Prediction Markets 2025: The Prediction Markets Explained

Prediction markets are exchange platforms where participants buy and sell contracts whose payoff depends on the outcome of a future event. The price of each contract reflects the collective judgment of traders about how likely the event is to occur. By design, those who hold better information or sharper analysis are rewarded, while those who misjudge lose money. Over time, this mechanism pushes market prices toward consensus probabilities that can be read as forecasts.

Although the idea of wagering on uncertain outcomes is centuries old, the modern study of prediction markets begins with organized academic experiments such as the Iowa Electronic Markets (IEM) in the late 1980s. These early platforms demonstrated that small, real-money markets could consistently outperform traditional opinion polls in predicting U.S. election results. Since then, prediction markets have been studied by economists, political scientists, statisticians, and computer scientists as laboratories for understanding how dispersed information is aggregated into prices.

The interest in prediction markets is not limited to politics. They have been applied to economic indicators, scientific replication studies, corporate project management, and even public health. Academic research shows that when contracts are clearly defined and trader participation is sufficiently broad, prediction markets often yield probability estimates that rival or surpass expert forecasts. They also update continuously, offering an advantage over slower-moving surveys or models.

Despite their appeal, prediction markets face important challenges. Regulators question whether they constitute gambling or financial instruments, ethicists debate whether it is acceptable to trade on outcomes involving human tragedy, and critics point out that thin participation or biased trader pools can distort results. These tensions shape the ongoing debate about their legitimacy, scope, and future role in forecasting.

This article provides a comprehensive exploration of prediction markets: how they work, their historical development, the evidence on their accuracy, design innovations that sustain them, and the ethical and legal controversies surrounding them. Drawing on decades of academic research and practical case studies, it examines both their promise and their limitations, offering a balanced view of a field where finance, information theory, and governance intersect.

What Is a Prediction Market?

Prediction markets are organized exchanges where participants trade contracts whose value depends on the outcome of future events. The central idea is simple: instead of relying only on polls, expert panels, or statistical models, markets allow individuals to express their expectations through financial stakes. The resulting prices represent a probability — not because anyone decreed it so, but because competition between buyers and sellers aligns the contract price with the collective estimate of how likely the event is.

These markets function in a manner similar to Futures or Binary Options, but with one crucial difference: instead of wheat, oil, or interest rates, the underlying asset is an event such as an election result, an economic indicator, or the approval of a new technology. For example, if a contract pays $1 if Candidate X wins, and it currently trades at $0.63, the market consensus interprets this as a 63% probability of victory. Unlike a mere opinion poll, every participant has something to lose if their expectation is wrong, which helps discipline overly optimistic or uninformed views.

Distinguishing Features

While prediction markets resemble other forms of speculation, several features set them apart:

Event-linked payouts: Settlement depends on whether a specific event occurs, rather than on price fluctuations of a commodity or security.
Probabilistic signals: Prices naturally map onto probabilities, offering an intuitive way to interpret collective expectations.
Information aggregation: Traders bring dispersed knowledge, news, and private insights into the market, which combines them into a single signal.

This makes them valuable not only for traders seeking profit, but also for researchers, journalists, policymakers, and businesses who want a real-time read on collective expectations.

Why They Matter

Prediction markets are important because they turn uncertain outcomes into tradable instruments. By doing so, they create a mechanism that rewards informed participants and penalizes poor judgment. Over time, this dynamic often produces forecasts that outperform expert committees or traditional polling.

They have been used to forecast:

Election results in the United States and Europe
Policy outcomes, such as the likelihood of central bank interest rate changes
Technological milestones, including space launches and artificial intelligence performance benchmarks
Public health developments, such as the timing of vaccine approvals

Through these applications, prediction markets show that collective judgment, when disciplined by incentives, can serve as a powerful forecasting tool.

How Prediction Markets Work

Prediction markets operate by transforming uncertain events into financial contracts. These contracts trade much like futures or options, but instead of being tied to the price of oil or interest rates, their value depends on whether a specified event occurs. The mechanism is straightforward, yet the implications are profound: by allowing people to back their beliefs with money, the market aggregates diverse information into a single, continuously updated probability.

Contract Structure

At the core of prediction markets are binary event contracts. A standard contract pays $1 if the event occurs and $0 if it does not. The contract trades at a price somewhere between those two extremes, reflecting the probability the market assigns to the event.

If a contract trades at $0.70, the market implies a 70% chance that the event will happen.
If it falls to $0.25, the implied probability is 25%.

This mechanism makes the prices directly interpretable as forecasts. Unlike derivatives markets where prices need careful conversion into probabilities, prediction markets produce probabilities as their primary output.

Some platforms extend beyond binary contracts to scalar markets, where the payoff depends on a range. For example, a contract might settle based on the level of inflation in a given year. Instead of a simple yes-or-no outcome, payouts vary along a scale, enabling forecasts of numerical values rather than discrete events.

Trading Dynamics

Like binary options brokers, prediction markets platforms bring together buyers and sellers whose views about the event differ.

Buyers purchase contracts when they believe the event is more likely than the market price suggests. If they think the true probability is 80% while the market prices it at 65%, buying at $0.65 offers expected profit.
Sellers (or short sellers) act in the opposite way, taking positions when they think the market overestimates the probability.

The market price reflects the balance of these trades, adjusting as new information arrives. For example, a poll release, a breaking news story, or an unexpected development can shift sentiment, leading to immediate price changes. In this way, prediction markets update forecasts continuously, unlike polls that are taken periodically.

