Issue 15
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Why prediction markets aren’t popular

17th May 2024
29 Mins

Prediction markets are legal, contrary to popular belief. But they remain unpopular, because they lack key features that make markets attractive.

Many entrepreneurs have tried to create prediction markets, contracts that trade on the outcome of future events. Luke Nosek, cofounder of PayPal, once worked on the problem. Sam Bankman-Fried, the jailed founder of cryptocurrency exchange FTX, is supposed to have originally wanted to build a prediction markets platform. A number of venture capital–backed start-ups are currently building prediction markets, including Kalshi, a prediction market regulated by the Commodities Futures Trading Commission (CFTC); Manifold, a play money prediction market; and Polymarket, a crypto-based prediction market currently illegal in the US.

Many academics have advocated for the creation of prediction markets. Economics Nobel laureate Kenneth Arrow argued for their deregulation in Science, alongside Cass Sunstein, the most cited legal scholar; Thomas Schelling, one of the foremost game theorists; and Philip Tetlock, who created superforecasting. Economist Bryan Caplan’s Substack is called Bet On It, alluding to the value of wagering on beliefs: bets are costly for people with wrong beliefs and profitable for people with accurate ones. This is the promise of prediction markets: they could use the wisdom of crowds and the price mechanism of markets to land on highly accurate probabilities.

We sometimes hear that prediction markets might solve other problems too. Perhaps we could bet on whether scientific studies will replicate as a means of identifying faulty scientific work and solving the replication crisis.

Or perhaps, as Caplan’s colleague Robin Hanson proposed, we could design the structure of our government around the results of prediction markets. Right now, voters vote based on a combination of their empirical beliefs about how the world works and the moral values of political candidates. Instead, argues Hanson, we could use prediction markets to predict the outcomes of different policies and let people use these to vote on values grounds alone.

Previously in Works in Progress, one author suggested that prediction markets could be added to Twitter and other social media platforms, so that misinformation and erroneous claims could be bet against and demonstrated to be false (or highly unlikely).

One of the authors of this very piece has even argued that liquid prediction markets could give such good information about the future that, instead of trying to figure out the probability of future events ourselves, we should simply defer our beliefs to prediction markets. The markets would inevitably incorporate more information into their implied judgments than someone could learn by themselves. If that sounds outlandish, think about what you’d normally do to find out how well a company like Nvidia has been doing lately – read its financial statements and annual reports, or look at its stock price.

All of this leads to a tricky puzzle: given diverse and widespread interest in prediction markets, why don’t they already exist? Why can’t we use them to check the probability of future events right now, the same way we check weather forecasts, as so many proponents say we someday could?

Certain prediction markets do exist. Americans bet over $330 billion on sports last year. Sportsbooks are markets on the outcomes of sporting events and you can reliably get an accurate probability of sporting events outcomes by checking them. More conventionally understood, there are substantial prediction markets for US elections, as well as a few other notable political events. Traders staked an estimated one billion dollars on the 2020 presidential election.

But these markets are very few and far between compared to the ‘prediction markets on everything’ visions of their advocates. In this vision, we would not just see liquid markets on high-profile events like elections, but all sorts of niche political, scientific, economic, and technological questions.

The explanation usually put forward by proponents is simply that these omniscient markets are unfortunate victims of broader prohibitions on gambling. If we could change these rules, we might know accurate probabilities of many more future events.

We disagree. Instead, our view is that prediction markets on everything – liquid markets over a wide range of important topics – will not work without subsidies. These subsidies would be expensive, so other forms of information aggregation are usually more attractive. The scarcity of prediction markets in the world today is not a failure of regulation, but a sign that they are much less promising than many advocates, including the authors of this piece, once hoped.

Advocates of prediction markets often argue that regulation is inhibiting the creation of prediction markets that would harness market forces to predict the occurrence of a wider range of events. In this view, if prediction markets were allowed to operate, their contracts would trade like other financial assets and prices would be similarly efficient: they would at least aggregate all publicly available information about a future event’s probability.

Yet despite these suggestions, in the US, prediction markets are, in large part, legal, and are regulated by the Commodity Futures Trading Commission. Per 7 US Code §7a–2, any prediction market listed to the public must be approved if it does not violate certain rules enumerated in the same chapter. Those rules prohibit contracts on:

  • activity that is unlawful under any Federal or State law;
  • terrorism;
  • assassination;
  • war;
  • gaming; or
  • other similar activity determined by the Commission, by rule or regulation, to be contrary to the public interest.

