Appendix H: Bibliography

This annotated bibliography collects the most important scholarly works, technical references, and practical resources relevant to the study of prediction markets. The entries are organized into ten thematic sections that mirror the arc of the textbook, from foundational theory through implementation and real-world application. Each entry includes a brief annotation explaining its relevance. Where possible, we have cited the most widely available version of each work. Readers pursuing deeper study in any area will find that the references within these sources form rich networks of further reading.

H.1 Foundational Works on Prediction Markets

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., Toder, H., & Wolfers, J. (2008). "The Promise of Prediction Markets." Science, 320(5878), 877–878. — A landmark collective statement by leading economists and decision scientists arguing that prediction markets aggregate information efficiently and that regulatory barriers to their use should be reduced. Essential reading for understanding the intellectual consensus behind prediction market advocacy.

2. Hayek, F. A. (1945). "The Use of Knowledge in Society." American Economic Review, 35(4), 519–530. — The classic essay on dispersed knowledge and the price system as an information aggregation mechanism. Provides the deepest intellectual foundation for why prediction markets work: prices summarize private information held by many individuals.

3. Hanson, R. (2003). "Combinatorial Information Market Design." Information Systems Frontiers, 5(1), 107–119. — Introduces the Logarithmic Market Scoring Rule (LMSR), the most important automated market maker for prediction markets. This paper is the technical bedrock of modern prediction market mechanism design and is referenced throughout this textbook.

4. Hanson, R. (2007). "Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation." Journal of Prediction Markets, 1(1), 3–15. — Extends the LMSR framework to combinatorial settings where traders can bet on combinations of outcomes. Demonstrates how scoring-rule-based markets can handle exponentially large outcome spaces.

5. Wolfers, J., & Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives, 18(2), 107–126. — The most widely cited survey article on prediction markets, providing accessible coverage of how they work, their empirical track record, and their limitations. An ideal starting point for any student of the field.

6. Wolfers, J., & Zitzewitz, E. (2006). "Interpreting Prediction Market Prices as Probabilities." NBER Working Paper No. 12200. — Examines the conditions under which prediction market prices can and cannot be interpreted as probabilities. Discusses the effects of risk aversion and heterogeneous beliefs on price-probability mappings.

7. Manski, C. F. (2006). "Interpreting the Predictions of Prediction Markets." Economics Letters, 91(3), 425–429. — A rigorous critique showing that prediction market prices need not equal mean beliefs under plausible assumptions about trader heterogeneity and risk preferences. Required reading for anyone making probability claims from market prices.

8. 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. Elsevier. — Summarizes the extensive empirical record of the Iowa Electronic Markets, demonstrating that prediction markets frequently outperform polls in forecasting elections.

9. Berg, J. E., & Rietz, T. A. (2003). "Prediction Markets as Decision Support Systems." Information Systems Frontiers, 5(1), 79–93. — Explores the use of prediction markets not merely as forecasting tools but as aids to organizational decision-making. Establishes the conceptual link between market prices and actionable intelligence.

10. Forsythe, R., Nelson, F., Neumann, G. R., & Wright, J. (1992). "Anatomy of an Experimental Political Stock Market." American Economic Review, 82(5), 1142–1161. — One of the earliest rigorous empirical studies of prediction markets, documenting the performance of the Iowa Electronic Markets in the 1988 U.S. presidential election. Establishes the empirical template for prediction market research.

11. Plott, C. R., & Sunder, S. (1988). "Rational Expectations and the Aggregation of Diverse Information in Laboratory Security Markets." Econometrica, 56(5), 1085–1118. — Foundational experimental economics paper demonstrating that laboratory markets can aggregate private information into prices consistent with rational expectations equilibrium. Provides key experimental evidence for the information aggregation hypothesis.

12. Surowiecki, J. (2004). The Wisdom of Crowds: Why the Many Are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations. Doubleday. — The popular book that introduced the concept of crowd wisdom to a mass audience. While not a technical reference, it frames the intuition behind prediction markets in accessible terms and motivated much subsequent research.

13. Sunstein, C. R. (2006). Infotopia: How Many Minds Produce Knowledge. Oxford University Press. — Examines prediction markets alongside other information aggregation mechanisms such as wikis and deliberation. Provides a balanced assessment of when markets succeed and when they fail relative to alternative institutions.

14. Rhode, P. W., & Strumpf, K. S. (2004). "Historical Presidential Betting Markets." Journal of Economic Perspectives, 18(2), 127–141. — Documents the extensive history of political betting markets in the United States dating back to the 19th century, showing that prediction markets are not a modern invention but have deep historical roots.

15. Tziralis, G., & Tatsiopoulos, I. (2007). "Prediction Markets: An Extended Literature Review." Journal of Prediction Markets, 1(1), 75–91. — A comprehensive survey of the prediction markets literature as of the mid-2000s, cataloging empirical findings, theoretical contributions, and open questions. Useful as a map of the field's early development.

16. Page, S. E. (2007). The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton University Press. — Provides formal models explaining why diverse groups outperform homogeneous experts, offering theoretical grounding for why prediction markets with diverse participants produce accurate forecasts.

17. Ottaviani, M., & Sørensen, P. N. (2010). "Noise, Information, and the Favorite–Longshot Bias in Parimutuel Predictions." American Economic Journal: Microeconomics, 2(1), 58–85. — Analyzes the systematic mispricing of low-probability and high-probability events in parimutuel markets. Important for understanding when and why prediction market prices deviate from true probabilities.

H.2 Market Microstructure and Mechanism Design

18. Chen, Y., & Pennock, D. M. (2010). "Designing Markets for Prediction." AI Magazine, 31(4), 42–52. — An accessible overview of computational approaches to prediction market design, covering scoring rules, market makers, and combinatorial markets. Bridges the computer science and economics literatures on mechanism design.

19. Pennock, D. M. (2004). "A Dynamic Pari-Mutuel Market for Hedging, Wagering, and Information Aggregation." Proceedings of the 5th ACM Conference on Electronic Commerce (EC '04), 170–179. ACM. — Introduces the dynamic pari-mutuel market mechanism, a hybrid of continuous trading and pari-mutuel pooling. Demonstrates how alternative market structures can improve liquidity and information aggregation.

