The 2nd 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 14-15, 2023).

The summer school will be held from June 11 to June 17 at Beslab of Universitat Pompeu Fabra, Spain.

• Goal: The goal of the summer school is to develop a foundation for using computational models and simulations 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:

• Two 90-minute lecture by the faculty
• 90 minutes programming lecture
• 90-minute hands-on group sessions in which students work to implement the computational model or participate in the experiment

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 31, 2023.

Organizers

• Herbert Dawid (Bielefeld University)
• John Ledyard (Caltech)
• Rosemarie Nagel (ICREA-UPF, and BSE)
• Yaroslav Rosokha  (Purdue University)

Guest lecturer

• Mikhail Anufriev (University of Technology Sydney)
• Cars Hommes (University of Amsterdam and Bank of Canada)
• Annie Liang (Northwestern. University)
• Valentyn Panchenko (University of New South Wales)

The 2nd Summer School of Computational and Experimental Economics

• From June 11th 2023 to June 17th 2023
• Organizers: Rosemarie Nagel, John Ledyard, Herbert Dawid, Yaroslav Rosokha
• Guest Lectures: Mikhail Anufriev, Cars Hommes, Annie Liang, Valentyn Panchenko

The 2nd Summer School of Computational and Experimental Economics

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:

• Dawid H. and G. Muehlheusser (2022), "Smart Products: Liability, Timing of Market Introduction, and Investments in Product Safety, Journal of Economic Dynamics and Control, 134, 104288.
• 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. 

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:

• 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.

John O. Ledyard

John O. Ledyard is the Emeritus Professor of Economics in the Division of the Humanities and Social at the California Institute of Technology. His primary research is on the theoretical foundations and applications of mechanism design.  Most recently he is working on connecting experimental findings for repeated games with learning models.

Selected Publications:

• “A Behavioral Model for Mechanism Design: Individual Evolutionary Learning” with Jasmina Arifovic, Journal of Economic Behavior & Organization, Volume 78, Issue 3, May 2011, Pages 374-395.  http://dx.doi.org/10.1016/j.jebo.2011.01.021
• “Individual Evolutionary Learning, Other-regarding Preferences, and the Voluntary Contributions Mechanism” with Jasmina Arifovic, Journal of Public Economics, 96, 2012, 808–823.  http://dx.doi.org/10.1016/j.jpubeco.2012.05.013 
• “ACE: A Combinatorial Market Mechanism” with Leslie Fine, Jacob K. Goeree, and Takashi Ishikida, in Handbook of Spectrum Auction Design, eds. Martin Bichler and Jacob Goeree, 2017
• "Learning to Alternate", with Jasmina Arifovic, Experimental Economics, 21(3), 692-721, 2018.
• “Individual Evolutionary Learning and Zero-Intelligence in the Continuous Double Auction”, with Jasmina Arifovic, Anil Donmez, and Megan Tjandrasuwita, to appear in Handbook of Experimental Finance.

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:

• Rosokha and Younge (2020). "Motivating Innovation: The Effect of Loss Aversion on the Willingness to Persist", Review of Economics and Statistics, 102(3), pp. 569-582
• Romero and Rosokha (2019). "The Evolution of Cooperation: The Role of Costly Strategy Adjustments" American Economic Journal: Microeconomics, 11(1), pp. 299-328
• Romero and Rosokha (2018) "Constructing Strategies in the Indefinitely Repeated Prisoner's Dilemma" European Economic Review, 104, pp. 185-219.

Cars Hommes

Cars Hommes holds a Ph-D in Economics since 1991 from the University of Groningen and is professor of Economic Dynamics at the University of Amsterdam and Senior Research Advisor at the Bank of Canada. His research interests include complex economic systems, behavioral and experimental macro-finance, agent-based models and their applications to policy analysis.

Selected publications:

• Poledna, S., Miess, M.G., Hommes, C.H. and Rabitsch, K. (2023), Economic forecasting with an agent-based model, European Economic Review 151, 104306.
• Hommes, C.H. (2021), Behavioral & experimental macroeconomics and policy analysis: a complex systems approach, Journal of Economic Literature 59(1), 149-219, March 2021.
• Assenza, T., Heemeijer, P., Hommes, C.H. & Massaro, D. (2021). Managing self-organization of expectations through monetary policy: a macro experiment, Journal of Monetary Economics 117, 170-186.
• Battiston, S., et al. (2016), Complexity theory and financial regulation. Economic policy needs interdisciplinary network analysis and behavioral modeling, Science 351, 6275, 818-819.

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.

Annie Liang

Annie Liang is an assistant professor of economics and of computer science (by courtesy) at Northwestern University. Her research is in economic theory—in particular, learning and information—and the application of machine learning methods for model building and evaluation. Prior to joining Northwestern, she was an assistant professor of economics at the University of Pennsylvania and a post-doctoral researcher at Microsoft Research. Annie is the recipient of an NSF CAREER award.

