Chapter 35: Further Reading

Smart Contract Market Mechanisms

Core Protocol Documentation

  1. Gnosis. Conditional Token Framework Documentation. The definitive technical reference for the CTF. Covers condition preparation, position splitting and merging, collection ID computation, the ERC-1155 interface, and deep position composition. The mathematical appendix on index sets and partition validation is particularly valuable for developers implementing CTF interactions. Available at docs.gnosis.io/conditionaltokens.

  2. Peterson, J., Krug, J., Zoltu, M., Williams, A. K., & Alexander, S. (2019). Augur: A Decentralized Oracle and Prediction Market Platform. arXiv:1501.01042v2. The Augur whitepaper describes the complete system: market creation, the LMSR market maker, the REP-based reporting system, dispute escalation, and the fork mechanism. Section 4 on the dispute resolution game theory is essential reading for understanding how economic incentives maintain oracle honesty. The paper's analysis of the "security budget" concept influenced all subsequent decentralized oracle designs.

  3. UMA Protocol. Optimistic Oracle Documentation. Comprehensive documentation of UMA's oracle system used by Polymarket. Covers the assert-dispute-resolve flow, bond sizing, the Data Verification Mechanism (DVM) backstop, and the commit-reveal voting scheme. The section on "Designing a good assertion" is practically useful for prediction market creators. Available at docs.uma.xyz.

  4. Polymarket. Technical Documentation and API Reference. While primarily focused on the API, this documentation describes Polymarket's order book structure, the Neg Risk Adapter for multi-outcome markets, and the CLOB integration with on-chain settlement. The contract addresses and ABIs are essential for programmatic interaction. Available at docs.polymarket.com.

Market Mechanism Design

  1. Hanson, R. (2003). Combinatorial Information Market Design. Information Systems Frontiers, 5(1), 107-119. Robin Hanson's paper on the Logarithmic Market Scoring Rule (LMSR), the automated market maker that powered Augur v1 and influenced all subsequent AMM designs for prediction markets. The paper shows how LMSR subsidizes market liquidity while bounding the market maker's maximum loss. Understanding LMSR is essential context for evaluating newer AMM designs (constant product, concentrated liquidity).

  2. Othman, A., Pennock, D. M., Reeves, D. M., & Sandholm, T. (2013). A Practical Liquidity-Sensitive Automated Market Maker. ACM EC. Extends LMSR with liquidity sensitivity, where the market maker adjusts its pricing curve based on trading volume. This paper bridges the gap between theoretical market scoring rules and practical on-chain implementations. The sensitivity parameter directly affects how much price moves in response to trades, analogous to the "depth" of a constant product AMM.

  3. Adams, H., Zinsmeister, N., Salem, M., Keefer, R., & Robinson, D. (2021). Uniswap v3 Core. While not specific to prediction markets, Uniswap v3's concentrated liquidity mechanism has been adapted for prediction market AMMs. The ability to concentrate liquidity within specific price ranges (e.g., around the current probability estimate) dramatically improves capital efficiency. Understanding this mechanism is valuable for designing or evaluating prediction market liquidity pools.

Oracle Design and Game Theory

  1. Hart, O. & Moore, J. (2004). On the Design of Hierarchies: Coordination versus Specialization. Journal of Political Economy, 113(4), 675-702. While not about blockchain oracles specifically, this paper provides the game-theoretic foundation for understanding hierarchical dispute resolution systems like Augur's. The insight that escalating costs filter out frivolous disputes while preserving the ability to correct genuine errors applies directly to dispute round design.

  2. Buterin, V. (2015). SchellingCoin: A Minimal-Trust Universal Data Feed. Blog post. Vitalik Buterin's early proposal for a Schelling-point-based oracle mechanism. The core idea --- that rational agents will coordinate on the truth as a focal point --- underlies Kleros, UMA's DVM, and Augur's dispute system. The post also describes the P+epsilon attack, which remains the primary theoretical threat to Schelling-point oracles.

  3. Breidenbach, L., Cachin, C., Chan, B., et al. (2021). Chainlink 2.0: Next Steps in the Evolution of Decentralized Oracle Networks. The Chainlink 2.0 whitepaper describes hybrid oracle networks that combine decentralized data aggregation with off-chain computation. While Chainlink's data feed model does not directly resolve prediction market questions, the paper's framework for evaluating oracle security (cryptoeconomic guarantees, data source diversity, reporting frequency) provides useful analytical tools for assessing any oracle mechanism.

Smart Contract Security

  1. Atzei, N., Bartoletti, M., & Cimoli, T. (2017). A Survey of Attacks on Ethereum Smart Contracts. POST 2017. A systematic survey of smart contract vulnerabilities including reentrancy, integer overflow, access control flaws, and denial of service. Each vulnerability is mapped to real-world exploits. For prediction market developers, the sections on reentrancy (relevant to payout/redemption functions) and access control (relevant to oracle resolution) are most critical.

  2. OpenZeppelin. Contracts Wizard and Security Guides. OpenZeppelin's security-focused contract library provides audited implementations of common patterns: access control (Ownable, AccessControl), reentrancy guards (ReentrancyGuard), and token standards (ERC-20, ERC-1155). Their security blog posts on common pitfalls in DeFi protocols are directly applicable to prediction market development. Available at docs.openzeppelin.com.

Empirical Analysis

  1. Angeris, G., Kao, H. T., Chiang, R., Noyes, C., & Chitra, T. (2019). An Analysis of Uniswap Markets. Cryptoeconomic Systems. Formal analysis of constant product AMMs with implications for prediction market liquidity pools. The paper derives conditions under which arbitrageurs keep the AMM price aligned with the external market price, which directly parallels how prediction market AMM prices should converge to the true probability as resolution approaches.

  2. Chen, Y. & Pennock, D. M. (2010). Designing Markets for Prediction. AI Magazine, 31(4), 42-52. A survey of prediction market design from an AI/mechanism design perspective. Covers market scoring rules, combinatorial markets, and the relationship between market microstructure and information aggregation. This broader view of mechanism design helps contextualize the specific smart contract implementations discussed in this chapter.

  3. Clark, J. & Essex, A. (2012). CommitCoin: Carbon Dating Commitments with Bitcoin. Financial Cryptography. An early paper on using blockchain timestamps for commit-reveal schemes. The commit-reveal pattern is central to Augur's dispute resolution and UMA's DVM voting. This paper explains the cryptographic primitives that make these schemes possible and analyzes their security properties in adversarial environments.