Prediction markets, also known as betting markets, information markets, decision markets, idea futures, or event derivatives, are open markets that enable the prediction of specific outcomes using financial incentives. They are exchange-traded markets established for trading bets in the outcome of various events. The most common form of a prediction market is a binary option market, which will expire at the price of 0 or 100%.

Prediction markets can be thought of as belonging to the more general concept of crowdsourcing which is specially designed to aggregate beliefs on particular topics of interest, where the market price can indicate what the crowd thinks the probability of the event is. Traders with different beliefs trade on contracts whose payoffs are related to the unknown future outcome and the market prices of the contracts are considered as the aggregated belief.

Prediction markets are considered gambling by many governments, and are banned in some locations. Some users and researchers have reported that prediction markets are similar to gambling and can cause addiction.

Before the era of scientific polling, early forms of prediction markets often existed in the form of political betting. One such political bet dates back to 1503, in which people bet on who would be the papal successor. Even then, it was already considered "an old practice". According to Paul Rhode and Koleman Strumpf, who have researched the history of prediction markets, there are records of election betting in Wall Street dating back to 1884. Rhode and Strumpf estimate that average betting turnover per US presidential election is equivalent to over 50 percent of the campaign spend.

Economic theory for the ideas behind prediction markets can be credited to Friedrich Hayek in his 1945 article "The Use of Knowledge in Society" and Ludwig von Mises in his "Economic Calculation in the Socialist Commonwealth". Modern economists agree that Mises' argument, combined with Hayek's elaboration of it, is correct. Prediction markets are championed in James Surowiecki's 2004 book The Wisdom of Crowds, Cass Sunstein's 2006 Infotopia, and Douglas Hubbard's How to Measure Anything: Finding the Value of Intangibles in Business.

Prediction markets are financial markets made up of binary contracts that resolve based on whether certain events happen or not. These contracts are usually exchange traded through a free floating order book system. The price of such contracts are set between $0.01 and $1 and represent the odds of an event occurring. Each event will have a “Yes” or “No” tradable contract. For example, if a “Yes” contract around an event occurring has a market price of $0.93 then the market is implying that there is a 93% that this event will take place.

In the same way the “No” contract in the same market will have a price of $0.07 and thus the market thinks this event has a 7% chance of occurring. The free-floating central limit order book (CLOB) has shown to be an incredibly efficient mechanism for matching pure supply and demand by giving participants the ability to submit trades at whatever price they choose and only being able to take on a trade or prediction if another market participant disagrees.

Prices in prediction markets are determined within one of the two predominant structures. The dominant structure of modern commercial markets is the central limit order book (CLOB), where participants place limit orders, accept limit orders, and prices are generated by supply and demand. Kalshi operates a fully centralized limit order book as a designated contract market regulated by the Commodity Futures Trading Commission (CFTC). As for Polymarket, it has a "hybrid-decentralized" order book where trades are matched off-chain and settled on-chain on Polygon.

Previously, Polymarket operated using an automated market maker (AMM), built on Hanson's scoring rule, until late 2022 when the firm switched to a full order book.

The other structure is the automated market maker, most notably the Logarithmic Market Scoring Rule (LMSR) introduced by Robin Hanson. While in an order book a transaction is matched between two traders, an LMSR market maker keeps a probability distribution over possible outcomes and offers to make trades at prices defined by the cost function, providing guaranteed liquidity even in the case of low trading activity.

For each outcome, the price is equal to the exponentiation of the ratio between the number of shares outstanding for that outcome and the liquidity parameter b, normalized so that prices for all the outcomes add up to one. The liquidity parameter regulates the sensitivity of prices to the trades and at the same time limits the maximum loss for the operator of the mechanism, which is known beforehand and bounded to b ln n for n outcomes.

The two structures involve a trade-off since an order book requires a willing counterparty for each transaction, while LMSR guarantees liquidity at the expense of a loss which is known in advance and bounded.

Academic literature has examined the meaning of prices in terms of probabilities. Justin Wolfers and Eric Zitzewitz showed that the market clearing price equals the mean trader belief under logarithmic utility and a symmetric distribution of beliefs, and that prices are close to the mean trader belief in many cases, but that they may be biased estimations of these beliefs. Charles Manski argues that prices partially identify mean beliefs, while Wolfers and Zitzewitz argue that prices are still meaningful given reasonable risk preferences.

Following the occurrence of the event under consideration in the contract, the winning outcome is determined and the contract is settled. The procedures of resolution vary greatly between regulated markets and decentralized ones.

In regulated exchanges, settlement happens according to a rulebook filed with a government regulator prior to the start of trading operations. Every single Kalshi contract is bound by a rulebook filed with the CFTC, which identifies authoritative data sources (Source Agencies) and a Payout Criterion for a "Yes" resolution. The Kalshi markets team relies on the results officially stated by these source agencies, settling each winning contract with a payment of one dollar while all the other contracts expire worthless.

Kalshi operates as a designated contract market under the Commodity Exchange Act.

In decentralized systems, the settlement of the contract is determined by an oracle that transfers the information about the result of the event to the blockchain. For example, Polymarket makes use of UMA (Universal Market Access), an "optimistic oracle," which means that the proposed resolution is considered true unless challenged within a dispute window. In case of a dispute, the Data Verification Mechanism (DVM) is launched: a Schelling-point mechanism in which UMA stakers commit votes in secret over a 24-hour period and reveal them during the following 24 hours.

According to UMA documentation, a dispute resolves when at least 65% of staked UMA both votes and agrees on a single outcome, and a vote that does not meet this threshold rolls into the following round. Stakers who abstain or vote against the majority are slashed, and the forfeited stake is redistributed to the majority voters. UMA also reports that its optimistic oracle currently resolves 99.8% of requests without escalating them to the DVM.