MO compares three market architectures for expressing shaped beliefs: binary prediction markets, options structures, and continuous prediction markets. The article traces the distribution gap from the Black-Scholes era through modern crypto markets and argues that continuous payout curves replace the workarounds traders currently use.

Follow-up to functionSPACE's V1 discretisation analysis, splitting Polymarket's 18,863 multi-market events into continuous (price brackets, weather ranges, margin percentages) versus categorical (teams, candidates) and re-running the pathology tests. Both types concentrate 90% of volume in the top 5-6 markets, but ghost markets turn out to be largely a categorical phenomenon: continuous events distribute volume more evenly across buckets and survive the liquidity cliff longer at high N. With continuous events overtaking categorical by event count in 2026Q1, the case for a continuous-distribution primitive applies to a growing share of the platform.

Beginner-oriented primer from Blockchain at Berkeley covering what prediction markets are, how order books translate bids and asks into probabilities, why they matter for business, media, and policy, and the Polymarket vs Kalshi comparison (offshore crypto-native vs CFTC-regulated; public onchain trades vs private USD activity). Good starting point to share with people new to the category.

Response to Axios and More Perfect Union coverage framing prediction markets as gambling. Park argues the investing/gambling line is about +EV, not the game itself, and that the flip side of speculation is always insurance. Contends prediction markets are structurally different from other derivatives because they are precise (binary payoffs create clean basis risk to truth) and have finite expiry, and that fears about insider trading are overblown since liquidity in obscure asymmetric markets will be negligible. Closes with a media-criticism argument that prestige outlets attack prediction markets because the markets threaten institutional control over truth.

Reframes prediction markets as consumer-wrapped binary options, drawing on the author's OTC commodity derivatives background. Introduces Minsky's 'vega wedge' as the structural overcharge that binary hedgers pay when they replicate via vanilla options (around 4.8% for BTC binaries, 7-20% for gold), and argues prediction markets can undercut that tax in categories with deep volume. Diagnoses what still keeps institutional capital out: no shared Black-Scholes-equivalent pricing language, missing risk infrastructure, and liquidity that remains retail-dominated.

Analyzes 36,777 Polymarket events to understand what happens when continuous questions are split into dozens of independent binary contracts. Volume follows an extreme Pareto distribution: the top 3 markets capture over 75% of trading activity regardless of event size, leaving a large fraction as untradeable ghost markets. The $0.01 tick size compounds the problem, creating a rounding tax that makes low-probability contracts structurally imprecise.

Uses the March 2026 Strait of Hormuz crisis to argue that binary order-book prediction markets hit an architectural ceiling when pricing granular, multi-outcome risk. Compares how traditional options solve this for tradable assets, then explains how automated market scoring rules (LMSR/CLMSR) offer protocol-native liquidity, coherent pricing, and capital efficiency for events without underlying assets. Walks through a concrete WTI crude oil scenario showing how scoring-rule markets reward precise thesis expression over simple directional bets.

Data-driven analysis of Kalshi's business model using all 203 million trades across $41.7B in volume. Reveals that Kalshi functions more like a poker rake than a sportsbook, charging fees via the formula fee = 0.07 × C × P × (1-P), which incentivizes trading near 50% probability. Key finding: sports comprise 82% of total volume, making Kalshi functionally a sports betting platform despite its CFTC-regulated derivatives positioning. Includes clear explanations of order book mechanics, binary contract pricing, and the regulatory framework (clearinghouse structure, no-action letters) alongside original data visualizations of volume distribution and resolution patterns.

Traces the quantitative finance toolkit from backtesting (Deflated Sharpe Ratio, combinatorial purged cross-validation) through factor models, Black-Litterman portfolio optimization, Bayesian regime detection, and machine learning, then argues each technique transfers directly to prediction markets. The core claim is that prediction markets are the purest testing environment for investment theory because binary resolution eliminates the unobservable noise that obscures strategy quality in traditional finance. Uses LMSR's mathematical identity with the softmax function to bridge quant finance and prediction market pricing.

Surveys the landscape of teams trying to add leverage to prediction markets and explains why most are converging on 1x to 1.5x rather than the 10x or 20x they advertise. The core problem is gap risk: binary outcomes resolve instantly, skipping the intermediate prices that liquidation engines need to function. Uses dYdX's TRUMPWIN perp on election night 2024 as a case study where sophisticated safeguards still broke under real conditions, then categorizes current approaches into three camps: constrain leverage, engineer around it with dynamic fees and circuit breakers, or ship and iterate.