Key Takeaways: Real-World Applications

The Application Landscape

  • Prediction markets have been deployed across at least ten major domains: elections, sports, corporate forecasting, scientific replication, pandemic tracking, geopolitical intelligence, economic indicators, climate, technology adoption, and government policy.
  • Domains follow a characteristic adoption curve: academic demonstration, pilot deployment, scaling challenges, institutional integration, and regulatory accommodation. Most domains sit between stages 2 and 4.
  • Proven use cases share five characteristics: clear resolution criteria, sufficient participant diversity, adequate incentives, institutional tolerance, and genuine information dispersion across participants.
  • The most common failure mode across all domains is insufficient participation (thin markets), not flawed market mechanism design.

Corporate Prediction Markets

  • Google, Hewlett-Packard, Microsoft, Intel, and Ford have all operated internal prediction markets. Google's is the longest-running and most carefully studied (since 2005).
  • Play-money markets with modest prizes generate accurate forecasts in organizations with competitive, data-driven cultures. Real money is not required for corporate applications.
  • Corporate markets add the most value on cross-functional questions where information is distributed across departments. Single-team questions (e.g., "Will our team ship on time?") add less value because the information is concentrated.
  • The most organizationally valuable function of corporate prediction markets is surfacing bad news — markets detect project slippage days or weeks before official status reports.
  • Systematic biases exist: employees are optimistic about their employer and about their own projects; newer employees are better calibrated than long-tenured ones; initial market-maker prices create anchoring effects.
  • Participation follows a power law: 10–15% of participants generate the majority of trading activity. Minimum viable participation is approximately 20–30 active traders per market.
  • Executive sponsorship is the single most important determinant of prediction market sustainability. Without a senior champion who uses market prices in decision-making, participation declines.

Scientific Forecasting

  • Replication Markets and similar platforms have demonstrated that prediction markets can predict which scientific findings will replicate with approximately 70–75% accuracy.
  • Markets outperform expert surveys on replication prediction, partly because markets aggregate more diverse information and partly because the incentive structure rewards accuracy over social conformity.
  • The base rate for replication in psychology is approximately 40–60% depending on the subfield and definition of "replication."
  • Markets on clinical trial outcomes aggregate information from diverse sources (researchers, industry analysts, physicians) and have shown moderate predictive power for Phase 3 success.
  • The key limitation of scientific prediction markets is thin participation: most questions attract too few traders for meaningful price discovery.

Pandemic Forecasting

  • COVID-19 provided the first large-scale stress test for pandemic prediction markets. Metaculus, Good Judgment Open, and Polymarket all operated active pandemic markets throughout 2020–2021.
  • Markets incorporated emerging scientific evidence 2–16 weeks faster than official institutional assessments (e.g., airborne transmission risk, IFR estimates, vaccine timeline).
  • The dominant failure mode was anchoring on historical precedent: forecasters underestimated pandemic scale (anchored on SARS/MERS) and overestimated vaccine timelines (anchored on traditional development processes).
  • Play-money and survey-based platforms (Metaculus, Good Judgment Open) outperformed the thin real-money market (Polymarket) for most pandemic questions, suggesting that participation breadth matters more than financial incentives for public health forecasting.
  • The CDC's multi-model ensemble slightly outperformed human forecasters, suggesting the optimal approach combines computational models with prediction market signals.

Geopolitical Forecasting

  • The IARPA-funded Good Judgment Project (2011–2015) demonstrated that trained forecasters using a prediction tournament structure could outperform professional intelligence analysts by 30% or more on geopolitical questions.
  • "Superforecasters" — the top 2% of participants — demonstrated remarkably stable accuracy over time and across question domains. Their key traits: cognitive reflection, comfort with numerical reasoning, active open-mindedness, and frequent updating.
  • Extremizing aggregation (pushing the average forecast away from 50%) consistently improves geopolitical forecasting accuracy, suggesting that raw averages are systematically underconfident.
  • Geopolitical forecasting suffers from ambiguous resolution criteria ("When has a 'conflict' begun?") and the potential for self-fulfilling or self-defeating prophecies when forecasts are published.

Economic and Financial Forecasting

  • Fed funds futures and TIPS breakeven spreads are the most mature and liquid prediction market-like instruments, processing billions of dollars in daily volume.
  • The implied probability of Fed rate changes, extracted from futures prices, outperforms economist surveys and most econometric models for short-horizon (1–3 month) forecasts.
  • TIPS breakeven inflation rates provide a market-implied inflation expectation that includes a risk premium (typically 0.2–0.5 percentage points), which must be subtracted for an unbiased forecast.
  • Economic prediction markets are subject to liquidity-driven distortions: flight-to-safety effects during crises can move TIPS breakevens for reasons unrelated to inflation expectations.

Climate and Weather

  • Weather derivatives (HDD/CDD futures) provide a well-functioning market for seasonal temperature forecasting, primarily used by energy utilities for hedging.
  • Climate prediction markets for longer-term questions (e.g., "Will global average temperature exceed 1.5C above pre-industrial by 2030?") remain in early stages with thin liquidity.
  • The main challenge for climate prediction markets is the extremely long time horizon: participants cannot be expected to wait decades for resolution, requiring creative contract design.

Technology Adoption

  • Prediction markets on technology adoption milestones (e.g., "When will Level 4 autonomous vehicles be commercially available?") are growing but face challenges in defining clear resolution criteria.
  • The S-curve model provides a useful framework for technology adoption forecasting: markets effectively estimate the parameters $K$ (ultimate adoption), $r$ (growth rate), and $t_0$ (inflection point).
  • Technology prediction markets are particularly susceptible to hype cycles: prices spike during peak enthusiasm and correct as reality sets in.

Cross-Domain Lessons

  • Participation is the master variable. Across all domains, the correlation between participant count and forecast accuracy is stronger than the correlation with incentive structure, market mechanism, or question design.
  • Markets add the most value when information is dispersed. If a single expert or model has most of the relevant information, a prediction market adds little. Markets shine when 50 people each have 2% of the puzzle.
  • Speed of information incorporation is more consistent than raw accuracy. Markets do not always produce the most accurate forecast, but they almost always reflect new information faster than bureaucratic alternatives.
  • Continuous probability distributions beat binary contracts. Platforms that allow forecasters to express distributional beliefs generate more informative and actionable forecasts than yes/no binary markets.
  • Combining markets with models is optimal. The best forecasting systems use prediction markets as one input in a multi-model ensemble, not as a standalone tool.
  • Institutional integration requires champions. Prediction markets that remain academic curiosities fail. Markets that have a senior decision-maker who regularly consults prices and visibly uses them succeed.

Practical Framework for Deployment

  • Before deploying a prediction market, evaluate: (1) Is information genuinely distributed? (2) Can questions be resolved objectively? (3) Is there a sufficient potential participant pool? (4) Is there institutional tolerance? (5) Is there an executive champion?
  • Start with 5–10 high-value questions with unambiguous resolution criteria. Demonstrate accuracy, then expand.
  • Expect the first year to be a learning period. Calibrate expectations accordingly.
  • Design for anonymity: participants must feel safe revealing bad news through their trades without career risk.
  • Monitor and address participation inequality: ensure that market prices reflect diverse information, not just the views of the most active traders.