Agent-Based Models (ABMs) are gaining great popularity in economics and social science because of their strong flexibility to describe the realistic and heterogeneous decisions and interaction rules between individual agents. In this work, we investigate for the first time the practicality of test-time training (TTT) of deep models such as normalizing flows, in the parameters posterior estimations of ABMs. We propose several practical TTT strategies for fine-tuning the normalizing flow against distribution shifts. Our numerical study demonstrates that TTT schemes are remarkably effective, enabling real-time adjustment of flow-based inference for ABM parameters.

翻译：智能体模型因其在描述个体智能体间现实且异质化的决策与交互规则方面具有强大灵活性，在经济学与社会科学领域日益受到广泛关注。本研究首次探讨了深度模型（如归一化流）的测试时训练在智能体模型参数后验估计中的实际应用性。我们提出了多种实用的测试时训练策略，用于针对分布偏移微调归一化流。数值研究表明，测试时训练方案具有显著有效性，能够实现对智能体模型参数的基于流的推断进行实时调整。