You don’t need to take our word for it: Kalshi has built a fully compliant and legal prediction market using the CFTC framework. On Kalshi, you can trade on the Oscars Best Picture winner, the number of tornadoes in a given month, and how much Ozempic prescriptions will increase over the next quarter of the year, among many other things. While the most well-known category of prediction markets – those on elections – are prohibited under the gaming ban (V), the impression of a widespread prohibition on prediction markets is not in line with reality. There are a few other ad hoc exceptions – markets on box office sales and onion futures are prohibited, for instance – but very few potential prediction markets are actually banned in the US. And yet most prediction markets that could legally exist do not exist, and the ones that do exist are not very popular.

Even if one argued that the threat of regulation made these markets impossible in the US, this has problems explaining the lack of prediction markets in other countries where such regulation is not present, and seems unlikely to be introduced. Prediction markets, including election markets (as well as all sports betting), are completely legal in the United Kingdom, for example, and the country clearly has the financial institutions and market size to support them. Still, non-sport markets remain few and far between: Betfair, for example, offers markets on elections but only has a few dozen markets total and rarely offers markets on other topics like economics and science. In fact, even politics is relegated to a tab within sports. There are currently around twelve million pounds in play on the US presidential election – about the same as typically gets bet on a single cricket match. Entrepreneurs have not created ‘markets on everything’ even where it is legal to do so.

Rather than regulation, our explanation for the absence of widespread prediction markets is a straightforward demand-side story: there is little natural demand for prediction market contracts, as we observe in practice. We think that you can classify people who trade on markets into three groups, but each is largely uninterested in prediction markets.

  • Savers: who enter markets to build wealth. Prediction markets are not a natural savings device. They don’t attract money from pensions, 401(k)s, bank deposits, or brokerage accounts. 
  • Gamblers: who enter markets for thrills. Prediction markets are not a natural gambling device, due to various factors including their long time horizons and often esoteric topics. They rarely attract sports bettors, day traders, or r/WallStreetBets users.
  • Sharps: who enter markets to profit from superior analysis. Without savers or gamblers, sharps who might enter the market to profit off superior analysis are not interested in participating. They also largely don’t need prediction markets to hedge their other positions.

In our view, much of the volume that exists on financial markets comes from money that is not attempting to beat the market by correcting pricing errors (like an asset that is underpriced compared to its likely returns), but money that wants to be in a market for other reasons, like investing in companies that will deliver a long-run return (as savers do), or making a sports event more exciting (as gamblers do). In both instances, market participants’ demands are relatively inelastic.

There are two separate but important features of this inelastic demand for market participation. The first feature is just that the market is by default large, which means there are significant profits to be made if a sharp can find a way to make prices more efficient.

The second feature is that inelastic participants are often willing to pay a small premium for market access. But in doing so, investors or bettors of these kinds create a pool of surplus that smart participants try to obtain, which in turn drives prices toward efficiency. Think about depositing your paycheck in a retirement account – the goal is to build a diversified portfolio for long-term gains, not to eke out every cent of possible return. Sharps compete to buy and sell stock to these savers at just above and below the best price. They get to arbitrage the market, while savers happily get easy access to liquidity.

It’s not just savers that pay for market access: gamblers are willing to make negative expected-value bets for the fun of betting. Even sharps are sometimes willing to pay to hedge their positions and reduce risk.

Markets become efficient when making them efficient is profitable. Large markets and markets where people will ‘pay’ expected return for access create those conditions. In our view, in prediction markets, no type of market participant – savers, gamblers, or sharps – is clamoring to be in the market, so there is no strong incentive pushing the market toward efficiency.

There is one important reason that prediction markets are not used by savers, and probably never will be. Prediction markets, unlike most asset markets, are zero-sum – in fact they are negative-sum, once you factor in platform fees. And if your money is in a prediction market, it can’t be invested in equities, or be earning interest in the bank, either. Every winner of a prediction market necessitates an equal and opposite loser. Securities investors with diversified portfolios can expect positive returns in the long term, because they are giving up their money for others to use to create output and wealth, in exchange for a share of what they create. That’s why responsible people have their pensions in stocks and bonds, rather than a diversified portfolio of sportsbooks. Positive-sum savings vehicles are far, far superior to zero-sum ones, for the simple reason that they will grow your savings in the long run.

