Chapter 39 Further Reading: Ethics of Prediction Markets

Ethical Foundations

Sandel, M. J. (2012). What Money Can't Buy: The Moral Limits of Markets. New York: Farrar, Straus and Giroux. Sandel's influential critique of market reasoning provides essential philosophical context for prediction market ethics. His argument that some goods are corrupted by being bought and sold applies directly to markets on death, suffering, and human dignity. Chapters on "incentives" and "naming rights" are particularly relevant to the commodification concerns discussed in this chapter.
Sunstein, C. R. (2006). "Deliberating Groups vs. Prediction Markets (or Hayek's Challenge to Habermas)." Episteme, 3(3), 192-213. Sunstein examines the tension between deliberative democracy (where citizens reason together) and prediction markets (where participants trade on beliefs). His analysis of the conditions under which markets outperform deliberation — and vice versa — provides a balanced framework for thinking about the democratic legitimacy of prediction markets.
Wolfers, J., & Zitzewitz, E. (2004). "Prediction Markets." Journal of Economic Perspectives, 18(2), 107-126. A foundational survey of prediction market economics that addresses ethical issues including manipulation, moral hazard, and market design. The authors' analysis of the social value of prediction markets provides the economic basis for the utilitarian arguments discussed in this chapter.

Bell, J. (1997). "Assassination Politics." (Self-published essay). The original essay proposing the assassination market concept. Required reading not because the proposal is ethically defensible — it is not — but because it articulates the most extreme form of moral hazard in prediction markets and forces engagement with the deepest ethical challenges. Available online through various archives.
Hanson, R. (2006). "Designing Real Terrorism Futures." Public Choice, 128(1-2), 257-274. Hanson responds to the DARPA FutureMAP controversy by designing a terrorism prediction market with explicit moral hazard safeguards. His analysis of conditional market design, position limits, and delayed settlement as mitigation strategies remains the most sophisticated technical treatment of the moral hazard problem.
Athanasoulis, S. G., & Shiller, R. J. (2001). "World Income Components: Measuring and Exploiting Risk-Sharing Opportunities." American Economic Review, 91(4), 1031-1054. Argues for the creation of markets on economic aggregates (GDP, income indices) that allow risk sharing. Relevant to the argument that aggregate-level prediction markets are ethically distinct from individual-level markets.

Camerer, C. F. (1998). "Can Asset Markets Be Manipulated? A Field Experiment with Racetrack Betting." Journal of Political Economy, 106(3), 457-482. An empirical study of whether prediction market (betting) prices can be manipulated. Camerer finds that manipulation attempts are largely corrected by market forces in liquid markets. This provides evidence for the "self-correcting" argument that manipulation is expensive and temporary in well-designed markets.
Hanson, R., Oprea, R., & Porter, D. (2006). "Information Aggregation and Manipulation in an Experimental Market." Journal of Economic Behavior & Organization, 60(4), 449-459. A laboratory experiment testing whether manipulation affects market accuracy. Finds that markets are remarkably robust to manipulation attempts: prices converge toward true values even when some participants are trying to manipulate them. This is reassuring for market integrity but does not eliminate the ethical concerns about attempted manipulation.

Buterin, V., Hitzig, Z., & Weyl, E. G. (2019). "A Flexible Design for Funding Public Goods." Management Science, 65(11), 5171-5187. Introduces the quadratic funding mechanism, which can be adapted to prediction market participation to address the wealth-weighted voice problem. The mathematical framework for quadratic mechanisms (where cost grows with the square of influence) is directly applicable to prediction market design.
Page, S. E. (2007). The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton: Princeton University Press. Page provides the theoretical foundation for why participant diversity matters in prediction markets. His "diversity prediction theorem" shows that collective accuracy depends on both individual accuracy and cognitive diversity. This supports the argument that excluding underrepresented populations reduces market accuracy.

Blaszczynski, A., & Nower, L. (2002). "A Pathways Model of Problem and Pathological Gambling." Addiction, 97(5), 487-499. A comprehensive model of gambling disorder that identifies three pathways: behaviorally conditioned gamblers, emotionally vulnerable gamblers, and antisocial impulsivist gamblers. This model can inform the design of responsible trading features for prediction market platforms.
Gainsbury, S. M. (2015). "Online Gambling Addiction: The Relationship between Internet Gambling and Disordered Gambling." Current Addiction Reports, 2(2), 185-193. Reviews the evidence on online gambling addiction. Relevant because prediction markets are primarily online platforms with similar risk factors. Recommends specific harm reduction measures including mandatory breaks, loss limits, and self-exclusion features.

Narayanan, A., & Shmatikov, V. (2008). "Robust De-anonymization of Large Sparse Datasets." IEEE Symposium on Security and Privacy, 111-125. Demonstrates that pseudonymous data (like blockchain transactions) can often be de-anonymized through linkage attacks. Directly relevant to the privacy concerns of blockchain-based prediction markets, where all transactions are publicly visible.
Dwork, C., & Roth, A. (2014). "The Algorithmic Foundations of Differential Privacy." Foundations and Trends in Theoretical Computer Science, 9(3-4), 211-407. The definitive reference on differential privacy, which provides the mathematical framework for publishing prediction market statistics without revealing individual participants' trades. Extended in Chapter 41.

Arrow, K. J., et al. (2008). "The Promise of Prediction Markets." Science, 320(5878), 877-878. The Nobel laureates' letter arguing for reduced regulatory barriers to prediction markets. Addresses the social value argument directly and argues that the benefits of prediction markets outweigh the risks. A useful counterpoint to the precautionary approach.