Simulating the economic impact of rationality through reinforcement learning and agent-based modelling
By Simone Brusatin, Tommaso Padoan, Andrea Coletta, Domenico Delli Gatti, Aldo Glielmo
Rating
1350
Battle Count: 79
Relevance
6/10
While not directly applicable to trading, the model provides insights into market dynamics and firm behavior that could inform trading strategies in certain markets.
Implementation Complexity
8/10
Requires implementation of both agent-based models and reinforcement learning algorithms, as well as integration of the two approaches.
Reproducibility
4/5
The paper provides detailed descriptions of the model, parameters, and experimental setup. Code availability is mentioned but not yet provided.
About this paper
Methodology: Rational macro ABM (R-MABM). Problem types: Reinforcement Learning, Economic Simulation, Multi-agent Learning.
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