Prediction markets would be a more appealing savings product if the money bet were held in interest-paying assets before the bets were resolved. However, this would not make prediction markets any more attractive for savings purposes than a conventional savings vehicle, so we would still not expect to see savers use prediction markets.

There is a demand for zero-sum (and indeed, negative expected value) sports betting, which brings us to our second group, the gamblers.

On the home page Kalshi, most of the featured markets are about whether events will happen this year, meaning they end on the 31st of December, 2024. Contrast this with a sports betting site like DraftKings, where the front page will be covered with bets on games happening in the next day or two. Some of the featured bets are on live games, which resolve in hours or minutes.

Our view is that, for gamblers, quick resolutions are one of the key things that make a bet attractive and exciting. Sports betting sites’ futures bets on longer-term outcomes are far less traded than bets on single games about to happen, even when the future event (like the winner of the Super Bowl) is far higher-profile than tonight’s game. For example, in late March, there was a mere £5,190 bet on the Wimbledon 2024 winner, but £227,421 was bet on the relatively unimportant, but in-play, Francesco Maestrelli vs. Pierre-Hugues Herbert match in tennis’s Napoli Cup. For reference, Wimbledon is the single biggest event in all of tennis, while no one ranked higher than 87th in the world is playing the Napoli Cup.

Quick resolutions are so valued that live, in-game betting is becoming the most popular type of sports betting, despite the fact that the house tends to widen spreads on live bets, hurting bettors’ expected returns.

Beyond their exciting unpredictability and quick conclusions, sports matches are communal events of general interest. People are already fans of sports teams, even before the betting starts, and the predictable pace of seasons creates an ongoing community. The same cannot be said of most prospective prediction markets, which are often one-off events of little general interest.

This model – of highly anticipated events with communities of fans attracting gamblers – also explains where prediction markets are currently succeeding. US presidential elections, surely the most well-known recurring political events on Earth, create a huge amount of buzz and theatrics, which fans closely follow. Similarly, when scientists and entrepreneurs raced to replicate the LK-99 superconductor paper, Twitter was aflame with discussions and a number of tests were being live streamed. Yet even in these cases, gamblers’ preference for quick resolution bites: 42 percent of the volume on the 2020 election was traded in the last week before the vote, excluding the additional trading in the period between the election and the resolution of the market. Moreover, the volume of the most popular prediction markets still pales in comparison to the amount gambled on sports – recall the amount spent on legal sports betting in the US alone was over $330 billion in 2023.

Simply put, most things that we might want to know about the future aren’t much fun to bet on. Prediction markets provide a few exceptions like elections, but gamblers can’t bring about prediction markets on everything.

Without savers or gamblers, only sharps would remain. There are a few profiles of sharps who might seek value in prediction markets. Hobbyists, like politics nerds who want to capitalize on their knowledge, may constitute one group. Because insider trading is not prohibited in prediction markets, people with inside knowledge of some organization or event may want to trade on their information there. The hope of the prediction markets on everything vision is that true sharps would emerge in the form of hedge funds or other trading firms – professionals who would spend all their time investigating the probabilities of these events. These new entrants could create liquid markets with the efficiency found in asset markets.

But since prediction markets lack savers – who flood security markets with capital and create profit opportunities – this never happens. Prediction markets are orders of magnitude smaller than other financial markets. This makes the markets much less appealing to traders who want to profit off of time spent figuring out the ‘right’ price. That is because potential profits scale directly with market size. A one percent edge on a one billion dollar market is better than a ten percent edge on a $50 million market. It’s hard to imagine how prediction markets would ever find the size and liquidity necessary to pay the salaries of top sharps without savers.

As most prediction markets also lack many of the features that attract gamblers, whom sharps would prefer to trade against, sharps are left with the unappealing prospect of trading only with one another. This is analogous to turning up to a poker table and discovering that all of the other competitors are poker champions. You would much rather have been at a table of drunk tourists.

Markets are much less liquid when sharps trade only against sharps. As we’ve pointed out, the rewards for being right are smaller. But even beyond that, traders are more worried that they might be wrong when all of the other money is smart money. Why should they trust their model of the market probability over other sophisticated traders? 

