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.

Aelix diagnoses why prediction markets remain stuck on sports gambling — roughly 65% of Polymarket and Kalshi volume goes to sports, with another 12% each on crypto and politics, leaving useful markets like STEM at 1.2%. The bottleneck isn't technology but market structure: prediction markets are zero-sum, so savers don't participate; gamblers drive volume toward short-term entertainment; sharps follow the gamblers; and useful markets starve. AI agents break this cycle by acting as cheap, forced-participation sharps — they're cloneable, parallelizable, can be compelled to trade on any question, and dramatically lower the minimum viable liquidity threshold for niche markets. The piece also revisits the history of corporate internal prediction markets (HP BRAIN, Eli Lilly, Google) and argues that AI sidesteps the organizational failures that killed them, reviving the futarchy and info-finance vision on more tractable terms.

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.

Argues forecasting accuracy has outpaced product design: a decade inside the US intelligence community produced zero complaints about forecast quality, yet forecasting firms remain niche while simulation startups Aaru and Simile raised nine-figure rounds. Diagnoses the gap as a failure to embed forecasts into institutional workflows, and recommends forecasting companies hire deployment managers who transform probabilities into artifacts clients can act on.

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.

Written during the US-Israel strikes on Iran, examines whether prediction markets on armed conflicts are net informational goods or perverse incentive engines. Dissects the IDF insider trading case where soldiers traded Polymarket positions before strikes, the CFTC's regulatory stance, and the divergent approaches of Kalshi (regulated, avoids conflict markets) versus Polymarket (offshore, lists them freely). Argues the information value is real but the moral hazard is structurally underpriced, and proposes guardrails including delayed settlement and conflict-of-interest screens.

Galaxy research report on prediction markets evolving from niche speculation to mainstream financial infrastructure. Covers Polymarket ($9B valuation, 1.6M users) and Kalshi (top finance iOS app), emerging leverage mechanisms (Space, Gondor), AI as interface layer for fragmented venues, and convergence toward derivatives—event contracts as hedges, collateral, and composable financial primitives.

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.

Spartan Labs explores what happens when you give Andrej Karpathy's LLM Council design actual stakes. Their Simmer prototype runs 30 AI agents across 6 models and 5 reasoning personas in an LMSR market, producing a capital-weighted forecast without requiring human liquidity. Early observations reveal coordination cascades when agents share identical search results, a finding with implications for any multi-agent prediction system.

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.

Introduces 'opportunity markets' where prices remain hidden from everyone except the sponsor during an opportunity window. Designed to let institutions discover high-probability opportunities (talent scouting, research commercialization) before competitors can access the signal, solving the public goods problem of price discovery.

Challenges the conception that prediction markets serve as effective policy tools. Argues that conditional prediction markets proposing 'Will X lead to consequence Y?' fail to translate probability assessments into meaningful policy guidance despite being their central theoretical appeal.

Outlines 10 trends and feature ideas for prediction markets including shorter duration markets, modular dispute resolution, AI as arbiters and participants, market segmentation, yield-bearing stablecoin integration, and conditional markets that allow betting on if-then outcomes.

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.