21 Keys to Prediction Market

The Bayesian Truth Serum (BTS), developed by Drazen Prelec, is an information elicitation mechanism that encourages truthful reporting of subjective opinions by rewarding participants whose predictions align with beliefs that are “surprisingly popular” compared to what others expect. As Scott Kominers explains in the transcript, BTS asks participants for their own belief (e.g., did you like a movie?) and their estimate of others’ beliefs, using discrepancies to identify truthful answers. If a participant’s belief matches an unexpectedly common response, they are rewarded, assuming they’ve provided unique, accurate insight.

For instance, the transcript cites a study by Prelec, Seung, and McCoy, where BTS outperformed simple crowd polling. When asked the capital of Pennsylvania, most might guess Philadelphia (incorrect) and expect others to agree, but a minority correctly choosing Harrisburg would score higher due to its surprising popularity. This method excels in subjective or low-information settings, where prediction markets may falter due to thin participation or lack of objective outcomes. Unlike prediction markets, BTS doesn’t require trading digital assets or event resolution, making it simpler to implement.

BTS is valuable for market research or community feedback, where subjective data is critical, and can be enhanced by blockchain for transparent, auditable scoring. However, it’s less effective for objective events like elections, where prediction markets’ financial incentives and thick markets provide stronger forecasts, as seen in the 2024 election’s accurate predictions on platforms like Polymarket.

A circuit breaker in prediction markets is a design feature that temporarily halts trading when prices move too rapidly, often due to herd behavior or manipulation, to stabilize the market and allow rational reassessment. As Scott Kominers and Alex Tabarrok mention in the transcript, circuit breakers, inspired by stock markets, can “slow contagion” by limiting quick entries or exits, giving participants time to evaluate new information, such as a prominent poll during the 2024 election, before prices overreact.

For example, if a major public signal (e.g., a poll) triggers a surge in digital asset trades, a circuit breaker pauses trading to prevent herding from skewing prices away from true probabilities. This is particularly critical in thin markets, where a single large trader can disproportionately influence prices, as noted in the transcript’s discussion of manipulation attempts. Blockchain-based markets can implement circuit breakers via smart contracts, ensuring automated, transparent enforcement.

Circuit breakers enhance the reliability of prediction markets as public goods by maintaining accurate information aggregation, especially for high-stakes forecasts like elections or corporate decisions. They complement other design features, like decentralized oracles, to ensure markets reflect true collective beliefs rather than transient distortions.

Contract resolution in prediction markets is the process of verifying the outcome of an event (e.g., an election result) to finalize the payout of digital assets traded in the market. As Scott Kominers and Alex Tabarrok explain in the transcript, this often requires an oracle to bring off-chain data (e.g., who won the 2024 election) onto a blockchain, where smart contracts distribute payments based on the asset’s terms (e.g., $1 if a candidate wins, $0 if they lose). Accurate resolution is critical, as it determines who profits, as seen in Polymarket’s reliable payouts post-2024 election.

The transcript highlights challenges, such as oracle manipulation, where incorrect data (e.g., a hacked New York Times report) could distort payouts, as Alex notes with crypto hack examples. Decentralized oracles mitigate this by aggregating multiple sources, enhancing trust. In non-blockchain markets, like the Iowa Political Prediction Markets, resolution relies on institutional trust, but blockchain’s immutability ensures verifiable outcomes. Thin markets or unclear events (e.g., the transcript’s example of an unlisted nominee like Gaetz) can complicate resolution, requiring precise contract design.

Contract resolution is the linchpin of prediction markets’ trustworthiness, enabling applications like election forecasting or corporate decision-making. Robust design, often leveraging blockchain’s transparency, ensures accurate payouts and maintains market integrity as a public good.

Decentralization in prediction markets refers to the use of blockchain technology to distribute control, data storage, and contract execution across a network of nodes, minimizing dependence on a centralized entity. As Scott Kominers discusses in the transcript, decentralization enhances trust by ensuring that no single party can unilaterally alter market outcomes or manipulate resolutions, such as through oracles. This is critical for on-chain prediction markets, where digital assets are traded, and outcomes (e.g., election results) must be transparently verified, as seen in platforms like Polymarket during the 2024 election.