20. 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. — Establishes deep connections between automated market making and online convex optimization, showing that market makers can be designed using tools from machine learning theory. A key paper linking prediction market design to computational learning theory.

21. Chen, Y., & Vaughan, J. W. (2010). "A New Understanding of Prediction Markets via No-Regret Learning." Proceedings of the 11th ACM Conference on Electronic Commerce (EC '10), 189–198. ACM. — Shows that cost-function-based market makers are equivalent to no-regret learning algorithms, providing a powerful new lens for understanding market maker behavior and convergence properties.

22. Hanson, R. (2003). "Combinatorial Information Market Design." Information Systems Frontiers, 5(1), 107–119. — (See entry 3 above for annotation.) Also foundational for market microstructure; the LMSR is the dominant automated market maker in the literature.

23. Othman, A., Sandholm, T., Pennock, D. M., & Reeves, D. M. (2013). "A Practical Liquidity-Sensitive Automated Market Maker." ACM Transactions on Economics and Computation, 1(3), Article 14. — Proposes a market maker that adapts its liquidity parameter based on trading volume, improving on the fixed-liquidity LMSR. Important for practical implementations where the designer does not know the appropriate liquidity level in advance.

24. 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. — The foundational model of bid-ask spreads arising from adverse selection. Essential background for understanding why market makers in prediction markets must manage the risk of trading against better-informed participants.

25. Kyle, A. S. (1985). "Continuous Auctions and Insider Trading." Econometrica, 53(6), 1315–1335. — Introduces the canonical model of strategic trading by an informed insider, yielding key results about price impact, market depth, and information revelation. Underpins the theory of how information enters prediction market prices.

26. Dudik, M., Lahaie, S., Pennock, D. M., & Rothschild, D. (2013). "A Combinatorial Prediction Market with Arbitrary Outcomes." Proceedings of the 14th ACM Conference on Electronic Commerce (EC '13), 427–428. ACM. — Extends combinatorial prediction market mechanisms to handle arbitrary outcome structures, broadening the applicability of automated market makers.

27. Brahma, A., Chakraborty, M., Das, S., Lavoie, A., & Magdon-Ismail, M. (2012). "A Bayesian Market Maker." Proceedings of the 13th ACM Conference on Electronic Commerce (EC '12), 215–232. ACM. — Proposes a market maker that maintains an explicit Bayesian posterior over states of the world, providing a principled framework for pricing in prediction markets with structured state spaces.

28. Ostrovsky, M. (2012). "Information Aggregation in Dynamic Markets with Strategic Traders." Econometrica, 80(6), 2595–2647. — Proves that prediction markets aggregate information even when traders are fully strategic and forward-looking, not just myopic. A deep theoretical result validating the information aggregation properties of markets.

29. Agrawal, S., Delage, E., Peters, M., Wang, Z., & Ye, Y. (2011). "A Unified Framework for Dynamic Prediction Market Design." Operations Research, 59(3), 550–568. — Provides a general mathematical framework unifying various prediction market mechanisms, including LMSR and dynamic pari-mutuel markets, under a common optimization-based formulation.

30. Milgrom, P. R. (1981). "Rational Expectations, Information Acquisition, and Competitive Bidding." Econometrica, 49(4), 921–943. — Foundational work on information aggregation in competitive settings. Establishes key results about when and how market mechanisms reveal private information, directly relevant to prediction market theory.

31. Shi, P., Velez, R. A., & Vohra, R. V. (2021). "On Forward Induction and Evolutionary Stability in a Market Scoring Rule." Journal of Economic Theory, 192, 105192. — Analyzes strategic behavior in market scoring rule prediction markets from a game-theoretic perspective, examining equilibrium selection and stability.

32. Frongillo, R., & Kash, I. A. (2015). "Vector-Valued Property Elicitation." Proceedings of the 28th Conference on Learning Theory (COLT 2015), 710–727. — Extends the theory of proper scoring rules to vector-valued properties, providing mathematical foundations relevant to multi-outcome prediction market design.

33. Sethi, R., & Vaughan, J. W. (2016). "Belief Aggregation with Automated Market Makers." Computational Economics, 48(1), 155–178. — Investigates how automated market makers aggregate heterogeneous trader beliefs into prices, providing both theoretical analysis and simulation results relevant to understanding LMSR behavior.

H.3 Probability, Forecasting, and Calibration

34. Tetlock, P. E. (2005). Expert Political Judgment: How Good Is It? How Can We Know? Princeton University Press. — The landmark study documenting the poor calibration of expert forecasters across political and economic domains, and showing that simple statistical models often outperform human experts. Motivates the use of prediction markets as an alternative to expert opinion.

35. Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown. — Presents the findings of the Good Judgment Project, identifying the traits and techniques of exceptionally accurate forecasters. Directly relevant to developing trading skill in prediction markets.

36. Brier, G. W. (1950). "Verification of Forecasts Expressed in Terms of Probability." Monthly Weather Review, 78(1), 1–3. — Introduces the Brier score, the most widely used proper scoring rule for evaluating probabilistic forecasts. A foundational reference for calibration assessment in prediction markets.

37. de Finetti, B. (1937). "La Prévision: Ses Lois Logiques, Ses Sources Subjectives." Annales de l'Institut Henri Poincaré, 7(1), 1–68. English translation in Kyburg, H. E., & Smokler, H. E. (Eds.), Studies in Subjective Probability (1964). — The foundational statement of subjective probability theory, establishing that coherent degrees of belief must obey the axioms of probability. The philosophical bedrock of prediction markets, which elicit subjective probabilities through economic incentives.

38. Good, I. J. (1952). "Rational Decisions." Journal of the Royal Statistical Society, Series B, 14(1), 107–114. — Introduces the logarithmic scoring rule and develops the idea that proper scoring rules incentivize honest probability reporting. A key precursor to the scoring-rule-based market makers used in modern prediction markets.

39. Savage, L. J. (1971). "Elicitation of Personal Probabilities and Expectations." Journal of the American Statistical Association, 66(336), 783–801. — Develops the theory of eliciting subjective probabilities using proper scoring rules, establishing key results on incentive compatibility that underpin prediction market design.

