As we can see from the Gartner Hype Cycle Graph for Social Software, Prediction Markets are now on the downside of the dreaded “Trough of Disillusionment” (2010). Last year, it was just entering this phase, and in 2008 it was at the most-hyped “Peak of Inflated Expectations”. The object of this paper is to examine the current status of the prediction market “industry”, discuss several troubling issues that are holding back enterprise prediction market adoption, and look at the prospects for the future. Even if you get really sleepy reading this paper, keep going to the very end, where I will reveal a very, very long-term prediction! Can you guess what it is about?
You’re probably already familiar with the following graph showing the Prediction Market growth trend. It’s the one that appears in many presentations on prediction markets. As far as I know, the graph hasn’t been updated since 2006. It sure did look like the market was going to experience explosive growth! Did it?
According to a McKinsey Global Survey of Web 2.0 adoption, enterprise prediction market “adoption” grew from less than 1% in 2007 to 8% in 2009. This is how Consensus Point disclosed this results of the McKinsey report. I looked at the actual McKinsey interactive graphs and found that prediction market adoption was 9% in 2008. Does this mean that prediction market adoption had already peaked in 2008? I thought we were just getting started! If the survey is correct, prediction markets experienced more than an eight-fold increase in usage in the last two years. Based on what we can see, there appears to be something wrong with the definitions of “adoptions” and “prediction markets”. Alternatively, prediction market adoption is taking place behind closed doors or it isn’t really happening at all.
If the adoption rate is correct, why aren’t we seeing a significant spike in reported success stories? There has been very little reporting of any prediction market results – good or bad. I suspect the companies that have “adopted” prediction markets have done so in very limited pilot studies. Here’s another possibility. A quick review of several vendor websites indicates that many of the success stories involve idea pageant (or idea market) “prediction markets”. I’m willing to bet that the companies that implemented these “markets” were included in those “adopting” prediction markets. While this type of market does involve collective intelligence, it isn’t really a prediction market.
To start the review of the current status of prediction markets, let’s check in with Jed Christiansen, who recently posted his take on the industry.
There was nothing new in Jed Christiansen’s Prediction Market Review for 2010. His comments are correct, but he didn’t provide much commentary about the reasons for the developments over the past year. Essentially, his summary was as follows:
- Real money betting sites are booming
- Free public prediction markets cannot survive without monetizing site traffic
- Software vendors are providing more consulting services to their clients
He sees the PM industry as “maturing”. Existing vendors will continue to establish themselves, “as more companies experiment with new management tools and techniques.” The problem with the industry is that the product is still in its infancy. I don’t think you can call a market “maturing”, when the majority of the clients are merely experimenting with the concept of prediction markets and the “product” is, basically, still a concept. As we saw at the beginning of this post, prediction markets appear to be firmly entrenched in the trough of disillusionment. Furthermore, Gartner estimates that mainstream adoption is 5-10 years away, the same estimate they gave in 2009 and 2008.
Not only is the industry mired in the trough of disillusionment, I think the primary researchers are stuck in one too (with one notable exception)! Over the last few years, there have been no important new research studies, no significant published prediction market trials, and no major prediction market issues resolved. It is as if the researchers don’t want to look too closely at the issues for fear that some of them may seriously undermine the usefulness or potential of prediction markets.
I exclude one researcher (and his team) from the list of disillusioned researchers. During the year, David Pennock and his group at Yahoo! Research, launched Predictalot to showcase a fairly complex example of a combinatorial prediction market. So far, it has been used to predict the winners of the NCAA March Madness basketball tournament and the World Cup. On a humorous note, Predictalot and its developers received the Best Prediction Market Development of the Year for 2010. I’ll have more to say about the significance of this development, below.
Let’s look at the reasons behind Jed’s industry developments, which will lead into a discussion of the issues holding back the adoption of prediction markets and the future prospects for the industry.
