预测清洗：多市场中中立、透明与治理的幻觉
Prediction Laundering: The Illusion of Neutrality, Transparency, and Governance in Polymarket
AI总结：
本文通过分析Polymarket平台，揭示预测清洗现象，指出其通过算法聚合掩盖不确定性与资本不对称，导致认知信任偏差，并提出摩擦积极设计以应对合成真相生产中的社会与财务摩擦。
摘要：
随着预测市场作为认知基础设施的依赖性增加，像Polymarket这样的平台被定位为提供客观、实时概率真相的提供者，然而它们所产生的信号往往掩盖了不确定性、策略性操纵和资本不对称，导致错误的认知信任。本文对Polymarket（N=27）进行了定性社会技术审计，结合数字人类学、解释性走查和半结构化访谈，探讨了概率权威的产生与争议。我们引入“预测清洗”概念，基于MacFarlane的知识传递框架，描述了如何通过算法聚合剥离主观、高不确定性赌注、策略性对冲和资本密集型鲸鱼活动的原始噪声。我们追踪了一个四阶段的清洗生命周期：结构性净化，其中集中化的本体论编写可投注的未来；概率扁平化，将异质动机压缩成单一信号；建筑掩蔽，将资本驱动的影响隐藏在看似共识背后；以及认知硬化，消除治理争议以产生客观的历史事实。我们展示了这一过程通过将真相解决转移给如Discord等非平台社区，导致认知眩晕和问责缺口。挑战无摩擦集体智慧的叙事，我们展示了认知分层，即技术精英审计底层机制，而更广泛公众消费经过净化、资本加权的信号，并最后倡导摩擦积极设计，揭示合成真相生产中固有的社会和财务摩擦。
英文摘要：
The growing reliance on prediction markets as epistemic infrastructures has positioned platforms like Polymarket as providers of objective, real-time probabilistic truth, yet the signals they produce often obscure uncertainty, strategic manipulation, and capital asymmetries, encouraging misplaced epistemic trust. This paper presents a qualitative sociotechnical audit of Polymarket (N = 27), combining digital ethnography, interpretive walkthroughs, and semi-structured interviews to examine how probabilistic authority is produced and contested. We introduce the concept of Prediction Laundering, drawing on MacFarlanes framework of knowledge transmission, to describe how subjective, high-uncertainty bets, strategic hedges, and capital-heavy whale activity are stripped of their original noise through algorithmic aggregation. We trace a four-stage laundering lifecycle: Structural Sanitization, where a centralized ontology scripts the bet-able future; Probabilistic Flattening, which collapses heterogeneous motives into a single signal; Architectural Masking, which conceals capital-driven influence behind apparent consensus; and Epistemic Hardening, which erases governance disputes to produce an objective historical fact. We show that this process induces epistemic vertigo and accountability gaps by offloading truth-resolution to off-platform communities such as Discord. Challenging narratives of frictionless collective intelligence, we demonstrate Epistemic Stratification, in which technical elites audit underlying mechanisms while the broader public consumes a sanitized, capital-weighted signal, and we conclude by advocating Friction-Positive Design that surfaces the social and financial frictions inherent in synthetic truth production.
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。
