The 6th edition of the EuADS summer school will be dedicated to

Data Science and AI meet Economics

and will take place in the city of Luxembourg from 5 to 8 July 2026.

Introduction

Data Science and Artificial Intelligence (AI) have become central to modern Economics as the discipline has shifted from theory to data and computation. Today, economists work with massive, complex datasets and study increasingly nonlinear, strategic, and dynamic systems —- areas in which Data Science, AI and Machine Learning provide powerful tools for efficiently processing high-dimensional, unstructured, and large-scale data. Data Science and AI methods improve causal inference, prediction, and forecasting. They also inspire new approaches to modeling dynamic optimisation and strategic behaviour.

Conversely, Economics contributes foundational ideas that shape how AI systems are designed, evaluated, and deployed. Indeed, some of the most significant concepts in contemporary AI originate from economic theory, which provides the basis for decision-making (statistical decision theory), strategic reasoning (game theory), incentive design (mechanism design), causal inference (trustworthy AI), welfare analysis (AI ethics), and behavioural modeling (human-centered AI).

Structure of the summer school

A series of five tutorials delivered by leading experts will address the synergies and mutual benefits between Data Science & AI and Economics.

Each tutorial will cover half a day, consisting of a theoretical introduction followed by a practical part.

Schedule at a glance (last update 06.04.2026)

The tutorials are aimed at PhD students, postdoctoral researchers, and early-career researchers with a basic knowledge in Data Science or AI. Knowledge of Economics is advantageous but not required as each speaker will briefly introduce the necessary economical topics.

An interest in interdisciplinary research and applications of data science is highly recommended. The summer school encourages active networking between participants and speakers.

Speakers

The following speakers have already confirmed. You will find their abstracts in the next section.

Andreas Haupt, Stanford University (USA)

Tutorial: Economics in Machine Learning from Human Preferences

Despoina Makariou, University of St.Gallen (Switzerland)

Tutorial: Machine Learning and Data Science for Risk and Insurance

Larysa Zomchak, National University of Lviv (Ukraine)

Tutorial: Complexity, Chaos, and Machine Learning in Economics: Tools for Understanding Unpredictable Systems

Massimiliano Marcellino, Bocconi University (Italy)

Tutorial: Machine Learning Based Macro-Econometrics

Abstracts

Abstracts of the tutorials for the summer school are listed below.

* Economics in Machine Learning from Human Preferences (Andreas Haupt)

The tutorial will highlight ideas from the behavioral sciences in Large Language Model training. Pre-training uses proper scoring rules to elicit calibration, which disappears in mid- and post-training due to reinforcement learning. Supervised finetuning matches moments. Reinforcement learning from human feedback uses discrete choice methods, and aggregates preferences according to the Borda count. Participants will leave with an overview of current training methodology, and a list of open problems for future work for Economists and Data Scientists in the training of human-centered language models.

* Machine Learning and Data Science for Risk and Insurance (Despoina Makariou)

This tutorial introduces how machine-learning and data-science methods can be applied to central problems in risk and insurance, which play an important role in economic and financial decision-making. The theoretical part will review core modelling concepts such as claims frequency and severity modelling, predictive approaches, and model evaluation, highlighting how these tools extend classical actuarial and econometric techniques. The tutorial will also cover key practical considerations, including data preparation, interpretability, and the challenges typical of insurance datasets. The hands-on component will demonstrate simple, reproducible workflows using public or simulated data to illustrate how machine-learning methods can be applied in insurance and risk-management contexts.

* Complexity, Chaos, and Machine Learning in Economics: Tools for Understanding Unpredictable Systems (Larysa Zomchak)

Economic systems are complex, nonlinear, and often chaotic, yet mainstream econometric practice has long relied on linear models that struggle to capture this reality. This tutorial revisits the foundations of econometric modelling through the lens of chaos theory, and explores how modern Data Science methods can extend, rather than replace, the econometric tradition.

The first part of the tutorial introduces chaos theory as an economic modelling framework: deterministic chaos, nonlinear dynamics, sensitivity to initial conditions, and strange attractors. Drawing on examples from business cycles, financial markets, and macroeconomic time series, participants will develop an intuition for why economic data so frequently defies classical model assumptions, and what chaos theory offers as an alternative perspective.

The second part focuses on non-linear econometric methods: threshold models, regime-switching models, GARCH-family volatility models, and cointegration in nonlinear settings. These tools are examined both theoretically and through applied examples, including GDP forecasting, inflation dynamics, and banking sector modelling. Special attention is given to the practical challenges of working with real economic data — structural breaks, mixed-frequency observations, and limited sample sizes.

The third part reflects on the role of Machine Learning (ML) as a complement to econometrics in chaotic economic environments: where ML adds value, where it falls short, and how hybrid approaches combining econometric rigour with ML flexibility are emerging at the research frontier.

This tutorial will blend theory, applied examples from European and transition economies, and open discussion.

* Machine Learning Based Macro-Econometrics (Massimiliano Marcellino)

A variety of Machine Learning methods have been recently imported into Macro-econometrics to allow for the use of both very large data sets and more flexible functional forms. We will review some of the resulting classical and Bayesian Machine Learning based macro-econometric methods, focusing on those that have been shown to produce good forecasts for economic and financial variables, with respect to standard macro-econometric models. We will use various empirical applications to illustrate the methods.