Real-money prediction markets are booming and expected to continue to boom, not because they are good predictors, but because betting is booming. The major players are Betfair and Intrade, neither of which spout on about the predictive abilities of their markets.
Discrete outcome markets (like horse races) are perfect for betting but not nearly as useful for making predictions and the decisions based upon them. Most of the markets generate predictions that are too general or too public to be useful. The value of information depends on having it before someone else and being able to act upon it. Since these markets are ill-suited for useful predictions, their success will depend almost entirely on the public’s desire for betting opportunities. Personally, I think these types of markets should be excluded from the definition of prediction markets. Horse race odds are considered to be pretty good predictors of the race outcome, but we don’t consider horse race betting pools to be prediction markets.
Public Prediction Markets
Most public prediction markets are not very useful, at all. Even if they were proven to be accurate, no one would pay for information that is already publicly available. With few ways to generate revenue, growth prospects are bleak. Hubdub ceased operations during the last year. While it was fun to play on their prediction markets, participants became disinterested as the novelty of “betting” on trivial outcomes wore off.
No amount of explaining will convince participants that it was a good thing that Susan Boyle lost Britain’s Got Talent, even though she had a 78% chance of winning. Once we’re done explaining that, we can take a stab at explaining why there was such a wide variance between Hubdub’s (78%) and Intrade’s (49%) likelihoods of her winning. Personally, I think she should have won!
HSX and IEM run somewhat more useful markets, but neither is very good at accurately forecast long-term outcomes. Forecasting short-term outcomes is not particularly useful. Unless HSX can be turned into a real-money market, the prospects for any commercial success are minimal. However, this and other public markets are still valuable for research purposes.
Don’t expect any growth in this sector.
Vendor Consulting Services
This is a growth area, because their clients are ill prepared to create useful prediction markets without guidance. Failed trials mean the client companies will stop experimenting with prediction markets. Vendors help their clients achieve reasonable prediction results. None of the existing vendors can survive on software sales, alone. Vendors should try to get as many trials as possible and investigate the unresolved prediction market issues (see below).
There will be few new vendors, because the prospects for enterprise prediction markets are not very rosy (more about this, below).
WHAT IS HOLDING BACK ENTERPRISE PREDICTION MARKETS?
It’s no secret that prediction markets have not taken off in the corporate world. Don’t corporate decision-makers know a good thing when they see it or is there something wrong with the product?
Since getting involved with prediction markets, I have maintained a list of issues that remain unresolved. In my opinion, not resolving these issues is the reason enterprise prediction markets have failed to take hold in the marketplace. Despite several researchers – especially Robin Hanson as the most published adherent – stating that prediction markets are at least as accurate as other forecasting methods, the case has not really been made (at least not to my satisfaction).
As we will see, prediction markets are unable to accurately predict long-term outcomes, and they have poor records for accuracy and reliability, all of which are crucial for enterprise adoption. I haven’t mentioned the issues of market design, participant training, number of participants, etc…, because these things are easily solvable. It makes little sense to tackle these issues, unless the important issues are resolved first.
“Just in Time” is Not Timely Enough
Prediction markets need to be able to forecast long-term events. In order to make long-term decisions, we need information about conditions, events and outcomes that will occur far off in the future. Well, at least longer than a month or two! While there have been several long-term prediction markets (public ones), not one has provided an accurate prediction of the future outcome, until very close to the time when the outcome was revealed. Such predictions, no matter how accurate, are not actionable. In other words, these markets have been wholly inadequate for management decision-making purposes. The use of prediction markets to forecast any long-term outcome is questionable, if not down-right dangerous.
The following two graphs of historical prices in two long-term (14 year) prediction markets are from Ideosphere. In both of these markets, the predictions only became reasonably accurate during the last year before the outcomes were revealed. Of course, some prediction market advocates will argue that the markets were accurate throughout the trading period. The market price, at any point in time, accurately reflects all available information in the market at that time. Consequently, the markets are considered “accurate”. However, they aren’t accurate, if our purpose is to rely on them to make decisions about outcomes in the long-future.
