Prediction markets have made uncertainty itself a tradable asset

“The history of prediction markets can be traced back to Francis Galton’s ox and Kenneth Arrow’s promise. But their recent stratospheric rise is reliant on our polycrisis era. Bets can be made on elections, interest rates and war. More uncertainty leads to more disagreement, more trading and larger markets. Chirantan Chatterjee explains what this reveals about the world. In Joseph Heller’s “Catch-22”, Milo Minderbinder turns war into a business model. Milo profits from both sides of the conflict with a chillingly simple logic: if there is demand, there is profit. What was once satire increasingly resembles economic reality. Today’s war profiteers do not need planes or supply chains. They need only a smartphone, a crypto wallet and a prediction market. But in doing so they take us back to an old economics concept: the wisdom of the crowds. In 1906, Francis Galton, an English statistician, observed a crowd guessing the weight of an ox at a country fair. Individually, guesses varied widely. But when he averaged them the result was remarkably accurate – closer than most individual estimates. From this simple exercise emerged one of the most enduring ideas in economics: under the right conditions, dispersed knowledge aggregated across individuals can outperform experts. A century later, in the aftermath of the global financial crisis, Kenneth Arrow and others extended Galton’s insight into the domain of markets. In 2008 they argued that legalising prediction markets, essentially allowing people to trade on future events, could improve social welfare. These markets would aggregate information efficiently, reduce uncertainty and minimise deadweight losses arising from imperfect knowledge. The logic was elegant. If markets can reveal prices for goods, why not for probabilities? Nearly two decades later, that vision has not only materialised, but scaled dramatically. Who could have predicted this? Prediction markets such as Kalshi and Polymarket allow users to trade contracts on elections, interest rates , wars , pandemics and more. What was once an academic curiosity has become a financial ecosystem. In 2025 prediction market volumes reached roughly $63–64 billion , up from under $16 billion in 2024. Some estimates suggest that volumes could exceed $300 billion in 2026 , with longer-run projections entering the trillions. Monthly trading volumes have surged from less than $100 million in early 2024 to more than $13 billion in a single month by late 2025. Polymarket recorded over $7 billion in trading volume in a single month in early 2026, with daily records exceeding $400 million . Uncertainty itself has become a tradable asset. This could be dangerous, since we may now be allowing platforms to artificially engineer uncertainty. But the economics behind this growth is deceptively simple. When outcomes are predictable, there is little to trade. When outcomes are uncertain, and people disagree, markets become active. Disagreement creates liquidity; uncertainty creates opportunity. More uncertainty leads to more disagreement, more trading and larger markets. This mechanism explains why prediction markets have expanded so rapidly. But it also reveals something deeper about the world in which they are emerging. Profiting from the polycrisis We are living in an era of overlapping crises, a “polycrisis” of geopolitical tensions, climate shocks, economic volatility, technological disruption and political fragmentation. Each layer of uncertainty feeds into the next. And prediction markets sit at the centre, monetising uncertainty. Politics has become more volatile. Populist leaders have destabilised traditional political equilibria. From America and Britain, and India to Turkey electoral outcomes are less predictable, policy trajectories more uncertain and media cycles faster and more polarised. From the perspective of prediction markets, this volatility seems like good news and the engine fuel. Populist politics supplies the uncertainty that prediction markets monetise. Yet the relationship between prediction markets and politics is no longer one-directional. Prediction markets are increasingly embedded in the information ecosystem. Probabilities are reported in news coverage, cited by analysts and shared widely on social media. A candidate’s “odds” are now part of their political narrative. Momentum becomes measurable and tradable. This creates a feedback loop. Markets influence media narratives. Media narratives shape public beliefs. Public beliefs influence political behaviour. Political behaviour feeds back into markets. Prediction markets do not merely reflect expectations, they help shape them Politicians may begin to optimise not only for votes, but for probabilities. Provocative statements, strategic ambiguity and media spectacle can move market prices in real time. Politics becomes a performance designed to shift probabilities . This brings us back to Galton and to a critical caveat in his insight. Galton’s crowd worked because individuals made independent guesses. Their errors were uncorrelated, allowing them to cancel each other out. Modern prediction markets operate under very different conditions. Participants today are influenced by shared information sources: social media, news cycles and political