Prediction markets are mechanisms that turn participant opinions into probabilities. Participants buy contracts on event outcomes, and the market price of a contract reflects the collective probability estimate. In 2024–2025, prediction markets experienced explosive growth: Polymarket processed roughly $3.7B on the single “US presidential winner” market and about $9B cumulative platform volume in 2024, with ~314,000 active traders (The Block; ChainCatcher). The platform runs hundreds of concurrent event markets at any given time. Growth was not monotonic — DLNews reported an ~84% drop in volume immediately after the November 2024 election, followed by a gradual recovery around sports, crypto, and macro events. This drawdown pattern is critical context for anyone designing tokenomics around event-driven platforms.
But behind the elegant idea lie serious tokenomics questions: how to set prices when liquidity is thin? Which mechanism is fairer — AMM or order book? How to defend against Sybil attacks?
How a Prediction Market Works
Core Mechanics
Each market is a binary (or multi-outcome) contract:
- “Yes” is priced from $0.01 to $0.99
- Price = participants’ probability estimate
- When the event occurs, the “Yes” contract settles at $1.00, “No” at $0.00
- Contract_price — current trading price
- Face_value — settlement value ($1.00)
- P_outcome — implied probability (computed)
- A contract price of $0.65 → the market estimates a 65% probability
Three Pricing Approaches
| Approach | Mechanism | Examples |
|---|---|---|
| AMM (LMSR) | Logarithmic cost function sets price | Gnosis, Zeitgeist, XO.Market (LS-LMSR) |
| Odds-driven vAMM | Virtual AMM with oracle-sourced odds and a singleton liquidity pool | Azuro (LiquidityTree + vAMM) |
| AMM (Uniswap-style) | Constant-product pool per outcome | Polkamarkets |
| CLOB | Central Limit Order Book — participants set prices themselves | Polymarket, Kalshi, Limitless Exchange |
| Hybrid | Off-chain order book + on-chain settlement | SX Bet |
AMM in Prediction Markets
LMSR — Logarithmic Market Scoring Rule
The classic AMM formula for prediction markets, proposed by Robin Hanson:
- qᵢ — number of contracts sold for outcome i
- n — number of outcomes; the sum runs over i = 1..n
- b — liquidity parameter (higher b = lower slippage)
- C(q) — cost function determining the current price (computed)
Price of a contract for a specific outcome:
- pᵢ — current contract price (= probability estimate, computed)
- The sum in the denominator runs over j = 1..n outcomes
- Σᵢ pᵢ = 1 (probabilities are normalized)
LMSR Problems
Fixed liquidity. The parameter b determines the market maker’s maximum loss. If b is too small — slippage is enormous on small volumes. If b is large — the market maker incurs heavy losses. There’s no dynamic adjustment to changing demand.
Inefficiency on volatile markets. LMSR was designed for prediction markets with gradual probability refinement. When markets move sharply (election results, sports events), the formula can’t adapt fast enough, and arbitrageurs extract profit from the market maker.
Scaling. For multi-outcome markets (10+ options), LMSR requires exponentially more liquidity. For a market with 20 outcomes, the b parameter needs to increase proportionally.
CLOB — Central Limit Order Book
Mechanics
CLOB works like traditional exchanges:
- A participant places a limit order: “Buy ‘Yes’ at $0.60”
- Another participant places a matching order: “Sell ‘Yes’ at $0.60”
- Orders match, the trade executes
Advantages Over AMM
| Criterion | AMM (LMSR) | CLOB |
|---|---|---|
| Slippage | Determined by parameter b, fixed | Depends on order book depth, adaptive |
| Pricing | Formula, lags the market | Participants set price in real time |
| Liquidity | Limited by initial market maker capital | Sourced from all participants |
| Multi-outcome | Exponential scaling requirements | Linear scaling |
| Arbitrage | Market maker bears losses | Losses distributed among counterparties |
Why CLOB Won
Polymarket — the largest prediction market by volume — uses CLOB based on the CTF Exchange protocol. The reasons:
Capital efficiency. Market makers place limit orders with managed risk, rather than pouring liquidity into a pool with unpredictable losses.
