Prediction Laundering: The Illusion of Neutrality, Transparency, and Governance in Polymarket

2026-02-05

AI Summary

This study examines how prediction markets such as Polymarket algorithmically aggregate information in ways that obscure subjectivity, uncertainty, and capital inequality, thereby producing an illusion of “objective consensus.” Through a sociotechnical audit employing digital ethnography, interpretive walkthroughs, and semi-structured interviews, and drawing on MacFarlane’s framework of epistemic transmission, the research introduces the concept of “prediction laundering” and articulates a four-stage lifecycle model. This model elucidates the mechanisms of epistemic stratification and governance deficits underlying synthetic truths. Findings reveal that platforms repackage highly uncertain events and whale-driven capital maneuvers as credible probabilities, inducing epistemic vertigo and accountability vacuums. The study argues that this dynamic exacerbates the cognitive divide between technical elites and the public, and calls for friction-positive design to reconfigure platform governance.

Technology Category

• Game Theory and Economic Paradigms: Auctions and Market-Based Systems
• Philosophy and Ethics of AI: AI & Epistemology
• Reasoning under Uncertainty: Causality

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Algorithmic accountability and transparency on the web
• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization

Abstract

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.

Problem

• prediction markets
• epistemic trust
• algorithmic aggregation
• capital asymmetry
• governance

Innovation

• Prediction Laundering
• Epistemic Stratification
• Friction-Positive Design
• Probabilistic Flattening
• Sociotechnical Audit