Tag Archives: Prediction Markets

Heads In The Sand

By Robin Hanson · December 31, 2011 9:05 pm · Comments (12)

The end of a Boston Globe article on The future of prediction:

But the real question, when it comes to predicting the future of forecasting, may not be whether we can or can’t forecast accurately — it’s whether we want to. Robin Hanson, an economist at George Mason University and a pioneer of prediction market design, thinks that what’s holding back our ability to predict is not technology or a lack of ingenuity. He believes companies and governments already have much of what they need to be a lot better at predicting the future, and that the reason they’re not taking more advantage of it is that in many cases, having accurate predictions in hand makes managers, CEOs, and government officials accountable in a way that lots of them don’t want to be.

That’s because knowing the future can be a scary thing: It means genuinely answering for the costs of our decisions, confronting the likelihood of failure, seeing that arrows point down as often as they point up. When we’re offered a look into the crystal ball, it may in fact be human nature to turn away.

“We’re two-faced,” Hanson said. “We like to talk as though we wanted better forecasts, but often we have other agendas. When the opportunity to know the future presents itself — as, increasingly, it will — we may end up discovering that we’d rather stay in the dark.”

When projects fails, project managers like to say “No one could have foreseen that. We did the best we could.” This strategy doesn’t work so well when prediction markets or other credible methods create clear public track records showing consensus estimates of a high chance of failure, and perhaps also what could have been done to reduce that chance.

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Fixing Election Markets

By Robin Hanson · November 4, 2011 10:00 am · Comments (11)

One year from now the US will elect a new president, almost surely either a Republican R or a Democrat D. If there are US voters for whom politics is about policy, such voters should want to estimate post-election outcomes y like GDP, unemployment, or war deaths, conditional on the winning party w = R or D. With reliable conditional estimates E[y|w] in hand, such voters could then support the party expected to produce the best outcomes.

Sufficiently active conditional prediction markets can produce conditional estimates E[y|w] that are well-informed and resistent to biases and manipulation. One option is to make bets on y that are called off if w is not true. Another is to trade assets like  ”Pays $y if w” for assets like “Pays $1 if w.” A basic problem this whole approach, however, is that simple estimates E[y|w] may reflect correlation instead of causation.

For example, imagine that voters prefer to elect Republicans when they see a war looming. In this case if y = war deaths then E[y|R] might be greater than E[y|D], even if Republicans actually cause fewer war deaths when they run a war. Wolfers and Zitzewitz discuss a similar problem in markets on which party nominees would win the election:

It is tempting to draw a causal interpretation from these results: that nominating John Edwards would have produced the highest Democratic vote share. …The decision market tells us that in the state of the world in which Edwards wins the nomination, he will also probably do well in the general election. This is not the same as saying that he will do well if, based on the decision market, Democrats nominate Edwards. (more)

However, this problem has a solution: conditional close-election markets — markets that estimate post-election outcomes conditional not only on which party wins, but also on the election being close. This variation not only allows a closer comparison between candidates’ causal effects on outcomes, but it is also more relevant to an outcome-oriented voter’s decision. After all, an election must be close in order for your vote to influence the election winner.

To show that conditional close markets estimate causality well, I’ll need to get technical. And use probability math. Which I do now; beware.

Continue reading "Fixing Election Markets" »

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Official Optimism

By Robin Hanson · November 2, 2011 8:30 am · Comments (16)

Governments consistently overestimate their future budgets:

Analyzing data for 33 countries, Frankel finds that the average upward bias in the official forecast of the budget balance, relative to the realized balance, is 0.2 percent of GDP at the one-year horizon, 0.8 percent at the two-year horizon, and 1.5 percent at the three-year horizon. The longer the horizon, and the more genuine uncertainty there is, the more scope there is for wishful thinking. The bias is not larger for the commodity producers, … or for the developing countries, than for others. …

Over-optimism in predicting growth appears linked to over-optimism in predicting budget balances. On average, the upward bias in growth forecasts is 0.4 percent when looking one year ahead, 1.1 percent at the two-year horizon, and 1.8 percent at three years. The bias in growth forecasting appears in the United States and most other industrialized countries, but not among the commodity producing countries in the sample. …

Over-optimism is more prominent, for both budget balances and for economic growth, during economic booms. …. Countries subject to a budget rule … make official forecasts of growth and budget deficits that are even more biased and more correlated with booms than do other countries. Evidently when such governments exceed the deficit limits set by the rules, they respond by adjusting their forecasts rather than by adjusting their policies …

