My current research agenda is centred on economic dynamics: how agents, firms, institutions, and economies learn, accumulate, adapt, stabilise, fail to stabilise, and move between regimes. This perspective brings together my work on growth theory, learning in games, macroeconomic dynamics, evolutionary processes, firm growth, and forecasting. The common thread is an interest in how economic systems change over time, and in the theoretical tools needed to study such change.

Growth Theory, Automation, and the Future of Labour

A central part of my current work studies the long-run implications of artificial intelligence and automation for economic growth and the structural role of labour. I am interested in how AI may alter the place of labour in production, how accumulation may become less dependent on labour income, and how economies move between labour-dependent and increasingly automated regimes. This connects naturally with broader questions about growth across different adjustment timescales, economic history, institutions, and structural transformation.

Learning Models and Lyapunov Economies

My work on learning and stability studies how economic systems evolve under different learning protocols and behavioural revision rules. The aim is to understand when such processes lead to equilibrium, when they generate persistent instability, and when they produce more complex long-run behaviour. This includes the use of Lyapunov stability theory and related tools from dynamical systems to analyse the structure of economic adjustment.

Multi-dimensional Social Learning

I also study social learning processes in which agents are described by multiple traits rather than by a single strategy. Standard imitative dynamics often focus on the spread of individual behaviours, but social and professional success typically depends on bundles of abilities, characteristics, and actions. This work develops models of how such multi-dimensional traits evolve under imitation, selection, and social comparison.

High-dimensional Fitness Landscapes

Related work investigates evolutionary dynamics in high-dimensional fitness landscapes. The central question is how ruggedness, dimensionality, and local adaptation affect the ability of evolutionary processes to reach high-fitness configurations. More broadly, this research studies how path dependence and local selection constrain long-run evolutionary outcomes.

Evolution of Firm Size and Growth

My research on firm growth studies how size-related frictions, heterogeneous entry times, and stochastic growth processes shape firm size distributions and aggregate outcomes. The broader aim is to understand how micro-level growth mechanisms generate persistent firm heterogeneity, skewed size distributions, and regularities in the distribution of economic activity.

Forecasting with Imperfect Models and Macroeconomic Learning

I am revisiting an older project on forecasting in macroeconomic environments. The focus is not only on expectation formation, but on how agents make predictions when no available model is fully correct. The project studies how agents validate imperfect models online, learn from prediction errors, and adapt their forecasting rules over time. This connects macroeconomic learning with model selection, no-regret prediction, and the stability of aggregate dynamics under persistent model misspecification.

Taken together, these projects form a research programme in economic dynamics. They use tools from economic theory, game theory, macroeconomics, evolutionary modelling, and dynamical systems to study how economic systems learn, grow, adapt, and move between regimes.

I am willing to supervise students on any of the aforementione topics as well as other topics related to economic dynamics, broadly defined.