Chapter 2 Further Reading

An annotated bibliography of key sources on the history of prediction markets, organized thematically. Each entry includes a brief description of its content and relevance.

Foundational Academic Papers

1. Hayek, F. A. (1945). "The Use of Knowledge in Society." American Economic Review, 35(4), 519-530.

The intellectual ancestor of all prediction market theory. Hayek argues that prices in decentralized markets aggregate dispersed knowledge that no central planner could possess. While Hayek was writing about commodity and labor markets, his argument applies directly to prediction markets: prices for event contracts aggregate the private information of many individuals into a single, publicly observable number. Essential reading for understanding why prediction markets work in principle.

2. Wolfers, J., & Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives, 18(2), 107-126.

The definitive survey of the prediction market field as of 2004. Wolfers and Zitzewitz review the theoretical foundations, summarize the empirical evidence from the IEM, InTrade, and other platforms, and discuss potential applications in business and government. This is the single best introduction to the academic literature and remains highly cited. Accessible to readers with intermediate economics knowledge.

3. Manski, C. F. (2006). "Interpreting the Predictions of Prediction Markets." Economics Letters, 91(3), 425-429.

The most important critique of prediction markets. Manski demonstrates that market prices cannot, in general, be interpreted as probabilities because the price-probability mapping depends on participants' risk preferences. This short, technically precise paper is important for understanding the limitations of prediction markets and the conditions under which the "prices equal probabilities" interpretation is valid.

4. Arrow, K. J., et al. (2008). "The Promise of Prediction Markets." Science, 320(5878), 877-878.

A letter signed by over 20 leading economists (including Nobel laureates Arrow, Vernon Smith, and Robert Shiller) calling for the legal barriers to prediction markets to be lowered. The letter argues that prediction markets produce social value through information aggregation and that regulatory restrictions based on anti-gambling sentiment are misguided. Important both for its content and for the prestige of its signatories.

5. Hanson, R. (2003). "Combinatorial Information Market Design." Information Systems Frontiers, 5(1), 107-119.

Hanson's technical paper introducing the logarithmic market scoring rule (LMSR). The LMSR solves the liquidity problem in prediction markets by providing an automated market maker that will always accept trades at some price. This paper is somewhat technical but is essential for understanding the mechanism design behind modern prediction market platforms.

Iowa Electronic Markets Research

6. Forsythe, R., Nelson, F., Neumann, G. R., & Wright, J. (1992). "Anatomy of an Experimental Political Stock Market." American Economic Review, 82(5), 1142-1161.

The first major academic paper analyzing the IEM. Describes the market's design, reports results from the 1988 presidential election, and establishes the research paradigm that subsequent IEM papers would follow. Important as a historical document and for its detailed description of how the IEM works.

7. Berg, J. E., Forsythe, R., Nelson, F. D., & Rietz, T. A. (2008). "Results from a Dozen Years of Election Futures Markets Research." In Handbook of Experimental Economics Results, Vol. 1, 742-751.

A comprehensive review of IEM performance across multiple election cycles. Provides the key statistical comparisons between IEM predictions and poll-based forecasts that have become central to the case for prediction markets. The most authoritative source on the IEM's track record.

8. Forsythe, R., Rietz, T. A., & Ross, T. W. (1999). "Wishes, Expectations and Actions: A Survey on Price Formation in Election Stock Markets." Journal of Economic Behavior & Organization, 39(1), 83-110.

Identifies the "wishful thinking" bias in prediction markets: partisans tend to overvalue contracts for their preferred candidate. This paper is important for understanding the behavioral biases that affect prediction market prices and for appreciating that markets, while generally accurate, are not immune to systematic distortions.

Historical Betting Markets

9. Rhode, P. W., & Strumpf, K. S. (2004). "Historical Presidential Betting Markets." Journal of Economic Perspectives, 18(2), 127-142.

Documents the extensive political betting markets that operated in the United States from the 1860s through the 1940s. Rhode and Strumpf show that these markets were large, liquid, and remarkably accurate — and that their decline was driven by legal changes rather than poor performance. Essential reading for understanding the deep historical roots of prediction markets.

10. Vaughan Williams, L. (2003). Betting to Win: A Professional Guide to Profitable Betting. High Stakes Publishing.

While focused on sports and racing betting, this book provides valuable historical context on the development of organized betting markets, including early papal election betting and the evolution of bookmaking. Useful for understanding the broader tradition from which prediction markets emerged.

