and economics of innovation, agent-based computational economics, and dynamic policy analysis.

Selected publications:

• Huberts, N., Wen, X., Dawid, H., Huisman K. and P.M. Kort (2025), 'Double Marginalization due to External Financing: Capacity Investment under Uncertainty', Management Science, 71(5), 3749-3767.
• Colombo, L., Dawid, H. and P. Harting (2024), 'R&D location in dynamic industry environments', Journal of Economic Geography, 24, 41-62.
• Dawid H, Harting P, van der Hoog S, Neugart M (2019), "Macroeconomics with heterogeneous agent models. Fostering transparency, reproducibility and replication", Journal of Evolutionary Economics 29(1): 467-538.
• Dawid, H. and D. Delli Gatti (2018), "Agent-based Macroeconomics", in C. Hommes and B. LeBaron (Eds.) "Handbook of Computational Economics Vol. 4: Heterogeneous Agent Modeling", North-Holland, pp. 63-156.
• Dawid, H., Harting, P. and M. Neugart (2018), "Cohesion Policy and Inequality Dynamics: Insights from a Heterogeneous Agents Macroeconomic Model", Journal of Economic Behavior and Organization, 150, 220-255.
• Dawid, H. and MacLeod, W.B. (2008), "Hold-up and the Evolution of Investment and Bargaining Norms", Games and Economic Behavior, 62, 26-52. 

Mikhail Anufriev

Mikhail Anufriev is a professor of Economic Dynamics at the University of Technology Sydney (UTS). He earned his doctoral degree at the Sant’Anna School of Advanced Studies in Pisa in 2005. His research primarily focuses on models of bounded rationality and individual learning, as well as economic and social network dynamics. In his work, he applies theoretical models, computational methods, and controlled laboratory experiments with human subjects. His recent work includes experimental investigations into how people make choices between alternatives with endogenous and unknown payoff structures.

Selected publications:

• Anufriev, M., Duffy, J., and Panchenko, V. (2022). Learning in two-dimensional beauty contest games: Theory and experimental evidence. Journal of Economic Theory, 201, 105417.
• Anufriev, M., Hommes, C., and Makarewicz, T. (2019). Simple forecasting heuristics that make us smart: Evidence from different market experiments. Journal of the European Economic Association, 17(5), 1538-1584.
• Anufriev, M., Chernulich, A., and Tuinstra, J. (2017). A laboratory experiment on the heuristic switching model. Journal of Economic Dynamics and Control, 91, 21-42.
• Anufriev, M., and Hommes, C. (2012). Evolutionary selection of individual expectations and aggregate outcomes in asset pricing experiments. American Economic Journal: Microeconomics, 4(4), 35-64.

Rosemarie Nagel

Rosemarie Nagel is an ICREA research professor at the Universitat Pompeu Fabra (UPF-BSE), research director of the experimental laboratory (LEEX-UPF), and member of CESifo. She earned her Ph.D. in the European Doctoral Program at the University of Bonn (1994). Her research interest is on experimental economics, behavioral economics, and neuroeconomics.

Selected publications:

• Camilo Moore, A,  F.  Germano,  R.  Nagel (2026). “Understanding Human Behavior via Similarity: A Geometric and Behavioral Rules-Based Approach to Games.” Working paper
• Mauersberger, F. and Nagel, R. (2018). Levels of Reasoning in Keynesian Beauty Contests: A Generative Framework in the Handbook of Computational Economics, Volume 4, Heterogeneous Agents. Editors: Cars Hommes and Blake LeBaron. Amsterdam: North-Holland.
• Bosch, A., J. G. Montalvo, R. Nagel, A. Satorra (2010). Finite Mixture Analysis of Beauty-Contest Data from Multiple Samples, Experimental Economics vol. 13(4): 461-475.
• "Neural correlates of depth of strategic reasoning in medial prefrontal cortex" (with Giorgio Coricelli), Proceedings of the National Academy of Sciences (PNAS): Economic Sciences, PNAS June 9, 2009 vol. 106 no. 23 9163-9168
• "One, Two, (Three), Infinity...: Newspaper and Lab Beauty-Contest Experiments", (with Antoni Bosch-Domènech, Jose García-Montalvo, and Albert Satorra), American Economic Review December 92 (5), 2002, pp 1687-1701.