Information Aggregation

The central claim of prediction markets is that they aggregate dispersed information. Each trader brings their own knowledge — from public data, private signals, professional expertise, or intuition. When they place trades, they reveal not just their opinion, but the strength of their conviction, proportional to the size of their investment.

This process means the final price is not an average of all opinions but a weighted measure: those more confident and willing to risk more capital exert greater influence. This weighting often improves accuracy, as casual or uninformed views are diluted by the financial incentives favoring well-grounded predictions.

Market-Making and Liquidity

For prediction markets to function effectively, they require liquidity — the ability for traders to buy and sell without large price distortions. Many platforms employ automated market makers (AMMs) to ensure that contracts can always be traded.

Traditional order book systems, such as those used in stock exchanges, match buyers and sellers directly. This works when many participants are present, but can fail in thin markets.
Automated market makers use algorithms to set prices based on supply and demand. They provide continuous liquidity but must be carefully calibrated to avoid distortions.

The presence of liquidity providers ensures that markets remain active, which is necessary for generating reliable probability estimates. Without sufficient trading, prices can be easily skewed by a small number of participants, undermining the information value of the market.

Resolution Mechanisms

Prediction markets depend on clear settlement criteria. Once the event occurs (or fails to occur), contracts must resolve transparently and unambiguously.

In regulated platforms such as Kalshi, resolution follows official data sources, for example Bureau of Labor Statistics releases for unemployment figures.
In decentralized platforms such as Augur or Polymarket, resolution depends on oracles — systems that verify outcomes on the blockchain. Disputes may require additional processes, such as arbitration by token holders.

Clarity is essential. Ambiguous contract wording can cause disputes that erode trust in the market. A poorly defined contract such as “Will peace be achieved in region X?” creates confusion over what counts as “peace.” In contrast, contracts tied to measurable outcomes — election winners, economic data releases, or specific court rulings — maintain credibility.

Incentive Structures

The effectiveness of prediction markets rests on their incentive design. Participants are rewarded for accurate forecasting and penalized for error. This mechanism discourages careless or dishonest predictions, unlike polls or surveys where misreporting carries no cost.

The self-correcting nature of these markets comes from arbitrage opportunities. If a contract trades at a price inconsistent with available information, informed traders can step in to profit, bringing the price back in line. Over time, this competitive pressure tends to improve forecast accuracy, though only if enough participants are present to exploit discrepancies.

Example in Practice

Consider a market on whether a particular bill will pass in the U.S. Congress by the end of the year.

Early in the session, the contract trades at $0.40, reflecting a 40% probability.
A major news outlet reports strong opposition from influential senators, and the price drops to $0.25.
Later, the bill gains unexpected bipartisan support, pushing the price to $0.65.
If the bill passes, the contract settles at $1; if it fails, at $0.

Throughout the process, the price acts as a live forecast, adjusting in real time to the flow of political developments.

Historical Development of Prediction Markets

Prediction markets did not emerge suddenly as a modern innovation. Their development has been gradual, shaped by academic research, early commercial experiments, regulatory interventions, and the rise of blockchain technology. Tracing this history reveals how the idea of trading contracts on uncertain events has moved from university research projects to international platforms and regulated exchanges.

The development of prediction markets reflects a cycle of innovation, expansion, and regulation. Beginning as university experiments, they grew into international commercial platforms, faced legal pushback, reemerged in decentralized form, and are now being tested as regulated exchanges.

The core idea has remained constant: contracts tied to uncertain events can transform private beliefs into public probabilities. What has changed over time is the scale of participation, the technology of trading, and the legal frameworks that govern them.

Early Academic Foundations: Iowa Electronic Markets

The Iowa Electronic Markets (IEM), launched in 1988 by the University of Iowa, are widely considered the origin of modern prediction markets. Conceived as a teaching and research tool, the IEM allowed participants to trade contracts on political elections with small stakes, usually capped at $500.

The design was simple but effective. Contracts paid $1 if a candidate won and $0 if they lost, with market prices reflecting the probability of each candidate’s victory. Researchers used these markets to test whether financial incentives could improve the accuracy of political forecasts.

Results were striking. In several U.S. election cycles, IEM prices provided more accurate predictions than national opinion polls. Academic papers analyzing IEM data circulated widely, and the project became a cornerstone of literature on collective forecasting. It also demonstrated that small-scale markets could serve as laboratories for understanding how information is aggregated.

Commercial Growth: TradeSports and Intrade

In the late 1990s and early 2000s, entrepreneurs sought to expand prediction markets beyond the classroom.

TradeSports, founded in Dublin in 2002, initially offered sports-related contracts but soon expanded to political and financial events.
Intrade, established in 2001, became the most influential commercial prediction market of its time. It allowed participants from around the world to trade contracts on elections, economic indicators, and geopolitical events.

Intrade’s markets were widely cited by the media, especially during U.S. presidential elections. Journalists highlighted the platform’s ability to provide real-time probability estimates, often contrasting them with opinion polls. By the 2004 and 2008 elections, Intrade was treated by many analysts as a barometer of public expectations.

However, success brought scrutiny. U.S. regulators, particularly the Commodity Futures Trading Commission (CFTC), argued that Intrade was offering unregistered derivatives. Combined with financial difficulties and legal challenges, Intrade ceased operations in 2013. Its closure marked the end of the first wave of large-scale, commercial prediction markets, but it also proved that demand existed for platforms that could provide probabilistic forecasts on real-world events.

