Why do prediction markets work?

Is there a paradox in the continued existence of prediction markets? Recently I’ve been wondering this. Let me start with a little background for those that are unfamiliar with the concept of prediction markets.

Prediction markets are markets that allow you to bet on the outcomes of real-life events. This gives financial incentives to predict accurately, and as such the market price of a given bet reflects a kind of aggregate credence for that event occurring. There’s a whole bunch of results, theoretical and applied, that indicate that prediction markets serve to give robustly accurate probability estimates for real-world events.

Here’s a great paper by Robin Hanson about a political system based on prediction markets, named futarchy. Essentially, the idea is that voters determine a nation’s values, so as to generate some average national welfare metric, and then betting markets are used to decide policy. Some quotes:

On info-failures as a primary problem for democracy

According to many experts in economics and development, governments often choose policies that are “inefficient” in the sense that most everyone could expect to gain from other feasible policies. Many other kinds of experts also see existing policies as often clearly inferior to known alternatives.

If inferior policies would not have been adopted had most everyone known they are inferior, and if someone somewhere knew or could have learned that they are inferior, then we can blame inferior policies on a failure of our “info” institutions. By “info” here I just mean clues and analysis that should change our beliefs. Our info institutions are those within which we induce, express, and evaluate the acquiring and sharing of info. They include public relations teams, organized interest groups, news media, conversation forums, think tanks, universities, journals, elite committees, and state agencies. Inferior policies happen because our info institutions fail to induce people to acquire and share relevant info with properly-motivated decision makers.

[…]

Where might we find better info institutions? According to most experts in economics and finance, speculative markets are exemplary info institutions. That is, active speculative markets do very well at inducing people to acquire info, share it via trades, and collect that info into consensus prices that persuade wider audiences. This great success suggests that we should consider augmenting our political info institutions with speculative market institutions. That is, perhaps we should encourage people to create, trade in, and heed policy-relevant speculative markets, instead of discouraging such markets as we do today via anti-gambling laws.

Laying out the proposal

In futarchy, democracy would continue to say what we want, but betting markets would now say how to get it. That is, elected representatives would formally define and manage an after-the-fact measurement of national welfare, while market speculators would say which policies they expect to raise national welfare. The basic rule of government would be:

    • When a betting market clearly estimates that a proposed policy would increase expected national welfare, that proposal becomes law.

Futarchy is intended to be ideologically neutral; it could result in anything from an extreme socialism to an extreme minarchy, depending on what voters say they want, and on what speculators think would get it for them.

Futarchy seems promising if we accept the following three assumptions:

  • Democracies fail largely by not aggregating available information.
  • It is not that hard to tell rich happy nations from poor miserable ones.
  • Betting markets are our best known institution for aggregating information.

On the success of prediction markets

Betting markets, and speculative markets more generally, seem to do very well at aggregating information. To have a say in a speculative market, you have to “put your money where your mouth is.” Those who know they are not relevant experts shut up, and those who do not know this eventually lose their money, and then shut up. Speculative markets in essence offer to pay anyone who sees a bias in current market prices to come and correct that bias.

Speculative market estimates are not perfect. There seems to be a long-shot bias when there are high transaction costs, and perhaps also excess volatility in long term aggregate price movements. But such markets seem to do very well when compared to other institutions. For example, racetrack market odds improve on the predictions of racetrack experts, Florida orange juice commodity futures improve on government weather forecasts, betting markets beat opinion polls at predicting U.S. election results, and betting markets consistently beat Hewlett Packard official forecasts at predicting Hewlett Packard printer sales. In general, it is hard to find information that is not embodied in market prices.

On the possibility of manipulation of prediction markets

We want policy-related info institutions to resist manipulation, that is, to resist attempts to influence policy via distorted participation. Speculative markets do well here because they deal well with “noise trading,” that is, trading for reasons other than info about common asset values. When other traders can’t predict noise trading exactly, they compensate for its expected average by an opposite average trade, and compensate for its expected variation by trading more, and by working harder to find relevant info. Theory says that if trader risk-aversion is mild, and if more effort gives more info, then increased noise trading increases price accuracy. And in fact, the most accurate real speculative markets tend to be those with the most noise trading.

What do noise traders have to do with manipulators? Manipulators, who trade hoping to distort prices, are noise traders, since they trade for reasons other than asset value info. Thus adding manipulators to speculative markets doesn’t reduce average price accuracy. This has been verified in theory, in laboratory experiments, and in the field.

Futarchy remains for me one of the coolest and most exciting ideas I’ve heard in political philosophy, and prediction markets fascinate me. But for today, I have the following question about their feasibility:

If the only individuals that are able to consistently profit off the prediction market are the best predictors, then why wouldn’t the bottom 50% of predictors continuously drop out as they lose money on the market? If so, then as the population of market participants dwindles you would end up with a small fraction of really good predictors, each of whom sometimes gets lucky and makes money and sometimes is unlucky and loses some. On average, these people won’t be able to make money any more (as the ability to make money relies on the participation of inferior predictors in the market), so they’ll drop out as well.

If this line of reasoning is right, then it seems like prediction markets should inevitably collapse as their user base drops out. Why, then, do sites like PredictIt keep functioning?

One possibility is that there’s something wrong with the argument. This is honestly where most of my credence lies; tons of smart people endorse the idea, and this seems like a fairly obviously central flaw in the concept for them all to miss. If this argument isn’t wrong, though, then we have an interesting phenomenon to explain.

One explanation that came to my mind is that the continued survival of prediction markets is only possible because of a bug in human psychology, namely, a lack of epistemic humility. People are on average overly confident in their beliefs, and so uninformed people will continue confidently betting on propositions, even when they are generally betting against individuals with greater expertise.

Is this really what’s going on? I’m not sure. I would be surprised if humans were actually overconfident enough to continue betting on a market that they are consistently losing money on. Maybe they’d find some way to rationalize dropping out of the market that doesn’t amount to them admitting “My opinion is not worth as much as I thought it was”, but surely they would eventually stop betting after enough losses (putting aside whatever impulses drive people to gamble on guaranteed negative-expected-value games until they lose all their money.) On the other hand, it could be that the traffic of less-informed individuals does not consist of the same individuals betting over and over, and instead a constant crowd of new sheep coming in to be exploited by those more knowledgeable. What do you think? How do you explain this?

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