After years of pretending being a prediction market consultant, Robin Hanson finally confesses nobody has ever cared about his stuff. – [LINK]

The Emperor of enterprise prediction markets is naked.

Robin Hanson:

I can confirm that this disinterest is real payday loans Philadelphia. For example, when I try to sell firms on internal prediction markets wherein employees forecast things like sales and project completion dates, such firms usually don’t doubt my claims that such forecasts are cheap and more accurate. Nevertheless, they usually aren’t interested.

The other take about forecast accuracy.

Technology Review publishes an uncritical article on collective forecasting used in business. – {CrowdCast}

How Bets Among Employees Can Guide a Company&#8217-s Future &#8211- Internal prediction markets enable colleagues to wager on the fate of crucial projects and the success of products in the pipeline. &#8211- Technology Review

If prediction markets are such a powerful tool, then why arent we able to use them to solve [INSERT YOUR FAVORITE WORLD PROBLEM HERE]?

Justin Wolfers is asked the question, but I would have a different answer than his.

The reason prediction markets are not widely used in business is that their many boosters (Robin Hanson, James Surowiecki, Justin Wolfers, etc.) have exaggerated their usefulness. Just because they are objective in their wisdom does not mean that they are very useful.

Objectivity is over-rated. This is a painful lesson for the handful of young startups who swallowed the prediction market myth. Next step: the dead pool.

Truth in Advertising – Meet Prediction Markets

Most published papers on prediction markets (there aren&#8217-t many) paint a wildly rosy picture of their accuracy. Perhaps it is because many of these papers are written by researchers having affiliations with prediction market vendors.

Robin Hanson is Chief Scientist at Consensus Point. I like his ideas about combinatorial markets and market scoring rules, but I think he over-sells the accuracy and usefulness of prediction markets. His concept of Futarchy is an extreme example of this. Robin loves to cite HP&#8217-s prediction markets in his presentations. Emile Servan-Schreiber (Newsfutures) is mostly level-headed but still a big fan of prediction markets. Crowdcast&#8217-s Chief Scientist is Leslie Fine- their Board of Advisors includes Justin Wolfers and Andrew McAfee. Leslie seems to have a more practical understanding than most, as evidenced by this response to the types of questions that Crowdcast&#8217-s prediction markets can answer well: &#8220-Questions whose outcomes will be knowable in three months to a year and where there is very dispersed knowledge in your organization tend to do well.&#8221- She gets it that prediction markets aren&#8217-t all things to all people.

An Honest Paper

To some extent, all of the researchers over-sell the accuracy and the range of useful questions that may be answered by prediction markets. So, it is refreshing to find an honest article written about the accuracy of prediction markets. Not too long ago, Sharad Goel, Daniel M. Reeves, Duncan J. Watts, David M. Pennock published Prediction Without Markets. They compared prediction markets with alternative forecasting methods for three types of public prediction markets: Football and baseball games and movie box office receipts.

They found that prediction markets were just slightly more accurate than alternative methods of forecasting. As an added bonus, these researchers considered the issue that prediction market accuracy should be judged by its effect on decision-making. So few researchers have done this! A very small improvement in accuracy is not considered material (significant), if it doesn&#8217-t change the decision that is made with the forecast. It&#8217-s a well-established concept in public auditing, when deciding whether an error is significant and requires correction. I have discussed this concept before.

While they acknowledge that prediction markets may have a distinct advantage over other forecasting methods, in that they can be updated much more quickly and at little additional cost, they rightly suggest that most business applications have little need for instantaneously updated forecasts. Overall, they conclude that &#8220-simple methods of aggregating individual forecasts often work reasonably well relative to more complex combinations (of methods).&#8221-

For Extra Credit

When we compare things, it is usually so that we can select the best option. In the case of prediction markets it is not a safe assumption that the choices are mutually exclusive. Especially in enterprise applications, prediction markets are heavily dependent on the alternative information aggregation methods as a primary source of market information. Of course, there are other sources of information and the markets are expected to minimize bias to generate more accurate predictions.

In the infamous HP prediction markets, the forecasts were eerily close to the company&#8217-s internal forecasts. It wasn&#8217-t difficult to see why. The same people were involved with both predictions! The General Mills prediction markets showed similar correlations, even when only some of the participants were common to both methods. The implication of these cases is that you cannot replace the existing forecasting system with a prediction market and expect the results to be as accurate. The two (or more) methods work together.

Not only do most researchers (Pennock et al, excepted) recommend adoption of prediction markets, based on insignificant improvements in accuracy, they fail to consider the effect (or lack thereof) on decision-making in their cost/benefit analysis. Even if some do the cost/benefit math, they don&#8217-t do it right.

Where a prediction market is dependent on other forecasting methods, the marginal cost is the total cost of running the market. There is no credit for eliminating the cost of alternative forecasting methods. The marginal benefit is that expected by choosing a different course of action than the one that would have been taken based on a less accurate prediction. That is, a slight improvement in prediction accuracy that does not change the course of action has no marginal benefit.

Using this approach, a prediction market that is only &#8220-slightly&#8221- more accurate, than those from alternative forecasting approaches, is just not good enough. So far, there is little, if any, evidence that prediction markets are anything more than &#8220-slightly&#8221- better than existing methods. Still, most of our respected researchers continue to tout prediction markets. Even a technology guru like Andrew McAfee doesn&#8217-t get it , in this little PR piece he wrote, shortly after joining Crowdcast&#8217-s Board of Advisors.

Is it a big snow job or just wishful thinking?

[Cross-posted from Toronto Prediction Market Blog]

Hyping enterprise prediction markets in Mashable

No Gravatar

Matt Fogarty of CrowdCast:

Business leaders rely on metrics and data to inform decisions around new products and opportunities, but traditional forecasting methods suffer from bias and lack of first-hand information. That’s why business forecasting is an ideal target for the application of crowd wisdom. While bets are made anonymously, some prediction market software applications have built-in reward systems for accurate forecasters. And the accuracy of prediction markets over traditional forecasting methods is proven again and again. […] Prediction markets will then aggregate this knowledge to produce actionable, people-powered forecasts. The result is an ultra-rich information source that will lay the foundation for smarter, better-informed company decisions. […]

CrowdCast is an enterprise software platform that helps companies make better forecasts by tapping the knowledge stored in their employees.

No Gravatar


Download this post to watch the video &#8212-if your feed reader does not show it to you.

Common pitfalls of enterprise prediction markets: participants who lack relevant information, too few participants, and too little trading.

No Gravatar


&#8220-Prediction markets seek information aggregation from a large group of diverse individuals by encouraging active participation.&#8220-


&#8220-The biggest challenge is getting people in the company to be active&#8221- [].