identities. Instead of independent signals, markets often aggregate correlated beliefs. In such environments, the crowd may amplify dominant narratives rather than aggregate dispersed information. Prices become hybrid objects, part information, part sentiment and part attention. But the deeper shift lies elsewhere. Prediction markets do not simply depend on uncertainty. They reward it. Higher uncertainty leads to higher trading volume. Higher volume benefits platforms, traders and liquidity providers. This creates a powerful incentive structure: uncertainty is no longer just observed; it is produced and monetised. Recent evidence suggests this is not merely theoretical. In April 2026 three Polymarket accounts reportedly earned over $600,000 by correctly betting on a ceasefire between Iran and the United States. These accounts had a track record of accurately predicting geopolitical events, raising concerns about possible information advantages. Elsewhere, Polymarket faced backlash for allowing bets on the fate of a downed military pilot, with lawmakers describing such markets as “disgusting” and calling for restrictions. Prediction markets are evolving into mainstream financial infrastructure. Institutional capital is flowing into the sector. The parent company of the New York Stock Exchange has committed up to $2 billion in investment into Polymarket. The system that is no longer peripheral. It is central and growing. The ethical and normative implications are significant When prediction markets extend into domains such as war, financial incentives become attached to real-world outcomes. Traders may profit from conflict, crises or institutional breakdowns. Information asymmetries can be exploited. This does not imply wrongdoing by any specific actor. There is no credible evidence that political leaders are directly trading on such markets. But the structure matters. When three conditions – access to privileged information, the ability to trade on outcomes and opacity in participation – hold then the incentives for insider trading become real. This is not conspiracy. It is economics, even if it sounds dismal. And it leads to a more unsettling conclusion: prediction markets create a world in which it becomes rational to ask whether power and profit are beginning to converge. What, then, should be done? The answer is not likely prohibition. Prediction markets have real value. They can improve forecasting, aggregate dispersed knowledge and provide real-time signals about future events. They also generate unprecedented data, which can be used to test whether they are functioning as intended. An empirical research agenda is both possible and necessary. Economists and policy makers can ask questions. Do large trades systematically precede geopolitical events? Do certain accounts generate abnormal returns consistent with insider information? Do market prices move before or after public news announcements? Does trading intensity correlate with spikes in political rhetoric or media coverage? By combining prediction market data with high-frequency global event datasets it is possible to test whether these markets are aggregating information or exploiting asymmetry. In this sense, prediction markets are both the problem and the laboratory to test for their social welfare effects. Which brings us back to Arrow. His original hypothesis can be summarised simply: markets can improve welfare by aggregating information efficiently. But the world has changed. A more realistic, updated version might look like this: prediction markets improve welfare when uncertainty is exogenous, information is dispersed and participants are independent. They may reduce welfare when uncertainty is endogenous, information is asymmetric and participation is correlated or strategic. The key distinction is no longer whether markets aggregate information but whether they are embedded in systems that produce or distort it. From Galton’s ox to platforms like Kalshi and Polymarket, the ambition has remained constant: to aggregate dispersed knowledge and reveal truth. But the context has transformed. We now live in a world where uncertainty is tradable, politics is financialised and markets may influence the outcomes they price. In such a world, the boundary between prediction and participation begins to dissolve. The original promise of prediction markets was to make society smarter about the future. The emerging reality is more ambiguous and more consequential. Prediction markets do not simply trade on uncertainty. They may be causing and extending it. And when that uncertainty is tied to war, public health or democratic stability, the costs are no longer confined to traders. They are borne by society itself courtesy the Milo Minderbinders of 2026 trading on prediction platforms. This article gives the views of the author, not the position of LSE Business Review or the London School of Economics. You are agreeing with our comment policy when you leave a comment. Image credit: rblfmr provided by Shutterstock. The post Prediction markets have made uncertainty itself a tradable asset first appeared on LSE Business Review .

Summary generated from the RSS feed of LSE Business Review. All article rights belong to the original publisher. Click through to read the full piece on blogs.lse.ac.uk/businessreview.