40. Gneiting, T., & Raftery, A. E. (2007). "Strictly Proper Scoring Rules, Prediction, and Estimation." Journal of the American Statistical Association, 102(477), 359–378. — The definitive modern treatment of proper scoring rules, providing a comprehensive mathematical framework. Essential reference for understanding the scoring rules that underlie automated market makers.

41. Dawid, A. P. (1982). "The Well-Calibrated Bayesian." Journal of the American Statistical Association, 77(379), 605–610. — Establishes fundamental results about calibration of probabilistic forecasts, showing conditions under which Bayesian forecasters are well-calibrated. Important for evaluating prediction market accuracy.

42. Murphy, A. H., & Winkler, R. L. (1987). "A General Framework for Forecast Verification." Monthly Weather Review, 115(7), 1330–1338. — Introduces a decomposition of forecast quality into calibration, resolution, and uncertainty components. Provides the conceptual framework used throughout this textbook for evaluating prediction market performance.

43. Satopää, V. A., Baron, J., Foster, D. P., Mellers, B. A., Tetlock, P. E., & Ungar, L. H. (2014). "Combining Multiple Probability Predictions Using a Simple Logit Model." International Journal of Forecasting, 30(2), 344–356. — Develops methods for aggregating multiple probabilistic forecasts, directly relevant to combining individual trader predictions and understanding how markets perform this aggregation.

44. Ranjan, R., & Gneiting, T. (2010). "Combining Probability Forecasts." Journal of the Royal Statistical Society, Series B, 72(1), 71–91. — Provides theoretical foundations for combining probability forecasts from multiple sources, relevant to understanding how prediction markets aggregate diverse opinions into a single price.

45. Lichtenstein, S., Fischhoff, B., & Phillips, L. D. (1982). "Calibration of Probabilities: The State of the Art to 1980." In Kahneman, D., Slovic, P., & Tversky, A. (Eds.), Judgment Under Uncertainty: Heuristics and Biases, 306–334. Cambridge University Press. — The classic review of calibration research, documenting systematic overconfidence and other biases in human probability assessment. Explains why naive probability estimates are often poorly calibrated and why markets may help correct these biases.

46. Cooke, R. M. (1991). Experts in Uncertainty: Opinion and Subjective Probability in Science. Oxford University Press. — A comprehensive treatment of expert probability elicitation, discussing calibration, coherence, and aggregation. Provides theoretical background for understanding why structured elicitation mechanisms like prediction markets outperform informal expert judgment.

47. Winkler, R. L. (1996). "Scoring Rules and the Evaluation of Probabilities." Test, 5(1), 1–60. — An extensive survey of scoring rules and their properties, covering both theoretical foundations and practical applications. A valuable reference for the scoring rule concepts used throughout this textbook.

48. Jose, V. R. R., Nau, R. F., & Winkler, R. L. (2008). "Scoring Rules, Generalized Entropy, and Utility Maximization." Operations Research, 56(5), 1146–1157. — Establishes deep connections between scoring rules, information theory, and utility theory. Shows how market scoring rules relate to fundamental concepts in decision theory and information economics.

49. Ungar, L., Mellers, B., Satopää, V., Tetlock, P., & Baron, J. (2012). "The Good Judgment Project: A Large Scale Test of Different Methods of Combining Expert Predictions." AAAI Technical Report FS-12-06, 37–42. — Describes the methodology and early results of the Good Judgment Project, the IARPA-funded forecasting tournament that demonstrated the power of aggregated human judgment. Directly informs the chapters on forecasting tournaments and prediction aggregation.

H.4 Trading Strategies and Behavioral Finance

50. Kelly, J. L., Jr. (1956). "A New Interpretation of Information Rate." Bell System Technical Journal, 35(4), 917–926. — Introduces the Kelly criterion for optimal bet sizing, showing that maximizing the expected logarithm of wealth leads to optimal long-run growth. The most important single paper for prediction market trading strategy.

51. Thorp, E. O. (1975). "Portfolio Choice and the Kelly Criterion." In Ziemba, W. T., & Vickson, R. G. (Eds.), Stochastic Optimization Models in Finance, 599–619. Academic Press. — Extends the Kelly criterion to portfolio settings and discusses practical considerations for implementation. Essential reading for traders managing positions across multiple prediction markets.

52. Thorp, E. O. (2017). A Man for All Markets: From Las Vegas to Wall Street, How I Beat the Dealer and the Market. Random House. — An autobiographical account by the pioneer of quantitative trading, covering his work in blackjack card counting and options pricing. Provides vivid illustrations of edge detection and bankroll management principles applicable to prediction markets.

53. MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (Eds.) (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific. — The definitive collection of papers on the Kelly criterion, including historical contributions, mathematical extensions, and practical applications. An essential reference for the bet-sizing chapters of this textbook.

54. Kahneman, D., & Tversky, A. (1979). "Prospect Theory: An Analysis of Decision under Risk." Econometrica, 47(2), 263–292. — Introduces prospect theory, demonstrating that people systematically deviate from expected utility theory through loss aversion, probability weighting, and reference dependence. Explains many of the behavioral biases observable in prediction markets.

55. Tversky, A., & Kahneman, D. (1974). "Judgment under Uncertainty: Heuristics and Biases." Science, 185(4157), 1124–1131. — The foundational paper on cognitive heuristics and biases, including anchoring, availability, and representativeness. Explains systematic errors in probability assessment that create trading opportunities in prediction markets.

56. Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux. — A comprehensive synthesis of decades of research on cognitive biases, dual-process theory, and decision-making. Provides essential background for understanding the behavioral anomalies that prediction market traders can exploit.

57. Thaler, R. H. (2015). Misbehaving: The Making of Behavioral Economics. W. W. Norton. — An engaging history of behavioral economics by one of its founders, covering key anomalies in financial markets. Directly relevant to understanding why prediction markets sometimes exhibit systematic mispricings.

58. Snowberg, E., & Wolfers, J. (2010). "Explaining the Favorite–Long Shot Bias: Is It Risk-Love or Misperceptions?" Journal of Political Economy, 118(4), 723–746. — Analyzes the favorite-longshot bias in prediction markets and betting markets, distinguishing between risk-preference and belief-based explanations. Important for understanding systematic pricing anomalies that traders encounter.