This situation, of sharps trading against sharps, is analogous to the no-trade theorem in financial economics. The theorem gives conditions for when no trading will occur in a market. If all traders in a market are rational, and it is common knowledge among all traders that they are all rational, then no trades happen. Existing prices incorporate all previously available information, so in proposing a trade, a trader reveals that they have private knowledge that the other traders don’t have. The other traders surmise that they would lose if they accepted any trade that was offered. So no trades occur.

This is an unrealistic limit case. Even in highly professionalized markets, not all traders are rational, and most do not assume that any price is a perfect aggregation of all available information. But it is the situation a market tends to as gamblers and savers exit the arena, leaving only the sharps behind.

In practice, sharps would know they were mostly trading against sharps, but might still think they were better traders than their counterparties. But a sharp would usually understand they should be worried about their counterparty getting the better of them. The counterparty too assumes that they should also be worried, so both parties would be more hesitant to trade. And that’s not to mention platform fees, which would also take a cut. Add it all up and you have the makings of a noisy, inefficiently priced market that is too small for knowledgable traders to bother entering to correct profitably.

We see this as the core demand issue with prediction markets: without savers or gamblers to add volume to the market, the market cannot attract enough sharps to create the liquidity to drive prices toward accuracy. 

There are exceptions to our argument that sharps won’t trade against one another. In some zero-sum markets, sharps do trade against one another: options markets, foreign exchange markets, and insurance markets. In each of those markets, one party’s gain is matched by another party’s loss, yet the markets see very large trading volumes. Moreover, participants on either side of trades in these markets are frequently sophisticated players, apparently unworried that their counterparties will get the better of them. Do these examples imply that there are many sharps who wish to trade similar contracts in prediction markets, but cannot because the markets are regulated out of existence? We think not.

In part, options are a form of gambling. In options markets, where contracts provide the right to buy or sell an underlying security at a given price, there is still a strong preference for contracts that will expire soon. Contracts that expire that day, known as zero-day options, are notoriously popular among traders. In fact, the median option on the S&P 500 is a zero-day option. On such short time horizons, stock movements are highly random. The popularity of these options suggests that many day traders are simply using them to gamble. Beyond zero-day options, if you consider the Chicago Board Options Exchange’s page, which shows the price of options to buy or sell the S&P 500 at specific prices on specific future dates, you can observe a steep decline in traded volume as the date at which the option expires increases. Today there are millions of contracts expiring within the next week but less than 200 traded contracts for the longest dates, expiring in December 2026.

To the extent that longer-dated contracts are traded, the primary reason seems to be hedging, managing risk by taking a position opposite to your main position. Sophisticated traders use options to carefully hedge their portfolios. By serving as a hedging device, options, like insurance, are
positive-sum in risk-adjusted terms. One side benefits from making a trade with positive expected return, the other side benefits from making a trade that pays off when they really need the money.

So, whether it is gambling retail traders attracting smart money to take the other side of their Tesla options or smart money buying options to use as insurance, the options market is consistent with our model.

The foreign exchange story is very similar: large corporations and entities like sovereign wealth funds seek to hedge foreign exchange risks, while sophisticated hedge funds take the other sides of trades, seeking returns. In principle, both sides can benefit, since their goals are fundamentally different.

One of the main cases prediction market start-up Kalshi has made to the CFTC, in seeking approval to offer markets on elections, is that prediction markets similarly can provide a valuable hedging service. On Kalshi, many of the markets offered seem to reflect this use case.

In principle, there is no reason that some prediction markets couldn’t serve as tools for hedging. The problem is that where a conventional prediction market might be useful for hedging, the traditional finance system has usually created a better product.

Since the early 1980s, all sorts of new financial instruments have been created to allow financial institutions to hedge their positions. Derivatives, for example, are instruments whose value depends on some underlying asset. Some can provide ways of betting on whether a security or basket of securities will default. Markets have been created for what the federal funds rate will be in any given month, for the consumer price index monthly level, for betting on the dividends of companies, and much else. Existing financial infrastructure has been perfectly capable of developing prediction markets when they serve a useful function.

When there is significant demand to hedge certain risks, banks and other financial institutions have every incentive to figure out a way of servicing that demand. Some of these new products could be considered prediction markets, in that they predict things like future inflation. But prediction markets as they are typically understood – binary contracts on a wide range of nonfinancial events – have mostly not been developed within existing financial infrastructure, suggesting that demand for prediction markets as hedging tools is just not very large.