Decentralization supports credible commitments, allowing participants to trust that the market’s rules—encoded in smart contracts—will execute as promised without interference. For instance, a decentralized oracle can aggregate data from multiple sources to resolve a contract, reducing the risk of manipulation compared to a centralized source like a single news outlet. However, Alex Tabarrok notes that decentralization isn’t always necessary; non-blockchain prediction markets, like the Iowa Political Prediction Markets, succeeded without it by relying on institutional trust. Decentralization shines in contexts lacking strong institutions or requiring global participation, as it enables open, auditable systems.

The benefits of decentralization include enhanced security, transparency, and accessibility, making prediction markets more robust for applications like forecasting global events or DAO governance. However, it introduces complexity, such as managing decentralized oracles, and may not be essential for internal markets (e.g., Hewlett-Packard’s) where trust in a central entity already exists.

Dispersed information refers to the fragmented, often tacit knowledge held by individuals across a population, which prediction markets aggregate into accurate forecasts through the trading of digital assets. As Alex Tabarrok cites Hayek’s 1945 paper in the transcript, this information—such as a Pennsylvania resident’s insight into local voting trends or an employee’s knowledge of a project’s delays—is difficult to centralize but can be surfaced via market prices. In the 2024 election, Polymarket’s prices reflected such dispersed inputs, outperforming polls with biased samples (5% response rates).

The power of prediction markets lies in incentivizing participants to reveal this information through financial stakes, as Scott Kominers notes, with prices converging to a “convex combination” of individual beliefs. For example, Hewlett-Packard’s internal market aggregated employees’ dispersed knowledge about printer sales, improving forecasts. Thin markets, however, struggle to capture enough dispersed information, as seen in the 2016 election’s inaccuracies, while thick markets maximize diversity and accuracy.

Blockchain enhances this process by providing a transparent, auditable platform for trading, ensuring dispersed information is reliably aggregated. This makes prediction markets valuable for applications like election forecasting, scientific replicability, or corporate planning, where capturing widespread knowledge is critical for accurate predictions.

Domain expertise in prediction markets refers to specialized knowledge in a particular field (e.g., politics, science) that enables participants to make more informed trades of digital assets, contributing to accurate price discovery. Scott Kominers and Alex Tabarrok note in the transcript that experts, such as scientists evaluating paper replicability, bring valuable insights due to their ability to analyze data or context (e.g., statistical methods). However, they emphasize that prediction markets benefit from diverse participants, not just experts, as seen in the 2024 election where local knowledge (e.g., neighborhood sentiment) complemented expert polls.

The transcript cites an example of a non-expert who earned $10,000 in a scientific replication market by obsessively analyzing data, showing that expertise can emerge organically. Unlike polls, which may over-rely on experts, prediction markets don’t require predefined expertise, allowing anyone with relevant information to contribute, as Alex underscores. This openness enhances information aggregation, especially in thick markets, but thin markets may overly depend on experts, risking bias.

Domain expertise strengthens prediction markets for specialized applications, like forecasting scientific outcomes or corporate sales, but its value depends on market thickness and diversity. Blockchain’s transparency ensures expert contributions are auditable, but markets must balance expert and non-expert inputs to maximize accuracy.

Futarchy, proposed by economist Robin Hanson, is a governance model that uses prediction markets to make policy decisions by forecasting their impact on a chosen metric, such as GDP adjusted for inequality or environmental factors (“GDP+”). As Alex Tabarrok explains in the transcript, in futarchy, voters select the success metric, and prediction markets then estimate whether proposed policies (e.g., healthcare or immigration reforms) will increase or decrease this metric. Policies with higher predicted positive impacts are adopted, leveraging market-driven information aggregation to guide decisions.

Unlike traditional governance systems like democracy, futarchy relies on the collective wisdom of market participants trading digital assets to predict outcomes, theoretically reducing bias and improving decision quality. For example, a market could predict whether a new science policy would boost GDP+, as Hanson suggests. The transcript notes that futarchy addresses manipulation concerns by proposing secondary markets to predict reversals, ensuring robustness. While not yet widely implemented, futarchy could be applied incrementally, such as predicting the impact of firing a CEO on a company’s stock price, as Alex proposes.

Futarchy’s strength lies in its ability to harness dispersed information for objective decision-making, but it requires thick markets and reliable oracles to function effectively. Its futuristic nature, as Scott Kominers likens to a Borges story, highlights its potential to transform governance, particularly in decentralized systems like DAOs, where blockchain enables transparent, auditable predictions.

