Polymarket: Decentralized Prediction Market

• Polymarket is a decentralized event prediction platform where participants trade shares on real-world outcomes securely using blockchain technology.
• It integrates automated market makers, off-chain order books, and a fully collateralized share structure to enable frictionless arbitrage and manipulation resistance.
• Recent research highlights its advanced market microstructure, efficient risk management, and robust empirical evaluation of prediction accuracy.

Polymarket is a decentralized event prediction market platform that enables participants to trade on the outcomes of real-world events by buying and selling shares representing specific, mutually exclusive, and exhaustive “conditions.” Built atop blockchain infrastructure, Polymarket integrates automated and order-book market making with composable tokenized share structures, dispute resolution via decentralized oracles, and detailed on-chain archiving. The system is designed to aggregate diverse information, manage liquidity and risk efficiently, and facilitate manipulation-resistant price discovery in a permissionless yet modular fashion. Recent research on Polymarket has focused on market microstructure, arbitrage, risk management, prediction accuracy, and platform design, positioning it as a reference implementation for modern decentralized prediction markets.

1. Architectural Evolution and Core Design

Early decentralized prediction markets (DePMs) such as Truthcoin and Augur v1 used active, risk-exposed market creators who posted initial prices and collateral, often relying on mechanisms like LMSR with direct operator exposure. In contrast, Polymarket follows the “splitting” paradigm proposed in later DePMs: users mint a complete bundle of outcome shares by depositing collateral and then trade those shares. This approach eliminates sponsor risk at inception by ensuring the system is always fully collateralized for all possible outcomes.

Trading is enabled both via automated market makers (AMMs) and off-chain order books with on-chain settlement. Settlement and outcome resolution use a hybrid model: straightforward market outcomes are settled directly, while disputed outcomes escalate to resolution via UMA’s Optimistic Oracle, where on-chain voting and slashing are applied as needed. Polymarket’s infrastructure is implemented as Ethereum-compatible smart contracts (e.g., Polygon), leveraging modular external components for share representation, trading, and resolution (Rahman et al., 17 Oct 2025).

Key innovations in Polymarket’s design include:

• Yes–No Bundle with Negative Risk (YNB-NR) share structure: Each outcome consists of a YES and a NO token, with internal conversion gadgets (j_{k_N} ≡ Σ_{ℓ∈Ω\{k}} j_{ℓ_Y}) ensuring pricing consistency and enabling frictionless arbitrage between complements.
• Composability and Modular Substitution: Polymarket’s architecture enables rapid module substitution (e.g., Gnosis Conditional Tokens Framework for shares, UMA’s Oracle for disputes, USDC for collateral), supporting risk and trust agility.
• Hybrid Trading Layer: Supports both AMM (for uninterrupted liquidity) and order book mechanisms (for depth and price discovery).
• Gasless Withdrawals: Utilizes OpenGSN relayers for minimizing end-user transaction costs during settlement (Rahman et al., 17 Oct 2025).

2. Market Microstructure: Workflow and Share System

Polymarket’s protocol can be decomposed into a seven-stage modular workflow (Rahman et al., 17 Oct 2025):