Academic-Commercial Hybrid: PredictIt

In 2014, PredictIt launched under a “no-action” letter from the CFTC, which allowed the platform to operate as a research project with limited stakes. Like the Iowa Electronic Markets, PredictIt restricted individual positions (typically to $850 per contract) to remain within an educational framework.

PredictIt specialized in political markets. Contracts covered elections, legislative votes, judicial rulings, and policy outcomes. The platform quickly became popular with journalists and researchers, who used its prices as indicators of shifting expectations in U.S. politics.

For nearly a decade, PredictIt served as a hybrid model: accessible enough to attract thousands of traders, but small enough to remain legally protected as a research initiative. That arrangement ended in 2022, when the CFTC revoked its no-action letter, leading to the platform’s eventual wind-down in 2023. PredictIt’s closure underscored the regulatory uncertainty that continues to shape prediction markets in the United States.

Decentralized Experiments: Blockchain-Based Platforms

The rise of blockchain technology in the 2010s created a new generation of prediction markets that operated without central authority.

Augur, launched in 2015 on Ethereum, allowed anyone to create and trade markets. Resolution relied on decentralized oracles, with disputes settled by token holders. While pioneering, Augur faced usability problems, thin liquidity, and slow adoption.
Gnosis, introduced in 2017, built tools for decentralized forecasting applications rather than focusing on a single platform. It emphasized modular infrastructure, enabling developers to integrate prediction markets into broader applications.
Polymarket, emerging in the 2020s, gained traction with a simpler interface and higher liquidity. Settling contracts in USDC (a dollar-pegged stablecoin), it became the most active blockchain-based market, covering politics, technology, science, and culture.

Decentralized markets highlighted both the promise and challenges of blockchain. They demonstrated that global, censorship-resistant trading was possible, but also revealed problems with liquidity concentration, resolution disputes, and regulatory crackdowns on platforms offering access to U.S. residents.

Regulated Exchange Models: Kalshi

A significant milestone came with Kalshi, founded in 2018 and approved by the CFTC in 2020 as a regulated exchange for event contracts. Unlike its predecessors, Kalshi operates within the framework of U.S. derivatives law, giving it legitimacy in mainstream finance.

Kalshi’s markets include contracts on economic indicators, policy outcomes, and other measurable events. By positioning itself as a financial exchange rather than a gambling platform, it seeks to attract institutional traders and businesses that want to hedge against real-world risks.

The approval of Kalshi signaled that prediction markets could be integrated into regulated finance under certain conditions. While restrictions remain (notably the prohibition on political election markets in the U.S.), Kalshi represents a turning point in the institutionalization of event contracts.

Case Studies of Forecasting Performance

Several episodes illustrate how prediction markets have interacted with major global events:

U.S. Elections (2008 and 2012): Markets often provided more accurate forecasts than polling averages, particularly in swing states.
Brexit (2016): Markets underestimated the likelihood of the Leave vote for much of the campaign but adjusted sharply in the final days.
COVID-19 Vaccines (2020): Markets on approval timelines offered useful probabilistic forecasts months before official announcements, though they occasionally mispriced regulatory delays.
Ethereum Merge (2022): Crypto-native markets priced technical upgrade timelines with a degree of accuracy that provided developers and investors with a transparent confidence measure.

These cases show both the strengths and weaknesses of prediction markets: their ability to adapt dynamically to information, but also their vulnerability to demographic biases, thin liquidity, and framing effects.

Part 4: Strengths and Limitations in Forecasting

Prediction markets attract interest because they often generate probability estimates that outperform traditional forecasting tools such as opinion polls, expert committees, or statistical models. Their design encourages participants to act on information rather than speculation, and this incentive mechanism helps discipline forecasts. Yet the performance of prediction markets is uneven. They are most reliable under certain conditions, but fragile when those conditions fail. Understanding both their strengths and their limitations is essential to evaluating their role as forecasting instruments.

Prediction markets show remarkable strengths as forecasting mechanisms: they incentivize accuracy, aggregate diverse information, and produce transparent, interpretable signals. Their track record includes notable successes in politics, business, and science.

At the same time, their limitations are real. Thin liquidity, participant bias, poorly defined contracts, and susceptibility to manipulation can compromise their reliability. These weaknesses do not invalidate prediction markets but set boundaries on where they are most effective.

Their role is best understood as complementary: they are neither flawless crystal balls nor mere gambling tools, but structured environments where incentives channel collective judgment into measurable probabilities.

Strengths of Prediction Markets

Incentive-Driven Accuracy

Unlike opinion polls, which collect stated preferences or beliefs without cost, prediction markets require traders to risk money. This incentive structure discourages careless guesses. Participants who are consistently wrong lose capital, while those who identify mispricings are rewarded. Over time, this mechanism tends to concentrate influence in the hands of informed participants.

This dynamic distinguishes prediction markets from surveys or crowdsourced platforms that treat all inputs as equal. Here, conviction must be backed by stakes. A trader who strongly believes a candidate will win, and is willing to commit substantial funds, exerts more weight on the forecast than someone who offers only casual interest.

Information Aggregation

Markets excel at drawing on dispersed information. Individual traders may have access to unique signals — private knowledge of a supply chain disruption, insider understanding of political coalitions, or specialized technical expertise. Alone, such information might remain fragmented. In a market, however, it is integrated into the collective price.