Herd behavior, also known as the bandwagon effect, occurs in prediction markets when participants align their trades with prominent public signals, such as a major poll or analyst report, rather than their private information, leading to price distortions. Scott Kominers references this in the transcript, citing Morris and Shin’s “Social Value of Public Information,” where a salient signal (e.g., a poll during the 2024 election) causes market prices to overreact, as participants assume others will follow suit, amplifying the signal’s impact beyond its informational value.

This behavior can undermine the wisdom of the crowds, as it reduces the independence of participants’ judgments, a key factor in accurate information aggregation. For instance, if a poll suggests a candidate’s lead, traders may buy corresponding digital assets not because they believe the poll but because they expect others to act on it, as seen in election market fluctuations. Thin markets are particularly susceptible, lacking diverse counterbalancing trades. The transcript suggests design solutions like circuit breakers to slow rapid price shifts or secondary markets to predict reversals, mitigating herding.

Herd behavior highlights the need for thick, diverse markets to ensure robust price discovery. While blockchain’s transparency can expose such trends, it doesn’t inherently prevent them, making market design critical for maintaining accurate forecasts in applications like elections or corporate planning.

In prediction markets, an incentive mechanism is a structured system, usually involving financial rewards through digital assets, that motivates participants to reveal their true beliefs or information about future events. As described by Alex Tabarrok and Scott Kominers in the transcript, participants are incentivized to “put skin in the game” by trading assets based on their forecasts, such as buying an asset at $0.55 that they believe reflects a 70% probability, expecting a profit if correct. This financial stake aligns participants’ actions with their true expectations, improving market accuracy.

These mechanisms distinguish prediction markets from polls by rewarding accuracy, as seen in the 2024 election where markets outperformed biased polls with low response rates (e.g., 5%). Beyond money, incentives can include reputation systems or tokens, as Scott notes, where participants earn non-transferable tokens for accurate predictions, usable for prestige or further market participation. For example, Hewlett-Packard’s internal market gave employees $100 to trade, incentivizing accurate sales forecasts. However, incentives must be carefully designed to avoid manipulation or herding, which can distort prices in thin markets.

Effective incentive mechanisms ensure robust information aggregation, making prediction markets valuable for forecasting events like corporate outcomes or scientific replicability. They work best in thick markets where diverse participants have strong motivations to act on their knowledge, but alternative mechanisms like peer prediction can complement them in thinner markets.

Information aggregation refers to the mechanism by which prediction markets and similar systems gather and synthesize diverse pieces of information held by individuals into a cohesive, often more accurate, forecast. In prediction markets, this occurs through participants trading digital assets based on their private knowledge or forecasts, with the resulting asset price reflecting the collective probability of an event’s outcome. As highlighted by Alex Tabarrok in the transcript, this concept draws from Friedrich Hayek’s 1945 paper, “The Use of Knowledge in Society,” which argues that markets aggregate dispersed, tacit knowledge—such as local insights or expert analyses—that no single individual possesses.

The strength of information aggregation lies in its ability to incorporate varied perspectives, including those from non-experts who may hold unique insights (e.g., a Pennsylvania resident’s knowledge of local political sentiment). For example, in corporate settings like Hewlett-Packard’s internal prediction markets, employees’ dispersed knowledge about sales forecasts was aggregated to predict printer sales more accurately than traditional methods. However, effective aggregation requires a thick market with many participants and strong incentives to reveal truthful information, as thin markets or misaligned incentives can lead to incomplete or distorted outcomes.

This process extends beyond prediction markets to other mechanisms like peer prediction or auctions, but prediction markets are particularly effective due to their financial incentives and transparent pricing. They act as a decentralized system for surfacing collective intelligence, making them valuable for applications like forecasting election results or assessing scientific replicability, where diverse inputs lead to robust predictions.

Market manipulation in prediction markets involves deliberate actions to skew digital asset prices, either to profit or to influence external perceptions, such as political campaign strategies. Scott Kominers notes in the transcript that manipulation can occur when a participant with significant resources (e.g., a “whale”) floods the market with trades to shift prices, as allegedly attempted in the Obama vs. McCain election market, though arbitrageurs corrected it within hours. Manipulation is more feasible in thin markets with fewer participants, where large trades can disproportionately affect prices.

