Chapter 11: Further Reading

Foundational Papers

Hayek and the Price System

  • Hayek, F. A. (1945). "The Use of Knowledge in Society." American Economic Review, 35(4), 519-530. The foundational essay on prices as information carriers. Essential reading for understanding why markets aggregate information. Hayek's argument about dispersed knowledge and the impossibility of central planning directly motivates the use of prediction markets.

Efficient Market Hypothesis

  • Fama, E. F. (1970). "Efficient Capital Markets: A Review of Theory and Empirical Work." Journal of Finance, 25(2), 383-417. The definitive statement of the EMH, classifying efficiency into weak, semi-strong, and strong forms. While written for financial markets, the framework directly applies to prediction markets.

  • Muth, J. F. (1961). "Rational Expectations and the Theory of Price Movements." Econometrica, 29(3), 315-335. The paper that introduced rational expectations, providing the formal framework for understanding how agents form beliefs consistent with market equilibrium.

No-Trade Theorem

  • Milgrom, P. and Stokey, N. (1982). "Information, Trade and Common Knowledge." Journal of Economic Theory, 26(1), 17-27. The seminal no-trade theorem paper. Understanding this result is essential for grappling with the fundamental question of why prediction markets have active trading.

Information Cascades

  • Bikhchandani, S., Hirshleifer, D., and Welch, I. (1992). "A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades." Journal of Political Economy, 100(5), 992-1026. The foundational paper on information cascades, showing how rational agents can rationally ignore their private information and follow the herd.

  • Banerjee, A. V. (1992). "A Simple Model of Herd Behavior." Quarterly Journal of Economics, 107(3), 797-817. An alternative model of herding behavior that complements the Bikhchandani et al. framework.

Prediction Market Theory

Information Aggregation

  • Wolfers, J. and Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives, 18(2), 107-126. An excellent survey of prediction market theory and evidence, covering information aggregation, efficiency, and applications. Recommended as a first read on the topic.

  • Manski, C. F. (2006). "Interpreting the Predictions of Prediction Markets." Economics Letters, 91(3), 425-429. A critical analysis of what prediction market prices actually mean. Manski argues that prices reflect risk-neutral probabilities, which may differ from true probabilities. An important counterpoint to naive interpretations.

  • Gjerstad, S. (2005). "Risk Aversion, Beliefs, and Prediction Market Equilibrium." Working paper. Examines how risk preferences affect prediction market prices and the relationship between prices and true probabilities.

Marginal Trader Hypothesis

  • Forsythe, R., Nelson, F., Neumann, G. R., and Wright, J. (1992). "Anatomy of an Experimental Political Stock Market." American Economic Review, 82(5), 1142-1161. The seminal paper on the Iowa Electronic Markets, introducing the marginal trader hypothesis and providing early evidence that a small fraction of informed traders drives market accuracy.

  • Berg, J. E., Nelson, F. D., and Rietz, T. A. (2008). "Prediction Market Accuracy in the Long Run." International Journal of Forecasting, 24(2), 285-300. Comprehensive analysis of IEM accuracy over two decades, comparing market forecasts to polls. Strong evidence for the marginal trader hypothesis.

Manipulation

  • Hanson, R. and Oprea, R. (2009). "A Manipulator Can Aid Prediction Market Accuracy." Economica, 76(302), 304-314. The counterintuitive finding that manipulation attempts can actually improve prediction market accuracy by attracting informed traders. Essential reading for the robustness discussion.

  • Camerer, C. F. (1998). "Can Asset Markets Be Manipulated? A Field Experiment with Racetrack Betting." Journal of Political Economy, 106(3), 457-482. An early empirical study of market manipulation, using racetrack betting as a laboratory.

Wisdom of Crowds

  • Surowiecki, J. (2004). The Wisdom of Crowds. New York: Doubleday. The popular book that brought crowd wisdom to a broad audience. Accessible and full of examples, though the theoretical treatment is informal. Good complement to the formal analysis in this chapter.

  • Galton, F. (1907). "Vox Populi." Nature, 75, 450-451. The original "wisdom of crowds" observation: the median estimate by fairgoers of an ox's weight was remarkably accurate. A delightful piece of scientific history.

  • Hong, L. and Page, S. E. (2004). "Groups of Diverse Problem Solvers Can Outperform Groups of High-Ability Problem Solvers." Proceedings of the National Academy of Sciences, 101(46), 16385-16389. A formal treatment of why diversity of perspective can be more valuable than individual ability. The mathematical framework directly applies to prediction market composition.