Unfortunately, even if these long-term markets are “accurate” several years away from the outcome, we have no way of knowing whether they can be relied upon. It is impossible to verify the calibration of these markets (though it has been claimed that they are – 30 days before the market close). It is difficult to imagine that these markets were calibrated back in 1998, where the market prices were approximately 75% – 80%, yet the eventual closing prices were 0%. It’s possible, but highly unlikely. It is much more likely that these markets were reflecting a significant amount of uncertainty about the outcome.
The longer the trading period of the market, the more sources of uncertainty there will be. The steady march of time gradually reduced the uncertainty in these markets. It is as simple as that. Even if it were possible to acquire enough information to reduce the uncertainty surrounding the outcome, it is highly unlikely that the incentives would be enough to cover the search costs.
I don’t have the answers as to why these markets have not worked, but here are a few possibilities:
- Traders are not patient enough to bet on long-term events. They want to make a trade and quickly find out whether they have won.
- The longer the time period between the prediction and the outcome, the more likely it is that there will be more random, intervening events that affect the outcome, increasing uncertainty.
- Intervening events that have a complex influence on the outcome will increase uncertainty around the prediction AND increase the likelihood of a wrong prediction. Such outcomes may not be predictable by any method.
As the markets move closer to the outcome, uncertainty about intervening events decreases. Generally, about 30 days before the outcome, the markets become reasonably accurate. In fact, for most of the period the prediction markets were in operation, the predictions were wildly inaccurate! The question is whether there this is enough advance notice for the prediction to be acted upon, making them useful.
Here is an example from IEM, used to show how even fairly heavily traded markets are unable to make actionable predictions until very near the market close.
Note in the Congressional Control Market for 2006, the market prediction was inaccurate until a few days before the election. For decisions that need to know which way the election would go, the prediction would likely be too late. Most long-term markets exhibit this characteristic.
The Hewlett Packard pilot was one of the first studies of enterprise prediction markets (my commentary, here). Even though it is over 10 years old, it is still the most often cited case! This pilot study found that 6 of 8 markets outperformed the company’s internal forecasts. That’s pretty good, except that the “better” predictions were only slightly better and three of the predictions were really poor (greater than 25% error). One of the study’s authors commented: “The accuracy improvement was not high enough to be adopted,” says Chen. “You need to be a lot more accurate before it’s worth it to implement a new process.”
We can say that these markets were effective aggregators of participant information. When you consider that the participants in the prediction market trials were also involved in making the internal forecasts, it is not difficult to understand why the prediction markets were better at predicting the internal forecasts than they were at predicting the actual outcomes! Unfortunately, prediction markets need to be good at predicting the future outcomes.
The General Mills trials showed that prediction markets were as good as internal methods, but they were not significantly better and some of the internal forecasters were also participants in the prediction markets. It should be kept in mind that these were very short-term predictions, such that it would have been almost impossible to act upon the predictions.
Pennock et al showed that prediction markets were accurate (in the cases they studied), but they were not significantly more accurate than alternative prediction methods. They concluded that in order for prediction markets to be useful, they must be significantly better than alternative forecasting methods. In the cases they studied, they found prediction markets were only slightly better than other methods. In previous posts, I introduced the concept of materiality to the analysis of prediction markets. Essentially, for a prediction market to be useful, it must be more accurate than the next best predictor, such that the more accurate prediction would make a difference to the decision-maker relying on the forecast. Then, we need to look at the costs and benefits to determine whether the use of prediction markets is a wise course of action.
One of the measures of accuracy is calibration. We can be fairly sure that horse race odds are well-calibrated with race outcomes, because we can analyse thousands of homogeneous races to prove the claim. Unfortunately, we are hard pressed to find more than a handful of similar PMs from which we might test the PM’s calibration with the outcomes. Yet claims are made that PMs are reasonably well-calibrated and “therefore, they are accurate.”