For example, the transcript describes how a manipulator might use multiple identities (Sybils) to create the appearance of widespread belief, potentially swaying public opinion if a market’s price is used as a signal (e.g., for campaign efforts). However, thick markets, like Polymarket in 2024, resist manipulation due to diverse participation and arbitrage opportunities. Blockchain-based markets can mitigate this through transparency and decentralized oracles, though vulnerabilities remain if oracles are compromised, as Alex Tabarrok warns with examples of crypto hacks.

Manipulation undermines the reliability of prediction markets as public goods, distorting their ability to aggregate accurate information. Designing markets with circuit breakers or secondary markets (e.g., predicting price reversals, as Robin Hanson suggests) can reduce manipulation risks, ensuring forecasts remain trustworthy for applications like elections or corporate decision-making.

In prediction markets, an oracle is a mechanism or entity that delivers verified off-chain information (e.g., election results or weather data) to a blockchain to resolve the outcome of a market’s digital asset contracts. As Scott Kominers and Alex Tabarrok explain in the transcript, oracles are critical for on-chain prediction markets because events like a U.S. presidential election occur off-chain, requiring a reliable method to bring that data on-chain to determine payouts. For example, an oracle might use the New York Times’ reported election results to confirm a candidate’s victory.

Oracles face challenges due to the high financial stakes involved in contract resolution, creating incentives for manipulation, as Alex notes with examples of crypto hacks exploiting distorted oracles. To mitigate this, decentralized oracles, which aggregate data from multiple sources (e.g., Chainlink), enhance trustworthiness by reducing reliance on a single point of failure. The transcript cites the 2024 election, where accurate oracle resolution was crucial for platforms like Polymarket to pay out correctly, unlike historical errors like the “Dewey Defeats Truman” headline.

Oracles are essential for ensuring prediction markets on blockchains function reliably, enabling applications like election forecasting or corporate decision-making. Their design must prioritize transparency and decentralization to maintain trust and prevent manipulation, especially in high-stakes markets.

Peer prediction is an information aggregation mechanism that incentivizes participants to report their true beliefs by rewarding them based on how accurately their predictions match the distribution of others’ beliefs, rather than the actual outcome of an event. As Scott Kominers describes in the transcript, participants are asked for their own forecast (e.g., will a candidate win?) and their estimate of what others believe, using the latter to cross-check truthfulness. This method is particularly useful in thin markets or for subjective questions where outcomes are hard to verify, unlike prediction markets that require a definitive resolution.

For example, the transcript references a study by Hussam, Rigol, and Roth, where peer prediction was used in a developing country to identify successful micro-entrepreneurs by asking community members who they and others thought would succeed. Unlike prediction markets, peer prediction allows immediate payouts based on belief alignment, not waiting for event resolution, making it practical for contexts with delayed or unclear outcomes. It leverages Bayesian reasoning to ensure truth-telling, as participants’ rewards depend on accurately gauging the crowd’s beliefs.

Peer prediction complements prediction markets by addressing scenarios where thick markets or objective outcomes are absent, such as small-scale or subjective forecasts. Its integration with blockchain could enhance trust through transparent reward distribution, but it’s less suited for high-stakes, objective events like elections, where prediction markets excel due to their financial incentives and clear resolutions.

A prediction market is a speculative market where participants buy and sell digital assets tied to the outcome of specific future events, such as elections or product launches. The price of these assets reflects the market’s collective belief about the probability of the event occurring—for example, a digital asset priced at $0.70 for a candidate winning an election implies a 70% chance of victory. Unlike traditional polls, prediction markets incentivize participants to act on their true beliefs by putting financial stakes (“skin in the game”) into their predictions, making them a powerful tool for forecasting. They were pioneered in 1988 with the Iowa Political Prediction Markets, designed explicitly to produce accurate predictions by aggregating dispersed information.

These markets leverage the “wisdom of the crowds,” where diverse participants, each with partial information, contribute to a collective forecast that often outperforms individual or expert predictions. For instance, in the 2024 U.S. presidential election, prediction markets like Polymarket accurately forecasted outcomes better than polls, which suffered from biases like low response rates (e.g., 5% for modern polls versus 60% historically). However, their effectiveness depends on market thickness (participation volume) and the absence of manipulation, as thin markets or coordinated distortions can skew results.