Agent-Based Modeling

  • Gode, D. K. and Sunder, S. (1993). "Allocative Efficiency of Markets with Zero-Intelligence Traders: Market as a Partial Substitute for Individual Rationality." Journal of Political Economy, 101(1), 119-137. The paper that introduced zero-intelligence traders, showing that market structure alone (even without intelligent agents) can achieve some degree of efficiency. A foundational result for agent-based modeling of markets.

  • LeBaron, B. (2006). "Agent-Based Computational Finance." In Handbook of Computational Economics, Vol. 2, Elsevier, 1187-1233. A comprehensive survey of agent-based models in finance, covering the technical details of building and analyzing ABMs.

  • Farmer, J. D. and Foley, D. (2009). "The Economy Needs Agent-Based Modelling." Nature, 460, 685-686. A brief but influential argument for why agent-based modeling is necessary for understanding economic systems, particularly financial markets.

Mechanism Design and Market Design

  • Hanson, R. (2003). "Combinatorial Information Market Design." Information Systems Frontiers, 5(1), 107-119. The paper that introduced the LMSR and combinatorial prediction markets. Essential for understanding how market structure affects information aggregation.

  • Hanson, R. (2013). "Shall We Vote on Values, But Bet on Beliefs?" Journal of Political Philosophy, 21(2), 151-178. The "futarchy" proposal: governance by prediction market. An ambitious vision for using conditional prediction markets to guide policy decisions.

  • Chen, Y. and Pennock, D. M. (2010). "Designing Markets for Prediction." AI Magazine, 31(4), 42-52. A practical guide to prediction market design, covering automated market makers, scoring rules, and the connection between them.

Empirical Studies

Election Predictions

  • Arrow, K. J., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J. O., ... and Zitzewitz, E. (2008). "The Promise of Prediction Markets." Science, 320(5878), 877-878. A collective letter from leading economists and computer scientists making the case for prediction markets. Published in Science, it represents the strongest mainstream endorsement of the field.

Corporate Applications

  • Cowgill, B. and Zitzewitz, E. (2015). "Corporate Prediction Markets: Evidence from Google, Ford, and Firm X." Review of Economic Studies, 82(4), 1309-1341. Rigorous analysis of internal prediction markets at major corporations. Shows that corporate prediction markets can outperform official forecasts.

Science and Replication

  • Dreber, A., Pfeiffer, T., Almenberg, J., Isaksson, S., Wilson, B., Chen, Y., ... and Johannesson, M. (2015). "Using Prediction Markets to Estimate the Reproducibility of Scientific Research." Proceedings of the National Academy of Sciences, 112(50), 15343-15347. A striking application of prediction markets to predict which published scientific results would replicate. Markets outperformed survey-based methods.

Forecasting Tournaments

  • Tetlock, P. E. and Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. New York: Crown. While not specifically about prediction markets, this book on the IARPA ACE forecasting tournament provides the best comparison framework for prediction market accuracy. Essential context for evaluating market performance.

Behavioral and Experimental Economics

  • Plott, C. R. and Sunder, S. (1988). "Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets." Econometrica, 56(5), 1085-1118. A landmark experimental paper showing that laboratory markets can aggregate information held by different traders. The experimental evidence for the Hayek Hypothesis.

  • Grossman, S. J. and Stiglitz, J. E. (1980). "On the Impossibility of Informationally Efficient Markets." American Economic Review, 70(3), 393-408. The famous paradox: if markets were perfectly efficient, no one would have incentive to gather information, so they could not be efficient. Provides the theoretical foundation for understanding why markets are never perfectly efficient.

Online Resources

  • Prediction Markets (Wikipedia). A well-maintained overview with links to major prediction market platforms and research. https://en.wikipedia.org/wiki/Prediction_market

  • Iowa Electronic Markets. The longest-running real-money prediction market in the United States. Historical data and research papers available. https://iemweb.biz.uiowa.edu/

  • Metaculus. A community forecasting platform that combines elements of prediction markets and forecasting tournaments. Active community with detailed question resolution criteria. https://www.metaculus.com/

  • Robin Hanson's blog (Overcoming Bias). Extensive writing on prediction markets, futarchy, and mechanism design by one of the field's pioneers. https://www.overcomingbias.com/

Suggested Reading Order

For readers new to the topic: 1. Surowiecki (2004) — accessible introduction to crowd wisdom 2. Wolfers and Zitzewitz (2004) — survey of prediction market theory 3. Hayek (1945) — the foundational philosophical argument 4. Berg, Nelson, and Rietz (2008) — empirical evidence

For advanced readers: 1. Milgrom and Stokey (1982) — no-trade theorem 2. Grossman and Stiglitz (1980) — impossibility of perfect efficiency 3. Hanson (2003) — mechanism design 4. Plott and Sunder (1988) — experimental evidence 5. Hanson and Oprea (2009) — manipulation robustness