Pricing Belief: The Rise of Prediction Markets

Prediction markets are quietly becoming one of the most efficient ways people have found to aggregate belief, uncertainty, and information. As they go mainstream, they challenge how we think about uncertainty, truth, and decision-making.

What Are Prediction Markets?

Prediction markets are platforms where participants trade contracts tied to specific future events. Each contract pays out a fixed amount if an event occurs. The price of the contract is based on the market’s collective estimate of the probability that the event will happen.

For example, a contract could pay $1 if a specific candidate wins an election. If that contract is trading at $0.70, the market is assigning a 70% probability to the outcome. Traders buy or sell based on their own beliefs, information, or models, and the resulting price aggregates these into a single estimate.

In the U.S., platforms like Kalshi operate as federally regulated prediction markets, offering contracts on economic data releases, elections, and policy outcomes.

How Prediction Markets Work

Prediction markets resemble financial markets, but their purpose is solely to forecast. The process involves:

• Event definition: A clear and measurable outcome (e.g., “Will X win NFL MVP?”).

• Contract creation: Contracts are issued that pay out based on the outcome %.

• Trading: Participants buy and sell contracts as new information emerges.

• Price discovery: Prices change as beliefs shift.

• Settlement: Once the event resolves, contracts pay out accordingly.

  
  

Why Prediction Markets Are Gaining Popularity

Prediction markets are gaining popularity for several reasons.

First, they are often highly accurate. Studies found that prediction market prices match or outperform traditional forecasting methods such as opinion polls and expert judgments. For example, the Iowa Electronic Markets (IEM), a prediction market run by the University of Iowa, beat 964 opinion polls in predicting five U.S. presidential elections from 1988 to 2004, coming closer to the final results 74% of the time.¹ ²

Second, incentives matter. Unlike surveys, prediction market participants have real financial exposure. Being wrong has a cost. This “skin in the game” discourages casual bias and overconfidence while rewarding careful reasoning and information gathering. Academic work shows that traders who consistently make poor predictions tend to lose money and exit the market, reducing their influence over time.³

Third, prediction markets react instantly to new information. Court rulings, earnings, injuries, and debates shift odds as beliefs change, not on a fixed schedule. This makes market prices valuable in fast-moving or uncertain environments. ³ ⁴

Finally, participation is broad. Anyone with insight, expertise, or a model can contribute by trading. Rather than relying on a single authority, prediction markets aggregate small, independent judgments into a single probability. Research on collective intelligence suggests that when participants act independently and are incentivized to be accurate, aggregated forecasts tend to outperform individual experts.⁵ ⁶

A live view of Robinhood’s prediction markets, showing real-time probabilities for a football game alongside player and team contracts.

My own Experience: What Trading These Markets Actually Feels Like

Over the past six months, I’ve interacted with prediction markets through platforms like Robinhood. I wasn’t approaching them primarily to generate returns, but to observe how collective belief behaves under uncertainty.

One thing became clear almost immediately: markets move before things feel obvious. Prices often shift ahead of headlines, long before an outcome feels culturally “locked in.”

Another lesson is how quickly confidence forms and how often it’s misplaced. A contract trading at $0.85 feels certain, yet it still fails 15% of the time. The gap between emotional certainty and probability is uncomfortable, and prediction markets force you to confront it directly.

Most interestingly, trading these markets changes how you think. You can’t say “I feel like this will happen.” You have to decide whether you believe it’s a 55% outcome or an 80% one.

Limitations and Criticisms

Despite the hype, prediction markets are not flawless.

Legal and regulatory restrictions limit their adoption in many countries. Liquidity can be tight, especially for niche events, making prices unreliable. Large traders can sometimes distort signals in small markets. And some events, especially those that are ethically sensitive, raise legitimate moral concerns.

Recent events illustrate this vividly. In early 2026, the prediction market Polymarket refused to pay out on wagers tied to whether the United States would “invade” Venezuela following a U.S. military operation that resulted in the capture of Venezuelan President Nicolás Maduro and other leaders. Polymarket ruled that the contract’s specific language, which required U.S. military operations intended to establish control over Venezuelan territory, had not been satisfied, even though many users felt the actions de facto constituted an invasion.

As of this writing, more than $10 million in bets remain unresolved or unpaid.

Disclaimer: Understanding these limits is essential if prediction markets are to be leveraged for decision-making.

What the Future Might Hold

Emerging technologies are expanding the possibilities for prediction markets. Decentralized platforms built on blockchain infrastructure promise greater transparency and global participation. Advances in AI may improve event definitions, detect manipulation, and analyze market dynamics more effectively.

Much of this recent growth has been enabled by infrastructure players like Kalshi, which operates as a CFTC-regulated exchange and provides the underlying market structure for many U.S.-based prediction contracts. Kalshi’s regulatory status has helped legitimize prediction markets as a forecasting tool rather than a form of gambling, opening the door for broader distribution through consumer-facing platforms.

Other players like Robinhood have played a more distributive role. Since launching at the end of 2024, prediction markets have become its fastest-growing product line by revenue, with 11 billion contracts traded by more than 1 million customers, according to the company.

Closing Thoughts

The rise of prediction markets is not just a finance trend; it also offers insight into human decision-making. Their growing visibility suggests a need for tools that prioritize incentives over opinions.

These markets show that given the right incentives, people become more honest with themselves about uncertainty. When we “bet” on our beliefs, we are forced to reckon with how strongly we truly believe something, and we update those beliefs quickly if new evidence emerges (because not doing so costs us). They reveal that our collective judgments can be remarkably rational when aggregated, even if any single individual’s judgment is fallible.

I don’t think prediction markets will replace experts, polls, or judgment anytime soon. But I do think they’re teaching us something important: how rarely we quantify our beliefs and how much clearer thinking becomes when we do.

That alone makes them worth paying attention to.

As one analysis summed up, if you “let a diverse crowd put money behind its beliefs, you get a stream of probabilistic information that is very difficult to replicate by any other means.”

Sources

1. Berg, J., Nelson, F., & Rietz, T. (2008). Prediction market accuracy in the long run. International Journal of Forecasting, 24(2), 285–300 – Compared 964 polls to simultaneous IEM market prices; found the market closer to the actual outcome 74% of the time, significantly outperforming polls, especially at longer horizons.

2. Wolfers, J., & Zitzewitz, E. (2004). Prediction Markets. Journal of Economic Perspectives, 18(2), 107–126 – Review of prediction market performance across domains.

3. Arrow, K., et al. (2008). The Promise of Prediction Markets. Science, 320(5878), 877–878 – Policy piece by leading economists. Argues that prediction markets can “predict a large variety of events” better than traditional instruments and should be freed from undue regulation.

4. Graefe, A. (2017). Prediction markets vs. alternative methods – Examination of election forecasts in Europe.

5. Hayek, F. (1945). The Use of Knowledge in Society. American Economic Review, 35(4), 519–530 – Classic insight: in any market, prices act as signals that efficiently aggregate dispersed information.