59. Ziemba, W. T., & Hausch, D. B. (1986). Betting at the Racetrack. Dr. Z Investments. — A practical guide to quantitative betting strategies in parimutuel markets, covering expected value calculation, bankroll management, and system design. Many techniques transfer directly to prediction market trading.

60. Vaughan Williams, L. (Ed.) (2011). Prediction Markets: Theory and Applications. Routledge. — An edited volume covering prediction market theory, empirical evidence, and applications, with contributions from leading researchers. Provides diverse perspectives on market design and performance.

61. Shiller, R. J. (2000). Irrational Exuberance. Princeton University Press. — Documents speculative bubbles in financial markets and the psychological forces driving them. Relevant to understanding when prediction markets might exhibit bubble-like behavior and how traders can protect against it.

62. Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House. — Examines the role of rare, high-impact events in forecasting and decision-making. Important for understanding tail risks in prediction markets and the limitations of probabilistic forecasting.

63. Lo, A. W. (2004). "The Adaptive Markets Hypothesis." Journal of Portfolio Management, 30(5), 15–29. — Proposes an evolutionary framework reconciling efficient markets with behavioral finance, arguing that market efficiency varies over time as participants adapt. Provides a useful lens for understanding how prediction market efficiency evolves.

64. Mauboussin, M. J. (2006). More Than You Know: Finding Financial Wisdom in Unconventional Places. Columbia University Press. — Explores decision-making at the intersection of psychology, complex systems, and investing. Contains accessible discussions of probability, edge detection, and process-oriented thinking applicable to prediction markets.

65. Fama, E. F. (1970). "Efficient Capital Markets: A Review of Theory and Empirical Work." Journal of Finance, 25(2), 383–417. — The seminal statement of the efficient market hypothesis. Essential background for understanding the theoretical basis for and limitations of prediction market efficiency.

66. Agrawal, A., Delage, E., Peters, M., Wang, Z., & Ye, Y. (2011). "A Unified Framework for Dynamic Prediction Market Design." Operations Research, 59(3), 550–568. — Develops optimization-based strategies for trading in dynamic prediction markets. Relevant to algorithmic trading approaches covered in the advanced chapters.

67. Poundstone, W. (2005). Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street. Hill and Wang. — A popular history of the Kelly criterion and its applications, from Claude Shannon's information theory to Ed Thorp's quantitative trading. Provides engaging historical context for the bet-sizing principles taught in this textbook.

H.5 Data Science and Machine Learning

68. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed.). Springer. — The standard graduate reference for statistical learning methods including regression, classification, ensemble methods, and model selection. Provides the mathematical foundations for the data-driven prediction models used in prediction market research.

69. James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer. — A more accessible companion to Hastie et al. (2009), suitable for readers building practical prediction models. Covers the supervised learning techniques most commonly applied to prediction market feature engineering.

70. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer. — A comprehensive textbook on probabilistic approaches to machine learning, including Bayesian methods, graphical models, and variational inference. Provides theoretical depth for readers implementing sophisticated prediction models.

71. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press. — A thorough treatment of machine learning from a probabilistic standpoint, covering Bayesian methods, graphical models, and approximate inference. Especially relevant for the chapters on Bayesian updating and model-based trading.

72. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. — The standard reference on deep neural networks, covering architectures, optimization, and regularization. Relevant to the advanced chapters on using deep learning for prediction market signal generation.

73. Breiman, L. (2001). "Random Forests." Machine Learning, 45(1), 5–32. — Introduces the random forest algorithm, one of the most successful ensemble methods for prediction tasks. Directly applicable to building prediction models that inform trading in prediction markets.

74. Chen, T., & Guestrin, C. (2016). "XGBoost: A Scalable Tree Boosting System." Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. ACM. — Presents the XGBoost gradient boosting framework, widely used in prediction competitions and applied forecasting. A key tool for feature-based prediction models discussed in the data science chapters.

75. Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). Chapman and Hall/CRC. — The standard reference on Bayesian statistical methods, covering prior specification, posterior computation, and model checking. Essential for the Bayesian approaches to prediction market modeling discussed in this textbook.

76. Silver, N. (2012). The Signal and the Noise: Why So Many Predictions Fail — but Some Don't. Penguin. — A popular account of prediction in politics, sports, economics, and other domains, drawing on the author's experience building election forecast models. Provides practical wisdom about calibration, model building, and intellectual humility.

77. McKinney, W. (2017). Python for Data Analysis (2nd ed.). O'Reilly Media. — The definitive guide to data manipulation with pandas in Python. A practical reference for readers implementing the data analysis pipelines described in the hands-on chapters.

78. VanderPlas, J. (2016). Python Data Science Handbook. O'Reilly Media. — Covers NumPy, pandas, Matplotlib, and scikit-learn with practical examples. A useful companion for the Python-based prediction market analysis exercises in this textbook.

79. Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). "Scikit-learn: Machine Learning in Python." Journal of Machine Learning Research, 12, 2825–2830. — The reference paper for scikit-learn, the most widely used Python machine learning library. Relevant to all practical machine learning implementations discussed in the textbook.

80. Hyndman, R. J., & Athanasopoulos, G. (2021). Forecasting: Principles and Practice (3rd ed.). OTexts. — A modern, freely available textbook on forecasting methods covering exponential smoothing, ARIMA, and machine learning approaches. Provides practical forecasting techniques applicable to time-series prediction in markets.

81. Provost, F., & Fawcett, T. (2013). Data Science for Business. O'Reilly Media. — Bridges the gap between data science techniques and business decision-making, with clear explanations of model evaluation, expected value frameworks, and classification. Relevant to turning model outputs into actionable prediction market trades.

82. Kuhn, M., & Johnson, K. (2013). Applied Predictive Modeling. Springer. — A practical guide to building and evaluating prediction models, with emphasis on real-world workflow including preprocessing, feature engineering, and model tuning. Directly applicable to the modeling pipelines used in prediction market analysis.

83. Raschka, S., & Mirjalili, V. (2019). Python Machine Learning (3rd ed.). Packt. — A hands-on guide to machine learning with Python using scikit-learn and TensorFlow. Provides practical implementation guidance for the ML-based trading models discussed in later chapters.

