Prediction markets want you to see them (and use them) as one of the best possible instruments for finding out what’s happening in the world.
Open any election market, and you’re immediately given a probability that shifts in real time as people trade. It’s a single number, easy to check in a few seconds, that condenses something as uncertain and volatile as an election campaign into a number that essentially tells you the likely outcome.
That clarity is their main appeal, and it explains why so many people now treat these markets as a running scoreboard for the race.
The catch is that the number exists only because many strangers are wagering on who will govern you, a premise plenty of Americans now find pretty distasteful.
A recent Ipsos poll Reuters published found that 69% of Democrats and 61% of Republicans opposed using prediction markets to bet on election results. They also opposed using prediction markets to bet on other things, like military actions and government policy decisions.
The survey interviewed 4,506 US adults from Sept. 30 to Oct. 5 and measured approval of betting, so those answers don’t show whether people trust the forecasts or which objections drove their views.
The results complicate the industry’s favorite defense, which is that they produce better information. While accuracy is prediction markets’ strongest card, it does nothing for the separate question of whether the bets behind those numbers should exist at all.
Voters can find election forecasts valuable and still object to a system in which the people who run elections, or influence how they’re run, can profit from the result. The poll shows that the industry’s argument for better information isn’t enough to win public support for election betting.
The industry now needs to defend both how it produces the number and how good that number is.
Prediction markets sell certainty in an uncertain world
In a basic election contract, the winning side pays $1, and the losing side pays nothing. If you buy a candidate’s victory at 60 cents, you make 40 cents if they win, or you lose your 60 cents if they don’t.
The market prices that as a 60% probability, which simplifies it and makes it easy for everyone to understand and trade. Underneath that percentage, buyers and sellers commit money to their expectations.
Someone who thinks the candidate’s prospects are better than the price implies has a reason to buy, while someone who thinks they’re worse has a reason to sell or take the other side. Get enough of those trades, and it pulls the price toward informed judgments.
Because anyone can bet on prediction markets, you can get a diverse mix of participants, like people who actually support the candidate in question and people who just like to gamble. The odds you see on a market don’t tell you which of them made the most trades.
Kalshi is trying to make that easier to see. Its Oct. 6 election update introduced low-volume labels and more prominent trade feeds, alongside scores comparing forecasts with outcomes.
The company also said something interesting: most of its traffic comes from people who watch rather than trade. That audience, the people watching the markets, is central to its information argument: relatively few people can produce a price that many more people see.
Prediction markets offer more than election-night entertainment, since the same contracts that give watchers a running forecast can also serve people with real money riding on a government decision.
Say a company stands to lose money if the government makes a particular decision, and it buys a contract that pays out when that decision goes against it. The payout can soften the loss, provided the contract’s wording matches the risk, and the traders on the other side of the bet simply expect a different outcome and have no business at stake.
That makes the market a good financial protection for those who need it, and it gives everyone else a forecast that’s backed by real money.
But a voter that’s just reading that forecast gets none of those benefits, and has no say in which events the platform chooses to list. Kalshi decides if an event is worth offering, then traders show up to bet on it, and the event becomes a product.
The prices then spread well beyond traders into news coverage and everyday conversation about the election, so the platform ends up asking to be treated as a source of public knowledge while still operating as a commercial market.
When knowing too much pays off
Kalshi said it fined and suspended four congressional candidates for trading on their own races. The problem is that these candidates were trading on elections in which they were also participants, putting them in a position where their political interests and financial incentives could overlap.
Even if a trade doesn’t involve confidential information or an attempt to influence the result, letting candidates profit from their own races creates an obvious conflict of interest.
The case points to two problems that arise when people close to an election can profit from it: insider trading, where someone bets on knowledge the rest of the market doesn’t have, and election tampering, where someone with a stake in the bet can influence the result.
That shows that the people betting on an election shouldn’t also be in a position to shape its outcome.
The same logic applies to a campaign employee who learns that their candidate will withdraw tomorrow and places a trade today. The resulting price might well become a better forecast, and that counts in favor of the information argument.
The people on the other side of the trade, though, lost money to knowledge no amount of research could have given them, because it came from the employee’s position inside the campaign.
And if the employee has any influence over the announcement, the problem gets bigger than just knowing something first. The trader may help decide the very event they expect to be paid on.
Kalshi says its rules exclude relevant political insiders and that it screens accounts and investigates suspicious trades. Those restrictions limit which information the exchange will allow people to profit from. Its ban on war and death contracts draws another boundary around what it can list.
There are good reasons to debate how tight these boundaries should be. Blanket bans can drive activity toward platforms where misconduct is harder to investigate, and traders are harder to sanction.
Conversely, an exchange can’t expect every objection to disappear once it explains that money improves forecasting. Some objections concern who can participate, and others concern rewarding outcomes people don’t want turned into commercial opportunities.
Prediction markets can gain a much more prominent position in election coverage by letting the market see how their forecasts were produced.
That means showing the trading behind the odds and comparing past probabilities with actual results, including forecasts made while the outcome was still uncertain. Published enforcement decisions should also explain who broke the rules and how, as well as what happened to their trades.
That way, we could have enough evidence about both the forecast and the market that’s producing it.
Platforms that want to be treated as sources of political information should expect that kind of examination. Money gives people a reason to investigate, but it also gives insiders a reason to exploit what they know.
The credibility these platforms gain will depend on how they handle both incentives. Prediction markets may become very good at telling voters who’s likely to win.
Whether voters are comfortable with the business behind those predictions is a separate question, and better odds alone cannot answer it.