Chapter 4: Further Reading

A curated list of references for deeper exploration of prediction market contracts, payoff mechanics, market microstructure, and related theory. Entries are organized by topic and annotated with brief descriptions of what each resource covers and why it is useful.

Foundational Theory

1. Arrow, K. J. (1964). "The Role of Securities in the Optimal Allocation of Risk-Bearing." Review of Economic Studies, 31(2), 91-96.

The seminal paper introducing Arrow-Debreu securities — the theoretical foundation for prediction market contracts. Arrow shows that a complete set of state-contingent claims (which is what multi-outcome prediction markets provide) enables optimal risk allocation. Somewhat formal but readable and historically important.

2. Debreu, G. (1959). Theory of Value: An Axiomatic Analysis of Economic Equilibrium. Yale University Press.

The full mathematical treatment of general equilibrium theory including contingent commodities. More technical than Arrow's paper but essential for understanding why complete markets (with contracts for every possible state) are theoretically optimal. Best suited for readers comfortable with mathematical economics.

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

An excellent overview of prediction markets from an academic perspective. Covers how market prices map to probabilities, the types of contracts used, and empirical evidence on market accuracy. A great starting point that bridges theory and practice.

Market Microstructure

4. Harris, L. (2003). Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press.

The definitive practitioner-oriented textbook on market microstructure. Covers order types, order book mechanics, bid-ask spreads, market makers, and execution quality in extraordinary detail. While focused on financial markets, virtually every concept applies directly to prediction market exchanges. Highly recommended.

5. Hasbrouck, J. (2007). Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading. Oxford University Press.

A more quantitative treatment of market microstructure with emphasis on empirical methods. Useful for readers who want to analyze order book data, measure transaction costs, or study price formation. Requires comfort with econometrics.

6. Glosten, L. R. & Milgrom, P. R. (1985). "Bid, Ask and Transaction Prices in a Specialist Market with Heterogeneously Informed Traders." Journal of Financial Economics, 14(1), 71-100.

Foundational paper on how bid-ask spreads arise from information asymmetry. In prediction markets, some traders have better information than others, and the spread reflects the risk of trading against informed participants. This paper explains the mechanism precisely.

Prediction Market Design and Mechanics

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

Robin Hanson's paper on designing markets for complex, multi-dimensional outcomes. Introduces the logarithmic market scoring rule (LMSR) — the automated market maker used by many prediction market platforms. Essential reading for understanding how markets provide liquidity for thinly traded contracts.

8. 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 represent. Manski argues that prices do not necessarily equal probabilities when traders are risk-averse or face wealth constraints. Important for understanding the limits of interpreting prices as pure probabilities.

9. Pennock, D. M. & Sami, R. (2007). "Computational Aspects of Prediction Markets." In Algorithmic Game Theory, Chapter 26. Cambridge University Press.

A survey of the computational mechanisms behind prediction markets, including market scoring rules, combinatorial markets, and automated market makers. Bridges computer science and economics perspectives.

Contract Design and Settlement

10. Tziralis, G. & Tatsiopoulos, I. (2007). "Prediction Markets: An Extended Literature Review." Journal of Prediction Markets, 1(1), 75-91.

A comprehensive literature review covering the history, design, and performance of prediction markets. Includes discussion of different contract types and resolution mechanisms across various platforms. A good reference for seeing the full landscape.

11. 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.

Summarizes the Iowa Electronic Markets' (IEM) experience with election contracts. The IEM was one of the earliest academic prediction markets, and this paper discusses contract design decisions, resolution challenges, and market performance over 12 years.

12. Abramowicz, M. (2004). "Information Markets, Administrative Decisionmaking, and Predictive Cost-Benefit Analysis." University of Chicago Law Review, 71(3), 933-1020.

A legal and policy analysis of prediction markets that includes detailed discussion of contract specification challenges. Particularly useful for understanding how resolution criteria should be designed and what happens when they are ambiguous.

Practical Platforms and Applications

13. Polymarket Documentation. "How Polymarket Works." https://docs.polymarket.com/

The official documentation for Polymarket, one of the largest prediction market platforms. Covers contract types, resolution processes, and trading mechanics. Useful for seeing how the theoretical concepts in this chapter are implemented in practice.

14. Kalshi Documentation. "Kalshi Exchange Rules." https://kalshi.com/docs/

The rulebook for Kalshi, a CFTC-regulated prediction market exchange in the United States. Contains detailed contract specifications, resolution procedures, and fee schedules. Valuable for understanding how a regulated exchange handles the issues discussed in this chapter.

15. Augur Whitepaper. Peterson, J. et al. (2015). "Augur: A Decentralized Oracle and Prediction Market Platform."

The whitepaper for Augur, a decentralized prediction market built on Ethereum. Covers how smart contracts handle settlement, how dispute resolution works through token-weighted voting, and the challenges of decentralized oracle design. Relevant for understanding blockchain-based prediction markets.

Risk Management and Position Sizing

16. Kelly, J. L. (1956). "A New Interpretation of Information Rate." Bell System Technical Journal, 35(4), 917-926.

The original Kelly Criterion paper. While we cover the Kelly Criterion in detail in a later chapter, this paper is relevant here because position sizing is intimately connected to contract payoff structure. Kelly's formula tells you the optimal fraction of your bankroll to wager based on your edge and the odds.

17. Thorp, E. O. (2006). "The Kelly Criterion in Blackjack, Sports Betting, and the Stock Market." In Handbook of Asset and Liability Management, Vol. 1, 385-428.

Edward Thorp's accessible treatment of the Kelly Criterion with practical examples. Includes discussion of binary bet sizing that maps directly to prediction market positions.

Behavioral and Empirical Insights

18. Snowberg, E., Wolfers, J., & Zitzewitz, E. (2013). "Prediction Markets for Economic Forecasting." In Handbook of Economic Forecasting, Vol. 2A, 657-687.

Analyzes how well prediction markets forecast economic variables — directly relevant to Case Study 2 in this chapter. Covers the accuracy of market-implied distributions for GDP, employment, and other indicators.

19. Page, L. (2012). "'It Ain't Over Till It's Over': Yogi Berra Bias on Prediction Markets." Journal of Economic Behavior & Organization, 84(3), 776-781.

Examines how prediction market prices behave as events unfold, finding that prices sometimes underreact to new information. Relevant for understanding the dynamics of position management and trade lifecycle discussed in Section 4.4.

Online Resources

20. Metaculus. "Forecasting Resources." https://www.metaculus.com/help/

While Metaculus is a reputation-based forecasting platform rather than a monetary prediction market, their documentation on question design and resolution is excellent. Their approach to resolution criteria is a model of clarity and can inform how you evaluate contract specifications on monetary platforms.