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

TL;DR

This paper critically examines how prediction markets like Polymarket produce seemingly objective probabilistic signals through a process called Prediction Laundering, which masks uncertainty, strategic influence, and governance issues, impacting epistemic trust.

Contribution

It introduces the concept of Prediction Laundering, a four-stage sociotechnical process revealing how subjective bets are transformed into seemingly objective signals in prediction markets.

Findings

Prediction Laundering involves four stages: Sanitization, Flattening, Masking, and Hardening.

The process creates epistemic vertigo and accountability gaps.

Broader publics rely on sanitized signals while technical elites understand underlying complexities.

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.