Further Reading: Real-World Applications

Corporate Prediction Markets

  1. Cowgill, B., & Zitzewitz, E. (2015). "Corporate Prediction Markets: Evidence from Google, Ford, and Firm X." Review of Economic Studies, 82(4), 1309–1341. The definitive empirical study of corporate prediction markets. Analyzes data from Google's play-money markets (2005–2013), Ford's real-money internal markets, and a third anonymized firm. Demonstrates that markets outperform internal forecasts, especially for cross-functional questions, and documents the optimism bias among employees trading on their own projects. Essential reading for anyone considering deploying an internal prediction market.

  2. Cowgill, B. (2009). "Putting Crowd Wisdom to Work." Google Working Paper. An earlier, more accessible account of Google's prediction market system. Describes the "Goobles" play-money mechanism, initial findings on accuracy, and the organizational challenges of adoption. Provides practical details on market design that the later academic paper omits.

  3. Chen, K. Y., & Plott, C. R. (2002). "Information Aggregation Mechanisms: Concept, Design and Implementation for a Sales Forecasting Problem." Working Paper, California Institute of Technology. The pioneering study of Hewlett-Packard's internal prediction markets for sales forecasting. Demonstrates that markets outperformed official HP forecasts in 6 of 8 trials. This paper is largely responsible for launching the corporate prediction market movement and remains a model of careful experimental design.

  4. Hopman, J. W. (2007). "Using Forecasting Markets to Manage Demand Risk." Intel Technology Journal, 11(2), 127–136. Documents Intel's use of prediction markets for semiconductor demand forecasting. Shows how markets aggregated information from across Intel's supply chain, providing early signals of demand shifts that conventional forecasting systems missed. Useful for understanding prediction market applications in manufacturing and supply chain contexts.

Scientific Forecasting

  1. Dreber, A., Pfeiffer, T., Almenberg, J., Isaksson, S., Wilson, B., Chen, Y., & Nosek, B. A. (2015). "Using Prediction Markets to Estimate the Reproducibility of Scientific Research." Proceedings of the National Academy of Sciences, 112(50), 15343–15347. The first large-scale demonstration that prediction markets can forecast replication outcomes. Markets predicted which of 44 psychology studies would replicate with approximately 71% accuracy, outperforming surveys of individual experts. Foundational for the "replication markets" literature.

  2. Camerer, C. F., et al. (2018). "Evaluating the Replicability of Social Science Experiments in Nature and Science Between 2010 and 2015." Nature Human Behaviour, 2(9), 637–644. Extends replication markets to high-profile social science papers published in Nature and Science. Confirms that market predictions of replication success are well-calibrated and that market prices incorporate information beyond what individual survey respondents provide. Includes discussion of how market design affects accuracy.

Geopolitical Forecasting

  1. Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown. The popular account of the IARPA-funded Good Judgment Project. Describes how "superforecasters" outperformed intelligence analysts by 30% or more on geopolitical questions. Identifies the key traits of superforecasters (cognitive reflection, active open-mindedness, frequent updating) and argues for prediction tournaments as a complement to intelligence analysis. Accessible to general audiences.

  2. Mellers, B. A., Ungar, L., Baron, J., Ramos, J., Gurcay, B., Fincher, K., ... & Tetlock, P. E. (2014). "Psychological Strategies for Winning a Geopolitical Forecasting Tournament." Psychological Science, 25(5), 1106–1115. The academic companion to Superforecasting. Presents the statistical evidence that training, teaming, and tracking improve geopolitical forecast accuracy. Documents the "extremizing" effect — that pushing aggregated forecasts away from 50% improves calibration — and provides the mathematical framework for performance-weighted aggregation.

Pandemic and Public Health Forecasting

  1. Metaculus. "COVID-19 Forecasting: A Retrospective." Metaculus Blog, 2021. Metaculus's own retrospective analysis of its pandemic forecasting track record. Includes calibration analyses, speed-of-update metrics, and comparisons with CDC ensemble models. Transparent about both successes (early warning signals) and failures (underestimating pandemic scale, pessimistic vaccine timelines). Freely available online.

  2. Recchia, G., Freeman, A. L. J., & Spiegelhalter, D. (2021). "How Well Did Experts and Laypeople Forecast the Size of the COVID-19 Pandemic?" PLoS ONE, 16(5). Systematically compares forecasts from epidemiological experts, prediction market participants, and laypeople across multiple dimensions of the COVID-19 pandemic. Finds that calibrated laypeople who updated frequently performed comparably to domain experts, supporting the "wisdom of crowds" hypothesis for pandemic forecasting.

Economic and Financial Forecasting

  1. Piazzesi, M., & Swanson, E. T. (2008). "Futures Prices as Risk-Adjusted Forecasts of Monetary Policy." Journal of Monetary Economics, 55(4), 677–691. The standard reference for extracting monetary policy expectations from fed funds futures. Demonstrates that futures prices, after adjusting for risk premia, provide superior forecasts of Fed policy actions compared to surveys and VAR models. Essential reading for understanding how financial markets function as prediction mechanisms.

  2. Gürkaynak, R. S., Sack, B., & Swanson, E. (2005). "Do Actions Speak Louder Than Words? The Response of Asset Prices to Monetary Policy Actions and Statements." International Journal of Central Banking, 1(1), 55–93. Analyzes how financial market prices respond to both Fed actions and communications. Demonstrates that forward-looking market prices incorporate information from Fed statements, not just actions, providing a real-time reading of market expectations about future policy.

Prediction Market Design and Theory

  1. Arrow, K. J., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J. O., ... & Zitzewitz, E. (2008). "The Promise of Prediction Markets." Science, 320(5878), 877–878. A short but influential advocacy piece signed by leading economists, arguing for relaxed regulation of prediction markets based on their demonstrated forecasting value. Provides a concise summary of the evidence across multiple application domains and remains the most frequently cited call for broader prediction market adoption.

  2. Hanson, R. (2003). "Combinatorial Information Market Design." Information Systems Frontiers, 5(1), 107–119. Describes the theoretical foundations of combinatorial prediction markets — markets that allow trading on combinations of events. Introduces the LMSR (Logarithmic Market Scoring Rule) that underlies many modern platforms. While primarily theoretical, this paper has had enormous practical impact on market design.

Climate and Technology Forecasting

  1. Roll, R. (1984). "Orange Juice and Weather." American Economic Review, 74(5), 861–880. A classic study showing that orange juice futures prices contain useful weather forecast information beyond what the National Weather Service provides. While not a prediction market paper per se, it demonstrates the principle that market prices aggregate distributed weather knowledge — a finding that generalizes to modern weather derivatives and climate prediction markets.