Given the above comments about long-term PMs, we have to ask, when is a PM “well-calibrated”? Is it when the market closes? If so, the prediction is useless, because it cannot be acted upon, even though it may be quite accurate. Is it 30 days before the outcome of a long-term PM? If so, this is a bit better, but still pretty useless. Is it near when the market opens and continuously until the market closes? This would be ideal, but it is highly unlikely to be the case.
Galton’s ox and the missing submarine stories are examples of collective intelligence, not prediction markets, yet they are frequently cited as proof that prediction markets are accurate.
In order to be useful in an enterprise setting, prediction markets must reliably provide accurate predictions of future outcomes. Furthermore, they must be at least as accurate and timely as other traditional forecasting methods, and hopefully, make predictions at a lesser cost. Here, reliability means consistency. The same type of prediction market must consistently provide more accurate forecasts than other available means.
In the discussion about long-term markets (above), we found that PMs were very unreliable until close to the time the outcome is revealed. This brings up a couple of crucial questions. How far in advance can prediction markets make accurate predictions? How will we know the point in time when a prediction is “accurate”?
Recall the Susan Boyle Britain’s Got Talent markets. Why are there wildly different predictions of the same outcome in different prediction markets? How do we know which market is accurate? Is it a matter of prediction market efficiency? If so, how do we know whether a market is efficient? Rajiv Sethi provides us with an approach to determining which market is more efficient, but not whether the market is sufficiently efficient. Are there differences in participant information in the two markets? Is there a lack of diversity in one of the markets? Evidence of Cascading? Herding? Are there inadequate incentives to acquire and reveal information in the markets? Does sufficient information exist in one or both of the markets? If not, both markets may be aggregating guesses rather than informed opinions.
Prediction markets are touted as being excellent information aggregation methods, and by all accounts, they probably are very good at this. It almost seems too obvious to mention, but I will anyway. In order for the markets to provide accurate, reliable predictions, there must be a sufficient amount of information available to be aggregated. No one is really looking at this issue, yet it is crucial to success of prediction markets. This is the issue of information completeness.
THE FUTURE OF PREDICTION MARKETS
Where to from here? Despite the significant unresolved issues, I still believe prediction markets have potential (though not as much as we all once thought).
Can PMs ever replace traditional forecasting processes?
Probably not. As discussed, the HP and General Mills prediction markets used individuals involved in the internal forecasting process. Accordingly, the HP predictions were closer to the internal forecasts than they were to the actual outcome. At General Mills, both the predictions and the internal forecasts were very close.
The nagging question is, if the internal forecasting processes had not been in place, would the prediction markets have been as accurate as they were? We may never know, because I doubt there are any companies willing to test this proposition. My intuition tells me that stand alone prediction markets would be less accurate than internal forecasts as well as PMs in conjunction with internal forecasts.
I’m not arguing that prediction markets are poor aggregators of information. The reason for the lesser accuracy of stand-alone prediction markets is that there is much less information to aggregate (without the internal processes to search for information).
Is there a place for PMs to supplement traditional forecasting methods?
Prediction markets involve a relatively small marginal cost. So, it is relatively painless to implement key prediction markets to supplement traditional forecasting methods. Some of the benefits are: the ability to quickly check the internal forecast for significant deviations from the prediction (which can be investigated), more information by incentivizing participants to search for more information, and a reduction of forecasting bias.
The real benefit, in my opinion, is that prediction markets provide a better measurement of uncertainty around the outcome than do traditional forecasting methods. It does this in the form of a distribution of predictions, which can be seen visually and measured by the standard deviation. The information can be used to identify the need for further information and can be used in risk management and contingency planning. In addition, management can measure the reduction of uncertainty over time as new information is revealed or possible sources of uncertainty are removed.
