Further Reading — Chapter 1: What Are Prediction Markets?
An annotated bibliography of foundational papers, books, platform documentation, and online resources for deeper exploration of prediction markets.
Seminal Academic Papers
1. Wolfers, J. & Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives, 18(2), 107–126.
The single best introductory survey of prediction markets. Wolfers and Zitzewitz explain how prediction markets work, review evidence on their accuracy, and discuss design considerations. They show that prices in liquid prediction markets are well-calibrated and compare favorably to polls and expert forecasts. Essential reading for anyone entering the field.
2. 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 letter co-signed by 22 prominent economists urging policymakers to remove legal barriers to prediction markets. The authors argue that prediction markets have demonstrated value as forecasting tools and that regulatory obstacles are holding back both research and practical applications. Remarkably brief and persuasive.
3. Manski, C. F. (2006). "Interpreting the Predictions of Prediction Markets." Economics Letters, 91(3), 425–429.
A theoretical caution: Manski argues that prediction-market prices do not, in general, equal mean beliefs of traders. Under risk aversion or heterogeneous wealth, the relationship between price and probability becomes more complex. This paper is a valuable counterpoint to the simplified "price = probability" narrative and is important for understanding the limits of the standard interpretation.
4. Berg, J. E., Nelson, F. D., & Rietz, T. A. (2008). "Prediction Market Accuracy in the Long Run." International Journal of Forecasting, 24(2), 285–300.
A long-run study of the Iowa Electronic Markets (IEM), which have been running since 1988. The authors find that IEM presidential-election prices outperformed polls 74 % of the time when compared in the final week before the election. The paper provides the most comprehensive evidence for prediction-market accuracy in a political-forecasting context.
5. Cowgill, B., Wolfers, J., & Zitzewitz, E. (2009). "Using Prediction Markets to Track Information Flows: Evidence from Google." Working paper.
The definitive study of corporate prediction markets. Cowgill analyzed Google's internal prediction markets, finding that they outperformed official forecasts, exhibited minimal bias, and efficiently aggregated information from across the company. The paper also documents interesting organizational dynamics, such as new employees being more optimistic than veterans.
6. Hanson, R. (2003). "Combinatorial Information Market Design." Information Systems Frontiers, 5(1), 107–119.
Robin Hanson's foundational paper on combinatorial prediction markets, where traders can bet on any combination of outcomes. Hanson introduces the logarithmic market scoring rule (LMSR), which became the most widely used automated market maker for prediction markets. Essential for understanding how modern prediction platforms handle liquidity without traditional order books.
7. Page, L. (2012). "Are Markets Efficient? Why Even Informed Traders May Be Wrong." Economics Letters, 114(3), 245–248.
Explores conditions under which prediction markets can fail, including correlated information among traders and systematic biases. Useful for understanding when you should be skeptical of a market's price signal.
8. Atanasov, P., Rescober, P., Stone, E., Swift, S. A., Servan-Schreiber, E., Tetlock, P., ... & Ungar, L. (2017). "Distilling the Wisdom of Crowds: Prediction Markets vs. Prediction Polls." Management Science, 63(3), 691–706.
A rigorous comparison of prediction markets and prediction polls (structured surveys where respondents give probability estimates) from the IARPA ACE tournament. The authors find that both approaches perform well, with prediction markets having a slight edge. They also explore hybrid approaches that combine the two.
Books
9. Surowiecki, J. (2004). The Wisdom of Crowds. New York: Doubleday.
The popular book that brought the "wisdom of crowds" concept to mainstream attention. Surowiecki explains the conditions under which crowds outperform experts (diversity, independence, decentralization) and provides engaging case studies. While not specifically about prediction markets, it provides the intellectual foundation for understanding why they work. Accessible to any reader.
10. Tetlock, P. E. & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. New York: Crown.
Tetlock's account of the Good Judgment Project, an IARPA-funded forecasting tournament that identified "superforecasters" — ordinary people who consistently outperformed intelligence analysts. The book covers prediction markets tangentially but provides deep insight into what makes individuals and groups good at forecasting. Essential background for anyone interested in the broader science of prediction.
