Prediction markets enable users to trade on the outcomes of various events. These platforms began scaling across the United States last year and now track a broad spectrum of events—from geopolitical developments to entertainment award winners. But what exactly are prediction markets?

As an economics researcher with long-standing expertise in market mechanisms and incentive structures, my answer is simple: prediction markets are fundamentally ordinary markets. Markets are foundational tools for resource allocation, directing goods and services to those who need them most. In this process, markets also exhibit information aggregation capabilities: the clearing of supply and demand integrates all participants’ knowledge into price signals.

Prediction market platforms and their associated instruments directly leverage this information aggregation power to forecast the likely outcomes of future events. Platforms introduce specific asset contracts tied to particular events—holders receive payouts if the pre-defined outcome materializes. Users trade these assets based on their own assessments of event probabilities. For years, many enterprises have used prediction markets to uncover tacit employee knowledge, helping gauge whether key products will launch on schedule. Researchers have also employed such tools to evaluate the reproducibility of experimental findings. Today, numerous media organizations collaborate with prediction markets, harnessing collective intelligence to supplement frontline reporting and traditional journalism, enriching content depth.

Prediction markets aggregate individual participants’ forecasts about the future, synthesizing these perspectives into a trading mechanism that estimates the probability of various events. Betting on event outcomes in these markets follows the same logic as predicting stock prices in equity markets or trading oil futures in commodity markets. The difference lies in the underlying assets: while oil prices are influenced by multiple complex factors, prediction market contracts only generate returns when a specified event occurs.

When oil prices rise, we can infer that demand exceeds supply—but we may not discern the root causes: Is it due to fears of escalating tensions in the Middle East, or the emergence of new applications for petroleum? Prediction markets allow for standalone trading contracts on individual possibilities, enabling precise disaggregation of forecasts. For example, a market titled “Whether the Strait of Hormuz will remain open at a specified time” could feature a contract rule stating: pay $1 per contract upon event realization. As users continuously trade, the market price becomes a probability barometer, reflecting the collective judgment of traders on the likelihood of the event.

The mechanics work as follows: suppose the current price of each contract is $0.50, indicating the market assigns a 50% probability to the event. If you believe the probability is higher—say, 67%—you can buy the contract at $0.50. If your forecast proves correct, your contract yields $0.67 in return. This purchase pushes up both the market price and the implied probability, signaling that other traders believe the market previously underestimated the event’s likelihood. Conversely, if someone perceives the current price as inflated, they may sell short or issue a bearish position, thereby lowering the market’s estimated probability.

Compared to alternative forecasting methods, well-functioning prediction markets offer distinct advantages. First, they deliver quantified probability outputs—a core strength. Public opinion polls and surveys only capture proportionality of views; deriving event probabilities from them requires additional statistical modeling to assess sample representativeness. Moreover, poll data typically represents static snapshots at a given moment, whereas prediction markets dynamically update in real time as new participants enter and new information emerges.

More crucially, prediction markets incorporate intrinsic incentive and constraint mechanisms. Both buyers and sellers invest real capital, and incorrect judgments result in financial loss. This compels participants to rigorously analyze their available information, focusing on domains where they possess superior knowledge. Conversely, the desire to profit from information and expertise motivates individuals to conduct thorough research and uncover relevant insights. A notable case occurred during the 2024 U.S. election cycle, when prediction market participants resorted to unconventional survey methods to gather intelligence beyond the reach of traditional polling firms.

Finally, prediction markets boast remarkably broad coverage. In theory, traders with insight into the oil industry can express their views via long or short positions on crude oil contracts. Yet in practice, countless events lack reliable forecasting channels through mainstream commodities or equity markets—precisely where prediction markets shine. Recently, several prediction markets have launched contracts assessing the performance of various AI models across different tasks. Such niche trends are difficult to reflect in traditional commodities markets. Anyone can build and fund a prediction market to address such specialized questions.

Prediction markets are not new. Early forms date back to 16th-century Europe, where people used them to predict the next pope. Modern prediction markets integrate knowledge from economics, statistics, market design, and computer science. In the 1980s, Charles Plott and S. S. Sanderson established the formal academic framework for this mechanism. Soon after, the world’s first modern prediction market—the Iowa Electronic Market—launched. Enabled by internet technology, this model has since aggregated fragmented global information, driving continuous growth.

However, unlocking the full potential of prediction markets still faces significant challenges. First, infrastructure-level issues persist: how to determine final event outcomes and achieve consensus; how to ensure market transparency and transaction traceability; and how to implement scalable dispute resolution mechanisms when contract settlements are contested or subject to manipulation.

Second, market design presents critical challenges. First, participants with core information must be actively involved. If all traders are uninformed, market prices become meaningless signals. Conversely, if informed actors avoid participation, predictions will be systematically biased. I proposed this insight as early as 2016: the prediction markets underestimated the probabilities of Brexit and Donald Trump’s first presidential election victory because participants failed to anticipate the rise of populism.

Additionally, insider trading poses risks—especially when insiders possess the ability to influence event outcomes. Imagine if members of a papal conclave placed bets on the “next pope” prediction market using confidential information, front-running trades, or even secretly manipulating the election to favor their holdings. Once widespread belief arises that markets are compromised by insider manipulation, rational participants will exit, leading to systemic collapse.

Another risk: malicious actors might deliberately manipulate prediction market prices to shape public perception of event probabilities. In such cases, prediction markets shift from aggregating collective wisdom to becoming tools for propaganda. For instance, a political campaign could deploy campaign funds to artificially inflate the market probability of its candidate’s victory, creating a false impression of momentum. However, prediction markets possess inherent self-correcting mechanisms: when prices deviate significantly from fair value, arbitrageurs step in to bet against the mispricing, counteracting irrational valuations.

These challenges underscore the need for refined rules governing participant access, contract design, and overall operational standards. Yet if industry practitioners can systematically address these issues, prediction markets will ultimately become indispensable tools for human foresight and navigating uncertainty.

By: Scott Duke Kominers, Research Partner at a16z crypto, translated by Chopper, Foresight News