activity
20242026
collaborators

6 papers

q-fin.TR2026

Machine Spirits: Speculation and Adaptation of LLM Agents in Asset Markets

Maxime Saxena, Marco Pangallo, Cars Hommes +2

As Large Language Models (LLMs) become increasingly integrated into financial systems, understanding their behavioural properties is crucial. Do LLMs conform to the rational expect…

cs.LG2026

Comparing Data Assimilation and Likelihood-Based Inference on Latent State Estimation in Agent-Based Models

Blas Kolic, Corrado Monti, Gianmarco De Francisci Morales +1

In this paper, we present the first systematic comparison of Data Assimilation (DA) and Likelihood-Based Inference (LBI) in the context of an Agent-Based Model (ABM). These models…

cs.AI2025

Learning Individual Behavior in Agent-Based Models with Graph Diffusion Networks

Francesco Cozzi, Marco Pangallo, Alan Perotti +2

Agent-Based Models (ABMs) are powerful tools for studying emergent properties in complex systems. In ABMs, agent behaviors are governed by local interactions and stochastic rules.…

cs.MA2025

Statistical Model Checking of NetLogo Models

Marco Pangallo, Daniele Giachini, Andrea Vandin

Agent-based models (ABMs) are gaining increasing traction in several domains, due to their ability to represent complex systems that are not easily expressible with classical mathe…

econ.GN2025

Can Generative AI agents behave like humans? Evidence from laboratory market experiments

R. Maria del Rio-Chanona, Marco Pangallo, Cars Hommes

We explore the potential of Large Language Models (LLMs) to replicate human behavior in economic market experiments. Compared to previous studies, we focus on dynamic feedback betw…

econ.GN2024

Data-Driven Economic Agent-Based Models

Marco Pangallo, R. Maria del Rio-Chanona

Economic agent-based models (ABMs) are becoming more and more data-driven, establishing themselves as increasingly valuable tools for economic research and policymaking. We propose…