Alex Tabarrok uses Polymarket's Maine Democratic primary market as a worked example of how conditional probability estimates can be reverse-engineered from prediction market price moves, then argues that Robin Hanson's decision markets (conditional contracts with refund provisions) would give cleaner, continuous estimates of exactly the quantities decision-makers need. A short, accessible case for moving beyond raw prediction markets toward decision markets and futarchy.

Scott Alexander reviews the Metaculus US Democracy Threat Index, a prediction market index tracking expected threats to American democracy through 2027-2028. The index peaked at 47% in late 2025 following election uncertainty before settling to 39% after orderly special elections and failed politicized prosecutions. Discusses conditional prediction markets as a tool to measure how electoral outcomes affect democratic health, and proposes a wishlist including cross-platform duplicates (Polymarket/Kalshi), better question design, and wider forecaster participation.

WaPo analyzes 1,276 Kalshi markets and 829 Polymarket markets across 344 primary races and finds prediction markets are well-calibrated: candidates given 70-80% probability win about as often as predicted. High-profile misses (Spencer Pratt in LA, Massie in KY) are not failures of the market but the expected 25% outcome in a properly calibrated system where probabilities are often misunderstood as certainties.

Draws on Isaiah Berlin's two concepts of liberty to frame two competing visions of markets: the 'market as camera' (Hayek's price discovery aggregating scattered private knowledge) versus the 'market as engine' (actively shaping the outcomes it measures). Uses prediction markets as a live case study, covering their history from 1880s New York curb betting through the Iowa Electronic Markets to Polymarket and Kalshi's $24B monthly volume. Highlights recent research showing prediction market accuracy is driven by fewer than 3% of skilled traders, not wisdom of crowds, and explores the political stakes of which outcomes get priced at all.

Provides a framework for understanding which prediction market categories can win institutional capital, using variance risk premium analysis to determine where event contracts beat options replication. Situates the current benchmark-building race (election ETFs, corporate event notes, AI capability markets) within the historical pattern of credit and crypto infrastructure formation.

Traces the history of prediction markets from 1419 Vatican papal elections through the Iowa Electronic Markets to the Polymarket era, arguing the sector is at an inflection point. Surveys the insider trading scandals (Musk tweets, French elections), moral hazard concerns (assassination markets), and the wave of new entrants (Robinhood, DraftKings, Crypto.com, FanDuel) that signal mainstream adoption. Concludes that prediction markets are evolving, not decaying, but need regulatory clarity and structural reform to mature.

Fits a Bayesian hierarchical model to 292 million trades across 327,000 contracts on Kalshi and Polymarket to decompose calibration errors into structured components: universal horizon effects, domain-specific biases, and trade-size scale effects, which together explain 87.3% of variance on Kalshi. Finds persistent underconfidence in political markets where prices compress toward 50%, and shows that large trades amplify this pattern on Kalshi but not on Polymarket, pointing to platform-specific microstructure differences.

History of U.S. prediction market regulation culminating in the February 2026 jurisdictional standoff between CFTC Chairman Mike Selig and Utah Governor Cox. Park traces the arc from the Iowa Electronic Markets' 1988 no-action letter, through Intrade's 2012 collapse and the binary options fraud era, to Kalshi's court win establishing that 'gaming' does not cover financial contracts on uncertain outcomes. Argues federal preemption under the Commodity Exchange Act will hold against state attorneys general, and that Bitwise's PredictionShares ETF launch marks the point where political event contracts become a mainstream financial product.

Examines Clinton and Huang's research on 2024 election market accuracy, finding PredictIt at 93%, Kalshi at 78%, and Polymarket at 67%, while also documenting significant cross-platform price divergences for identical contracts near Election Day. Raises concerns about Kalshi's media partnerships with CNN and CNBC, arguing they create incentives for sensational coverage of market movements and potential manipulation of thin markets.

Critical analysis of prediction market reliability during the 2024 US election. Documents how four coordinated accounts controlled 23% of Polymarket's open interest, 41% of volume appeared to be wash trading, and argues current platforms lack the structural conditions for reliable forecasting.

Speculates on how Polymarket's 2024 presidential election market could be manipulated through its oracle system. Argues that Fox News was chosen as an oracle despite being unlikely to call the election for a non-Trump candidate, and that UMA token holders could sway disputed resolution votes given UMA's small market cap.

Characterizes Polymarket as a crypto media/creator economy platform rather than just an event-trading platform. Notes the 166:1 ratio of monthly visits to MAU suggests significant non-trading visitors, and that Polymarket users are older and less focused on maximizing risk-reward compared to typical crypto traders.

Compares prediction markets with traditional polls and expert commentary along two axes: grassroots vs top-down and expertise density. Uses the 2024 Biden-Trump race to show how Polymarket priced in Biden's withdrawal probability while polls measured only head-to-head support.

Analysis of Polymarket's growth trajectory and business model. Notes that 99.2% of trading volume concentrates in political markets and two-thirds of cumulative volume occurred in the last six months, raising questions about sustainability beyond election cycles.