1. Underlying Infrastructure: Smart contracts on Ethereum-compatible chains.
2. Market Topic: Open market creation, occasionally curated or clarified to prevent ambiguity or hidden presumptions.
3. Share Structure and Pricing: YNB-NR ensures all YES shares sum to \$1 (completeness), all NO shares sum to$|\Omega| - 1$, with internal conversion formulas:
   • $j_{k_N} ≡ \sum_{ℓ∈Ω\setminus\{k\}} j_{ℓ_Y}$
   • $\sum_{ℓ∈Ω\setminus\{k\}} j_{ℓ_N} ≡ j_{k_Y} + \$1 \cdot (|\Omega| - 2)$$</li> </ul></li> <li><strong>Trading</strong>: Initial liquidity via complete share splitting (collateral to shares). Subsequent trades use off-chain orderbooks or on-chain AMMs; shares can be withdrawn to external DeFi venues.</li> <li><strong>Resolution</strong>: Hybrid approach — “auto-resolve” for clear outcomes, escalation to UMA’s oracle in the event of disputes.</li> <li><strong>Settlement</strong>: Gasless “pull” by users ensures settlement costs are not borne by the platform; strict solvency invariant is maintained:</p> <p>$$\text{Treas}_M \geq \sup_{\omega \in \Omega_M} \sum_j S_j(M) \cdot R_j(\omega)$</li> <li><strong>Archiving</strong>: Critical settlement events on-chain; detailed orderbook data via Polymarket API.</li> </ol> <p>This design enables efficiency and composability but requires careful trade-off management between decentralization, efficiency, expressiveness, and manipulation resistance. For example, modularity allows easy replacement of failed components (such as oracles or collateral tokens), but partial centralization may be involved in topic creation and some aspects of off-chain data availability (<a href="/papers/2510.15612" title="" rel="nofollow" data-turbo="false" class="assistant-link" x-data x-tooltip.raw="">Rahman et al., 17 Oct 2025</a>).</p> <h2 class='paper-heading' id='arbitrage-price-efficiency-and-market-behaviors'>3. Arbitrage, Price Efficiency, and Market Behaviors</h2> <p>Despite the intended design that collective outcome prices in each market sum to \$1 (enforcing no-arbitrage under completeness and exclusivity), empirical studies reveal persistent pricing discrepancies and arbitrage on Polymarket (Saguillo et al., 5 Aug 2025):
     • Market Rebalancing Arbitrage: Intra-market mispricings where the sum of YES token prices deviates from one (i.e., $\sum_i val(Y_i, t) \neq 1$), leading to risk-free profit via synthetic portfolios.
     • Combinatorial Arbitrage: Inter-market strategies exploiting logical dependencies across markets (e.g., candidate win and margin markets); formalized via the difference in aggregated probabilities of logically linked subsets.
       • Arbitrage exists if:

       $$\sum_{c\in S} val(T_c, t) < \sum_{c'\in S'} val(T_{c'}, t)$$

       with neutral (riskless) portfolios constructed across $S$ and the complement of $S'$.

     Sophisticated participants have extracted approximately \$40 million of profit via algorithmic execution and rapid exploitation of price inconsistencies, as confirmed through on-chain event analysis. The analysis employs a heuristic-driven reduction, using embeddings and LLM-based semantic parsing to link and compress conditions, reducing the combinatorial explosion in search for arbitrages (Saguillo et al., 5 Aug 2025).

     4. Market Making, Liquidity, and Risk Management Paradigms

     Several market-making mechanisms are actively researched and implemented on Polymarket:

     • Cost-function Market Makers: Polymarket has employed mechanisms such as the logarithmic market scoring rule (LMSR), but recent advances propose mechanisms with constant loss (e.g., Constant Log Utility Market Maker (CLUM)), bounded loss over large or countably infinite outcome spaces, and efficient binary search-based approximation for real-time pricing (Papireddygari et al., 14 Oct 2025).
     • Interval Securities and Log-Time Operations: Tree-based LMSR and multi-resolution linearly constrained market makers allow expressive trading on intervals and continuous outcomes with logarithmic-time pricing and bounded loss (Dudík et al., 2021).
     • Parimutuel Mechanisms with Ambiguity Aversion: The Knightian Pari-mutuel Mechanism (KPM) optimizes market making under ambiguity aversion via a max-min program over eligible probability distributions constrained by Kullback–Leibler divergence from a prior:

       $$\mathcal{Y} = \{ p \in \mathbb{R}_+^n : \sum_i p_i = 1,\ \sum_i p_i \ln(p_i/q_i) \leq \theta \}$$

       This approach allows explicit hedging of worst-case risk, bounded losses, and polynomial-time clearing, supporting robust and automated price discovery in high-frequency environments (Roh et al., 2015).

