This is the most formal treatment of futarchy's microstructural challenges to date, modeling how selection bias and equilibrium multiplicity emerge from the joint information structure of action and outcome beliefs. The paper's central insight — that on-chain wallet transparency creates a cross-market welfare channel with a narrow interior optimum — has direct implications for how conditional prediction markets should be designed on blockchain venues.

A clear-eyed diagnosis of why decision markets (futarchy) haven’t taken off despite the elegant theory. Identifies two structural problems: thin markets that can’t attract enough informed traders for idiosyncratic decisions, and a conditional-futures architecture where token-price-as-KPI is too noisy to incentivize rational trading. Covers MetaDAO and Combinator as current implementations grappling with these issues.

Long essay from the Bento.fun founder positioning prediction markets as the next stage in the history of expression: from print to radio to social media, each medium widened who could speak, but only markets demand that speakers bear consequence for being wrong. Pulls in Hayek on price as coordination, Taleb on skin in the game, and Hanson on futarchy to argue the same primitive now extends to politics, sports, journalism, and science. Useful as a values-level framing of why staked speech might out-trust cheap talk in an AI-saturated information environment.

Argues prediction markets' next phase involves Impact Markets (pricing assets conditional on events, e.g., 'BTC price if Fed cuts 75bp') and Decision Markets (using conditional valuations to automate governance). Claims Impact Markets enable true economic hedging by collapsing multi-step inference into direct price discovery.

Argues prediction markets treat reflexivity as a bug, but hyperstition markets weaponize it as a feature. Where prediction markets ask 'what will happen?', hyperstition markets ask 'what can we make happen?' Positions this as futarchy with execution built in—betting YES means coordinating action toward manifestation. The market discovers the price of coordination through dynamic subsidies.

Comprehensive taxonomy of 14 prediction market mechanism types beyond standard binary markets. Covers bonding curve markets, opinion markets (beauty contests), opportunity markets (private prices), hyperstition markets (self-fulfilling coordination), futarchy (MetaDAO), perpetual markets, quantum markets (capital-efficient parallel conditionals), and no-loss PMs. Each design optimizes for different goals: accuracy, speed, coordination, or outcome manifestation.

VC landscape analysis covering incumbents (Polymarket vs Kalshi metrics) and emerging players (Limitless, Onit, Hedgehog, Inertia). Explores advanced concepts including futarchy (MetaDAO), conditional DeFi markets, and beauty contest games. Outlines investment criteria: prosumer appeal, category focus, permissionless market creation, and parlay support.

Podcast discussion separating hype from reality in prediction markets. Covers foundational mechanics, comparative advantages over pollsters and experts, and future applications including corporate decision-making, scientific reproducibility, and governance innovations.

Argues that prediction markets represent one application within a broader 'info finance' ecosystem. Proposes these mechanisms can improve governance, scientific research, journalism, and social media through information-pricing mechanisms that go beyond simple betting.

Dan Schwarz, who built both of Google's internal prediction markets across two decades, tells the inside story of why they struggled and what it means for the future. Prophit (2005–2011) attracted 20% of Google employees but died when regulatory approval for an external launch stalled. Gleangen (2020–2022) reached over 10,000 users but faced a deeper problem: managers valued transparency and adjustability over raw accuracy, and the forecasts were asking internal questions when executives needed competitor intelligence on OpenAI and Microsoft. The piece closes with AI agents as the cost-reducing wedge that might finally make corporate prediction markets viable.

Argues that asset futarchy solves trustless joint ownership by making treasury raids economically irrational: exploiting minority shareholders requires buying their tokens above fair value while simultaneously depressing conditional market prices, making the attack self-defeating by construction. Examines MetaDAO's implementation and Proposal 6, where an attempted governance attack was repelled through this mechanism. Also addresses limitations including soft rug pulls, settlement price complexity, and regulatory constraints around insider trading.