发表机构
Open University of Cyprus（塞浦路斯开放大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究自适应多智能体系统中跨政策体系的迁移学习问题，比较白板学习者和迁移学习者，通过实验表明先前监管经验在政策关系稳定时可复用，变化时需谨慎对待，在方法上给出相应策略。
AI 中文摘要
政策模型通常假定政策工具与其结果之间的关系在不同制度条件下保持稳定。但在自适应社会技术系统中这一假设可能不成立，监管变化会改变激励机制，智能体可进行策略性响应，政策变量到总体结果的映射也会改变。本文将这种体系变化作为自适应多智能体系统中的迁移学习问题研究。把政策体系表示为由可观测输入分布和将政策变量映射到结果的目标函数所引发的学习问题。比较了在新体系中搜索灵活假设类别的白板学习者和有效假设类别受前一体系结构知识限制的迁移学习者。通过程式化排放监管实验环境和动态ABM稳健性实验表明，当限制保留新目标函数并降低有效复杂度时迁移有益，反之则有害。贡献在于方法层面：当先前监管经验捕捉到稳定结构不变量时应复用，而政策变化改变政策-结果关系时要谨慎对待。
英文摘要
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. A policy regime is represented as a learning problem induced by an observable input distribution and a target function mapping policy variables to outcomes. We compare a blank-slate learner that searches a flexible hypothesis class in the new regime with a transfer learner whose effective hypothesis class is restricted by structural knowledge from the previous regime. Transfer is beneficial when this restriction preserves the new target function while reducing effective complexity; it is harmful when the restriction excludes the new target and creates misspecification. A stylized emissions-regulation experimental environment and a dynamic ABM robustness experiment support the claim. When the target regime preserves an affine monotone tax-emissions relation, transfer improves empirical small-sample performance. When the target regime introduces a threshold break, the same transferred structure produces negative transfer: held-out error remains high, online prediction generates more mistakes, and repeated online streams show larger cumulative and final-window error under misspecification. The contribution is methodological: previous regulatory experience should be reused when it captures stable structural invariants, but treated cautiously when policy change alters the policy-outcome relationship.
Comments17 pages, 3 figures, 8 tables. Simulation-based methodological study of positive and negative transfer across adaptive policy regimes