     Recent research also explores multi-outcome, arbitrarily correlated events and optimal AMMs leveraging differentiable economics and optimal transport duality, facilitating bundled pricing and explicit profit-maximization under adverse selection (Curry et al., 14 Feb 2024). Derivative layers for event probabilities—including belief variance swaps, correlation swaps, and first-passage notes—are proposed to handle belief volatility, jumps, and cross-event risks in a tractable, standardizable fashion (Dalen, 17 Oct 2025).

     5. Behavioral Dynamics and Empirical Forecast Performance

     Polymarket’s large, transparent dataset enables unprecedented measurement and modeling of trader behavior and market efficiency:

     • Political Leaning Inference: By aggregating detailed dimensions of trading behavior, the Political Betting Leaning Score (PBLS) quantifies a user’s partisan orientation using price, decay, size, party direction, and frequency factors. Robust correlations between PBLS and trading profit/loss, as well as realized behavior, have been validated using internal and external benchmarks (Chen et al., 20 Jul 2024).
     • Prediction versus Polling: Comparative analyses of Polymarket data and traditional polling for the 2024 U.S. presidential election indicate that Polymarket provided superior predictive accuracy, particularly in swing states. Bayesian structural time series models demonstrate that Polymarket rapidly and granularly incorporates event news, outperforming polls in both short-term responsiveness and final forecast accuracy, in alignment with “Wisdom of Crowds” theory (Cutting et al., 11 Jul 2025).
     • Liquidity Provision and Settlement Assets: Research on BTC-denominated prediction markets highlights the inefficiency of stablecoin markets (due to opportunity costs and currency conversion loss) and outlines the mechanics, risk exposures, and capital efficiency of bootstrapping BTC (as a deflationary settlement asset) via cross-market making, AMM, or DeFi borrowing methods (Shabashev, 15 Sep 2025).

     6. Open Research Challenges and Future Directions

     Polymarket, as a modular DePM implementation, exposes several open problems:

     • Formalizing Market Predicates: Developing machine-checkable, unambiguous event specifications to mitigate ambiguity, hidden assumptions, and boundary category confusions in permissionless markets.
     • AMM Optimization: Designing AMMs optimized for event contracts, where share prices are strictly bounded in $[0, 1]$ and may resolve abruptly; current DeFi AMMs may yield abrupt spikes on boundary resolution, challenging capital efficiency and dynamic liquidity management (Rahman et al., 17 Oct 2025).
     • Manipulation Resistance and Resolution Robustness: Improving oracle and dispute mechanisms, especially guarding against collusion, “whale” dominance, and systematic manipulation, possibly by robust integration of AI-based resolution modules or advanced slashing incentives (Rahman et al., 17 Oct 2025).
     • Market Data Archival: Ensuring full, reproducible, and verifiable orderbook and trade data archiving, beyond settlement information—critical for empirical research and transparency.
     • Composability and Trust Minimization: Advancing protocol-level modularity to allow granular, trust-minimized substitution of core infrastructure components (e.g., share, trade, and oracle modules) to maintain security and liveness in adversarial environments.

     7. Significance within the Broader Prediction Market Ecosystem

     Polymarket exemplifies the modern paradigm of decentralized prediction markets, integrating advances in cost-function-based market making, permissionless yet modular market creation, and rigorous on-chain settlement and archiving. Its hybrid microstructure, robust arbitrage environment, and behavioral traceability provide a testbed for economic and computational experiments in information aggregation, market efficiency, and mechanistic design. As DePMs continue to mature, Polymarket’s composable architecture and open research agenda position it as a critical locus for both practical deployment and theoretical investigation toward scalable, manipulation-resistant, and information-efficient event forecast markets (Rahman et al., 17 Oct 2025, Saguillo et al., 5 Aug 2025, Dalen, 17 Oct 2025, Curry et al., 14 Feb 2024, Papireddygari et al., 14 Oct 2025, Dudík et al., 2021, Roh et al., 2015, Chen et al., 20 Jul 2024, Cutting et al., 11 Jul 2025, Shabashev, 15 Sep 2025).