Chapter 28: Further Reading

Foundational Works

1. Arrow, K.J., Forsythe, R., Gorham, M., Hahn, R., Hanson, R., Ledyard, J.O., Levmore, S., Litan, R., Milgrom, P., Nelson, F.D., Neumann, G.R., Ottaviani, M., Schelling, T.C., Shiller, R.J., Smith, V.L., Snowberg, E., Sunstein, C.R., Tetlock, P.C., Tetlock, P.E., Thaler, R.H., Toder, E., & Wolfers, J. (2008). "The Promise of Prediction Markets." Science, 320(5878), 877--878.

A landmark letter signed by a who's-who of economists and social scientists making the case for legalizing and expanding prediction markets. Provides the intellectual justification for why market design matters: well-designed markets aggregate information better than any known alternative. Essential context for understanding the stakes of good design.

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

The definitive survey article on prediction markets as of the mid-2000s. Covers the basics of how prediction markets work, their accuracy relative to polls and experts, and the key design considerations. Sections on contract design and market structure directly inform this chapter's discussion of outcome space design and resolution criteria.

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

Robin Hanson's seminal paper on the Logarithmic Market Scoring Rule (LMSR), which is the market-making mechanism assumed throughout this chapter's discussion of liquidity seeding and AMM design. Understanding the LMSR is essential for implementing the subsidy strategies described in Section 28.7.

Market Design and Question Formulation

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

While not specifically about prediction markets, this book provides the best available treatment of question design for forecasting. The discussion of "Fermitization" (breaking vague questions into specific, measurable components) directly parallels the SMART framework presented in Section 28.2. The book's treatment of calibration and scoring is essential background for Section 28.9.

5. Atanasov, P., Rescober, P., Stone, E., Swift, S.A., Servan-Schreiber, E., Tetlock, P., Ungar, L., & Mellers, B. (2017). "Distilling the Wisdom of Crowds: Prediction Markets vs. Prediction Polls." Management Science, 63(3), 691--706.

Directly addresses the effectiveness of prediction market subsidization. Shows that even modest subsidies ($50--$500) significantly improve participation and accuracy. The empirical findings inform the cost-effectiveness analysis in Section 28.7.5 and provide concrete evidence for subsidy allocation decisions.

6. Lichtenstein, S., Fischhoff, B., & Phillips, L.D. (1982). "Calibration of Probabilities: The State of the Art to 1980." In D. Kahneman, P. Slovic, & A. Tversky (Eds.), Judgment Under Uncertainty: Heuristics and Biases. Cambridge University Press.

The foundational work on calibration measurement. The Brier score and calibration curve methods used in Section 28.9 derive from this tradition. Understanding calibration is essential for evaluating whether a prediction market's design is producing well-calibrated probability estimates.

Resolution and Edge Cases

7. Metaculus. "Metaculus Question Writing Guide." Available at: metaculus.com/help/question-writing/

Metaculus has developed one of the most sophisticated question-writing frameworks in the prediction market space. Their public guide covers resolution criteria design, edge case handling, and the "fine print" methodology discussed in Section 28.10.2. A practical complement to the theoretical framework in this chapter.

8. Polymarket. "Resolution Rules." Available at: polymarket.com

Polymarket's publicly available resolution rules for their markets provide real-world examples of how the principles in Section 28.3 are applied at scale. Studying their resolution criteria for election markets, economic indicators, and event-based markets offers concrete illustrations of the resolution source hierarchy and edge case handling.

Liquidity, Incentives, and Market Quality

9. Chen, Y. & Pennock, D.M. (2010). "Designing Markets for Prediction." AI Magazine, 31(4), 42--52.

An accessible overview of automated market maker design for prediction markets, with specific attention to how AMM parameters (particularly the LMSR's $b$ parameter) affect market quality. Directly relevant to the liquidity seeding discussion in Section 28.7 and the quality metrics in Section 28.9.

10. Cowgill, B. & Zitzewitz, E. (2015). "Corporate Prediction Markets: Evidence from Google, Ford, and Firm X." Review of Economic Studies, 82(4), 1309--1341.

The most comprehensive study of internal corporate prediction markets. Provides evidence on how market design choices (incentive structure, participation rules, question framing) affect accuracy and participation in real organizational settings. Essential reading for anyone designing markets for corporate or institutional use.

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

A careful analysis of what prediction market prices actually mean. Manski shows that under risk-averse preferences, market prices may not equal probabilities. This has direct implications for market design: the resolution criteria must be designed so that prices have a clear probabilistic interpretation, as discussed in Section 28.5.

Automated and Scalable Market Creation

12. Witkowski, J. & Parkes, D.C. (2012). "A Robust Bayesian Truth Serum for Small Populations." Proceedings of the AAAI Conference on Artificial Intelligence.

Addresses the challenge of resolution when ground truth is unavailable -- relevant to automated resolution systems (Section 28.11.5) and the design of markets where objective resolution sources do not exist.

13. Abernethy, J., Chen, Y., & Vaughan, J.W. (2013). "Efficient Market Making via Convex Optimization, and a Connection to Online Learning." ACM Transactions on Economics and Computation, 1(2), Article 12.

Provides the theoretical foundation for cost-function-based market makers, generalizing the LMSR. Relevant for understanding the mathematical properties that make automated market makers suitable for scalable market creation and the quality metrics that can be derived from cost-function properties.

Platform Design and Operations

14. Berg, J.E., Forsythe, R., Nelson, F.D., & Rietz, T.A. (2008). "Results from a Dozen Years of Election Futures Markets Research." In C.R. Plott & V.L. Smith (Eds.), Handbook of Experimental Economics Results, Vol. 1. North-Holland.

Reports on the Iowa Electronic Markets (IEM), one of the longest-running prediction market platforms. Provides longitudinal evidence on how market design evolves over time and how operational decisions (market creation timing, resolution procedures, participant rules) affect market quality across many election cycles.

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

Places prediction markets in the broader context of collective intelligence mechanisms. Sunstein's discussion of the conditions under which markets outperform (or fail relative to) deliberation, polls, and expert panels provides important context for understanding when the effort of careful market design pays off -- and when simpler mechanisms might suffice.