Simulating the economic impact of rationality through reinforcement learning and agent-based modelling

arXiv:2405.02161v1 Announce Type: new
Abstract: 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, agents within an ABM follow predetermined, not fully rational, behavioural rules which can be cumbersome to design and difficult to justify. Here we leverage multi-agent reinforcement learning (RL) to expand the capabilities of ABMs with the introduction of fully rational agents that learn their policy by interacting with the environment and …

abstract agent agents arxiv assumptions cs.ai cs.ce cs.lg cs.ma design econ.gn economic economic impact economics equilibrium frameworks general however impact limitations modelling q-fin.ec reinforcement reinforcement learning rules simulation through type