Transfer Learning Across Policy Regimes in Adaptive Multi-Agent Systems
arXiv:2607.09685v1 Announce Type: cross Abstract: Policy models often assume that the relationship between a policy instrument and its outcome remains stable across institutional conditions. In adaptive socio-technical systems this assumption may fail: regulatory change can alter incentives, agents can respond strategically, and the mapping from policy variables to aggregate outcomes can change. This paper studies such regime change as a transfer-learning problem in adaptive multi-agent systems.
Record details
Published: 14 July 2026
Source: arXiv cs.CY
Category: Research
Topics: Regulation · Agents & autonomy
Retrieved: 14 July 2026
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ethics.ai (14 July 2026), “Transfer Learning Across Policy Regimes in Adaptive Multi-Agent Systems,” evidence record 1548, https://ethics.ai/record/1548 (originally published by arXiv cs.CY).
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