When Prediction Markets Become Weapons

This article addresses how institutional adoption of prediction markets, coupled with current market shortfalls, produces opportunities for bad actors to more effectively spread misinformation. 

I) The Theory 

First, I’d like to establish the baseline for what a prediction market aims to achieve: The Wisdom of the Crowds. To illustrate, I’ll quote one of the most widely referenced examples of this effect: the Francis Galton ox-weight story (The following is an abbreviated excerpt from the first chapter of James Surowiecki’s aptly titled book, The Wisdom of Crowds, in which Galton, a British scientist, observes this phenomenon when attending a fair in Plymouth): 

A fat ox had been selected and placed on display, and members of a gathering crowd were lining up to place wagers on the weight of the ox… Eight hundred people tried their luck… Many of them were butchers and farmers, who were presumably expert at judging the weight of livestock, but there were also quite a few people who had, as it were, no insider knowledge of cattle... "[similar to] those clerks and others who have no expert knowledge of horses, but who bet on races, guided by newspapers, friends, and their own fancies." … When the contest was over, [Galton] added all the contestants' estimates, and calculated the mean of the group's guesses. That number represented, you could say, the collective wisdom of the Plymouth crowd. If the crowd were a single person, that was how much it would have guessed the ox weighed…The crowd had guessed that the ox, after it had been slaughtered and dressed, would weigh 1,197 pounds. After it had been slaughtered and dressed, the ox weighed 1,198 pounds. 

The astonishing accuracy of the Plymouth crowd has become something close to scripture in the prediction market industry. Polymarket and Kalshi have been quick to market themselves as platforms that aggregate "collective intelligence” and “harness the wisdom of crowds.” The above story is “proof” that when the guesses of a large, diverse crowd are aggregated, the errors cancel out and the truth emerges. Looking at a binary market, we can see that it roughly parallels the story posited above: 

Say you have a conjecture regarding the probability of X event happening. A contract trading at a $0.30 YES and a $0.70 NO means the market collectively believes there's a 30% chance of that outcome occurring. YES and NO contracts sum to $1, and you receive $1 if X event happens, $0 if it doesn't. If you believe the true probability is 55%, you buy, and if you're right, you profit $0.25 per share. The more confident you are, the more you buy and the more you shift the market in that direction as a result. In aggregate, those who believe an event has a higher chance of happening will buy, and those who believe otherwise will sell. This collectively nudges the price around; all valid information points the price in one direction, while erroneous guesses point in different directions and cancel each other out. This continues until the price converges and represents the collective opinion of the market.

Hayek’s seminal paper on information, The Use of Knowledge in Society, argued that every individual carried unique, localized information about their industry, neighborhood, and circumstance that no central authority could ever fully collect or process. Prices, in Hayek's framework, were the mechanism by which said dispersed knowledge was aggregated. Robin Hanson, the economist who laid the theoretical groundwork for modern prediction markets, extended that logic to outcomes. If you could create markets on future events, prices would aggregate dispersed beliefs about those events in the same way commodity prices aggregated dispersed beliefs about supply and demand. This, at its core, is what a binary market represents. 

II) Reality departs from theory. 

The first pothole in the application of the “wisdom of crowds” idea is that it assumes actors have equal market influence. In practice, prediction market accuracy reflects the wisdom of an informed minority, not the wisdom of crowds. The following paper illustrates this: They determine that skilled winners, whose trades are systematically profitable and thus drive prices closer to resolution value, make up just 3.14% of accounts. Lucky winners (positive returns indistinguishable from random chance) make up 29%; unlucky losers (losses indistinguishable from random chance) make up 61.4%; and unskilled losers (consistent loss in a statistically meaningful way) make up 6.4%. The remaining 0.1% are market makers. The 3.14% of skilled traders' order flow predicts both future prices and outcomes, and they are the first to trade in the direction of news when it arrives. The 3.14% are right, consistently, and the market reprices around them. Thus, platforms like Kalshi and Polymarket aren't aggregating the beliefs of millions of participants but rather exist as a medium in which a small number of skilled traders impose accurate prices on a much larger pool of noise and profit from the difference. [To the readers skeptical of whether or not that 3.14% includes traders with inside information, the paper finds that documented cases of insider trading fall within the lucky winners category, as insider trading often represents large, intermittent, single-time bets, not consistent price discovery.] 

Second, information tends to be interdependent, meaning that market participants tend to follow the trades of others rather than trading based on independent information. Take the following conclusion drawn from this 2025 paper after analyzing US, Croatian, and German parliamentary markets: 

Rather than generating forecasts through the averaging of independent judgments, these markets operate through structured competition among specialized participants with different information access, analytical capabilities, and strategic approaches. Elite traders with superior information or analytical capabilities establish price trends that cascade through the market ecosystem, while other participants respond to these signals with varying levels of acceptance or resistance based on their own information sets and strategic positioning. This competitive dynamic can produce remarkably accurate forecasts despite—or perhaps because of—its hierarchical structure, as it enables sophisticated participants to exert disproportionate influence on price formation while creating financial incentives for information revelation through profit opportunities. 