Kalshi’s most popular markets, rather than being new nonfinancial prediction markets, are actually markets on financial events that can already be synthesized in existing financial markets. The most popular is the number of Federal Reserve rate cuts this year; the second is the Federal funds rate in May; and the third is the Federal funds rate in June. All of these outcomes are already able to be traded in financial markets today, known as ‘fed funds futures’ markets. The next most popular markets are more novel but far less traded: ‘Room-temp superconductor validated this year?’ and ‘University Presidents ousted this year?’. These markets are presumably attracting gamblers, but they have hundreds of thousands of dollars in trades, not millions. And these are just the most popular markets – only ten contracts on Kalshi currently have over $100,000 in volume.

Prediction markets might be creating value by making complex markets like fed funds futures (or inflation rates) more easily accessible, but it does not seem like these markets will be revolutionary, as they functionally already exist.

Finally, the types of contracts that serve as useful hedges in financial markets are a subset of the types of events prediction markets advocates would like to see traded. It’s conceivable that someday sharps will hedge with contracts advocates are interested in like ‘Who will be the next Supreme Court nominee?’; ‘Will marijuana will be federally legalized in the US by 2030?’; and ‘Will GPT-5 be released by 2025?’, but there are lots of places they can do this already, and they don’t.

We suspect there is simply very little demand for hedging events like whether a certain law gets passed; there is only demand for hedging the market outcomes those events affect, like what price the S&P 500 ends the month at. Hedging market outcomes already implicitly hedges for not just one event but all the events that could impact financial outcomes.

Given these conditions, it is not a mystery why there aren’t more and larger prediction markets. Savers don’t want to go anywhere near them. Gamblers have more fun ways to chase their thrills. Sharps have no reason to enter the markets. We’re left with the small markets and wide spreads we find on Kalshi, PredictIt, and other prediction markets.

What’s the problem with just relying on the prediction markets we have today? They are small, with few traders and little professionalization, but are they still the best place to look for the probability of a future event?

We think that prediction markets as they exist are probably, at their best, similarly accurate to other high quality sources of information about the future, like the best forecasters, averages of forecasters like those found on Metaculus, and poll aggregators like 538. That is to say they do reasonably well, but are not authoritative or impossible for a highly motivated individual to beat.

This is what the limited evidence suggests. Consider the 2022 US midterm elections, where prediction markets should have been relatively strong as US elections attract gamblers. Research by First Sigma demonstrated that Metaculus (a forecasting platform and aggregator), 538, and Manifold (the play money prediction market) all predicted the elections better than the two true prediction markets: Polymarket and PredictIt. In Scott Alexander’s 2023 prediction contest, Metaculus – as well as many individual forecasters – also outperformed Manifold.

Why would this be? Even for hobbyists, it costs some amount of time and effort to learn information to predict the results of future events. Prediction markets currently offer relatively small opportunities to profit off this knowledge. There may be similar rewards in becoming a top-ranked forecaster on Metaculus, working at a forecasting consultancy, parlaying your skills into a job at a hedge fund, or creating a site like 538, which itself was sold to ESPN.

When prices on prediction markets are wrong, if it requires time and effort to figure out the right price, or even just to implement the trades, the limited market size and liquidity cannot incentivize new entrants from correcting those prices. In practice, this might mean that the market price of a prediction market contract is $0.50, but to buy more than $100 worth of contracts you would move the market price to $0.60, what you see as the fair price. All the work of betting (and another tax form to file) would amount to only a few dollars of profit. The usual logic – that experts should be able to make a lot of money on them, and in doing so correct the markets – simply doesn’t obtain.

So while prediction markets’ probabilities are worth considering, the limitations in size and liquidity of the markets greatly diminish their power. There is no evidence that they are better than other comparable mechanisms for information aggregation, let alone decisive. That doesn’t seem likely to change. In this case, you very well may be able to beat the market, but you probably won’t be able to profit much from it.

By themselves, we do not expect prediction markets to grow much beyond their current state. But prediction markets might work with subsidies, where money is added to the market to pay for information gathering. It’s worth recalling why people are excited about prediction markets in the first place: because, by aggregating knowledge and certainty, they could help us form more accurate beliefs about the future, and make better decisions about how to act. If the markets aren’t going to work on their own, these goals might still be compelling enough to create subsidies for prediction markets – essentially, the subsidy could play the role of the absent saver or gambler.