This feature allows prediction markets to act as a real-time aggregator of news and expectations. When a new poll is released, or a scientific paper changes assumptions about a vaccine’s efficacy, the effect is quickly incorporated into prices. The result is a dynamic, continuously updated forecast, unlike expert forecasts that are revised at intervals.

Transparency and Interpretability

Prediction market prices are directly interpretable as probabilities. A contract trading at $0.72 signals a 72% implied chance of occurrence. This simplicity distinguishes them from many other financial instruments, where probabilities must be extracted using complex models.

Transparency also comes from the visibility of price movements. Observers can track how expectations change in response to events, offering insight into both the forecast and the information environment shaping it.

Empirical Evidence of Success

Numerous studies have compared prediction markets with other forecasting tools. In U.S. elections, markets have frequently matched or exceeded polling averages in accuracy. In corporate contexts, internal markets have provided early warnings of product delays or missed targets, outperforming official forecasts produced by managers.

In scientific domains, markets have produced probabilistic estimates that sometimes anticipate outcomes more reliably than expert committees. For example, markets on biotechnology approvals and climate-related developments have demonstrated the ability to condense uncertainty into actionable signals.

Limitations of Prediction Markets

Liquidity Constraints

Prediction markets rely on active participation. When trading volumes are low, prices can be distorted by a handful of traders. This problem is common in niche or technical markets where only a small number of participants have interest or expertise.

Thin markets produce noisy signals that should not be mistaken for collective wisdom. For example, decentralized blockchain markets often struggle with liquidity outside high-profile events such as presidential elections. The resulting prices can fluctuate erratically, undermining credibility.

Participant Bias

Markets are only as representative as the participants who trade in them. If traders come disproportionately from certain demographics, political leanings, or geographies, the market may reflect those biases rather than objective probabilities.

A well-known example occurred in U.S. political markets, where overrepresentation of partisan traders sometimes skewed contract prices away from polling consensus. Although arbitrage opportunities should theoretically correct these distortions, they persist when opposing traders are absent or liquidity is limited.

Event Definition and Resolution Ambiguity

Prediction markets depend on clear contract wording and unambiguous resolution criteria. Poorly defined contracts introduce disputes that weaken trust. For instance, a contract asking “Will there be peace in region X by year-end?” raises questions about what constitutes peace: a ceasefire? a formal treaty? reduced violence?

Ambiguities of this kind can discourage participation and undermine confidence. Regulated exchanges like Kalshi attempt to address this by tying contracts to official data releases, but decentralized markets continue to grapple with oracle disputes and unclear criteria.

Susceptibility to Manipulation

In theory, markets are self-correcting: if one actor attempts to manipulate prices, others can profit by trading against them. However, in practice, manipulation remains a concern, especially in thinly traded markets.

Wealthy actors may deliberately distort prices to influence public perception, particularly in political markets.
Rumor campaigns can momentarily sway prices before being corrected.
Low-liquidity contracts can be moved significantly with relatively small trades.

While arbitrage mechanisms eventually restore balance, temporary distortions can reduce trust in the reliability of forecasts.

Limits of Scope

Not all questions are suited for prediction markets. They function best when outcomes are:

Well defined
Observable within a fixed timeframe
Of interest to enough participants to sustain liquidity

Questions about vague or long-term events — for instance, “Will climate change mitigation succeed?” — lack the precision needed for clear settlement. Similarly, questions too specialized to attract traders may generate unreliable prices.

Case Studies: Success and Failure

Success Example: U.S. Presidential Elections

During the 2012 U.S. election, prediction markets consistently priced President Obama’s reelection probability above 60%, even during periods when some polls suggested a tighter race. The market’s stability reflected informed expectations about turnout patterns and electoral college dynamics that were not fully captured in polling.

Failure Example: Brexit Referendum

In 2016, markets underestimated the likelihood of the United Kingdom voting to leave the European Union. For much of the campaign, contracts priced the probability of “Leave” at less than 30%. On the day of the vote, the market shifted upward, but still failed to reflect the eventual outcome. Analysts later suggested demographic biases among traders and overreliance on polling signals as contributing factors.

Mixed Example: COVID-19 Vaccine Approvals

Markets on vaccine approval dates in 2020 provided useful probabilistic ranges months before announcements. Yet, some contracts mispriced regulatory delays, demonstrating both the strengths and weaknesses of markets in rapidly evolving scientific contexts.

Part 5: Current Uses Across Politics, Business, and Science

Prediction markets have moved beyond their academic origins to become practical tools in several fields. Their ability to condense dispersed knowledge into probabilities makes them attractive to political analysts, business strategists, and scientific communities. Yet, the way markets are used differs across domains, reflecting variations in incentives, data availability, and cultural acceptance.

The applications of prediction markets reveal both versatility and constraints. In politics, they provide real-time probabilistic signals that enrich electoral coverage and policymaking analysis. In business, they serve as internal tools for harnessing employee knowledge and guiding strategic planning. In science, they help address reproducibility and forecast technological progress. In public health, they offer adaptive signals in crisis conditions.

Despite these benefits, adoption remains uneven. Markets require liquidity, clear event definitions, and cultural acceptance to function well. Where these conditions are absent, their outputs risk being noisy or misleading.

Prediction markets are therefore not universal forecasting tools but context-dependent instruments. Their strongest contributions arise when they operate in environments where incentives align, participation is broad, and outcomes are measurable within defined timeframes.