Valentyn Panchenko

Valentyn Panchenko is a Professor in Economics at UNSW Business School, UNSW Sydney. He received a PhD degree from the University of Amsterdam in 2006. Valentyn's main research area is Econometrics including big data, networks, dependence modelling, Granger causality, model evaluation and prediction, and structural modelling. He is also interested in economic models with bounded rationality, heterogeneous agents, learning and economic interactions, and economic experiments.

Selected publications:

• Anufriev M; Arifovic J; Ledyard J; Panchenko V, 2022, 'The role of information in a continuous double auction: An experiment and learning model: The Role of Information in CDA', Journal of Economic Dynamics and Control, 141, pp. 104387 - 104387, http://dx.doi.org/10.1016/j.jedc.2022.104387
• Anufriev M; Duffy J; Panchenko V, 2022, 'Learning in two-dimensional beauty contest games: Theory and experimental evidence', Journal of Economic Theory, 201, pp. 105417 - 105417, http://dx.doi.org/10.1016/j.jet.2022.105417
• Blavatskyy P; Ortmann A; Panchenko V, 2022, 'On the Experimental Robustness of the Allais Paradox', American Economic Journal: Microeconomics, 14, pp. 143 - 163, http://dx.doi.org/10.1257/mic.20190153
• Diks C; Panchenko V; van Dijk DV, 2011, 'Likelihood-based scoring rules for comparing density forecasts in tails', Journal of Econometrics, 163, pp. 215 - 230, http://dx.doi.org/10.1016/j.jeconom.2011.04.001

Yaroslav Rosokha

Yaroslav Rosokha is an Associate Professor of Economics and a member of the Vernon Smith Experimental Economics Laboratory at the Krannert School of Management at Purdue University. His recent work considers behavioral and environmental factors that influence cooperation in social dilemmas. In his research, Dr. Rosokha relies on the methodology of experimental economics in combination with computational modeling and game theory.

Selected publications:

• Hudja, S., Roberson, B., and Rosokha, Y. (2025). "Public leaderboard feedback in sampling competition: an experimental investigation". Review of Economics and Statistics, 107(2), 555-569
• Rosokha, Y., and Wei, C. (2024). "Cooperation in queueing systems". Management Science, 70(11), 7597-7616.
• Gill, D., and Rosokha, Y. (2024). "Beliefs, learning, and personality in the indefinitely repeated prisoner’s dilemma". American Economic Journal: Microeconomics, 16(3), 259-283.
• Romero, J., and Rosokha, Y. (2023). "Mixed Strategies in the Indefinitely Repeated Prisoner's Dilemma". Econometrica, 91(6), 2295-2331.

The 4th Summer School of Computational and Experimental Economics

Course lectures will be available soon. The following information describes the lectures from the 2024 edition.

Herbert Dawid

Introduction to Agent-based Modelling

This lecture gives a general motivation and introduction to agent-based modeling in Economics. It briefly discusses the methodological and conceptual foundations of this approach, key aspects of designing and analyzing agent-based economic models and illustrates the approach with well-known examples from the literature.

Literature:

• Dawid, H. (2015), “Modeling the Economy as a Complex System”, in B. Alves Furtado, P.A.M. Sakowski, M.H. Tovolli (Eds.), "Modeling Complex Systems for Public Policies", IPEA, Brasilia, pp. 191-216.
• Delli Gatti,D., Fagiolo, G., Gallegati, M., Richiardi, M. and Russo A. (Eds.) (2018), Agent-Based Models in Economics: A Toolkit. Cambridge and New York: Cambridge University Press.
• Farmer, D. J., and Foley, D. (2009). “The Economy needs Agent-based Modelling”, Nature 460, 685-686.
• Miller, J.H. and S. E. Page (2007), Complex Adaptive Systems: An Introduction to Computational Models of Social Life, Princeton University Press.
• Nelson, R. and Winter, S.G. (1982), An Evolutionary Theory of Economic Change, Harvard University Press, Cambridge, Mass.
• Schelling, T. (1969), “Models of Segregation”, American Economic Review, 59, 488-493.
• Wilensky, U. and W. Rand (2015), “An Introduction to Agent-Based Modelling: Modelling Natural Social, and Engineered Complex Systems with NetLogo”, MIT Press.

Agent-based Macroeconomics

This lecture provides an overview over well established agent-based macroeconomic models with particular focus on the foundations of the behavioral rules incorporated in these models. Also issues of empirical grounding and calibration of these models is briefly discussed.

