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

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.

💡 Research Summary

The paper conducts a qualitative sociotechnical audit of Polymarket, a blockchain‑based prediction market, using digital ethnography, interpretive walkthroughs, and 27 semi‑structured interviews. It introduces the concept of “Prediction Laundering,” adapted from MacFarlane’s knowledge‑laundering theory, to describe how high‑uncertainty bets, strategic hedges, and whale‑scale capital are stripped of their original context and presented as a single, seemingly objective probability. The authors map this process onto a four‑stage lifecycle: (1) Structural Sanitization – the platform scripts which futures are bet‑able through predefined event templates and on‑chain oracles, thereby excluding non‑legible possibilities; (2) Probabilistic Flattening – automated market makers collapse heterogeneous motives into a single 0‑1 price, erasing the underlying uncertainty; (3) Architectural Masking – the user interface hides capital concentration, creating the illusion of a crowd‑derived consensus while a small number of large traders drive price movements; (4) Epistemic Hardening – governance disputes and fact‑checking are off‑loaded to external communities (Discord, Telegram), so the platform appears to deliver a “clean” signal without visible friction.

Through this lens the authors identify “Epistemic Stratification”: technical elites audit the underlying mechanisms, but the broader public consumes a capital‑weighted signal, leading to accountability gaps and “epistemic vertigo.” The paper critiques the prevailing narrative that prediction markets embody frictionless collective intelligence, arguing instead that they reproduce existing power asymmetries and obscure the labor required to resolve disputes and verify outcomes.

In response, the authors advocate for “Friction‑Positive Design.” They propose UI/UX interventions that surface betting volume, liquidity provider influence, oracle reliability, and ongoing governance debates as metadata, allowing users to perceive the uncertainty and power dynamics embedded in the price. Such design shifts the focus from superficial transparency to genuine responsibility and informed trust.

Overall, the study reframes prediction markets not merely as efficient price mechanisms but as sociotechnical infrastructures that manufacture synthetic truths. It calls for scholars, designers, and policymakers to scrutinize the epistemic authority of these platforms, to expose the hidden frictions, and to embed accountability into their core architecture.