Political Forecasting

Elections as Natural Test Cases

Political elections have been the most visible domain for prediction markets. Elections are well-defined, time-bounded events with outcomes that are easily verifiable, making them ideal for contract design. Markets on U.S. presidential races, European parliamentary elections, and referendums have drawn significant participation.

Journalists frequently cite these prices as real-time indicators of public expectations. For example, during the 2008 and 2012 U.S. elections, prediction market prices often tracked — and occasionally anticipated — shifts in polling averages. Because contracts settle on objective outcomes, the credibility of political markets has been relatively high compared with surveys, which depend on sampling methods.

Legislative and Policy Outcomes

Beyond elections, markets have also been used to predict policy decisions. Platforms like PredictIt hosted contracts on whether the U.S. Congress would pass specific bills or whether central banks would adjust interest rates. These contracts provided probabilities that policymakers themselves sometimes monitored as an external check on expectations.

For instance, markets around the U.S. Federal Reserve’s interest rate decisions offered signals that financial professionals compared with official futures markets. The overlap showed how event-based contracts could complement existing economic instruments.

Business and Corporate Applications

Internal Prediction Markets

Corporations have experimented with internal prediction markets to forecast project deadlines, product launches, and financial performance. Instead of relying solely on top-down reporting, firms create markets where employees trade contracts linked to company-specific outcomes.

Studies have shown that employees often possess information not visible to management. For example, engineers might anticipate delays in a product rollout, or sales teams may sense customer resistance before it shows in official reports. Internal markets channel these dispersed insights into prices that management can interpret as probabilities.

Notable corporate experiments include:

Google: Ran internal markets on product launches and company performance, finding that prices often anticipated outcomes earlier than formal forecasts.
Hewlett-Packard: Used markets to predict printer sales, with results that outperformed traditional forecasting methods in accuracy.
Siemens and Eli Lilly: Tested internal markets on R&D outcomes and deadlines, providing managers with probabilistic assessments of project feasibility.

Strategic Planning and Risk Management

Businesses outside of technology have also considered prediction markets for strategic risk management. For example, markets on commodity supply chains, regulatory changes, or competitive product launches can help firms hedge against uncertainty. Although adoption is limited due to cultural resistance and regulatory uncertainty, the potential for improving decision-making remains significant.

Scientific and Academic Uses

Replication Markets

One of the most promising applications in science has been the use of markets to forecast the replicability of research findings. Following growing concerns about reproducibility in psychology, medicine, and economics, researchers created markets where participants bet on whether published results would replicate in independent studies.

These replication markets have demonstrated strong predictive power. Traders were able to identify which results were likely to hold up under scrutiny, often outperforming expert surveys. The approach provides a scalable mechanism for prioritizing replication efforts and allocating research resources more efficiently.

Technology and Innovation Forecasts

Markets have also been applied to technological milestones. For example, contracts have been created around the timing of space exploration achievements, artificial intelligence benchmarks, and biomedical breakthroughs. In some cases, these markets have provided early warning signals when expectations were unrealistic, tempering hype cycles with probabilistic estimates.

A practical illustration is the use of markets to forecast progress in machine learning competitions. When participants and observers traded contracts on the likelihood of specific performance thresholds being reached, prices converged toward accurate probabilities.

Public Health Applications

Prediction markets have also been used in public health contexts. During the COVID-19 pandemic, markets quickly emerged around vaccine approval timelines, production capacity, and policy responses. Although not perfect, they provided continuously updated signals that complemented epidemiological models and official projections.

Markets also allowed policymakers and analysts to gauge how quickly new information — such as trial results or regulatory delays — was absorbed into collective expectations. This adaptability made them useful in environments where uncertainty shifted daily.

Media and Public Discourse

The media’s embrace of prediction markets has been a double-edged sword. On one hand, journalists value the clarity of market prices as probabilistic signals, especially during election cycles. On the other, markets can be misunderstood or misrepresented, with probabilities treated as certainties or as substitutes for nuanced analysis.

Nonetheless, coverage in outlets like The New York Times, The Economist, and Bloomberg has helped integrate prediction markets into mainstream discourse. This visibility has raised awareness of their utility, but has also heightened scrutiny regarding regulation and potential manipulation.

Comparative Strengths Across Domains

Domain	Strengths of Use	Weaknesses / Challenges
Politics	Clear outcomes, strong media visibility, high liquidity in major elections	Susceptible to partisan bias, regulatory restrictions
Business	Captures insider knowledge, improves forecasts, aids risk management	Cultural resistance, confidentiality concerns
Science	Identifies replicable results, prioritizes research resources	Low participation outside specialized circles
Public Health	Rapid adaptation to new data, complements models	Vulnerable to misinformation during crises

Part 6: Regulatory and Legal Issues

Prediction markets sit at the intersection of finance, gambling, and information exchange, which makes their legal status highly complex. Regulators often disagree on whether these markets should be classified as financial derivatives, betting products, or research tools. The ambiguity has created a patchwork of rules across jurisdictions, shaping where and how prediction markets can operate.

The regulatory and legal environment for prediction markets is fragmented and uncertain.

In the United States, the CFTC asserts oversight, leading to a restrictive climate, especially for political contracts.
In the UK and parts of the EU, markets operate under gambling regulation, but this narrows their scope.
In Asia-Pacific, regulation is generally prohibitive, with few exceptions.
Blockchain-based platforms have introduced decentralization, but also raised new enforcement challenges.

