Agent-based simulators provide granular representations of complex intelligent systems by directly modelling the interactions of the system's constituent agents. Their high-fidelity nature enables hyper-local policy evaluation and testing of what-if scenarios, but is associated with large computational costs that inhibits their widespread use. Surrogate models can address these computational limitations, but they must behave consistently with the agent-based model under policy interventions of interest. In this paper, we capitalise on recent developments on causal abstractions to develop a framework for learning interventionally consistent surrogate models for agent-based simulators. Our proposed approach facilitates rapid experimentation with policy interventions in complex systems, while inducing surrogates to behave consistently with high probability with respect to the agent-based simulator across interventions of interest. We demonstrate with empirical studies that observationally trained surrogates can misjudge the effect of interventions and misguide policymakers towards suboptimal policies, while surrogates trained for interventional consistency with our proposed method closely mimic the behaviour of an agent-based model under interventions of interest.

翻译：基于智能体的模拟器通过直接建模系统中各智能体的交互，提供了对复杂智能系统的精细化表示。其高保真特性支持超局部政策评估及假设情景测试，但高昂的计算成本限制了其广泛应用。代理模型可缓解这些计算限制，但必须在相关政策干预下与基于智能体的模型保持行为一致性。本文借助因果抽象领域的最新进展，构建了针对基于智能体的模拟器的干预一致性代理模型学习框架。所提方法可在复杂系统中快速开展政策干预实验，同时确保代理模型在相关干预场景下以高概率与基于智能体的模拟器保持行为一致。实证研究表明，基于观测数据训练的代理模型可能错误评估干预效果，并引导政策制定者选择次优政策；而采用本文方法进行干预一致性训练的代理模型，在相关干预条件下能精准模拟基于智能体的模型行为。