Literature:

• Main Text
  • Dawid, H. and Delli Gatti, D. (2018), “Agent-based Macroeconomics, in Hommes C, LeBaron B (Eds.): Handbook of Computational Economics, Vol. 4 - Heterogeneous Agent Models, Amsterdam: Elsevier, p. 63-156.
• Background:
  • Assenza, T., Delli Gatti, D. & Grazzini, J. (2015), `Emergent dynamics of a  macroeconomic agent based model with capital and credit', Journal of Economic Dynamics and Control, 5-28.
  • Dawid, H. and P. Harting (2011): “Capturing Firm Behavior in Agent-Based Models of Industry Evolution and Macroeconomic Dynamics," in Applied Evolutionary Economics, Behavior and Organizations, ed. by G. Buenstorf, Edward-Elgar, 103-130.
  • Dawid, H., Harting, P., van der Hoog, S. & Neugart, M. (2019), A heterogeneous agent macroeconomic model for policy evaluation: Improving transparency and reproducibility, Journal of Evolutionary Economics, 29, 467-538.
  • Delli Gatti, D., Gaffeo, E., Gallegati, M., Giulioni, G., Palestrini, A. (2008). Emergent Macroeconomics: An Agent-Based Approach to Business Fluctuations. Springer: Berlin.
  • Delli Gatti D. and J. Grazzini (2020), “Rising to the Challenge: Bayesian Estimation and Forecasting Techniques for Macroeconomic Agent-Based Models“, Journal of Economic Behavior and Organization, 178, 875-902.
  • Dosi, G., Fagiolo, G. & Roventini, A. (2010), `Schumpeter meeting Keynes: a policy-friendly model of endogenous growth and business cycles', Journal of Economic Dynamics and Control 34, 1748-1767.
  • Sinitskaya, E. and L. Tesfatsion (2015): “Macroeconomies as constructively rational games," Journal of Economic Dynamics and Control, 61, 152-182.

Mikhail Anufriev

Title: Testing the Adaptive Belief Scheme

The lecture discusses how experiments have been used to test key assumptions of the adaptive belief scheme proposed by Brock and Hommes. It provides an overview of several recent studies that feature model horse races based on experimental data and highlights the challenges that arise in this research.

Literature

• Brock, W.A. and Hommes, C.H. (1997). A Rational Route to Randomness. Econometrica, 65, 1059-1095.
• Brock, W.A., and Hommes, C.H. (1998). Heterogeneous beliefs and routes to chaos in a simple asset pricing model. Journal of Economic Dynamics and Control, 22(8-9), 1235-1274.
• Anufriev, M., and Hommes, C. (2012). Evolutionary selection of individual expectations and aggregate outcomes in asset pricing experiments. American Economic Journal: Microeconomics, 4(4), 35-64.
• Anufriev, M., Duffy, J., and Panchenko, V. (2022). Learning in two-dimensional beauty contest games: Theory and experimental evidence. Journal of Economic Theory, 201, 105417.
• Anufriev, M., Chernulich, A., and Tuinstra, J. (2018). A laboratory experiment on the heuristic switching model. Journal of Economic Dynamics & Control, 91, 21-42.
• Chernulich, A. (2021). Modelling reference dependence for repeated choices: A horse race between models of normalisation. Journal of Economic Psychology, 87.

Rosemarie Nagel

Methodology

This lecture introduces the methods of experimental economics with especial attention to computational economics. We will discuss what is an economic experiment (field vs lab experiment), the different areas in experimental economics and behavioral economics, the link between experimental economics, theory and computational work, important design issues. All this will be discussed with the classical Keynesian Beauty Contest game. This introduction is meant to give a quick introduction to those who have never followed an experimental economic course. In class, students will participate in a series of experiments to motivate experientially the lecture content.

General literature:

• Handbook of Experimental Economics, Vol1 (1996), Vol2 (2016), editors J. Kagel &A. Roth
• Camerer, C. (2003), "Behavioral Game Theory," Princeton University Pre2016ss
• Friedman, D. and Sunder, S. (1994), Experimental Methods. Cambridge Univ. Press: Chapters 1-2: 1-20..
• Smith, V.L. (2002), "Method in Experiment: Rhetoric and Reality." Experimental Economics 5(2): 91-110.