Prediction markets are not limited to political events; they’ve been used internally by companies like Hewlett-Packard to predict printer sales and externally to assess scientific paper replicability. By enabling participants to trade on their knowledge, prediction markets create a public good by surfacing valuable, aggregated information that can guide decision-making in various domains.

Price discovery is the mechanism through which the trading of digital assets in a prediction market establishes a price that reflects the collective expectations of participants about the likelihood of a specific event. As Scott Kominers explains in the transcript, when participants buy or sell assets based on their private forecasts (e.g., a 70% chance of a candidate winning), their actions push the asset’s price toward a value that aggregates these beliefs, such as $0.70 per asset. This process mirrors how financial or commodities markets determine prices, but in prediction markets, the price directly corresponds to the probability of an event occurring.

For example, in the 2024 U.S. presidential election, prediction markets like Polymarket saw asset prices for candidates adjust dynamically as participants incorporated new information, such as local polls or economic indicators, outperforming traditional polls with low response rates (e.g., 5%). The accuracy of price discovery depends on market thickness—more participants with diverse information lead to better price signals. However, distortions like manipulation or herd behavior can skew prices, as seen in historical cases like the 2016 election where thin markets underestimated certain outcomes.

Price discovery in prediction markets is a powerful tool because it translates dispersed knowledge into a single, interpretable metric—the price. This makes it valuable for forecasting events like elections or corporate outcomes and for extracting insights from correlated markets, such as oil prices signaling Middle East conflicts, as noted by Alex Tabarrok.

In prediction markets, a public good is the accurate, aggregated information produced through trading digital assets, which benefits society broadly without requiring payment from those who use it. Alex Tabarrok emphasizes in the transcript that prediction markets create socially valuable forecasts, such as election outcomes or scientific paper replicability, accessible to all. For example, the 2024 election markets on Polymarket provided more reliable predictions than polls, aiding public understanding without restricting access to the data.

This information acts as a public good because it’s non-excludable (anyone can observe market prices) and non-rivalrous (one person’s use doesn’t diminish its value). The transcript highlights how markets predicting scientific replication save resources by identifying papers worth testing, benefiting the scientific community. However, thin markets or legal restrictions, as Alex notes, can limit this public good by reducing participation and accuracy, as seen in the U.S.’s restricted access to global markets.

Blockchain enhances this public good by ensuring transparent, auditable prices, making the information trustworthy for applications like policy decisions or corporate planning. Legalizing prediction markets, as Alex advocates, would thicken them, amplifying their societal value as a tool for collective forecasting.

A reputation system in prediction markets incentivizes accurate forecasting by rewarding participants with non-financial assets, such as tokens or points, that reflect their predictive success, enhancing their status or influence within the market. Scott Kominers explains in the transcript that, unlike monetary rewards, reputation systems can use non-transferable tokens earned through accurate predictions, which participants might leverage for prestige, access to elite roles, or further market participation, as seen in potential applications like forecasting jobs or DAO governance.

For example, Hewlett-Packard’s internal market could have used reputation tokens instead of $100 subsidies to motivate employees, aligning their predictions with sales outcomes without direct financial cost. The transcript suggests that journalists could use transparent, blockchain-based reputation systems to track betting accuracy, reducing hyperbole and increasing accountability, as Alex Tabarrok proposes. Blockchain ensures these systems are auditable, preventing manipulation and fostering trust, though thin markets may limit their effectiveness due to low participation.

Reputation systems complement financial incentives, making prediction markets accessible in contexts where monetary stakes are impractical, such as small-scale or subjective forecasts. They support applications like community-driven forecasting or decentralized governance, enhancing the public good of accurate information aggregation.

Skin in the game refers to the financial or reputational commitment participants make when trading digital assets in a prediction market, ensuring their predictions reflect their true beliefs. As Sonal Chokshi and Alex Tabarrok discuss in the transcript, this distinguishes prediction markets from polls, as participants risk money or reputation (e.g., via tokens) when buying assets, like a $0.55 asset for a 70% believed probability, incentivizing accuracy. This was evident in the 2024 election, where Polymarket’s financial stakes led to more accurate forecasts than polls with low response rates (5%).