H.6 Blockchain and Decentralized Systems

84. Nakamoto, S. (2008). "Bitcoin: A Peer-to-Peer Electronic Cash System." — The foundational white paper introducing Bitcoin and the blockchain concept. Provides the technical basis for understanding decentralized prediction markets that operate on blockchain infrastructure.

85. Buterin, V. (2014). "A Next-Generation Smart Contract and Decentralized Application Platform." Ethereum White Paper. — Introduces Ethereum and the concept of Turing-complete smart contracts. Essential background for understanding platforms like Augur, Polymarket, and other blockchain-based prediction markets.

86. Peterson, J., Krug, J., Zoltu, M., Williams, A. K., & Alexander, S. (2019). "Augur: A Decentralized Oracle and Prediction Market Platform." Augur White Paper v2.0. — The technical specification for Augur, the first major decentralized prediction market. Covers market creation, trading, reporting, and dispute resolution on the Ethereum blockchain.

87. Clark, A., & Oracle Team. (2021). "Chainlink 2.0: Next Steps in the Evolution of Decentralized Oracle Networks." Chainlink White Paper. — Describes the architecture of Chainlink, the leading decentralized oracle network. Directly relevant to understanding how decentralized prediction markets obtain reliable real-world data for settlement.

88. Angeris, G., & Chitra, T. (2020). "Improved Price Oracles: Constant Function Market Makers." Proceedings of the 2nd ACM Conference on Advances in Financial Technologies (AFT '20), 80–91. ACM. — Analyzes the properties of constant function market makers (CFMMs) used in DeFi, establishing results about price accuracy and arbitrage. Relevant to understanding the AMM mechanisms underlying decentralized prediction markets.

89. Adams, H., Zinsmeister, N., & Robinson, D. (2020). "Uniswap v2 Core." Uniswap White Paper. — Describes the Uniswap v2 automated market maker protocol. While designed for token exchange rather than prediction, the constant product market maker mechanism has influenced prediction market AMM design.

90. Daian, P., Goldfeder, S., Kell, T., Li, Y., Zhao, X., Bentov, I., Breidenbach, L., & Juels, A. (2020). "Flash Boys 2.0: Frontrunning in Decentralized Exchanges, Miner Extractable Value, and Consensus Instability." 2020 IEEE Symposium on Security and Privacy, 910–927. IEEE. — Exposes the problem of frontrunning and miner extractable value (MEV) in decentralized exchanges. Critical for understanding the manipulation risks specific to blockchain-based prediction markets.

91. Szabo, N. (1997). "Formalizing and Securing Relationships on Public Networks." First Monday, 2(9). — An early articulation of the smart contract concept by its originator. Provides historical and conceptual context for the programmable markets that enable decentralized prediction platforms.

92. Buterin, V. (2019). "Prediction Markets: Tales from the Election." Vitalik Buterin's Blog. — A discussion of how prediction markets performed during elections, with commentary on the potential and limitations of blockchain-based implementations. Offers the perspective of Ethereum's creator on decentralized prediction markets.

93. Gudgeon, L., Werner, S., Perez, D., & Knottenbelt, W. J. (2020). "DeFi Protocols for Loanable Funds: Interest Rates, Liquidity and Market Efficiency." Proceedings of the 2nd ACM Conference on Advances in Financial Technologies (AFT '20), 92–112. ACM. — Analyzes the emerging DeFi ecosystem, providing context for understanding how decentralized prediction markets interact with lending, borrowing, and other DeFi protocols.

94. Xu, J., & Vadgama, N. (2022). "From Banks to DeFi: The Evolution of the Lending Market." In Enabling the Internet of Value, 53–66. Springer. — Surveys the transition from traditional to decentralized financial infrastructure, relevant to understanding the ecosystem within which decentralized prediction markets operate.

95. Wood, G. (2014). "Ethereum: A Secure Decentralised Generalised Transaction Ledger." Ethereum Yellow Paper. — The formal specification of the Ethereum Virtual Machine. Provides the low-level technical foundation for understanding how prediction market smart contracts execute.

H.7 Regulation, Law, and Policy

96. Commodity Futures Trading Commission (CFTC). (2014). "CFTC Letter No. 14-130: No-Action Relief for the University of Iowa to Operate the Iowa Electronic Markets." — The key regulatory document granting a no-action exemption to the Iowa Electronic Markets. Illustrates the regulatory framework under which academic prediction markets operate in the United States.

97. Bell, T. W. (2006). "Prediction Markets for Promoting the Progress of Science and the Useful Arts." George Mason Law Review, 14(1), 37–92. — A legal analysis arguing that prediction markets serve socially valuable functions and should receive regulatory accommodation. Provides the strongest legal case for prediction market legalization.

98. Hahn, R. W., & Tetlock, P. C. (Eds.) (2006). Information Markets: A New Way of Making Decisions. AEI-Brookings Joint Center for Regulatory Studies. — An edited volume examining the policy case for prediction markets, with contributions addressing regulatory barriers, design considerations, and potential applications in government decision-making.

99. Abramowicz, M. (2007). Predictocracy: Market Mechanisms for Public and Private Decision Making. Yale University Press. — Explores the potential for prediction markets to improve public policy decisions, examining applications in law, governance, and institutional design. Makes the case for embedding prediction markets in government processes.

100. Posner, E. A. (2006). "The Regulatory Framework for Prediction Markets." In Hahn, R. W., & Tetlock, P. C. (Eds.), Information Markets, 29–52. AEI-Brookings. — Analyzes the complex regulatory landscape facing prediction markets in the United States, covering the intersection of gambling law, securities regulation, and commodity futures regulation.

101. Grimmelmann, J. (2015). "Illegal Futures: Prediction Markets and Intellectual Property." In Intellectual Property and the Common Law, 152–174. Cambridge University Press. — Examines the legal challenges of prediction markets that involve intellectual property and trade secrets, relevant to understanding constraints on corporate prediction markets.

102. CFTC. (2012). "Order Approving Nadex's Self-Certified Political Event Contracts." — Regulatory approval allowing Nadex to list binary options on political events, establishing an important precedent for regulated prediction market contracts in the United States.