The unresolved tension lies between viewing prediction markets as valuable forecasting tools versus dismissing them as forms of gambling. Until this debate stabilizes, markets will remain constrained, operating at the margins of finance and policy rather than in the mainstream.

United States

Commodity Futures Trading Commission (CFTC) Oversight

In the United States, the Commodity Futures Trading Commission (CFTC) is the principal regulator for derivatives markets. Because prediction contracts resemble futures — contingent claims on uncertain events — the CFTC has asserted jurisdiction over them. This has led to restrictions on public-facing platforms.

Iowa Electronic Markets (IEM): One of the earliest academic platforms, the IEM was allowed to operate under a “no-action” letter from the CFTC, provided it remained small in scale and served research purposes.
PredictIt: Launched in 2014 as an academic project in collaboration with Victoria University in New Zealand, PredictIt was granted similar allowances. However, in 2022 the CFTC rescinded its no-action letter, citing violations of conditions. The move sparked lawsuits and highlighted the precarious legal foundation of such markets.
Kalshi: A newer U.S.-based platform, Kalshi pursued full regulatory approval to operate as a Designated Contract Market (DCM). It has sought to list contracts on events such as inflation releases or congressional control. Yet even Kalshi’s efforts have faced CFTC pushback, especially when contracts touch on political events.

The CFTC’s reluctance stems partly from concerns about election betting, which regulators view as too close to gambling and vulnerable to manipulation. At the same time, there is recognition that event contracts could serve economic and informational value. This tension defines the U.S. landscape.

Gambling Laws and Political Sensitivities

Outside the CFTC, prediction markets run into state-level gambling prohibitions. Betting on elections, for example, is widely restricted due to fears of undermining democratic legitimacy. Regulators worry that financial incentives could encourage manipulation of outcomes — for instance, through voter suppression efforts or misinformation campaigns.

Thus, even when contracts do not fall neatly under commodity law, they risk being treated as illegal wagers. The result is a regulatory climate that discourages mainstream adoption, pushing many prediction markets into academic or gray-market status.

European Union and the United Kingdom

The United Kingdom has historically been more permissive. Betting exchanges such as Betfair allow users to wager on political events, sports, and other outcomes. These are regulated as gambling products rather than financial instruments. This framing has provided legal clarity but also limits their use as research or corporate forecasting tools, since they are viewed primarily as entertainment.

In the European Union, regulation varies by country. Some member states treat prediction markets as a subset of online betting, while others classify them as financial derivatives requiring licensing. The lack of harmonization complicates cross-border platforms.

For instance:

In Ireland and the UK, political betting is accessible through regulated bookmakers.
In Germany and France, strict gambling laws restrict such activity.
EU-wide financial directives such as MiFID II introduce additional obligations if contracts are classified as financial instruments.

Asia-Pacific

In most Asian jurisdictions, prediction markets are tightly restricted under gambling laws. Online betting is illegal or highly controlled in countries such as China, India, and Japan. Exceptions exist in Australia and New Zealand, where academic or small-scale markets have operated with regulatory tolerance.

Victoria University of Wellington has hosted markets used for research and teaching, providing one of the few institutionalized models in the region.

Blockchain-Based and Decentralized Platforms

The rise of blockchain technology has introduced decentralized prediction markets such as Augur, Gnosis, Polymarket, and Omen. These platforms are not controlled by a central operator but run on smart contracts. Anyone can create a market, and outcomes are resolved via decentralized oracles.

This decentralization presents novel regulatory challenges:

Jurisdiction: With no central entity, regulators struggle to identify who is responsible.
Enforcement: Even if regulators target individual developers or interfaces, the underlying contracts may remain accessible.
Financial risk: Users trade with cryptocurrencies, which adds layers of volatility and compliance concerns regarding anti-money laundering (AML) and know-your-customer (KYC) rules.

Polymarket, for example, faced enforcement action by the U.S. CFTC in 2022, resulting in a settlement that required restricting U.S. customers. Yet decentralized platforms continue to operate globally, creating a regulatory “whack-a-mole” problem.

Core Legal Questions

Several unresolved legal debates shape the regulatory environment:

Classification: Should prediction markets be regulated as financial derivatives, gambling products, or research tools? Each classification triggers different laws and agencies.
Legitimacy of Political Contracts: Election-related contracts are among the most controversial. Critics argue they could erode trust in democratic institutions, while supporters see them as valuable forecasting tools.
Consumer Protection: Regulators worry about fraud, market manipulation, and retail investors losing money.
Free Speech Considerations: Some argue that placing bets on events is a form of expression or information exchange, raising constitutional questions in certain jurisdictions.

The Balance Between Innovation and Risk

Regulators face a dilemma. On one hand, prediction markets promise informational and economic benefits. They can sharpen forecasts, aid policy planning, and provide transparency. On the other hand, they resemble gambling products that raise ethical and political sensitivities, especially around elections.

So far, most regulators have erred on the side of caution, restricting markets rather than experimenting with broader legalization. The exception has been academic projects under limited exemptions, or entertainment-focused betting exchanges under gambling regulation.