One of the most promising applications is in project management. Task and project completion forecasts involve the most bias, and prediction markets have the potential to significantly decrease this bias. While long-term predictions are not particularly useful, short-term ones appear to be reasonably accurate and prediction markets have been shown to quickly aggregate known information. In managing projects, it is important to obtain very short-term forecasts for task completion, so that corrective action may be taken. Prediction markets appear to be particularly well suited to this task.
Projects can be separated into tasks along the critical path, and PMs can be put in place to predict completion dates for these tasks. Because completion dates are continuous variables, coming close to the actual outcome will often be good enough, even if the prediction market is not a perfect predictor.
An interesting avenue of research would be to create a combinatorial prediction market in which all of the critical tasks are linked to the total project completion date. (See additional comments below).
While they are not really prediction markets – they’re more like weighted opinion polls or high-tech suggestion boxes – they are usually counted as being “prediction markets”. Oddly, these types of information markets make up the majority of “prediction markets” in use. They also have the greatest growth potential.
Idea pageants generate ideas quickly, at a very low cost. They are relatively easy to understand and implement. These applications don’t need a high level of accuracy to be useful – companies can investigate the top 10 ideas vs. needing to know the best one. Management doesn’t have to delegate all authority to the market. Weak or impractical ideas are quickly filtered out, but decision-makers are free to investigate all ideas, not just those that have high probabilities of success.
Based on the knowledge that the further away from the outcome, the greater the possible number of events occurring that would affect the outcome, predictions will be inaccurate and/or widely dispersed, until near the time the outcome becomes known. These intervening events are random, but the likelihoods are not (in most cases). Another possible application is to create markets, similar to idea markets, except that they would identify possible future events that might affect the outcome that we are trying to predict. This information, combined with prediction markets to estimate the likelihoods of these events occurring would add useful information to the market predicting the outcome of interest.
For example, we could predict the likelihood of a truckers strike during the third quarter, which could be used to make a better prediction of third quarter revenue (the outcome of another prediction market). Eventually, it might be possible to link the potential intervening events to the outcome in a combinatorial prediction market.
COMBINATORIAL PREDICTION MARKETS
Continuing with the previous example, we might apply Robin Hanson approach. Much of his work in the area of combinatorial prediction markets focuses on conditional probabilities. He might run two prediction markets. The first would predict 3rd Quarter revenue given a truckers strike. The Second market would predict 3rd Quarter revenue, given no strike. The difference between the two predictions would be the forecast cost of a trucker strike (in terms of revenue lost). Robin calls these decision markets, and they form the backbone of his futarchy concept. Decision markets represent one form of a combinatorial prediction market.
With great fanfare, Crowdcast released their innovative trading platform designed to make trading more intuitive. Essentially, it is a mechanism to allow traders to bet on user-defined spreads. For example, revenues will fall between $1.2m and $1.4m or $1.85m and $2.12m. It allows traders to make combination bets for any range they choose. While I think this innovation has potential, there may be a number of tricky issues regarding the effects of assumptions required to make this platform work. Still, it is a promising development.
Combinatorial prediction markets make an awful lot of sense, if they can be practically implemented. The above types of combinatorial prediction markets are relatively easy to implement. Perhaps the most difficult to design and implement is the type of combinatorial prediction market developed by David Pennock and his group. While it is used for sports betting (play market), the concepts may be applied to enterprise prediction markets.
Predictalot provides a working example of a fairly complex combinatorial prediction market, which involves combinatorial betting on the NCAA March Madness and the World Cup. For example, if Duke is predicted to win the championship, this automatically increases the likelihood of Duke winning in all of the rounds leading up to the final. Also, if Duke is predicted to win in the first round, this increases the likelihood of Duke winning the championship. This platform allows bettors to bet only on those things that they have knowledge.
