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 search methods in the calibration of a well-known macroeconomic ABM on real data, and further assess the performance of "mixed strategies" made by combining different methods. We find that methods based on random-forest surrogates are particularly efficient, and that combining search methods generally increases performance since the biases of any single method are mitigated. Moving from these observations, we propose a reinforcement learning (RL) scheme to automatically select and combine search methods on-the-fly during a calibration run. The RL agent keeps exploiting a specific method only as long as this keeps performing well, but explores new strategies when the specific method reaches a performance plateau. The resulting RL search scheme outperforms any other method or method combination tested, and does not rely on any prior information or trial and error procedure.
- Pub Date:
- February 2023
- Computer Science - Machine Learning;
- Computer Science - Artificial Intelligence;
- Computer Science - Multiagent Systems;
- Economics - General Economics
- 7 pages and 4 figures + appendix, presented at the AAAI bridge program 'AI for Financial Institutions' (https://aaai23.bankit.art/) and at ICLR bridge program 'AI4ABM' (https://ai4abm.org/workshop_iclr2023/)