Chapter 31: Further Reading

Foundational Papers

1. Hanson, R. (2013). "Shall We Vote on Values, But Bet on Beliefs?" Journal of Political Philosophy, 21(2), 151--178.

The definitive statement of the futarchy proposal. Originally circulated as a working paper in 2000, this paper lays out the complete mechanism: citizens vote on welfare metrics, prediction markets determine which policies maximize those metrics. Hanson addresses the key objections (manipulation, measurement, democratic legitimacy) and argues that futarchy dominates both democracy and dictatorship under broad conditions. Essential reading for this chapter.

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

While primarily about market design (see Chapter 30), this paper introduces the conditional market structure that underpins futarchy. Hanson's LMSR cost function and its extension to conditional outcomes provide the technical foundation for decision markets. The discussion of how to price $P(Y \mid D)$ using a combinatorial structure directly informs Section 31.2.

3. Othman, A. & Sandholm, T. (2010). "Decision Rules and Decision Markets." Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS).

Provides a rigorous game-theoretic analysis of decision market equilibria. Proves that truthful reporting of causal beliefs is a Bayesian Nash Equilibrium under certain conditions, but also identifies problematic multiple equilibria. The paper shows that the "thick market" assumption is critical -- in thin markets, manipulation and self-fulfilling prophecies become real risks.

Causal Inference

4. Rubin, D.B. (1974). "Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies." Journal of Educational Psychology, 66(5), 688--701.

The foundational paper for the potential outcomes framework used throughout this chapter. Rubin's notation -- $Y(A)$ and $Y(B)$ for potential outcomes under treatments A and B -- provides the language for formalizing what decision markets can and cannot identify. Understanding the distinction between $\mathbb{E}[Y \mid D = A]$ and $\mathbb{E}[Y(A)]$ is central to Section 31.4.

5. Pearl, J. (2009). Causality: Models, Reasoning, and Inference (2nd ed.). Cambridge University Press.

The alternative framework for causal inference using directed acyclic graphs and the do-calculus. Pearl's distinction between $P(Y \mid X)$ (observational) and $P(Y \mid \text{do}(X))$ (interventional) maps directly onto the challenge of interpreting conditional market prices as causal effects. Chapter 3 on interventions is particularly relevant.

6. Chen, Y., Kash, I., & Ruberry, M. (2014). "Eliciting Predictions and Recommendations for Decision Making." Proceedings of the 15th ACM Conference on Economics and Computation (EC).

Proposes "decision scoring rules" that directly incentivize traders to report causal effects rather than conditional expectations. This theoretical innovation addresses the selection bias problem at the mechanism design level, though it requires additional structural assumptions. Directly relevant to the theoretical discussion in Section 31.3.4.

Corporate Prediction Markets

7. 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 empirical study of corporate prediction markets. Documents Google's internal markets, including calibration analysis, participation patterns, and the optimism bias. Also covers Ford's vehicle demand forecasting and a third anonymized firm. Essential evidence for the practical feasibility of decision markets in corporate settings.

8. Chen, K.-Y. & Plott, C.R. (2002). "Information Aggregation Mechanisms: Concept, Design, and Implementation for a Sales Forecasting Problem." Caltech Social Science Working Paper 1131.

Documents HP's pioneering experiments with corporate prediction markets for sales forecasting. Shows that markets with only 8--12 participants outperformed HP's official forecasts, primarily by aggregating information from different departments. Demonstrates that thin markets can still produce valuable signals.

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

An accessible overview of prediction markets with discussion of their potential for decision-making. Includes early arguments about how conditional markets could be used for policy evaluation. Provides the broader context for understanding why decision markets were proposed.

Blockchain and DAO Implementations

10. Gnosis. "Conditional Tokens Framework." Available at: github.com/gnosis/conditional-tokens-contracts.

The technical specification for the conditional token framework used in blockchain-based futarchy implementations. Details the token splitting and merging mechanics, AMM integration, and resolution protocols. This is the infrastructure underlying Meta-DAO and other on-chain futarchy experiments discussed in Section 31.10.

11. Buterin, V. (2014). "An Introduction to Futarchy." Blog post.

Vitalik Buterin's influential essay introducing futarchy concepts to the blockchain community. Proposes using prediction markets for DAO governance and analyzes the key challenges (metric selection, manipulation, liquidity). This essay catalyzed the development of on-chain futarchy implementations.

12. Meta-DAO. "Futarchy Protocol Documentation." Available at: docs.metadao.fi.

Technical documentation for the most prominent live futarchy implementation on Solana. Describes the conditional token structure, the AMM design, the decision threshold, and the resolution mechanism. Includes empirical data from actual governance decisions and analysis of market accuracy.

Arrow's impossibility theorem demonstrates fundamental limitations of voting systems. Futarchy can be understood as an attempt to circumvent these limitations by replacing preference aggregation (voting) with belief aggregation (prediction). Understanding Arrow's theorem provides context for why market-based alternatives are theoretically appealing.

14. Page, L. & Clemen, R. (2013). "Do Prediction Markets Produce Well-Calibrated Probability Forecasts?" The Economic Journal, 123(568), 491--513.

Empirical analysis of prediction market calibration with implications for decision markets. Shows that while markets are generally well-calibrated, systematic biases exist (particularly the favorite-longshot bias). These biases have direct implications for the reliability of conditional market prices as decision inputs.

15. Conitzer, V. & Sandholm, T. (2011). "Prediction Markets, Mechanism Design, and Cooperative Game Theory." Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI).

Connects prediction markets to the broader mechanism design literature. Analyzes the incentive properties of decision markets from a mechanism design perspective, including strategy-proofness, individual rationality, and budget balance. Shows that no mechanism can simultaneously satisfy all desirable properties.