Selected Publications:

• Measuring the Completeness of Economic Models (with Drew Fudenberg, Jon Kleinberg, and Sendhil Mullainathan), Journal of Political Economy, Vol. 130(4), 2022
• Dynamically Aggregating Diverse Information (with Xiaosheng Mu and Vasilis Syrgkanis), Econometrica, Vol. 90 (1), 2022
• Complementary Information and Learning Traps (with Xiaosheng Mu), Quarterly Journal of Economics, Vol. 135 (1), 2020
• Predicting and Understanding Initial Play (with Drew Fudenberg), American Economic Review, Vol. 109 (12), 2019

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

The 2nd Summer School of Computational and Experimental Economics

Herbert Dawid

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

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.

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

• 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. 

Cars Hommes

Title: Overview market experiments.

Abstract: This talk will survey learning-to-forecast experiments and present a behavioral theory of heterogeneous expectations that fits lab evidence.

Literature

Hommes, C.H. (2021), Behavioral & experimental macroeconomics and policy analysis: a complex systems approach, Journal of Economic Literature 59(1), 149-219, March 2021.

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.

John Ledyard

Title: Overview of Public Goods Experiments

The lecture provides an overview of public goods experiments and some agent-based models related to them.  We will cover (i) what is a public good, how much should be provided, and who should pay, (ii) some common institutions for dealing with public goods, and (iii) some designed mechanisms for dealing with public goods.  

Literature main papers:

• Ledyard, Chapter 2 in Handbook of Experimental Economics, 1995\
• Arifovic, Ledyard  2012. "Individual evolutionary learning, other-regarding preferences, and the voluntary contributions mechanism," Journal of Public Economics, Elsevier, vol. 96(9-10), pages 808-823.

Background:

• Arifovic, Ledyard, "A Behavioral Model for Mechanism Design: Individual Evolutionary Learning," Journal of Economic Behavior \& Organization,  2011
• Attiyeh, Franciosi, Isaac, "Experiments with the pivot process for providing public goods" Public Choice, 2000
• Carpenter, Jeffrey, "Punishing free-riders: How group size affects mutual monitoring and the provision of public goods", Games and Economic Behavior, 2007
• Clarke, "Multipart pricing of public goods" Public Choice, 1971
• Chen, Chapter 67 in Handbook of Experimental Economics Results, 2008
• Chen, Plott, "The Groves-Ledyard mechanism: an experimental study of institutional design", Journal of Public Economics, 1996
• Chen, Tang, "Learning and Incentive Compatibile mechanisms for public goods provision: an experimental study" Journal of Political Economy, 1998
• Cherry, T., S. Kroll, J. Shogren, "Voting, Punishment, and Public Goods", Economic Inquiry, 2007
• Green JR, Laffont J-J. Incentives in Public Decision Making. Amsterdam: North-Holland; 1979.
• Groves, "Incentives in Teams" Econometrica,  1973
• Groves, Ledyard, "Optimal allocation of public goods: a solution to the `free rider' problem'", Econometrica 1977
• Isaac-Walker, "Group size effects in public goods provision: the voluntary contribution mechanism," Quarterly Journal of Economics,  1988
• Kagel, Chapter 7   in Handbook of Experimental Economics, 1995
• Vickrey, "Counterspeculation, auctions, and competitive sealed tenders" \\ Journal of Finance, 1961
• Walker, "A simple incentive compatible scheme for attaining Lindahl \\ allocations"  Econometrica 1981

Annie Liang

Title: Machine Learning and Economic Modeling

Machine learning algorithms can be very effective at prediction, but are often opaque and uninformative about underlying economic forces. Can these black box methods help us to build better interpretable models and predictions of behavior? This lecture covers recent work that compares and combines traditional economic modeling with methods from machine learning.

Literature:

• Predicting and Understanding Initial Play (with Drew Fudenberg), American Economic Review, Vol. 109 (12), 2019
• Measuring the Completeness of Economic Models (with Drew Fudenberg, Jon Kleinberg, and Sendhil Mullainathan), Journal of Political Economy, Vol. 130(4), 2022
• How Flexible is that Functional Form? Quantifying the Restrictiveness of Theories (with Drew Fudenberg and Wayne Gao), Working Paper
• The Transfer Performance of Economic Models (with Isaiah Andrews, Drew Fudenberg, Lihua Lei, and Chaofeng Wu), Working Paper

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

The 2nd Summer School of Computational and Experimental Economics

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

Application - Deadline: March 31, 2023

1. Send to [email protected] one attached file: File should be your Curriculum Vitae in a email with the following subject: COMP23_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: COMP23_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 April 28 and have three options: 

1. Students who still plan to participate must register by May, 6th, 2023. Together with the registration, a registration fee of 300 Euros or 330 US-Dollar 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 May, 6th, 2023

Students who no longer plan to participate should inform us as soon as possible, in any case before May, 6th, 2023.

Applications are already closed.