不变因果路由：用于在线市场经济中规范治理
Invariant Causal Routing for Governing Social Norms in Online Market Economies
AI总结：
本文提出不变因果路由（ICR）框架，通过因果推理和不变因果发现，实现在线市场经济中稳定社会规范的治理，提升政策的可解释性和有效性。
摘要：
社会规范是通过代理之间反复互动在经济系统中内生性产生的稳定行为模式。在在线市场经济中，此类规范——如公平曝光、持续参与和平衡再投资——对于长期稳定性至关重要。我们旨在理解驱动这些新兴规范的因果机制，并设计能够将它们引导至期望结果的原理性干预措施。这具有挑战性，因为规范源于无数微观层面的互动，这些互动汇总为宏观层面的规律性，使因果归因和政策可转移性变得困难。为此，我们提出了不变因果路由（ICR），一种因果治理框架，该框架识别出在异质环境中稳定的政策-规范关系。ICR将反事实推理与不变因果发现相结合，以区分真正的因果效应与虚假相关性，并构建可解释、可审计的政策规则，这些规则在分布变化下仍保持有效。在由真实数据校准的异质代理模拟中，ICR比相关性或覆盖性基线产生了更稳定的规范、更小的泛化差距和更简洁的规则，证明了因果不变性为治理提供了一个原理性且可解释的基础。
英文摘要：
Social norms are stable behavioral patterns that emerge endogenously within economic systems through repeated interactions among agents. In online market economies, such norms -- like fair exposure, sustained participation, and balanced reinvestment -- are critical for long-term stability. We aim to understand the causal mechanisms driving these emergent norms and to design principled interventions that can steer them toward desired outcomes. This is challenging because norms arise from countless micro-level interactions that aggregate into macro-level regularities, making causal attribution and policy transferability difficult. To address this, we propose \textbf{Invariant Causal Routing (ICR)}, a causal governance framework that identifies policy-norm relations stable across heterogeneous environments. ICR integrates counterfactual reasoning with invariant causal discovery to separate genuine causal effects from spurious correlations and to construct interpretable, auditable policy rules that remain effective under distribution shift. In heterogeneous agent simulations calibrated with real data, ICR yields more stable norms, smaller generalization gaps, and more concise rules than correlation or coverage baselines, demonstrating that causal invariance offers a principled and interpretable foundation for governance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