As a result of budget institutions created in 2000, Chile’s official forecasts of growth and of budget balance have not been overly optimistic, even in booms. (more)

The key institutional innovation [in Chile] is that there are two panels of experts whose job it is each mid-year to make the judgments, respectively, what is the output gap and what is the medium term equilibrium price of copper, rather than leaving the job to government officials. …. A reinforcement of the Chilean idea would be to give the panels legal independence. There could be laws protecting them from being fired, as there are for governors of independent central banks. (more)

Prediction markets forecasting budget balances and growth rates would be easy, and they’d reliably resist political pressure for overly optimistic estimates. So why even bother with trying to figure out how to design expert panels that can remain both expert and independent? Either Frankel naively thinks this easy, he is ignorant of the market solution, or doesn’t really want to promote accurate budget estimates.

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Predict Yourself

By Robin Hanson · September 4, 2011 7:00 pm · Comments (9)

To act more on far ideals, predict what you will do:

Asking participants to predict their future vaccination behavior … substantially increased vaccination rates among patients with high short-term vaccination barriers (who, in the absence of this intervention, have low vaccination acceptance rates). These findings are consistent with past research on temporal construal, which suggests that people asked to think about a future behavior tend to focus its abstract benefits, and disregard concrete barriers that might impede it. (more)

Consider personal prediction markets, which predict what you will do in the future, such as whether you will lose weight, get married, get an A, get promoted, etc. By allowing your associates to participate in such markets, you could let them (anonymously) tell you what they really think about what you will do. Looking often at the predictions of such markets, and asking yourself if those predictions are wrong, could help you to live up to your far ideals about what you should and will do with your life.

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Join The DAGGRE Team

By Robin Hanson · August 31, 2011 11:55 pm · Comments (18)

A few weeks back Tyler Cowen posted an appeal from Philip Tetlock:

Starting in mid-2011, five teams will compete in a U.S.-government-sponsored forecasting tournament. Each team will develop its own tools for harnessing and improving collective intelligence and will be judged on how well its forecasters predict [government-chosen] major trends and events around the world over the next four years. … [We] will be one of the five teams competing – and we’d like you to consider joining our team as a forecaster.

You may have seen other teams’ appeals as well. Today I can announce that GMU hosts one of the five teams, please join us! Active participants will earn $50 a month, for about two hours of forecasting work. You can sign up here, and start forecasting as soon as you are accepted.

The government sponsor is IARPA (Intelligence Advanced Research Projects Activity), under the ACE (Aggregative Contingent Estimation) program, and our team is DAGGRE (Decomposition-Based Elicitation & Aggregation).

Our approach has three distinctive features, all visible to participants:

  1. We use an edit-based interface – a current consensus on all questions is visible to all participants, and any user may change any part. Each edit is scored on whether it moves the consensus closer to or further from the truth.  (This is equivalent to a market-maker-based prediction market).
  2. For each question IARPA assigns, we “decompose” it by adding related questions, and letting participants forecast both related questions and how they relate to the assigned questions. For example, users can assume answers to some questions, and then forecast other questions conditional on their assumptions. (This is equivalent to a combinatorial prediction market.)
  3. We will sometimes walk users through a special elicitation process that has been shown in field and lab experiments to produce more accurate estimates.

(Items #2,3 might not show for a week or two.) We are eager to see how our approach compares to the other approaches. Come get paid to help us find out!

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New Scientist Contest

By Robin Hanson · August 16, 2011 5:30 pm · Comments (17)

New Scientist magazine set up a contest between new prediction techniques, including prediction markets:

We decided to see how the latest techniques would stand up to the task of predicting what people will buy. … Over the past four months, we have set four teams the task of trying to predict the sales of each issue of New Scientist, using some of the most promising and innovative approaches available. … Continue reading "New Scientist Contest" »

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Tests For Hedgehogs?

By Robin Hanson · July 20, 2011 10:25 am · Comments (8)

Philip Tetlock famously showed that hedgehogs, who focus on one main analytical tool, are less accurate than foxes, who used a wide assortment of analytical tools, on simple long-term forecasts in political economy.

Over at Cato Unbound, two famous hedgehogs recently replied to Tetlock. John Cochrane argued that no one can do well at the unconditional forecasts that Tetlock studied, but that hedgehogs shine at conditional forecasts, such as GDP change given a big stimulus. Bruce Bueno De Mesquita noted that his hedgehoggy use of game theory is liked by the CIA and by peer review.

