Robin Hanson:

A track record tech must be combined with a social equilibrium that punishes those with poor records, and thus encourages rivals and victims to collect and report records. The lesson I take for forecast accuracy is that 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! Today, the simplest way to create forecast track records that get attention and respect is by making bets. In a bet, the parties work to define the disputed issue clearly enough to resolve later, and the bet payoff creates a clear record of who was right and wrong. Anti-gambling laws now discourage such bets—shouldn’t we at least eliminate this impediment to more respect for forecast accuracy records?

And once bets are legal we should go further, to revive our ancestors’ culture of respect for bets. It should be shameful to visibly disagree and yet evade a challenge to more clearly define the disagreement and bet a respectable amount on it.

Somebody should tell Robin Hanson about Long Bets, an awesome betting program (with a non-profit twist) from the Long Now Foundation.

Robin Hanson, is what I would term a market fundamentalist – somebody who believes that markets are the primary mechanism for achieving the public good. Recent events have demonstrated that this creed is morally bankrupt. We are, in the words of Michael Sandel, “at the end of an era of market triumphalism”.

Robin Hanson:

I deny the accusation, though I’m not sure why I should have to. Having a high opinion of the eventual info power of decision markets [*] is very different from saying “markets are the primary mechanism for achieving the public good.” Shouldn’t you have to first provide support for your accusation, rather than me somehow having to first prove bald accusations wrong?

[*] That’s at the core of the issue. Somebody who has a “high opinion” of something that has not given stellar results since 1988 is obviously exaggerating. I personally have a somewhat good opinion of prediction markets and conditional prediction markets, but not a “high opinion” —sense my nuance. Niall O’Connor has been too strong in his “accusation” ( ), but he is a bit in the right. [UPDATE: Niall O'Connor has an honest reply to Robin Hanson.]

#1. (Public or enterprise) traders mainly bring to the exchange some bits of information they acquired from some official or semi-official sources. The enterprise traders bring in the information they got from second hand, mainly. Few of them are in direct contact with the facts in question, or know the big picture. They also bring their bias, granted.

#2. Enterprise prediction markets are not the only thing “completely different to anything they’ve ever encountered [before]“. If you think of it, the Internet was “completely different”, too. Yet, the companies have all established an Internet presence and are all trying to market thru the Web. If EPMs have not yet been adopted, it is because a) their advocates have greatly exaggerated their benefits b) their advocates have not been specific enough as to when and where exactly EPMs can make a difference.

#3. If the EPMs “do the same thing differently” (I concur), then their advocates should go on the record as to why and when using traders is so beneficial to the forecasting process.

#4. When people hear about the wisdom of crowds in the media, in articles about prediction markets, it is in relation to that damn alleged added accuracy. I didn’t invent that.

#5. Surely. But do those 16 traders remain active after the novelty has worn off?

Number one, I don’t understand why information aggregation would be a “bottom-up” approach (as opposed to “top-down”). Our traders bring bits of information to the market —but these bits of information were originally produced by the traditional sources (news, political polls, political forecasters, opinion leaders, etc.). I don’t understand why this “bottom-up” metaphor would apply to the prediction markets. Generally speaking, I hate metaphors.

Number two, according to Jed, the slow adoption of enterprise prediction markets would be due to the “cognitive dissonance” that they provoke in the (weak) mind of the (freal) business managers. In other words, EPMs are such a novelty, and the corporate forecasters such a bunch of retarded people, that it will take decades before commercial organizations get to adopt the prediction market tool.

The reality is different.