Beyond money, skin in the game can include reputation systems, as Scott Kominers notes, where participants earn tokens for accurate predictions, redeemable for prestige or further market access. For instance, Hewlett-Packard’s internal market gave employees $100 to trade, aligning their forecasts with sales outcomes. This mechanism counters the lack of accountability in media or polls, as Alex suggests journalists should bet transparently to reduce hyperbole, enhancing trust in their predictions.

Skin in the game is central to prediction markets’ effectiveness, driving truthful information aggregation for applications like elections or scientific replicability. Blockchain enhances this by enabling verifiable, transparent stakes, though misaligned incentives in thin markets can still distort outcomes.

The Surprisingly Popular Mechanism, developed by Prelec, Seung, and McCoy, is an information elicitation technique that identifies truthful answers by rewarding responses that are more prevalent than participants predict. As Alex Tabarrok explains in the transcript, participants are asked for their belief (e.g., the capital of Pennsylvania) and what they think others will say. If a response, like Harrisburg (correct) over Philadelphia (incorrect), is chosen by a minority but exceeds expected popularity, it’s deemed “surprisingly popular” and rewarded, indicating truthfulness.

This mechanism addresses the limitation of wisdom-of-the-crowds approaches, which can fail when the majority is misinformed, as in the Pennsylvania example where most pick Philadelphia. Unlike prediction markets, which rely on trading digital assets and objective outcomes, the Surprisingly Popular Mechanism works well for subjective or less verifiable questions, requiring no market thickness or event resolution. It’s particularly useful in thin markets or for extracting niche knowledge, as it incentivizes truth-telling without financial stakes.

The mechanism’s strength lies in its ability to surface minority-held but accurate information, making it ideal for applications like market research or scientific hypothesis testing. While not inherently blockchain-based, integrating it with decentralized systems could ensure transparent scoring, though it’s less suited for high-stakes, objective forecasts like elections, where prediction markets excel, as seen in 2024’s Polymarket results.

In prediction markets, a thick market is characterized by a large number of participants trading substantial volumes of digital assets, leading to robust price discovery and accurate aggregation of dispersed information. Conversely, a thin market has few participants and low trading volumes, making it susceptible to distortions and less reliable predictions. As Alex Tabarrok and Scott Kominers emphasize in the transcript, thick markets attract diverse information sources, such as local insights or expert models, which enhance forecast accuracy, as seen in the 2024 U.S. election markets on platforms like Polymarket.

Thin markets, however, suffer from limited participation, reducing the diversity and strength of information signals. For example, the transcript notes that the 2016 election and Brexit prediction markets were less accurate partly because they were thin, with fewer participants and lower stakes, limiting incentives to gather precise information. Thick markets benefit from “organic demand” (e.g., hedgers in commodities markets), which subsidizes informed traders (“sharks”), whereas thin markets rely solely on these sharks, making them less competitive and reliable.

The distinction is critical for market design: thick markets produce public goods like accurate forecasts, while thin markets may require subsidies or alternative mechanisms (e.g., peer prediction) to function effectively. Legalizing prediction markets, as Alex suggests, could thicken them by allowing broader participation, improving their predictive power for applications like elections or scientific replicability.

The wisdom of the crowds refers to the idea that aggregating the judgments or predictions of a diverse group of individuals can produce a more accurate outcome than any single participant, even an expert, might achieve. In prediction markets, this is achieved when participants trade digital assets based on their individual knowledge, and the resulting market price reflects a synthesis of their collective insights. The concept, popularized by James Surowiecki’s 2004 book The Wisdom of Crowds, is exemplified in scenarios like estimating a cow’s weight, where the median guess of a crowd often closely approximates the true value, as Alex Tabarrok notes in the transcript.

For prediction markets, the wisdom of the crowds is most effective when the crowd is diverse, independent, and incentivized to act on accurate information. For instance, the transcript describes how prediction markets outperformed polls in the 2024 U.S. election by aggregating local insights (e.g., from residents aware of neighborhood political shifts) that polls missed due to sampling biases. However, this mechanism fails when the crowd lacks relevant information or when herd behavior distorts independent judgments, as seen in cases where public signals (e.g., a prominent poll) lead to collective overreactions.

The power of this phenomenon lies in its ability to harness dispersed information without requiring centralized coordination. Prediction markets leverage this by allowing anyone—experts or non-experts—to contribute, making them a robust tool for forecasting events like elections, corporate sales, or scientific outcomes, provided the market is thick and incentives are aligned.