The future of futurism: crowds or entrepreneurs?

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Chris Masse has already linked to The Economist story on futurists, which ends with a plug for prediction markets:

The most heeded futurists these days are not individuals, but prediction markets, where the informed guesswork of many is consolidated into hard probability. Will Osama bin Laden be caught in 2008? Only a 15% chance, said Newsfutures in mid-October 2007. Would Iran have nuclear weapons by January 1st 2008? Only a 6.6% chance, said Inkling Markets. Will George Bush pardon Lewis “Scooter” Libby? A better-than-40% chance, said Intrade. There may even be a prediction market somewhere taking bets on immortality. But beware: long- and short-sellers alike will find it hard to collect.

Like Chris, I&#8217-m partial to the plug for prediction markets, but the story from the past year that best fits the five pieces of advice to futurists in the article (think small, think short-term, admit uncertainty, embed in an industry, and listen more) was not about the &#8220-wisdom of crowds.&#8221- Rather, this profile by Michael Lewis of hedge fund entrepreneur/insurance risk modeler John Seo in the NYT Magazine seems to fit the bill.

[NOTE: This post is a somewhat revised version of a posting on Knowledge Problem: What will futurists do in the future? Chris has also already linked to the story on John Seo that was published in August 2007.]

In a truly efficient prediction market, the price will come to reflect the influence of all available information.

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Justin Wolfers in the Wall Street Journal:

[…] Through this process of different people trading based on their own observations about the race, prediction markets prices come to aggregate disparate pieces of information into a single summary measure of the likelihood of various outcomes. Moreover, if this market operates efficiently, it will appropriately summarize all of this information and the price will become the most statistically accurate forecast of the election outcome. […]

If I may, I would like to jot down some thoughts related to my concept of prediction market journalism.

  1. The explainer on prediction markets is pretty good.
  2. Crappy URL: http://online.wsj.com/article/SB119902559340658043.html?mod=rss_Politics_And_Policy
  3. No way to leave a comment.
  4. WSJ did list (in one of the sidebar boxes) BetFair along with InTrade &#8212-good point.
  5. WSJ didn&#8217-t list NewsFutures and Inkling Markets but listed their own play-money, bots-driven prediction exchange (WSJ Political Market) &#8212-bad point (conflict of interest).
  6. No external links embedded in Justin Wolfers&#8217- text &#8212-there are very good resources listed in the sidebar boxes, though (but the links use JavaScript and are not direct).
  7. No static or dynamic prediction market charts, even though Justin Wolfers spent a good deal of air time analyzing the recent prediction market events &#8212-a concept he formalized with Eric Zitzewitz.
  8. No tips &#8212-&#8221-I can&#8217-t predict what these trends will be […]&#8220-. Sounds like the prediction market approach (declaring that the market is a better forecasting tool than the polls or the experts) kills any anticipation and scenario planning. It shouldn&#8217-t be like that. Prediction market journalism can&#8217-t be only about analyzing the past. More on that in the coming weeks on Midas Oracle &#8212-not in the WSJ.

For all these reasons, I can give more than a straight B to Justin Wolfers&#8217- copy. You can do better than that, prof. :-D

Do sports prediction markets corrupt sport? No.

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Mark Davies (&#8221-managing director of corporate affairs at BetFair&#8221- = their spin doctor) in The Guardian:

Does the existence of betting exchanges corrupt sport?

NO

In the world of finance, it has always been far easier for employees to have a negative impact on a company&#8217-s share price than a positive one. Even a chief executive would be hard pushed to cause a price rise on any given day, but anyone with physical access to the company can very easily cause a fall. No one would suggest people should only be able to buy shares, and not sell them. Instead, regulators ensure that sanctions against corruption tip the balance heavily against trying it. Make the penalty draconian, and you deal with corruption at its heart.

