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

TL;DR

The paper interrogates prediction markets like Polymarket as epistemic infrastructures, arguing they practice Prediction Laundering to sanitize subjective bets into apparently neutral signals. Through a qualitative sociotechnical audit (N=27) combining digital ethnography, interpretive walkthroughs, and interviews, it identifies a four-stage lifecycle—Structural Sanitization, Probabilistic Flattening, Architectural Masking, and Epistemic Hardening—that concentrates capital influence and offloads governance to off-platform communities. The authors demonstrate Epistemic Stratification and Accountability Gaps, challenging the wisdom-of-crowds ideal and highlighting how the interface obscures the social and financial frictions behind a clean probability. They propose Friction-Positive Design and related interventions to surface capital asymmetries, interpretive context, and resolution trails, aiming to realign prediction markets with accountable, transparent governance.

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.

Figures (1)

• Figure 1: Methodological workflow and data integration diagram illustrating our two-phase research design moving from macro-level platform ethnography to micro-level participant sessions. The Ethnographic Data Archive from Phase 1 directly informs the Design Probes in Phase 2 to enable a confrontation between users and real-world platform artifacts.