Chapter 5: Further Reading

Platform-Specific Resources

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 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 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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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"