许立言

Xu Liyan

关键词：城市信息治理 ；选择偏误 ；信息偏误 ；反事实偏误 ；行为科学 ； 自然实验

Keywords：Urban Information Governance; Selection Bias; Information Bias; Counterfactual Bias; Behavioral Science; Natural Experiment

摘要：

城市信息学的长足进步为更加精细化、智能化的城市治理提供了新的机 遇。通过社会感知途径获取的大量新数据包含了丰富的城市态势和市民行为信 息，具有数据挖掘和知识发现上的可观潜力，但其有偏性是值得高度重视的问题。 本文以社会感知数据偏误为研究对象，首先枚举了城市治理中的各类常见新数 据及其应用场景，并扼要讨论了其中选择偏误、信息偏误和反事实偏误等三种 基本偏误的构成和主要性质。继而，本文从行为科学角度，基于“暴露—认知— 行动”框架分析了导致数据偏误问题的行为根源，并提出了一种基于自然实验 的因果推断以实现各类偏误纯净效应分解的方法。最后，本文以城市信息化治 理中常见的网格员巡查记录、执法监控视频等典型数据资源所反映的违章停车、 违章游商、噪声扰民等问题为例，展示了在准确认识数据偏误的基础上，对其 进行校正、归因和“助推”式利用，以服务于城市态势感知、问题发现与溯源、 针对性政策与法规制定等城市治理目标的应用途径。

Abstract：

Recent progress of urban informatics provides new opportunities for more refined and intelligent urban governance. The large amount of new data acquired through Social Sensing contains rich information on urban dynamics and citizens’ behaviors, and implies potential for data mining and knowledge discovery. However, observational bias in Social Sensing data is worthy of deep concerns. This paper, in investigating the data bias problem, firstly enumerates various kinds of common new data and their application scenarios in urban governance, and briefly discusses the main properties of the three basic bias types, namely selection bias, information bias, and counterfactual bias. Next, this paper analyzes the behavioral roots of the biases from the perspective of behavioral science with an “exposure-cognition-action” framework, and then proposes a methodological framework which utilizes natural experiment designbased causal inference to realize the decomposition of the pure effects of the three bias types. Finally, this paper takes the typical data resources such as urban management inspection records and law enforcement surveillance videos as examples, and demonstrates ways of correction, attribution, and “nudge”-style utilization of data bias with the proposed methodological framework which helps improve people’s well-being.

许立言，博士，北京大学建筑与景观设计学院，北京大学建筑与景观设计 学院，研究员，博士生导师 ；时空智能与地理设计实验室主任。 xuliyan@pku.edu.cn

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