Part 7: Theoretical Foundations and Academic Research

Prediction markets have been examined across economics, political science, statistics, and computer science as mechanisms for turning dispersed private knowledge into public probabilities. The core theoretical claim—that prices encode information—echoes a classic insight: prices act as signals that coordinate fragmented knowledge. F. A. Hayek’s argument that markets condense “widely diffused knowledge” into observable price signals provides the intellectual backdrop for why event-contingent prices can be interpreted as forecasts. home.uchicago.edu oll.libertyfund.org sites.santafe.edu

Markets as information aggregators

Modern surveys of prediction markets formalize this intuition: with many participants facing incentives to be right (and costs for being wrong), trading pushes contract prices toward probability-weighted consensus. Wolfers and Zitzewitz’s review in the Journal of Economic Perspectives shows that across multiple settings, market-generated probabilities are typically well-calibrated and competitive with standard forecasting benchmarks. aeaweb econpapers.repec.org

Two frameworks often used to rationalize these results are the Efficient Market Hypothesis (prices reflect available information) and Rational Expectations (aggregated forecasts converge to true expectations under common knowledge and incentives). While neither holds perfectly in real markets, they provide a tractable baseline for interpreting event prices as probabilities rather than mere “odds.” Empirical overviews in economics have repeatedly found that combining market prices with polls or models often improves accuracy relative to either source alone. American Economic Association

Evidence on accuracy: elections and beyond

The longest, most intensively studied series comes from the Iowa Electronic Markets (IEM). In a widely cited study comparing market predictions to 964 polls across five U.S. presidential cycles (1988–2004), Berg, Nelson, and Rietz report that the market was closer to the eventual outcome about 74% of the time. Their working paper (and the later journal version) remains a canonical reference on long-run election accuracy. biz.uiowa.edu ScienceDirect

Independent analyses reach similar conclusions: comparative work finds markets match or outperform polling aggregates in several cycles, particularly when liquidity is healthy and resolution criteria are unambiguous. ubplj.org

The academic literature doesn’t stop at politics. In science, prediction markets have been used to quantify replicability. Dreber, Pfeiffer, Almenberg and coauthors set up markets on 44 psychology studies in the Reproducibility Project; market prices correlated strongly with replication outcomes and outperformed survey judgments of experts and participants. Their PNAS paper is now a standard reference for using markets as meta-scientific tools. PNAS projects.iq.harvard.edu PubMed

Design: making markets work with limited liquidity

A practical challenge—especially outside headline elections—is thin participation. Robin Hanson’s Logarithmic Market Scoring Rule (LMSR) addresses this by acting as a cost-function market maker that always quotes prices and guarantees bounded loss to the operator. LMSR underpins many small-scale or internal markets (e.g., within firms) where order-book liquidity would otherwise be unreliable. Hanson’s original paper remains the foundational treatment. hanson.gmu.edu courses.cs.duke.edu

Related research in computer science extends LMSR-style market makers and analyzes their properties in combinatorial or multi-outcome settings, which is relevant when forecasting vectors of events (e.g., electoral college configurations). ResearchGate

Manipulation, bias, and robustness

Do concentrated actors skew prices? Evidence from the IEM suggests that manipulation attempts tend to be short-lived: informed traders and arbitrage restore prices as mispricings appear, provided participation is sufficient and questions are well-specified. Berg and Rietz summarize lessons on market design, manipulation, and accuracy in a political-science symposium paper, emphasizing clear resolution criteria and adequate depth. biz.uiowa.edu

Bias is not absent. Markets can reflect participant demographics or partisan preferences, especially when one side’s traders dominate. Comparative analyses show that such distortions diminish when trader diversity increases and when there are liquid, opposing views willing to take the other side. This helps explain why large national elections are usually better calibrated than niche, local, or technical markets. ubplj.org

What the literature says overall

Synthesis: Peer-reviewed surveys conclude that prediction markets typically yield well-calibrated probabilities that compete with or surpass conventional forecasts in many contexts. They update faster than polls and can be usefully combined with statistical models. American economic association
Mechanism: The theoretical foundation is straightforward—prices as information—tracing back to Hayek’s account of decentralised knowledge and forward to modern market-design work (LMSR) that keeps prices informative even when natural liquidity is low. home.uchicago.edu hanson.gmu.edu
Scope: Strongest results appear where outcomes are clear, timed, and observable (national elections; binary regulatory decisions; pre-specified scientific replications). Performance weakens when questions are vague, participation is thin, or incentives are asymmetric—conditions documented across political and academic settings. biz.uiowa.edu

Part 8: Ethical and Legal Challenges

Prediction markets raise persistent ethical, legal, and normative questions that go beyond technical design. Because they involve wagering on uncertain events—often in politics, economics, or even human tragedy—regulators and ethicists debate their legitimacy and impact. While some see them as extensions of financial derivatives or decision-support tools, others categorize them as gambling or even forms of moral hazard.

The ethical and legal challenges of prediction markets reveal deep tensions. They promise more accurate forecasting and better decision-making but raise questions about gambling, manipulation, insider trading, and the morality of wagering on human suffering. Different societies draw boundaries differently: some tolerate or even experiment with regulated markets, while others prohibit them outright.

The academic consensus is that design and governance matter. When markets are well-structured, limited to clearly defined outcomes, and transparently regulated, they can serve as valuable tools for collective intelligence. When poorly designed or politically mishandled, they risk being perceived as gambling schemes or exploitative wagers.

Gambling, Finance, or Free Speech?