103. Goodell, J. W., & Vähämaa, S. (2013). "US Presidential Elections and Implied Volatility: The Role of Political Uncertainty." Journal of Banking & Finance, 37(3), 1108–1117. — Examines how political uncertainty affects financial markets, providing empirical evidence relevant to the regulatory debate over whether prediction markets create or merely reflect uncertainty.

104. Zitzewitz, E. (2006). "Price Discovery Among the Punters: Using New Financial Betting Markets to Predict Intraday Volatility." Working Paper, Dartmouth College. — Analyzes the price discovery function of betting markets in financial contexts, contributing empirical evidence to the regulatory debate about whether prediction markets provide socially useful information.

105. Ledyard, J. O. (2006). "Designing Information Markets for Policy Analysis." In Hahn, R. W., & Tetlock, P. C. (Eds.), Information Markets, 37–64. AEI-Brookings. — Discusses how to design prediction markets specifically for policy-relevant information aggregation, addressing incentive compatibility, manipulation resistance, and institutional design.

106. Pirrong, C. (2014). "The Economics of Commodity Trading Firms." Working Paper, University of Houston. — Provides context on how commodity futures regulation applies to prediction market contracts. Relevant to understanding the regulatory constraints facing platforms seeking CFTC approval.

107. Kalshi, Inc. (2020). "Application for Registration as a Designated Contract Market." Filing with the CFTC. — The regulatory filing that led to Kalshi becoming the first CFTC-regulated exchange dedicated to event contracts. A primary document in the modern regulatory history of prediction markets.

H.8 Ethics and Social Impact

108. Hanson, R. (2006). "Designing Real Terrorism Futures." Public Choice, 128(1–2), 257–274. — Addresses the controversy over the Policy Analysis Market ("terrorism futures") by analyzing whether such markets create moral hazard or merely aggregate existing information. A key reference on the ethics of prediction markets for sensitive topics.

109. Wolfers, J., & Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives, 18(2), 107–126. — (See entry 5 above.) Also relevant to the ethics discussion because it addresses manipulation concerns and the social value of information aggregation.

110. Sandel, M. J. (2012). What Money Can't Buy: The Moral Limits of Markets. Farrar, Straus and Giroux. — A philosophical examination of the expansion of markets into domains previously governed by non-market norms. Raises important questions about whether some prediction markets — such as those on deaths, disasters, or personal outcomes — cross ethical boundaries.

111. Rothschild, D. (2009). "Forecasting Elections: Comparing Prediction Markets, Polls, and Their Biases." Public Opinion Quarterly, 73(5), 895–916. — Compares the accuracy of prediction markets to polls for election forecasting, raising questions about when markets add social value versus merely repackaging existing public information.

112. Rhode, P. W., & Strumpf, K. S. (2008). "Manipulating Political Stock Markets: A Field Experiment and a Century of Observational Data." Working Paper, University of North Carolina. — Provides empirical evidence on attempts to manipulate prediction markets, finding that manipulation attempts generally have limited and temporary effects. Important for assessing whether prediction markets are vulnerable to deliberate distortion.

113. Arnuk, S. L., & Saluzzi, J. (2012). Broken Markets: How High Frequency Trading and Predatory Practices on Wall Street Are Destroying Investor Confidence and Your Portfolio. FT Press. — Critiques high-frequency trading and market microstructure abuses in financial markets. Relevant to ethical concerns about whether sophisticated traders in prediction markets exploit less-informed participants.

114. O'Neil, C. (2016). Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. Crown. — Examines how algorithmic systems can perpetuate bias and harm. Relevant to ethical considerations around prediction markets that may affect vulnerable populations or embed systemic biases.

115. Zuboff, S. (2019). The Age of Surveillance Capitalism: The Fight for a Human Future at the New Frontier of Power. Public Affairs. — Analyzes the commodification of personal data and behavioral prediction by technology companies. Raises ethical questions about prediction markets that rely on extensive personal data or create incentives for surveillance.

116. Rajczi, A. (2008). "A Liberal Theory of Prediction Markets." Ethical Theory and Moral Practice, 11(4), 367–384. — Provides a philosophical analysis of the moral permissibility of prediction markets from a liberal political theory perspective, examining arguments about autonomy, harm, and the public interest.

117. 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. — Compares prediction markets to prediction polls as elicitation mechanisms, with implications for ethical questions about which method best serves the public interest while minimizing potential harms.

118. Cowgill, B., Wolfers, J., & Zitzewitz, E. (2009). "Using Prediction Markets to Track Information Flows: Evidence from Google." Proceedings of the 1st Dartmouth Conference on Auctions, Market Mechanisms, and Their Applications (AMMA '09). — Analyzes internal prediction markets at Google, raising ethical questions about corporate use of employee beliefs for strategic advantage and the potential for prediction markets to create unwanted incentive structures within organizations.

H.9 Real-World Applications and Case Studies

119. 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 corporate prediction markets, documenting their operation and performance at major companies. Essential reading for understanding how prediction markets function in organizational settings.

120. Chen, K. Y., & Plott, C. R. (2002). "Information Aggregation Mechanisms: Concept, Design, and Implementation for a Sales Forecasting Problem." California Institute of Technology Social Science Working Paper No. 1131. — Describes the implementation of prediction markets for sales forecasting at Hewlett-Packard, one of the earliest and most influential corporate applications.

121. Polgreen, P. M., Nelson, F. D., & Neumann, G. R. (2007). "Use of Prediction Markets to Forecast Infectious Disease Activity." Clinical Infectious Disease, 44(2), 272–279. — Demonstrates the use of prediction markets for forecasting influenza activity, showing that markets can produce useful public health predictions. Highly relevant given the subsequent use of similar approaches during the COVID-19 pandemic.

122. Luckner, S., Weinhardt, C., & Studer, R. (2006). "Predictive Power of Prediction Markets: A Study on the FIFA World Cup 2006." Proceedings of the 2nd Workshop on Prediction Markets, EC '06. — Applies prediction markets to sports forecasting, providing a natural test case where objective outcomes are clearly defined and frequent.