Betting on sport is no different. The only people who can corrupt sport are those taking part – a fact unchanged by the existence of betting exchanges. If you prevent people from succumbing to the temptation, would-be corrupters have no one to help them . You and I cannot rig a race just because we can bet against its outcome: we need someone who can affect the result. If that person might lose a livelihood, would they risk it for a fast buck?

Attack corruption at source, and it does not matter where the bet was placed. Nevertheless, some still long for the days when more traditional bookmakers held every card (an interesting notion considering what has historically been their dubious reputation)- others prefer a Tote monopoly- and some believe that banning bets against outcomes would constrain corrupters.

This series of arguments is based on the naive belief that a black market does not exist. This is absurd. Asian syndicates behind apparently rigged football matches (like those who turned floodlights out at grounds in the late 1990s) are no more dependent on Britain&#8217-s legitimate market than Colombian drugs barons are on sales of aspirin at Boots. The difference between legal, regulated, transparent betting – nowhere more so than on the leading betting exchange [= BetFair], where every transaction is open to scrutiny from 29 different sporting regulators – and the murky, illegal market, is the difference between chalk and cheese.

Black markets thrive where legal ones offer poor value. Now that the exchanges offer the best value, those previously tempted by odds on the black market are returning to the legal fold. Corruption-free sport comes from total transparency. The exchanges are the only part of the market that offer it. People get hung up on &#8220-betting to lose&#8221-.

Leave aside the obvious: bets to win (most clearly demonstrated in two outcome sports like tennis or snooker) are direct bets on the opposite outcome to lose. &#8220-Betting to lose&#8221- is just betting at value: if the price unfairly reflects the realistic chance of something happening, why should you not bet against it?

Value bets, placed for or against, are perfectly legitimate- acting to impact a given outcome adversely is corrupt. But banning the former through fear of the latter is like banning cutlery because some people use knives to harm. It is not the knives doing the damage, but the criminals using them. Legal betting does not corrupt sport- people do – and they are more likely to do it when they think they w ill not get caught. Measures to protect sport are not best aimed at open, transparent, and audited betting markets but through its participants, where the corruption can occur.

Excellent.

Prediction markets do react to stale news.

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Gilder and Lerman hypothesised that past/present events can potentially assist in predicting future prices in prediction markets. They empirically revealed that prediction markets are surprisingly predictable, even by purely market-historical techniques.

Taking hold of the baton from Gilder and Lerma, Panos Ipeirotis and George Tziralis developed techniques for extracting news flow signals to see whether they can indeed be utilised to predict the future performance of markets on the InTrade prediction exchange. On the question of whether Hillary Clinton will be the Democratic Presidential Nominee in 2008, they noted-

Our sentiment index (in maroon) is close to 1 when we predict that the market will move higher, and it is close to 0 when we predict that the market will move down. Typically, it works pretty well for predicting long periods of price increases and declines. To put our money where our mouth is, the signal for the last few days shows that Hillary&#8217-s market price will edge lower in the next few days/weeks.

Following on from this we looked at the Intrade prediction market and the Betfair markets on whether Hillary Clinton will be the Democratic Presidential Nominee in 2008, as of 10.45 GMT on December 3 2007. Whilst the Intrade market suggested that Clinton&#8217-s probability of victory was 67%, the Betfair market gave a reading of 69%.

We returned to the Intrade prediction market and the Betfair market on whether Hillary Clinton will be the Democratic Presidential Nominee in 2008 at 08.45 GMT on December 7 2007.

Whilst the Intrade prediction market had previously suggested that Clinton&#8217-s probability of victory was 67%, it was now suggesting that her probability of victory was 64%.

The Betfair market which had given a reading of 69% on Decmber 3 as regards her probability of winning the democratic nomination, was now suggesting that her probability of victory was only 50%.

It is quite clear, that the both sets of markets are responding to stale news, with Intrade significantly lagging behind Betfair, as regards its ability to aggregate all available news flow. Those that had sold Clinton on Betfair at 1.44 on December 3, on the back of Panos Ipeirotis and George Tziralis&#8217- advice, are now sitting on a healthy profit. The claim that prediction markets are innefficient would seem to be gathering momentum&#8230-. with the most likely cause being the fact that they are not liquid enough.

http://www.bettingmarket.com/predictionstale.htm

Do you see a sixth dimension to the prediction markets?