Price accuracy. On election markets where probabilities shift 1–2% per day, CLOB participants reflect information more precisely than the LMSR formula with its lag.
Composability. CLOB is compatible with professional trading strategies: market-making, cross-platform arbitrage, delta hedging.
The Fairness Problem: Normalized Volatility
On prediction markets, a fairness issue arises: outcomes with different volatility have different attractiveness for speculators.
Example
Two markets:
- “Candidate A wins the election” — price fluctuates from $0.45 to $0.55 (low volatility)
- “Bitcoin above $100K by June” — price fluctuates from $0.20 to $0.80 (high volatility)
A trader earns more on the second market with the same capital — not because they forecast better, but because the market is more volatile.
Normalized Volatility Formula
- Sigma_p — standard deviation of contract price
- P_avg — average contract price over the period
- P_avg × (1 − P_avg) — maximum possible variance for a Bernoulli distribution
- V_norm — normalized volatility, enables cross-market comparison (computed)
Normalized volatility enables comparing markets with different base probabilities and adjusting fees or margin requirements accordingly.
Sybil Arbitrage
Attack Mechanics
Sybil arbitrage — creating multiple accounts to exploit bonuses or subsidies on a prediction market:
- The platform subsidizes liquidity or offers bonuses for first bets
- The attacker creates N accounts
- Half the accounts bet “Yes,” the other half bet “No”
- One outcome wins, and the attacker collects bonuses from the winning accounts
- N — number of Sybil accounts (each makes one bet and receives one bonus)
- Bonus — bonus per account
- Fee — platform fee rate
- Bet_size — bet amount per account
- Profit — attack profit (computed); attack is profitable if Bonus > Fee × Bet_size
Defenses
| Method | Mechanism | Weakness |
|---|---|---|
| KYC | Document verification | Reduces anonymity, loses crypto-native audience |
| Proof of Humanity | Biometrics or social graph | Costly, false positives |
| Deposit collateral | Minimum stake to participate | Increases barrier to entry |
| Behavioral analysis | Algorithms detecting coordinated actions | Arms race |
Platform Tokenomics
Prediction market platforms use different tokenomic models:
Polymarket: No Governance Token, But a Platform Stablecoin
Polymarket operates without a governance token. Historically, bets were placed in USDC with minimal fees, and monetization relied on trading volume and data.
In April 2026, Polymarket announced CTF Exchange V2, introducing Polymarket USD — a platform-native, 1:1 USDC-backed stablecoin — together with Managed Optimistic Oracle V2 and a 37-address resolver whitelist (Crypto Times; Polymarket docs). The platform still has no freely traded governance token, but it is no longer “USDC-only”: users interact through a wrapped platform stablecoin that sits on top of USDC.
Advantage: no governance-token friction; users stay in stablecoins. Disadvantage: no on-chain governance instrument; value capture is indirect (volume and data).
Azuro: Token for Liquidity
Azuro uses its AZUR token for:
- Staking in liquidity pools
- Governance voting on market parameters
- Incentivizing data providers (oracles)
On the mechanism side, Azuro is an odds-driven virtual AMM: data providers push odds to the protocol, a singleton LiquidityTree pool absorbs bets across many markets, and LP profit comes from the spread around the oracle odds — not from an LMSR cost function (Azuro Medium, “Introducing Azuro’s Liquidity Tree”; Messari, “Understanding Azuro”). Classifying Azuro as “AMM (LMSR)” is a common but incorrect shorthand.
Advantage: liquidity providers are motivated by the token, not just fees. Disadvantage: an additional asset increases complexity and risk for users; pricing quality depends on data-provider honesty.
When AMM, When CLOB
Choosing the pricing mechanism
Prediction markets and mechanism design
Prediction markets are a special case of mechanism design. Pricing formulas, incentive systems, and attack defenses are built on the same principles as any tokenomic system.
Mechanism design