123. Gruca, T. S., & Berg, J. E. (2007). "Public Information Bias and Prediction Market Accuracy." Journal of Prediction Markets, 1(3), 241–251. — Examines how the availability of public information affects prediction market accuracy, finding that markets can be biased toward publicly available signals. Important for understanding market limitations.

124. Servan-Schreiber, E., Wolfers, J., Pennock, D. M., & Galebach, B. (2004). "Prediction Markets: Does Money Matter?" Electronic Markets, 14(3), 243–251. — Compares real-money and play-money prediction markets, finding that play-money markets can be nearly as accurate as real-money markets. Important for understanding the role of financial incentives in prediction market design.

125. Lichtman, A. J. (2016). Predicting the Next President: The Keys to the White House (2016 ed.). Rowman & Littlefield. — Presents the "Keys to the White House" model for predicting presidential elections, providing context for comparing structural models to prediction market prices for political forecasting.

126. Tung, C. Y., Chou, T. C., & Lin, J. W. (2015). "Using Prediction Markets of Market Scoring Rule to Forecast Infectious Diseases: A Case Study in Taiwan." BMC Public Health, 15, Article 766. — Extends prediction market applications to public health in an Asian context, demonstrating the cross-cultural applicability of prediction market mechanisms for disease surveillance.

127. Dreber, A., Pfeiffer, T., Almenberg, J., Isaksson, S., Wilson, B., Chen, Y., Nosek, B. A., & Johannesson, M. (2015). "Using Prediction Markets to Estimate the Reproducibility of Scientific Research." Proceedings of the National Academy of Sciences, 112(50), 15343–15347. — Uses prediction markets to forecast the replication success of published psychology studies, demonstrating a novel application of prediction markets to meta-science. A striking illustration of how markets can assess the credibility of scientific claims.

128. Camerer, C. F., Dreber, A., Holzmeister, F., Ho, T.-H., Huber, J., Johannesson, M., Kirchler, M., Nave, G., Nosek, B. A., Pfeiffer, T., Altmejd, A., Buttrick, N., Chan, T., Chen, Y., Forsell, E., Gampa, A., Heikensten, E., Hummer, L., Imai, T., ... Wu, H. (2018). "Evaluating the Replicability of Social Science Experiments in Nature and Science between 2010 and 2015." Nature Human Behaviour, 2(9), 637–644. — Extends the use of prediction markets for replication assessment to high-profile social science studies published in top journals. Demonstrates that prediction markets can accurately identify which published findings will replicate.

129. Leigh, A., & Wolfers, J. (2006). "Competing Approaches to Forecasting Elections: Economic Models, Opinion Polling and Prediction Markets." Economic Record, 82(258), 325–340. — A comparative analysis of different election forecasting approaches, establishing the relative strengths and weaknesses of prediction markets versus polling and fundamental models.

130. Ozimek, A. (2014). "The Regulation and Value of Prediction Markets." Mercatus Center Working Paper No. 14-12. — Assesses the economic value of prediction markets for society and argues for regulatory reform. Provides concrete examples of applications across domains and estimates of potential social benefits.

131. Rothschild, D., & Sethi, R. (2016). "Trading Strategies and Market Microstructure: Evidence from a Prediction Market." Journal of Prediction Markets, 10(1), 1–29. — Analyzes the trading strategies employed by participants in Intrade during the 2012 U.S. presidential election. Provides empirical insight into how real traders behave in prediction markets.

H.10 Software, Tools, and Technical References

132. Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Rio, J. F., Wiebe, M., Peterson, P., ... Oliphant, T. E. (2020). "Array Programming with NumPy." Nature, 585(7825), 357–362. — The reference paper for NumPy, the fundamental package for scientific computing in Python. NumPy underlies all numerical computations in the code examples throughout this textbook.

133. Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., ... SciPy 1.0 Contributors. (2020). "SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python." Nature Methods, 17(3), 261–272. — The reference paper for SciPy, which provides optimization, statistical, and numerical routines used in prediction market modeling and calibration analysis.

134. McKinney, W. (2010). "Data Structures for Statistical Computing in Python." Proceedings of the 9th Python in Science Conference (SciPy 2010), 56–61. — The original paper introducing pandas, the primary data manipulation library used in the textbook's code examples for handling prediction market data.

135. Hunter, J. D. (2007). "Matplotlib: A 2D Graphics Environment." Computing in Science & Engineering, 9(3), 90–95. — The reference paper for Matplotlib, the plotting library used to create the visualizations of market prices, calibration curves, and portfolio performance throughout this textbook.

136. Seabold, S., & Perktold, J. (2010). "Statsmodels: Econometric and Statistical Modeling with Python." Proceedings of the 9th Python in Science Conference (SciPy 2010), 92–96. — Introduces the statsmodels library for statistical modeling in Python, used in this textbook for time series analysis, regression, and hypothesis testing on prediction market data.

137. Chollet, F. (2017). Deep Learning with Python. Manning Publications. — A practical guide to deep learning using the Keras framework. Relevant to the chapters on building neural network models for prediction market signal generation.

138. Solidity Documentation Team. (2023). Solidity Documentation. https://docs.soliditylang.org/. — The official documentation for the Solidity programming language used to write smart contracts on Ethereum. Essential reference for readers implementing or auditing prediction market smart contracts.

139. OpenZeppelin. (2023). OpenZeppelin Contracts Documentation. https://docs.openzeppelin.com/contracts/. — Documentation for the most widely used library of audited smart contract components. Relevant to understanding the security infrastructure underlying decentralized prediction markets.

140. Brownlee, J. (2017). Introduction to Time Series Forecasting with Python. Machine Learning Mastery. — A practical guide to time series forecasting in Python, covering methods from classical statistics to deep learning. Useful for building models that track prediction market price dynamics.

141. Lubanovic, B. (2019). Introducing Python: Modern Computing in Simple Packages (2nd ed.). O'Reilly Media. — An accessible Python introduction suitable for readers new to programming who wish to implement the code examples in this textbook.

142. Sweigart, A. (2019). Automate the Boring Stuff with Python (2nd ed.). No Starch Press. — A practical Python programming guide covering web scraping, data manipulation, and task automation. Relevant to the chapters on collecting and processing prediction market data from web sources and APIs.