1. As one can plainly see, the Americans love technology very much. For instance, it took less than 2 years for the iPod, the iPhone, FaceBook, or Twitter, to reach mass adoption. When a brand-new technology is truly awesome, the Americans are quick to acquire it. If enterprise prediction markets were such a revolutionary and powerful forecasting tool, it would have found a market already —just like the iPod, the iPhone, FaceBook or Twitter did.
2. The benefits of enterprise prediction markets have been over-hyped by a bunch of Ivory Tower economic canaries who have no experience whatsoever in forecasting and business administration.
3. The added accuracy of the enterprise prediction markets is marginal —and anyway does not fill the gap with omniscience (contrary to people’s expectations). The value of EPMs lays in some specific situations where the competitive forecasting tools have limitations. As of today, to the notable exception of Inkling Markets [deep respect to NewsFutures and CrowdCast, anyway], prediction market consultants have been beaming out generalities about markets that play well with some business media, instead of publishing case studies.
4. The hurdles of running enterprise prediction markets have not been disclosed fully to the public. The main challenge is to find enough active traders. Remember than InTrade (who aims at the Americans) is powered by a few thousands of active traders, and BetFair (who aims at the British), by a few hundreds of thousands. Let’s remember that America is populated by 300 million people, and the United Kingdom, 60 million. In both countries, only a very small part of the population engages in event derivative trading — far less than 1%, and it is closer to 0% than to 1%, actually. In the context of a Fortune-500 company, which is of course much smaller than a country, the pool of potential active participants whose trading activity is sustained over time is quite tiny.
5. The field of enterprise prediction markets does not need to turn every Ivory Tower economic canary into a “chief scientist”. What companies need are Chief Forecasting Officers, who will analyze, impartially, for each specific situation, which tool is appropriate —a traditional forecasting tool (most of the times), or the EPM solution (at times). This analysis should not be done by a prediction market afiscionados, who has a bias, but by a professional and scientific forecaster who can do a benefit/cost analysis of each tool for each specific situation. EPMs will take off when the consultants and managers cease to be mezmerized by the economic canaries and start getting down and dirty with business cases in each industry.

UPDATE: Jed’s reply

UPDATE: My reply

Harrah’s is setting up a pilot prediction market to forecast customer activity in one of its domestic casino operations. [...]

Since the power of prediction markets hinges on effectively tapping into cognitive diversity throughout an organization, Page also argues convincingly that if members of a group do not have enough diversity in their perspectives, prediction markets can actually produce dismal results. [...]

Until now, few of the companies sponsoring successful pilots or tests have deployed prediction markets on a broad or sustained basis. Why not? One explanation is that prediction markets are deeply subversive. After all, lots of midlevel executives are consumed with the task of forecasting. If prediction markets do a better job of it, doesn’t that discredit the efforts (and perhaps even the motives) of these executives? But as prediction markets shift their focus toward new knowledge creation, they may become less threatening within corporations. [...]

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I don’t buy this explanation —nor do I buy that other one.

My view is that we haven’t yet demonstrated clearly when and how prediction markets can be useful.

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My comment to Alan Abramowitz and John Tierney:

“They follow the polls. That’s it.”

Yes, they follow the polls. No, that’s not it.

Traders also dig the news of the day and make anticipations about the outcome. For instance, towards the end of the 2008 Democratic primary, the polls and the mass media were still giving Hillary Clinton a very good standing, whereas the prediction markets (informed by a bunch of political experts who did the counting of the delegates and super-delegates) were telling us that she was as toasted as Lehman Brothers in the middle of the credit crunch crisis.

Are prediction markets useful? If John Tierney wants to answer this question, he should pick up a prediction market and put it in the social context of that day. Some prediction markets are more useful than others. In the case of the 2008 Democratic primary (a complicated matter), the prediction markets sided with the best informed political experts against the mass media and the polls. So to speak, they were an umpire. In that case, we see the emergence of a social utility. We now have the case for the media citing more the probabilities of the liquid (play-money and/or real-money) prediction markets.

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Previously: #1 – #2 – #3 – #4 – #5

External Link: Club of Growth

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Terrified.

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Other things to keep in mind:

• This year happened to have a lot of favorite-longshot States, which turned-out to be favorable to 538′s error relative to markets.
• Prediction markets register information in real time. Since the difference in error is small, this is important.
• Markets are more flexible, and useful in situations where you don’t have a rich data set and obvious statistical analyses. Elections are just one type of question. Even if you have data, it might be less expensive to set up a new contract than to undertake the analysis.
• And of course, prediction markets have functions aside from forecasting, and provide incentives for uncovering new information.

Congrats for the launch of AskMarkets. Best wishes to your prediction exchange and consulting firm.

Here’s the perfect opportunity to ask you the “question that kills”:

What was the social utility of the political election prediction markets during the 2008 campaign?

In other words, why should the media have informed people about the InTrade probabilities at a time Nate Silver did a near-perfect job forecasting the 2008 US elections?

What’s the added value of the political election prediction markets over the poll aggregators?

Can you cite one prediction market (other than the “who’s gonna become president?” prediction market) that has a high social utility?

Each time I ask this question to one of the prediction market luminaries (or so they think they are), I get back the same glance I would get from a dead trout —so I would appreciate if you could attempt to answer my question by publishing a blog post on Midas Oracle.

Best regards,

Chris Masse, bombastic blogger

http://www.midasoracle.org/

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