9 citations · 16 across the 5 of their papers we have counts for
6 papers
JaxMARL-BC: Multi-Agent Reinforcement Learning for Macroeconomic Modelling, in Seconds
Daniele Montagnani, Andrea Coletta, Antonietta Mira +1
Multi-agent reinforcement learning (MARL) offers a principled way of populating simulated economies with many heterogeneous decision makers. Its adoption, pioneered in finance, has…
Robust Causal Discovery in Real-World Time Series with Power-Laws
Matteo Tusoni, Giuseppe Masi, Andrea Coletta +3
Exploring causal relationships in stochastic time series is a challenging yet crucial task with a vast range of applications, including finance, economics, neuroscience, and climat…
BeforeIT.jl: High-Performance Agent-Based Macroeconomics Made Easy
Aldo Glielmo, Mitja Devetak, Steven Hoekstra +5
BeforeIT is an open-source software for building and simulating state-of-the-art macroeconomic agent-based models (macro ABMs) based on the recently introduced macro ABM developed…
Chat Bankman-Fried: an Exploration of LLM Alignment in Finance
Claudia Biancotti, Carolina Camassa, Andrea Coletta +2
Advancements in large language models (LLMs) have renewed concerns about AI alignment - the consistency between human and AI goals and values. As various jurisdictions enact legisl…
Simulating the Economic Impact of Rationality through Reinforcement Learning and Agent-Based Modelling
Simone Brusatin, Tommaso Padoan, Andrea Coletta +2
Agent-based models (ABMs) are simulation models used in economics to overcome some of the limitations of traditional frameworks based on general equilibrium assumptions. However, a…
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMs
Aldo Glielmo, Marco Favorito, Debmallya Chanda +1
Calibrating agent-based models (ABMs) in economics and finance typically involves a derivative-free search in a very large parameter space. In this work, we benchmark a number of s…