The same combinatorial prediction market concept could be applied to project management. It is difficult to predict the completion date of a complex project (Predictalot Champion). Some participants will have specialized knowledge of the task (Predictalot Team) they are working on, but little knowledge of other tasks along the critical path. A combinatorial market would allow participants to trade on those outcomes in which they have knowledge. The market structure will implicitly incorporate the predictions of tasks into the prediction of the overall project completion date. Similarly, the prediction of the overall project completion date will influence the predictions of the various tasks along the critical path.
This is an important development, because traders may have specific or local knowledge about one or more components of an outcome, though they have little knowledge about the eventual outcome itself. A single prediction market for the project outcome may fail, because there is not enough information about the outcome to generate an accurate prediction.
While it is true that a project outcome could be split into several prediction markets to predict the required tasks. The problem is that each prediction market may be too thinly traded to generate an accurate prediction. Also, there is no automatic inclusion of the task predictions in the project outcome prediction. A combinatorial prediction market has the potential to solve this problem and generate better predictions of the outcome.
Looking at a more generalized application, many outcomes are dependent (or conditional) on other events, actions or conditions. In order to better predict an outcome, we would like to know the factors that will have an effect on the outcome (discussed in the Idea Pageant section, above), and we would like to know how likely these factors are to arise. We could set up a series of separate prediction markets to predict the likely effects of each of the factors that will affect the outcome. The results of these markets would be available to the traders predicting the outcome of interest. While this is better than existing prediction models, it’s not ideal. Alternatively, the factors can be combined with the outcome in a combinatorial prediction market, allowing the likely effects of the factors to be automatically incorporated in the outcome prediction.
Certainly food for thought, and it is the reason that I selected Predictalot as the most important development in the area of prediction markets for 2010.
YOUR REWARD FOR READING THIS FAR!
No discussion of the future of prediction markets would be complete without commenting on the most comprehensive system of prediction markets ever conceived. Of course, I’m talking about Futarchy , one of the New York Times buzzwords for 2008. Sadly, for Robin Hanson, its creator, Futarchy has failed to take hold, anywhere. If the concept had any merit, Surely, at the very least, it would have been implemented in some small, South Pacific island nation by now (it hasn’t happened). About a year ago, I commented on the Future of Futarchy, where I dismissed the concept. Despite this, I see that in December 2010 Robin Hanson is still trying to promote the idea! While I disagree with Futarchy, I do heartily endorse his use of decision markets.
If there were a long-term prediction market on whether Futarchy would be implemented anywhere in the world in Robin Hanson’s lifetime, the price would be flat-lining on $0.00. Occasionally, the market price it would jump up to $0.50 (reflecting Robin’s trades), only to be smacked down by Mencius Moldbug’s trades. I suspect there will be a smirk on Robin’s face each time the market corrects his attempt to manipulate the market.
This market illustrates another key aspect of prediction markets. The outcome must be clearly defined. In this market, “Robin Hanson’s lifetime” is defined to mean his lifetime in his current body. It’s no secret that Robin wishes to have his head lopped off (when he dies, not before) and cryogenically frozen, to be thawed at some time in the future when bodies will be more “durable” or when brains can be downloaded into some robot-like “life” form. No word, yet, about whether the good professor’s wife will be similarly decapitated. Without this clear definition of the outcome, we wouldn’t be able to collect our bets, and it is likely that if brain cloning is possible, so is Futarchy!
So, my forecast is that Futarchy will never come to fruition and it should be cryogenically frozen now, too.
It has been quite an undertaking putting this paper together. Undoubtedly, I have missed a few key items, for which I apologize. As always, your comments are appreciated. While there have been few new developments, there are still many tasks to be completed, if enterprise prediction markets are to gain traction in the market. In writing this paper, it became evident how most of the major issues remain unresolved. I hope that some of the researchers will get over their disillusionment and ascend the slope of enlightenment! If so, I promise to get out of my own trough of disillusionment with respect to prediction markets!