5 citations · 7 across the 2 of their papers we have counts for
3 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…
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…