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Midas Oracle is about the event derivative markets and:

  1. their profit opportunities (sought by the traders)-
  2. their predictive power (investigated by the economists)-
  3. their entertainment ability (delivered by the play-money prediction markets)-
  4. their hedging utility (employed by the risk managers and monitored by the CFTC)-
  5. their decision-making capacity (alleged by Robin Hanson).

E-mail me or leave a comment below.

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UPDATE: Xpree&#8217-s Mat Fogarty&#8230-

their training / motivational ability (corporations like their employees to be engaged and knowledgeable about key metrics – PMs reward this process)

How Google ranks the software providers of enterprise prediction markets

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[InTrade is #1 but their software is not yet available for enterprise prediction markets, as I understand it.]

#1. NewsFutures

#2. Inkling Markets

#3. Consensus Point

#4. Zocalo

#5. HSX Research

Source: The Google Search ranking of the &#8220-prediction markets&#8221- webspots&#8230-

External Link: Jed Christiansen&#8217-s review of the software providers of enterprise prediction markets. (I will take a deeper look to it at a later time, and will maybe blog about it.)

Could a political campaign use prediction markets?

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This is cross-posted from our Inkling Markets blog where we have far fewer readers than the illustrious Midas Oracle. :)&#8230-

&#8212-

There are several prediction marketplaces out there for the upcoming U.S. election season and probably more to come. All the ones we know of are intended for participation by the general public. But what if a Presidential campaign ran an internal marketplace? How could prediction markets be used to give a campaign a competitive advantage? We put our political operative hats on for a few minutes and came up with some scenarios:

Resource Allocations

Speaking to veterans of previous presidential campaigns, one of the biggest issues mentioned was building consensus internally on resource allocation across the primaries, then for the general election. Conflicting polling data and infighting among advisors often led to the abandonment of several states where post-mortem analysis of actual voting patterns showed the candidate would have had a chance. Using prediction markets as input to resource allocation decisions, questions could be asked that compare performance metrics across different states, i.e. levels of support among certain voter blocs, predicted endorsements, outcomes of local elections that could impact the general election, etc. This type of information is hard to gather through traditional polling mechanisms but could easily be captured across participants from individual states, locales, and the general campaign.

Fundraising Forecasts

We assume existing forecasting methods used by campaigns are fairly accurate at anticipating how much money will be raised on a quarterly basis from a defined donor list. What may not be as well defined, however, is the impact of various campaign maneuvers on donation levels. For example, a campaign could internally test various scenarios with national campaign staff, field workers, even undecided voters to see if certain activities drive increased fundraising. If the campaign goes through with the activity, the campaign could evaluate the market and pay it out. If not, the market could simply be refunded. Of course, a campaign could also use prediction markets as further input to official forecasts across the different fundraising channels, allowing a more diverse group of people who may have additional insight beyond the &#8220-MBA types&#8221- at campaign headquarters crunching numbers.

Risk Management

(Using Inkling,) questions in a prediction market could be generated by the national campaign and staff at the local level. This &#8220-web of questions&#8221- would be especially useful when trying to anticipate risks to the campaign. The prediction market could be a clearing house of the whispers, rumors, and self-perceived weaknesses of the campaign to continuously test their merit or impact on the campaign. For example, someone from the local staff may be aware of a negative perception the candidate suffers from in a particular voting district. They could run a prediction market about its impacts in an upcoming primary, i.e. &#8220-Will the candidate be perceived as weak on X in analysis of post-appearance local media coverage?&#8221- If the stock price remains low, that issue probably does not need to be dealt with specifically. If it&#8217-s high, it may be an issue the campaign chooses to address proactively ahead of the predicted negative coverage.