The classification of prediction markets differs widely across jurisdictions. In the United States, regulators such as the Commodity Futures Trading Commission (CFTC) have generally restricted public-facing event markets, citing concerns about gambling and systemic risk. For example, in 2012 the CFTC blocked the North American Derivatives Exchange from offering contracts on political outcomes, framing them as “gaming” rather than hedging instruments. (CFTC release)

Academic work has noted that the boundaries are conceptually blurred. Robin Hanson and others argue that prediction markets can be seen as a form of speech market—a structured way of expressing beliefs—raising First Amendment questions in the U.S. (Hanson, 2007). Other scholars compare them to insurance or hedging tools, suggesting that banning them may limit risk management opportunities for firms and individuals.

Ethical Risks: Betting on Harmful Events

A persistent ethical concern is markets on harmful or morally sensitive outcomes. When contracts are tied to violence, terrorism, or natural disasters, critics argue that traders could have incentives to cause the event to profit from it. This debate reached a peak in 2003 when DARPA’s proposed Policy Analysis Market, designed to predict geopolitical instability, was attacked in the U.S. Senate as a “terrorism betting parlor.” The project was shut down within days. (New York Times coverage)

The concern extends to pandemic forecasting, corporate failures, or mortality-linked contracts. Even when no actual manipulation occurs, the optics of profiting from tragedy can erode public trust. Scholars like Sunstein (2006) warn that such markets risk undermining their legitimacy if they appear exploitative.

Manipulation and Market Integrity

Ethical issues also arise from manipulation attempts. Political partisans, corporations, or interest groups may try to distort prices to create the illusion of consensus or inevitability. While studies on the Iowa Electronic Markets and other platforms suggest such manipulation is usually short-lived—because informed traders arbitrage away mispricing—the ethical problem remains: manipulation can mislead the public and erode trust in democratic processes.

Further, when prediction markets are cited in media coverage of elections or policies, even temporary distortions can influence voter perceptions. This raises the question of whether markets should be protected from—or regulated like—other opinion-shaping institutions such as polls.

Privacy and Information Inequality

Prediction markets create incentives for individuals with private or insider information to trade. While this is celebrated as a strength (more informed markets), it raises legal and ethical questions:

Insider trading: If employees trade on private corporate or government data, should it be regulated like securities law?
Information inequality: Wealthier or better-informed individuals may disproportionately profit, raising distributional concerns.
Privacy risks: Internal corporate markets risk revealing sensitive strategic information if not properly designed.

Some scholars argue that carefully designed market rules, anonymization, and restricted participation can mitigate these issues, but no consensus exists.

Regulatory Experiments and Exceptions

Despite restrictions, several regulated or tolerated markets exist. The Iowa Electronic Markets operate under a no-action letter from the CFTC, provided they remain small-scale and educational. (CFTC No-Action Letter)

More recently, platforms like Kalshi have pursued formal regulatory approval as event contract exchanges, highlighting the difficulty of distinguishing prediction from speculation. Meanwhile, blockchain-based platforms (e.g., Polymarket) operate transnationally, often facing enforcement actions when serving U.S. users without registration. (CFTC settlement with Polymarket, 2022)

These cases illustrate an unsettled landscape: prediction markets live at the intersection of finance, gambling, speech, and public policy.

Normative Debates

Beyond legality, scholars ask whether prediction markets ought to exist for certain subjects:

Democratic values: Do election markets enhance democracy by providing transparent expectations, or undermine it by commodifying civic choice?
Corporate responsibility: Should firms use internal markets to guide decisions, even if they risk leaks?
Public policy: Could governments use prediction markets to improve decision-making, or would official reliance politicize and delegitimize them?

Cass Sunstein and Eric Posner have debated these issues, with some advocating “futarchy” (governance by prediction markets) while others warn of technocratic overreach and equity concerns.

Conclusion:

Prediction markets transform uncertainty into tradable contracts, making collective expectations visible in the form of prices. Beginning as small experiments like the Iowa Electronic Markets, they have evolved into research tools, policy discussions, and even blockchain-based platforms. Their theoretical roots draw from Hayek’s account of information in markets, rational expectations models, and modern market design. Across multiple studies, prediction markets have demonstrated that when liquidity, participation, and incentives align, they often provide probability estimates that are more accurate—and faster to update—than conventional forecasts.

The research evidence is substantial: from presidential elections where they rival polling averages, to scientific replication projects where they outperform expert surveys, prediction markets have shown an ability to aggregate scattered knowledge into reliable signals. Design innovations such as the Logarithmic Market Scoring Rule make them feasible even in thin markets, while corporate and academic experiments demonstrate practical uses beyond speculation.

Yet their success is not universal. Ethical, legal, and structural limitations impose boundaries. Poorly specified contracts, thin participation, or skewed trader demographics can reduce reliability. Regulatory concerns—whether classifying prediction markets as gambling, finance, or protected speech—continue to shape where and how they are permitted. Ethical debates about markets on violence, mortality, or disasters highlight the need for careful governance.

The overall picture is one of both promise and tension. Prediction markets embody a powerful mechanism for information aggregation and probabilistic forecasting, but their usefulness depends heavily on context, design, and legitimacy. Where they are well-regulated and responsibly structured, they can supplement polls, models, and expert judgment with real-time, incentive-driven probabilities. Where they are misused or poorly understood, they risk being dismissed as gambling or worse, as exploitative wagers on human tragedy.

In sum, prediction markets stand at a crossroads between finance, governance, and epistemology. They are not replacements for expertise, policy debate, or statistical models, but complements that can sharpen forecasts and reveal hidden signals. Their history and research base suggest that they are more than curiosities: they are laboratories of collective intelligence, showing how structured incentives can transform uncertainty into actionable knowledge.