[论文解读] Invariant Causal Routing for Governing Social Norms in Online Market Economies

Xiangning Yu, Qirui Mi+5 more|arXiv (Cornell University)|Mar 4, 2026

Game Theory and Applications被引用 0

一句话总结

这篇论文提出了不变量因果路由（ICR），一个三阶段框架，利用 PNS 基因因果推断来识别对分布变动不变的策略–规范路径，并将它们整合成一个简洁、可审核的规则路由器，以在在线市场实现稳定的规范。

ABSTRACT

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 extbf{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.

研究动机与目标

• 了解平台政策如何因果影响在线市场中社会规范的出现与稳定。
• 在异构环境中的分布变化下，识别保持不变的策略–规范路径。
• 开发一个可审核、可解释的基于规则的治理机制，并在不同种子和情境中实现泛化。
• 为为什么在不同群体和情境中某些干预成功或失败提供因果解释。

提出的方法

• Stage I 使用必要性与充分性概率（PNS）来检验在给定情境下平台策略是否因果性地使某一群体达到其规范区间。
• Stage II 学习一个最小的一次匹配规则路由器 S*，通过在最大化因果收益的同时确保覆盖初始条件桶的广泛性，将情境映射到干预措施。
• Stage III 通过对比目标策略与基线策略下的杠杆分布来解释干预成功或失败的原因，进行关键因素归因。
• 该框架依赖双世界成对运行来估计 PNS，并采用分桶贪心加修剪的过程来组装一个简洁、不变的路由器。
• 规范实现被操作化为在烧入期后进入并在定义的公差带内持续存在的宏观统计量。

Figure 1 . Social norms in onlineeconomies.

实验结果

研究问题

• RQ1 RQ1: 不同平台目标如何因果影响不同用户群体中社会规范的出现与稳定？
• RQ2 RQ2: 在分布转移下，最短且最稳定的不变量因果路由策略是什么？
• RQ3 RQ3: 哪些机制解释群体或情境间的规范分歧，哪些因素是这些分歧的必要或充分条件？

主要发现

Norm	Baseline	Current	Group	PNS
RI-1	NI	FAI	ALL	0.966 [0.904, 0.993]
RI-1	GMV	FAI	ALL	0.981 [0.899, 1.000]
RI-1	BAL	FAI	ALL	0.962 [0.893, 0.992]
ST-1	BAL	GMV	ALL	0.857 [0.722, 0.933]
ST-1	FAI	GMV	ALL	0.887 [0.774, 0.947]
ST-1	UW	GMV	ALL	0.905 [0.779, 0.962]
RI-2	NI	BAL	ALL	0.833 [0.735, 0.900]
RI-2	FAI	BAL	ALL	0.864 [0.761, 0.927]

• PNS 认证的因果路径表明，在所研究的情境下，特定的策略切换能可靠地推动所有群体的规范实现。
• 一个紧凑的规则路由器 S* 能在不同种子和初始条件下泛化，实现在分布外鲁棒性方面的强性能。
• 修剪在保持因果有效性微小损失的同时保持简洁，而基于相关性的基线在分布变化下更脆弱。
• Stage III 的归因揭示在相同初始条件下，哪些平台杠杆和用户反应推动了成功的规范稳定。
• 不变量因果路由比相关性 baselines 或覆盖性 baselines 具有更小的泛化差距和更高的鲁棒性。

Figure 2 . Three-stage framework for discovering causal strategies in social norm formation. Stage I tests causal effects by identifying implicit contracts $(\theta,g\mid\psi)$ where policy $\theta$ enables group $g$ to reach the norm under context $\psi$ . Stage II learns a compact rule router $S^{