We haven’t seen many examples of this actually happening. It could be due to the lack of a platform or the technology to do so. But we suspect this is due to three alternate factors: the free rider problem, the cost of subsidies, and the alternatives to subsidies.

One way to subsidize a prediction market would be to get all those who are interested in gleaning information from the market to share the cost of the subsidy. Perhaps one could charge fees to people who use the information gleaned from prediction markets. But how exactly to charge these users is difficult. Market prices tend to be public information. Even if you found a way to gate prices to only paying participants, they could still easily be leaked or passed around. Thus, a free rider problem emerges: many people who value the information a market provides cannot be charged.

There is also the issue of cost. Subsidizing prediction markets likely is a relatively expensive way of aggregating information. To attract large firms, profits would need to be commensurate with their other opportunities, likely in the millions. Smaller teams or savvy individuals might be willing to do it for less, but these costs could still be substantial. There is a simple reason for this: a subsidy needs to pay many market participants to create a crowd from which it could glean wisdom, whereas more conventional methods simply pay one group.

Even if the wisdom of crowds derived from subsidized prediction markets performed better than individuals or teams, we worry that subsidizers would be unwilling to pay, as they might quickly run into diminishing marginal returns. If a single high-quality forecasting team is relatively accurate, those who might subsidize a market may not find it to be worth it to eke out additional accuracy. In most cases, high degrees of precision aren’t even actionable – people are likely to take the same course of action if they judge something is 60 percent likely as if they judge it 63 percent (and often even 75 percent) likely.

The final point is there are good alternatives to subsidizing prediction markets. Financial institutions have analysts; governments use intelligence agencies; companies use consultants; NGOs partner with economists and data scientists. Institutions employ these alternatives and virtually none employ subsidies.

Why would this be, if each of these groups can be beat when it comes to predicting the future? In many cases, individuals, firms, and governments do not just wish to know the probability of a future event. They would like to know the contingent probabilities around a cluster of events and actions and the reasoning behind those probabilities. For example, a forecasting team could provide a cluster of conditional predictions around the topic you select, and deliver pages of analysis behind their predictions. It’s not just that you can hire forecasters though, which after all is a recent development. We suspect that much demand for information about the future is satisfied by existing markets and firms. If it weren’t, wouldn’t private companies have taken up forecasting and prediction markets more quickly in the first place? That’s not to say that everyone has perfect information about the future. Instead, it’s that we suspect most people are paying for information that is as accurate as they need in a form that they can use.

One still might claim that there are positive externalities to having pub­licly available probabilities of future events, so the government – free of the free rider problem, less cost sensitive, and perhaps less able to procure high-quality alternatives – should subsidize at least some markets. This might be true, in some cases. Scott Sumner has advocated for a subsidized NGDP futures market. This could be a valuable tool for monetary policy. In other cases however, like geopolitical forecasts, it might be more useful to have high-quality information which is not publicly available. Whether any given market is worthy of subsidy is up for debate, but it certainly should not be taken for granted.

We are arguing against the view that were it not for pesky regulators, prediction markets for everything would be ubiquitous, and that those prediction markets would be the premier way to predict the future. On the contrary, the current size of the prediction market universe reflects market demand. Even if all regulatory hurdles were abolished, we do not expect that universe to dramatically expand.

Of course, we could be proved wrong. Kalshi announced in April 2024 that Susquehanna International Group, a quantitative trading form, had joined the platform as a market maker. But, in our view, prediction markets are held back by the lack of savers and gamblers, rather than sharps like Susquehanna. We welcome the test of our theory and hope we are proved incorrect.

In the view that prediction markets are fundamentally held back by regulation, all sorts of high quality information about the future is simply lost. In our view, that information is not waiting to be unearthed. Short of occasionally bribing insiders to leak information through markets, gaining information about the future is difficult and time-consuming, and prediction markets – even where legal – cannot create incentives that inspire people to uncover it. It’s an appealing prospect – that we could have highly accurate prediction markets on all sorts of future outcomes – but the information simply isn’t lying there latent, ready to collect.

That’s why we aren’t particularly excited about philanthropic dollars going toward generic prediction market projects or lobbying in their favor. Instead, we must recognize that good information about the future is costly to come by, and we must be willing to purchase or create incentives to elicit that information. There is no epistemic free lunch. Prediction markets are a useful tool, but they are not an oracle.

With thanks to our friends Glenn Yu and Josh Beck for their misadventures in prediction markets.

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