11. Sunstein, C. R. (2006). Infotopia: How Many Minds Produce Knowledge. Oxford University Press.
A wide-ranging exploration of how groups can aggregate information, including chapters on prediction markets, wikis, and deliberation. Sunstein provides a balanced analysis of when prediction markets work and when they fail, with attention to the legal and institutional barriers.
12. Hanson, R. & Simmons, K. (2023). If Then: How the Simulmatics Corporation Invented the Future. (Note: Robin Hanson's work on futarchy is available across many papers and blog posts rather than a single book; see his blog below.)
While Hanson has not published a single comprehensive book on prediction markets, his body of work — blog posts, papers, and talks — constitutes the most important single intellectual contribution to the field. Start with his paper on "Shall We Vote on Values, But Bet on Beliefs?" (2013) for the futarchy concept.
Platform Documentation and Data Sources
13. Polymarket Documentation
URL: https://docs.polymarket.com/
Polymarket's official documentation covers their contract structure (binary outcomes on the Polygon blockchain), how resolution works via UMA's optimistic oracle, and their API for fetching market data. The API documentation is particularly useful for the programming exercises in this book.
14. Metaculus
URL: https://www.metaculus.com/
Metaculus is a community forecasting platform that uses a continuous probability-slider interface rather than a traditional order book. It hosts questions on science, policy, technology, and geopolitics. Metaculus provides an excellent public track record and calibration analysis, making it a valuable resource for studying forecasting accuracy. Note: Metaculus is not a traditional prediction market (no buying/selling of contracts).
15. Kalshi
URL: https://kalshi.com/
Kalshi is the first CFTC-regulated prediction market exchange in the United States. It offers binary contracts on economics, politics, climate, and other topics. Kalshi's regulatory status makes it a useful case study in how prediction markets interact with financial regulation.
Blog Posts and Online Resources
16. Hanson, R. "Overcoming Bias" (blog).
URL: https://www.overcomingbias.com/
Robin Hanson's long-running blog, where he discusses prediction markets, futarchy, decision theory, and more. Many of the key ideas in prediction-market design were first developed on this blog. The archive is vast; start with posts tagged "prediction markets."
17. Nosek, B. & others. "Prediction Markets for Science." (Various articles and reports, ~2015–2023.)
A growing body of work exploring the use of prediction markets to forecast the replicability of scientific studies. Prediction markets on whether published studies would replicate outperformed individual experts and surveys, demonstrating a novel application of the technology.
18. Tabarrok, A. & Cowen, T. "Marginal Revolution" (blog).
URL: https://marginalrevolution.com/
Alex Tabarrok and Tyler Cowen's economics blog frequently covers prediction markets, especially around elections and policy decisions. Tabarrok is a vocal advocate for legalizing prediction markets and has written extensively on the topic.
Online Courses and Tutorials
19. Tetlock, P. "Forecasting" (Coursera / University of Pennsylvania, various offerings).
While not exclusively about prediction markets, Philip Tetlock's online course covers the science of forecasting, including calibration, Brier scores, and the role of prediction markets. Complements this textbook's focus on markets with a broader view of the forecasting ecosystem.
20. "Prediction Markets: Bottlenecks and the Next Major Unlocks." (Research report by Star Xu, 2024.)
A research overview of the state of prediction markets as of 2024, covering blockchain- based platforms, liquidity challenges, and the regulatory landscape. Useful for understanding the current state of the industry.
How to Use This List
- Start with Wolfers & Zitzewitz (2004) for the academic foundation and Surowiecki (2004) for the intuition.
- Go deeper with Hanson (2003) for market-maker design and Manski (2006) for theoretical nuance.
- Get practical with Polymarket, Kalshi, and Metaculus documentation.
- Stay current with the Overcoming Bias and Marginal Revolution blogs.
End of Further Reading — Chapter 1