III) The information problem 

I choose to draw attention to the above two phenomena, concentrated information signaling and interdependent trading, because prediction markets are increasingly being incorporated as signals in news and financial infrastructure: 

1. Jan 7, 2026—Polymarket and Dow Jones’s partnership makes Polymarket's real-time data available across the Wall Street Journal, Barron's, MarketWatch, and Investor's Business Daily. Market updates increasingly integrate with prediction market statistics. 

2. Dec 2, 2025—Kalshi's data is now integrated into CNN's coverage. This is so that reporters can better “tap into real-time prediction market data to better inform and fact-check [their] reporting.” 

3. Oct 7, 2025—The Intercontinental Exchange (ICE), the same exchange operator that runs the NYSE, invested $2B in Polymarket and now distributes Polymarket probability data through its Consolidated Feed alongside securities pricing and corporate actions data. 

While I am a massive proponent for institutional recognition and adoption of prediction markets, I’m a bit wary of incorporating signals that are generated by markets whose accuracy depends on a small number of skilled traders operating on a platform with markets that hold relatively thin liquidity (more on this in a moment). The increasing recognition of prediction markets as official news sources provides greater incentives for market manipulation. 

The conventional argument against the possibility of manipulation is the depth of a market. A market with more participants has more cash and thus is more liquid ("deep") and harder to shift. This is very well documented. The 2024 election markets had over $3.7 billion in trading volume, far too deep for consistent manipulation. However, in a slimmer market, the “Popular Vote Winner 2024," which asked which candidate would win the popular vote, ended with $628M in total lifetime volume. A trader with the alias “Theo” was able to, through many accounts, place multiple $500 thousand to $1 million sums on Trump winning, which ended up shifting the contract's price from 26% to 39% in just a few hours. While large, deeply liquid flagship markets exist, the vast majority of political and economic markets sit under $500 million in depth, which, as we saw in the Theo case, are manipulable. The ICE pulls information from markets, liquid or not. Even so, $500 million is far too large for any long-term price manipulation by an individual actor. That is, however, not true for targeted, short-term manipulation by a well-resourced one. 

Take Russia's Internet Research Agency and its attempts to undermine the integrity of the 2016 U.S. electoral process. What made that operation effective wasn't that it changed votes directly. It was the fact that it generated content that looked like organic public sentiment (thousands of fake

accounts on Facebook and Twitter posting divisive and polarized sentiments), which got picked up by the media as representative of real opinion and consequently shaped the informational environment that voters and decision-makers operated in. Prediction market prices can sit in the same signal category. They’re real-time, numerical, and are increasingly treated as authoritative. However, a Polymarket probability listed alongside a Wall Street Journal article carries with it far more precision and credibility than a viral tweet does. 

I believe, therefore, the risk lies less in the simple manipulation of individual markets themselves but rather in the manipulation of prediction markets in parallel with traditional misinformation campaigns. This sort of market manipulation wouldn’t require moving the final price or outcome. It would require moving the price just enough, and at the right moment, to influence a media cycle or a decision-maker who reads the WSJ in the morning. Take the following hypothetical operation: 

1. A coordinated deployment of hundreds of anonymous wallets (Polymarket requires no KYC), based in different geographic regions, well in advance of the planned operation. These wallets may sit inactive or trade varying sums of money on various contracts, periodically getting lost in the thousands of wallets created every day. 

2. When a bad actor decides to launch a misinformation campaign, they can simultaneously engage these various wallets to inject money into desired markets and shift a contract's probability by several points for, say, 48 to 72 hours, at a cost that is very well within the operational budget of a state-level influence operation. 

3. The price would then begin to self-correct as skilled traders arbitrage it back. However, the ability of these skilled traders to reverse the price back and the time it would take them could be greatly drawn out by the spread of misinformation. While the 3.14% of skilled accounts undoubtedly use a mix of high-frequency mechanisms to drive information discovery, they still do so on public information. If false information successfully crowds out the truth, the time it would take for the price to revert to accurate levels would be significantly drawn out. In that time, whatever signal the bad actor was attempting to send had already been created and read. 

4. Major news outlets and large financial institutions reliant on ICE data now hold tampered information. A diplomat may walk into a room with a different sense of where American public sentiment stands on a policy he is about to negotiate. The board of a defense contractor reads through an ICE data service summary before a capital allocation meeting and sees that a Polymarket "US forces enter Iran by [X date]" contract has moved 8 points overnight with no accompanying news. They interpret this as crowd-sourced intelligence of escalation and accelerate procurement decisions. 

While this may seem far-fetched, a glance at history will tell you that mankind has been known to go to far greater and more outrageous lengths to gain an advantage over the other. I particularly believe that in the scheme of global politics, any minute edge over your opponent is invaluable. I’m confident that such manipulation within prediction markets will become an attractive tool for bad actors to use in the near future, that is, if they are not already being utilized.