Policy Predictions

Given the interest shown to any major candidate, a prediction market gives a campaign an outlet for those supporters wishing to participate in a more meaningful way than simply donating money. A market geared towards public policy across a wide range of issues, both national and local, would be an excellent resource to send people to. Currently most candidate&#8217-s online presence is focused largely on networking, information dissemination, event notifications, and fundraising. A broadly available prediction market would allow people to provide input on what they think will happen from a policy perspective, i.e. will a particular bill pass? How much funding will an initiative receive, etc.? The campaign could then take these predictions as input to shape policy. A similar marketplace could also be set up with a more limited audience of dedicated national and local campaign staff. This marketplace could be augmented with policy experts from around the world to provide additional perspective.

Competitive Analysis

Intended for a tightly controlled group of trusted participants, prediction markets could be run on the performance of the other candidates related to their fundraising levels, endorsements, primary performance, etc. to see how one candidate compares to another. This information would be very useful for strategy formulation. Four years ago some of the candidates tapped the blogosphere to drive early campaign participation and fundraising success.

This year most candidates are trying to build up and leverage their online social networks a la Facebook. Will this campaign season also be the year we see a candidate tapping the collective wisdom of his/her sprawling campaign apparatus?

Polls over prediction markets?

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&#8216-Are Political Markets Really Superior to Polls as Election Predictors&#8217- is a paper by Robert Erikson and Christopher Wlezian that calls into question whether prediction markets, and specifically IEM, is as accurate as proponents of prediction markets claim. The paper was highlighted here where it was suggested that advocates of prediction markets were turning a blind eye to it.

In the paper the authors show that while when you compare IEM with raw polls, IEM outperforms the polls however when you manipulate the poll data the polls are more accurate. The generate the manipulation the authors looked at poll data from elections from 1952 onwards which show that over time the early leader tends to lose that lead. They then used that relationship to manipulate polls for elections from 1988 onwards and compared the result with the IEM forecasts. The manipulated polls showed a higher level of forecast accuracy.

I think this is an interesting piece of research but it is a stretch to use this to claim that polls are more accurate than prediction markets. The fundamental problem is that when newspapers (or anyone else for that matter) quotes polls, they don&#8217-t refine them using historical data, they quote the actual poll result. If anything the authors have shown a small bias in IEM that one would now expect to get traded out (like the longshot/favorite bias in betting markets or the January effect in financial markets).

Fundamentally, the thing to note is that while polls make prediction markets more accurate, the converse does not hold.

Prediction Markets = Clear Expiry + Disperse Information + Participation Incentives

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Jed Christiansen at Forbes (just after John Delaney&#8217-s ill-written and pointless comment):

A market effectively aggregates the information from everyone participating. So anything where:

  • there is a clear result
  • information is dispersed between people and/or locations
  • people have an incentive to participate in the market

will likely provide better results than any other forecasting method. Experts just aren&#8217-t as good as they (or anyone else) think they are. It&#8217-s simply better to ask the crowd in these cases.

Missing from Jed Christiansen&#8217-s comment is the emphasis on long series for comparison. Takes time and hundreds of prediction markets to prove the wisdom of crowds.

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UPDATE: Jed Christiansen comments&#8230-

Chris, I agree that for probability assessment, a number of measurements are required to assess success. However, for metrics (ie, sales of widget X, rating of product Y) it doesn&#8217-t require a long series at all. Depending on how poor the current forecasting model is performing, a prediction market could prove successful after just a few measurements.

Meet David Jack, the managing director of TradeFair.

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David Jack is on the right&#8230- (here with Andrew &#8220-Bert&#8221- Black, the BetFair co-founder).

David Jack, the managing director of TradeFair

David Jack (Managing Director of TradeFair, a BetFair spin-off)

Previously: Binaries and Spreads: BetFair spins off TradeFair.

NEXT: Why does Tradefair care about Prediction Markets – by TradeFair&#8217-s David Jack – 2007-12-06