从因果到相关——大数据背景下侦查思维的转型  

From Causation to Correlation—the Transformation of Investigative Thinking in the Context of Big Data

作　　者：石腾 Shi Teng(School of Criminal Justice,China University of Political Science and Law,Beijing 100088)

机构地区：[1]中国政法大学刑事司法学院,北京100088

出　　处：《西部学刊》2024年第18期50-54,共5页Journal of Western

摘　　要：为有效应对日益复杂的犯罪模式和海量数据带来的挑战,大数据背景下侦查思维正经历一场深刻的变革。因果性侦查思维已经难以同步跟上犯罪模式的变化速度,其与现实情境之间出现“相位差”,正在向相关性思维转变,强调从海量数据中发现潜在关联,实现从数据洞察到决策行动的高效转化。虽然相关性思维在处理复杂数据关系上展现出优势,但因果性思维在确保侦查逻辑的严谨性和证据的完整性方面仍具有不可替代的价值。因此,大数据侦查思维应运而生,它巧妙地结合了两种思维模式,既追求数据驱动的洞察力,又保证了推理的逻辑连贯性,不仅能够提升侦查效率,促进侦查工作的现代化和智能化,还为实现数字化治理提供了新的视角和策略。In order to effectively respond to the challenges posed by increasingly complicated crime patterns and massive amounts of data,we are transforming investigative thinking in the context of big data.There is a“phase gap”between causal investigative thinking and real-life situations,meaning that existing modes of thinking are unable to catch the pace of the changes in crime patterns.Causal investigative thinking,therefore,is shifting towards a correlative one,which emphasizes the discovery of potential links from large amounts of data and the efficient transformation from data insight to decision-making action.Although correlation thinking shows advantages in dealing with complex data relationships,causal thinking is still of irreplaceable value in ensuring the rigor of investigative logic and the integrity of evidence.Consequently,big data investigative thinking has emerged,which skillfully combines the two modes of thinking,pursuing data-driven insights while ensuring logical coherence of reasoning.Big data investigative thinking not only enhances investigative efficiency and promotes the modernization and intelligence of investigation,but also provides new perspectives and strategies for the realization of digital governance.

关 键 词：大数据 侦查思维 因果性 相关性 数字化社会治理 

分 类 号：C91-0[经济管理] D925.2[社会学]