143. Wes McKinney and the pandas development team. (2023). pandas Documentation. https://pandas.pydata.org/docs/. — The official pandas documentation, serving as the primary reference for the data manipulation operations used throughout the textbook's code examples.

144. Kluyver, T., Ragan-Kelley, B., Perez, F., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J., Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., & Willing, C. (2016). "Jupyter Notebooks — A Publishing Format for Reproducible Computational Workflows." Proceedings of the 20th International Conference on Electronic Publishing, 87–90. — The reference paper for the Jupyter Notebook environment used for the interactive exercises and demonstrations throughout this textbook.

Additional Key References

145. Gjerstad, S. (2005). "Risk Aversion, Beliefs, and Prediction Market Equilibrium." Working Paper, University of Arizona. — Develops theoretical models showing how risk aversion affects prediction market prices and equilibrium, contributing to the understanding of when prices diverge from aggregate beliefs.

146. Fountain, J., & Harrison, G. W. (2011). "What Do Prediction Markets Predict?" Applied Economics Letters, 18(3), 267–272. — Critically examines what precisely prediction market prices represent, raising important conceptual questions about the interpretation of market-generated probabilities.

147. Mellers, B., Stone, E., Atanasov, P., Rohrbaugh, N., Metz, S. E., Ungar, L., Bishop, M. M., Horowitz, M., Merkle, E., & Tetlock, P. (2015). "The Psychology of Intelligence Analysis: Drivers of Prediction Accuracy in World Politics." Journal of Experimental Psychology: Applied, 21(1), 1–14. — Identifies the psychological characteristics that predict forecasting accuracy in geopolitical contexts, informing the discussion of what makes an effective prediction market trader.

148. Oprea, R., Porter, D., Hibbert, C., Hanson, R., & Tila, D. (2007). "Can Manipulators Mislead Market Observers?" Working Paper, University of California, Santa Cruz. — Experimental evidence on whether and how manipulation distorts prediction market prices, finding that manipulation attempts are often corrected by informed traders.

149. Rothschild, D. (2015). "Combining Forecasts for Elections: Accurate, Relevant, and Timely." International Journal of Forecasting, 31(3), 952–964. — Develops methods for combining prediction market prices with polling data to produce superior election forecasts. Demonstrates the complementarity of different forecasting approaches.

150. Goel, S., Reeves, D. M., Watts, D. J., & Pennock, D. M. (2010). "Prediction Without Markets." Proceedings of the 11th ACM Conference on Electronic Commerce (EC '10), 357–366. ACM. — Compares prediction markets to alternative crowd forecasting methods, finding that simpler aggregation mechanisms sometimes perform comparably. Important for understanding when prediction markets add value over simpler approaches.

151. Witkowski, J., & Parkes, D. C. (2012). "Peer Prediction Without a Common Prior." Proceedings of the 13th ACM Conference on Electronic Commerce (EC '12), 964–981. ACM. — Develops incentive-compatible mechanisms for eliciting truthful reports without requiring a common prior, relevant to peer-prediction approaches that complement or substitute for prediction markets.

152. Milgrom, P. R., & Stokey, N. (1982). "Information, Trade, and Common Knowledge." Journal of Economic Theory, 26(1), 17–27. — The foundational no-trade theorem showing that rational agents with common priors cannot agree to disagree. Explains why prediction markets require some degree of heterogeneous beliefs or non-common-knowledge information to generate trading volume.

153. Hong, L., & 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. — Provides formal mathematical results on why cognitively diverse groups outperform homogeneous expert groups. Offers theoretical support for why prediction markets with diverse participants produce accurate forecasts.

154. Schervish, M. J. (1989). "A General Method for Comparing Probability Assessors." Annals of Statistics, 17(4), 1856–1879. — Develops the theory of dominance among probability forecasters, providing rigorous foundations for comparing prediction market participants based on their forecast quality.

155. Gillen, B. J., Plott, C. R., & Shum, M. (2017). "A Pari-Mutuel-Like Mechanism for Information Aggregation: A Field Test Inside Intel." Journal of Political Economy, 125(4), 1075–1099. — Documents the use of a novel prediction market mechanism at Intel Corporation for forecasting production yields, demonstrating practical corporate applications and mechanism design choices in a real-world industrial setting.

156. Deck, C., Lin, S., & Porter, D. (2013). "Affecting Policy by Manipulating Prediction Markets: An Experimental Study." Journal of Economic Behavior & Organization, 85, 48–62. — Experimental investigation of whether prediction markets can be manipulated when market prices directly influence policy decisions. Addresses a key concern about the use of prediction markets in governance.

157. Rajiv, S., & Segal, I. (2020). "Conditional Prediction Markets." Working Paper, Stanford University. — Develops the theory of conditional prediction markets, which aggregate beliefs about how one event depends on another. Relevant to the chapters on decision markets and causal inference.

158. Jian, L., & Sami, R. (2012). "Aggregation and Manipulation in Prediction Markets: Effects of Trading Mechanism and Information Distribution." Management Science, 58(1), 123–140. — Compares continuous double auction and market scoring rule mechanisms in terms of aggregation speed and susceptibility to manipulation. Important for practical mechanism selection decisions.

159. Schlag, K. H., Tremewan, J., & van der Weele, J. J. (2015). "A Penny for Your Thoughts: A Survey of Methods for Eliciting Beliefs." Experimental Economics, 18(3), 457–490. — A comprehensive survey of belief elicitation methods including scoring rules, prediction markets, and betting mechanisms, with discussion of their relative advantages and limitations.

160. Armantier, O., & Treich, N. (2013). "Eliciting Beliefs: Proper Scoring Rules, Incentives, Stakes and Hedging." European Economic Review, 62, 17–40. — Examines practical issues in belief elicitation, including how stakes, hedging opportunities, and incentive structures affect the accuracy of elicited probabilities. Relevant to understanding prediction market design choices.

This bibliography reflects the state of the literature as of early 2026. The field of prediction markets is evolving rapidly, particularly in areas related to blockchain-based platforms, regulatory developments, and machine learning applications. Readers are encouraged to supplement these references with current working papers and preprints available through SSRN, arXiv, and NBER.