Chapter 5: Further Reading

Platform-Specific Resources

Polymarket

• Polymarket Documentation: https://docs.polymarket.com/ — Official API documentation for both the Gamma and CLOB APIs.
• Conditional Token Framework Specification: https://docs.gnosis.io/conditionaltokens/ — The technical specification for the token standard that Polymarket uses, originally developed by Gnosis.
• UMA Optimistic Oracle: https://docs.uma.xyz/ — Documentation for the oracle system used by Polymarket for market resolution.
• CFTC Settlement with Polymarket (2022): CFTC Order No. 22-01, available at cftc.gov — The 2022 consent order detailing Polymarket's settlement for offering unregistered binary options.

Kalshi

• Kalshi API Documentation: https://trading-api.readme.io/ — Official REST API docs with Swagger/OpenAPI specifications.
• Kalshi CFTC DCM Approval: Search for Kalshi's Form DCM filing on the CFTC website — Details of the regulatory approval process.
• Kalshi v. CFTC (Election Contracts Case): The federal court decision allowing Kalshi to offer congressional control contracts, an important legal precedent for U.S. prediction markets.

Metaculus

• Metaculus FAQ and Methodology: https://www.metaculus.com/help/faq/ — Detailed explanation of scoring, aggregation, and platform mechanics.
• Metaculus API: https://www.metaculus.com/api/ — API endpoint documentation.
• Metaculus Journal: https://www.metaculus.com/journal/ — Research articles and analysis published by the Metaculus team.

Manifold Markets

• Manifold Markets GitHub Repository: https://github.com/manifoldmarkets/manifold — The fully open-source codebase.
• Manifold API Documentation: https://docs.manifold.markets/api — REST API documentation with examples.
• Manifold Markets Blog: https://news.manifold.markets/ — Platform updates, design decisions, and market analysis.

PredictIt

• CFTC No-Action Letter to Victoria University of Wellington (2014): CFTC Letter No. 14-130, available at cftc.gov — The original letter authorizing PredictIt's operation.
• CFTC Withdrawal of No-Action Relief (2022): CFTC press release announcing the withdrawal of PredictIt's no-action letter.

Academic Papers

Prediction Market Accuracy and Design

• Arrow, K. J., Forsythe, R., Gorham, M., et al. (2008). "The Promise of Prediction Markets." Science, 320(5878), 877-878. A landmark endorsement of prediction markets by a group of prominent economists.

• Wolfers, J., & Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives, 18(2), 107-126. A comprehensive academic survey of prediction markets covering theory, evidence, and design.

• Manski, C. F. (2006). "Interpreting the Predictions of Prediction Markets." Economics Letters, 91(3), 425-429. Important caveats about interpreting prediction market prices as probabilities.

• Page, L. (2012). "'Are prediction markets more accurate than polls?' Lessons from the 2008 US presidential election." Journal of Prediction Markets, 2(1). Empirical comparison of prediction market accuracy vs. polling.

Calibration and Forecasting

• Tetlock, P. E. (2005). Expert Political Judgment: How Good Is It? How Can We Know? Princeton University Press. The foundational work on forecaster calibration and the superiority of "foxes" over "hedgehogs."

• Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown. Popular account of the Good Judgment Project, demonstrating that some forecasters are consistently better than others.

• Satopaa, V. A., Baron, J., Foster, D. P., et al. (2014). "Combining Multiple Probability Predictions Using a Simple Logit Model." International Journal of Forecasting, 30(2), 344-356. Methods for aggregating multiple probability forecasts.

Market Microstructure

• Hanson, R. (2003). "Combinatorial Information Market Design." Information Systems Frontiers, 5(1), 107-119. Theoretical foundations for automated market makers in prediction markets.

• Hanson, R. (2007). "Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation." Journal of Prediction Markets, 1(1), 3-15. The LMSR (Logarithmic Market Scoring Rule) that influenced many AMM designs.

• Othman, A., & Sandholm, T. (2013). "The Gates Hillman Prediction Market." Review of Economic Design, 17(2), 95-128. Analysis of a real-world internal prediction market at Carnegie Mellon.

Regulation

• Sunstein, C. R. (2006). "Deliberating Groups versus Prediction Markets (or Hayek's Challenge to Habermas)." Episteme, 3(3), 192-213. Theoretical argument for prediction markets as superior information aggregators compared to deliberative groups.

• Bell, T. W. (2006). "Prediction Markets for Promoting the Progress of Science and the Useful Arts." George Mason Law Review, 14(1), 37-92. Legal analysis of prediction markets and proposals for regulatory frameworks.

Crypto-Native Markets

• Clark, J. (2021). "Decentralized Prediction Markets: Design, Governance, and Regulation." Working paper examining the unique challenges of blockchain-based prediction markets.

• Gnosis Conditional Token Framework Whitepaper: https://docs.gnosis.io/conditionaltokens/docs/introduction/ — Technical whitepaper describing the conditional token framework.

Books

• Abramowicz, M. (2008). Predictocracy: Market Mechanisms for Public and Private Decision Making. Yale University Press. Explores how prediction markets could transform governance and decision-making.

• Surowiecki, J. (2004). The Wisdom of Crowds. Doubleday. The popular classic on why groups often outperform individual experts, with prediction markets as a key mechanism.

• Rhee, R. J. (2009). Understanding Prediction Markets. Cambridge University Press. Academic overview of prediction market theory and practice.

Data Sources

• Polymarket Data: Historical market data available through the Gamma API and CLOB API. Third-party data aggregators like Polymarket Whales also provide analysis.

• Kalshi Data: Historical trades and price data available through the Kalshi API. Kalshi also publishes market reports.

• Metaculus Data: Historical question data and predictions available through the API. Metaculus has published several research datasets.

• Manifold Data: Full market history available through the API and direct database access (Supabase). As an open-source project, all data is accessible.

• PredictIt Historical Data: Academic datasets of PredictIt trades were shared with researchers. Some datasets are available through academic data repositories.

Blogs and Online Resources

• Asterisk Magazine: Publishes longform articles on forecasting and prediction markets.
• Scott Alexander (Astral Codex Ten): Frequent commentary on prediction markets and forecasting.
• Nate Silver's Substack: Election modeling and prediction market analysis from the founder of FiveThirtyEight.
• Forecasting Research Institute (FRI): Research organization focused on improving forecasting methods.
• Good Judgment Project: Ongoing forecasting tournament with research publications.

Tools and Libraries

• py-clob-client: Polymarket's Python client library for interacting with the CLOB API. Available on PyPI and GitHub.
• httpx: Modern Python HTTP client library used throughout this chapter's examples. https://www.python-httpx.org/
• manifoldpy: Unofficial Python wrapper for the Manifold Markets API.
• ergo: Python library for working with probability distributions, useful for Metaculus-style continuous forecasting.

Chapter 5 of "Learning Prediction Markets — From Concepts to Strategies"