Today at Cato Unbound, I note that since Tetlock’s data is hardly universal, that leaves room for counter-claims that he missed important ways in which hedgehogs are more accurate. But I find it disappointing, and also a bit suspicious, that neither Cochrane nor De Mesquita express interest in helping to design better studies, much less in participating in such studies. I note that “it is certainly possible to collect and score accuracy on conditional forecasts”, and conclude:

Research patrons eager to fund hedgehoggy research by folks like Cochrane and De Mesquita show little interest in funding forecasting competitions at the scale required to get public participation by such prestigious folks. So hedgehogs like Cochrane and De Mesquita can continue to claim superior accuracy, with little fear of being proven wrong anytime soon. All of which brings us back to our puzzling disinterest in forecast accuracy, which was the subject of my response.

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Respect Forecast Accuracy

By Robin Hanson · July 13, 2011 11:05 am · Comments (13)

The topic at Cato Unbound this month is “What’s Wrong with Expert Predictions.” Dan Gardner and Philip Tetlock’s lead essay points out a puzzling lack of interest in forecast accuracy:

Corporations and governments spend staggering amounts of money on forecasting, and one might think they would be keenly interested in determining the worth of their purchases and ensuring they are the very best available. But most aren’t. They spend little or nothing analyzing the accuracy of forecasts and not much more on research to develop and compare forecasting methods. Some even persist in using forecasts that are manifestly unreliable. … This widespread lack of curiosity … is a phenomenon worthy of investigation.

My response essay considers this puzzle. The editor summarizes:

Robin Hanson argues that most people aren’t interested in the accuracy of predictions because predictions often aren’t about knowing the future. They are about affiliating with an ideology or signaling one’s authority. … He suggests that one way to make predictions more accurate might be to lift both the social stigma and legal prohibitions against gambling.

Key quotes:

Even if disinterest in forecast accuracy is explained by forecasting being only a minor role for pundits, academics, and managers, might we still hope for reforms to encourage more accuracy? …

Hope … mainly comes from the fact that we pretend to care more about forecast accuracy than we actually seem to care. We don’t need new forecasting methods so much as a new social equilibrium, one that makes forecast hypocrisy more visible to a wider audience, and so shames people into avoiding such hypocrisy. …

It isn’t enough to devise ways to record forecast accuracy—we also need a new matching social respect for such records. Might governments encourage a switch to more respect for forecast accuracy? Yes: by not explicitly discouraging it!

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Me on Freakonomics

By Robin Hanson · June 30, 2011 9:00 pm · Comments (3)

For five minutes near the end (48:45 to 53:45) of this hour long Freakonomics radio show on “The Folly of Prediction,” I discuss promising applications of prediction markests. We end it this way:

Dubner: So that sounds very logical, very appealing; how realistic is it?

Hanson: Well it depends on there being a set of customers who want this product. So, you know, if prediction markets have an Achilles heel it is certainly the possibility that people don’t really want accurate forecasts.

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Letting Leaders Off

By Robin Hanson · May 30, 2011 8:00 pm · Comments (10)

Bryan Caplan:

The gold standard of modern social science is … a “random controlled trial.” … And yet… real-world policy-makers continue to neglect, evade, and actively oppose experimental tests of efficacy. … Tim Harford explains why:

Politicians resist pilot schemes with objective measures of success. … politically inconvenient is the fact that half of the pilot schemes will fail… so the pilot will simply produce stark evidence of that failure. …

This is all a nice example of a theme I’ve been pushing for a while ….

Political agency problems are often a byproduct of voter irrationality. The principals give their agents grossly suboptimal incentives, then complain that the agents fail to carry out their assignments. … Pay-for-performance is a good idea, but the public is too irrational to accept it.

Note that private CEOs are also quite reluctant to run randomized trials of their business ideas. Yes some trials happens in marketing, but firms overall still display a puzzling neglect of randomized trials, and of prediction markets. Both mechanisms offer more accurate info, but at the cost of a high rate of clear public embarrassments – clear evidence showing that you endorsed crap.

Yes firms do implement incentive pay more often, but firms still remain puzzlingly reluctant to correct such incentives for overall trends in the economy or the local industry. Maybe voters are more reluctant than stockholders to discipline their agents, making the private sector more efficient at managing many forms of activity. But in both cases there remains a puzzling reluctance to force leaders to prove their value.

My hypothesis: leaders have status, with which voters and stockholders want to affiliate. While people talk about being offended by leader dominance, they are actually quite eager to submit, and reluctant to risk leader wrath by questioning leader quality. The people’s romance with the state makes them even more reluctant to hold political leaders accountable, so this effect is even worse in politics.

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