The DARPA Controversy and Policy Applications

11. Hanson, R. (2006). "Designing Real Terrorism Futures." Public Choice, 128(1-2), 257-274.

Hanson's retrospective analysis of the FutureMAP controversy. He describes what the Policy Analysis Market was actually designed to do (as opposed to how it was characterized in the media), analyzes why the project was killed, and argues that a properly designed version could still be valuable for intelligence analysis. Essential reading for understanding the gap between the actual proposal and its public reception.

12. Sunstein, C. R. (2006). Infotopia: How Many Minds Produce Knowledge. Oxford University Press.

Cass Sunstein (later Obama's regulatory czar) examines prediction markets as one of several mechanisms for aggregating collective knowledge. The book provides accessible treatment of the theoretical case for prediction markets and discusses their potential applications in government and business. More readable than most academic papers on the topic and provides important policy context.

Books on Forecasting and Collective Intelligence

13. Surowiecki, J. (2004). The Wisdom of Crowds. Doubleday.

The popular book that introduced the concept of crowd wisdom to a general audience. While not exclusively about prediction markets, Surowiecki discusses the IEM and other markets extensively as examples of how decentralized groups can outperform experts. Important for understanding the intellectual context in which prediction markets gained public attention in the mid-2000s.

14. Tetlock, P. E. (2005). Expert Political Judgment: How Good Is It? How Can We Know? Princeton University Press.

Tetlock's landmark study showing that expert political predictions are, on average, only slightly better than chance. This book is the intellectual foundation for the Good Judgment Project and the broader "forecasting movement." It explains why alternatives to expert judgment — including prediction markets — are needed.

15. Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown.

The follow-up to Expert Political Judgment, describing the Good Judgment Project and its finding that ordinary volunteers using structured methods can outperform intelligence analysts with access to classified information. Important for understanding the connection between prediction markets and the broader forecasting community.

Platform-Specific Sources

16. Pennock, D. M. (2004). "A Dynamic Pari-Mutuel Market for Hedging, Wagering, and Information Aggregation." Proceedings of the 5th ACM Conference on Electronic Commerce, 170-179.

Pennock's technical paper on market mechanisms, drawing on his experience with the Hollywood Stock Exchange. Important for understanding how play-money markets can be designed to produce accurate forecasts. Somewhat technical but highly relevant to market design.

17. Clark, A. (2013). "InTrade's Demise: What Went Wrong." Various media sources (Financial Times, Bloomberg, Reuters coverage from March-April 2013).

Media coverage of InTrade's closure provides important details about the financial irregularities, regulatory actions, and customer impact. While not a single source, the collective media coverage from this period is essential for understanding what happened to InTrade. The Financial Times and Bloomberg coverage is particularly detailed.

18. CFTC Order of Settlement with Polymarket (2022). CFTC Docket No. 22-05.

The official CFTC settlement order against Polymarket provides insight into how U.S. regulators view blockchain-based prediction markets. Important for understanding the regulatory environment in which modern platforms operate. Available on the CFTC website.

19. Kalshi, Inc. v. CFTC (2024). U.S. District Court for the District of Columbia.

The landmark federal court ruling that allowed Kalshi to list political event contracts over the CFTC's objection. This case is essential reading for anyone interested in the regulatory future of prediction markets in the United States. The judge's opinion provides a detailed analysis of the CFTC's authority over event contracts.

Broader Context

20. Abramowicz, M. (2007). Predictocracy: Market Mechanisms for Public and Private Decision Making. Yale University Press.

An extended argument for using prediction markets in government and corporate decision-making. Abramowicz addresses many of the practical and philosophical objections to prediction markets and proposes specific institutional designs. More ambitious and speculative than the empirical papers listed above, but thought-provoking.

Online Resources

While academic papers and books provide the deepest analysis, several online resources offer valuable supplementary material:

IEM Official Website (https://iemweb.biz.uiowa.edu/): Current market prices and historical data from the Iowa Electronic Markets.
Metaculus (https://www.metaculus.com/): Active forecasting platform with thousands of questions and an engaged community. A living example of reputation-based prediction.
Manifold Markets (https://manifold.markets/): Active play-money prediction market with an open API. Good for hands-on experimentation.
Polymarket (https://polymarket.com/): The leading cryptocurrency-based prediction market. Current market prices and resolution criteria.
Kalshi (https://kalshi.com/): The first CFTC-regulated prediction market exchange. Current contracts and educational resources.