Transfer Learning Across Policy Regimes in Adaptive Multi-Agent Systems

Jul 14, 2026

A new preprint examines how transfer learning can be applied to adaptive multi-agent systems facing policy regime changes. The authors compare blank-slate learners with transfer learners that reuse structural knowledge from previous regimes, using an emissions-regulation simulation. Results show that transfer learning improves performance when the policy-outcome relationship remains stable, but can lead to negative transfer when a regime change introduces a threshold break. The paper provides a methodological framework for determining when regulatory experience should be reused or discarded.

Why it matters: This work offers a formal approach to understanding the risks and benefits of transfer learning in policy modeling for adaptive socio-technical systems, informing the design of AI-driven regulatory tools.

Full story at: arXiv Computers and Society ↗