Papers related to the Beauty Contest game and level-k:

• Fudenberg, Drew, and Annie Liang. 2019. "Predicting and Understanding Initial Play." American Economic Review, 109 (12): 4112-41.
• Mauersberger, F. and Nagel, R. (2018). Levels of Reasoning in Keynesian Beauty Contests: A Generative Framework in the Handbook of Computational Economics, Volume 4, Heterogeneous Agents. Editors: Cars Hommes and Blake LeBaron. Amsterdam: North-Holland.
• Vincent P. Crawford, Miguel A. Costa-Gomes, and Nagore Iriberri (2012) "Structural Models of Nonequilibrium Strategic Thinking: Theory, Evidence, and Applications," Journal of Economic Literature, 51(1):5–62, 2013.
• Antoni Bosch-Domènech , Jose García-Montalvo, Rosemarie Nagel, and Albert Satorra, "One, Two, (Three), Infinity...: Newspaper and Lab Beauty-Contest Experiments", American Economic Review December 92 (5), 2002, pp 1687-1701. 

Valentyn Panchenko

Title: Individual Evolutionary Learning

In this lecture, we will introduce Individual Evolutionary Learning (IEL), a portable algorithm for learning inspired by genetic algorithms and tailored for economic decision-making. We will explore its implementation details and key modeling choices. Additionally, we will demonstrate that IEL algorithms can generate simulated data consistent with human behavior in diverse experimental settings. These results strongly endorse the use of the IEL model as a testbed for counterfactual analysis and the design of new experimental treatments.

Literature:

• Arifovic, J.; Ledyard, J., 2012, ‘Individual evolutionary learning, other-regarding preferences, and the voluntary contributions mechanism’, Journal of Public Economics, 96(9-10), pp.808-823, https://doi.org/10.1016/j.jpubeco.2012.05.013
• Anufriev M; Arifovic J; Ledyard J; Panchenko V, 2022, 'The role of information in a continuous double auction: An experiment and learning model: The Role of Information in CDA', Journal of Economic Dynamics and Control, 141, pp. 104387 - 104387, http://dx.doi.org/10.1016/j.jedc.2022.104387
• Anufriev M; Arifovic J; Ledyard J; Panchenko V, 2013, 'Efficiency of continuous double auctions under individual evolutionary learning with full or limited information', Journal of Evolutionary Economics, 23, pp. 539 - 573, http://dx.doi.org/10.1007/s00191-011-0230-8
• Arifovic, J.; Boitnott, J.F.; Duffy, J., 2019, ‘Learning correlated equilibria: An evolutionary approach’, Journal of Economic Behavior & Organization, 157, pp.171-190. https://doi.org/10.1016/j.jebo.2016.09.011

Yaroslav Rosokha

Title: "Reinforcement Learning"

Reinforcement learning has been used as a model of behavior in psychology, economics, and computer science. The central maxim of reinforcement learning is the “Law of Effect” (Thorndike 1898). It states that the likelihood of implementing an action will increase with the success of that action. In economics, simple action-reinforcement models have been applied to explaining human behavior in individual tasks, strategic games, and markets since the early 90s. More recently, there has been a revival of interest in reinforcement learning. In particular, the focus has been on incorporating a richer class of models from the computer science literature known as Q-learning.

Literature:

• Sutton and Barto. Reinforcement Learning. Chapter 6: Temporal-Difference Learning
• Erev, Ido, and Alvin E. Roth. "Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria." American economic review (1998): 848-881
• Calvano, Emilio, Giacomo Calzolari, Vincenzo Denicolo, and Sergio Pastorello. "Artificial intelligence, algorithmic pricing, and collusion." American Economic Review 110, no. 10 (2020): 3267-3297

The 4th Summer School of Computational and Experimental Economics

To participate in our Summer School, please do the following steps.

Application - Deadline: March 6, 2026

1. Send to [email protected] one attached file: File should be your Curriculum Vitae in a email with the following subject: COMP26_CV_firstname_last_name
2. Fill the following application form. You will see a confirmation message after submitting the form. Before, remember to send your curriculum to [email protected].
3. Letter of Recommendation:

Please ask the person who gives you the letter of recommendation to send it as an attached PDF document to the address [email protected], with the following subject: COMP26_LETTER_firstname_lastname

Please contact us at [email protected] for another way for sending us the letter, such as ordinary mail or fax, but we strongly encourage to use the e-mail and PDF option to speed up the process.

Registration

Students who have been accepted for the school will be informed by March 16th and have three options: 

1. Students who still plan to participate must register by April, 6th, 2026. Together with the registration, a registration fee of 300 Euros needs to be paid (see below). 
2. Students who need accommodation must make a reservation at the hostel to be announced in the acceptance letter until April, 13th, 2026.

Students who no longer plan to participate should inform us as soon as possible, in any case before April, 6th, 2026.