I’m curious why Andrew McAfee would cite this example in support of prediction markets. Though he mentions that his executive students had conducted a “simple poll”, in fact, they really created a pari-mutuel market, which provided the “risk and gain”. It was not a prediction market, as the executives believed it was. There was no buying and selling of shares. They simply purchased their “horses” (time slots). It might be more appropriate to call the entries “tickets” rather than “shares” and “bets” rather than “investments”.

The “consensus” ticket was correct. No surprise here (favourite), and the payoff was slightly higher than it should have been, because of the slight long shot bias. Andrew might have corrected them on their terminology, too. Prediction markets are not run to obtain a *consensus* of opinion. They are run to *aggregate* information from the market participants.

It really would have been interesting to sit in on the class, had the fellow arrived at one of the other time periods. Would the executives have cheered that their “prediction market” had worked like a charm? Surely, someone would have jumped up and said “but it’s well-calibrated!” Had the wrong “horse” won, would *any* of the executives have been confident enough to present a proposal on the use of prediction markets in their companies? I’ll bet all of them would have been a bit concerned that the front runner didn’t win.

Andrew writes that it showed the “power of incentives”. Actually, it did not, at least in the sense we usually define “incentives” in prediction markets. In a true [prediction market], the incentive is presented when the market price is not “accurate”. The informed trader either knows this or has an incentive to obtain more information and set the price right. His incentive is the profit from trading to correct an inaccurate market price. There was no change in the price of any of the “shares” in the market they described. Hence, there was no “incentive” in this sense.

Andrew also states that this experiment shows how easy it is to set up “convincing demonstrations” of prediction markets. I agree, they are easy to set up (at least technically). Making them work is much, much more difficult.

I’ve read Andrew McAfee’s other posts on prediction markets, and it appears he’s been slipped a bit too much of the PM Kool-Aid that is going around. If you don’t read the prediction market research papers with a critical eye, you can get caught up in the over-enthusiastic reporting of results and come to the conclusion that prediction markets are a *proven* technology for greatly improving corporate forecasting problems. Nothing could be further from the truth at this point. Worse, there is not much useful research being published.

I think Andrew McAfee should have presented his students with a more balanced summary of the strengths and weaknesses of prediction markets.

That’s my take on it. However, I am happy that the executives were interested in prediction markets! There is some hope, yet.

[Disclosure: He is on the CowdCast board. ]

Andrew McAfee asks a question at the bottom of his post. Here is my answer. The reason for not adopting collective forecasting (prediction markets being the most complex method of information aggregation that predicts) would be that the benefit (objectivity in forecasting) is too small for those executives who don’t really need that surplus of objectivity.

If you have other questions, professor McAfee, just ask. I’ll have the answers for you.

Previously: CrowdCast = Collective Forecasting = Collective Intelligence That Predicts

Two questions to wind up this post. First, how could I have made better use of crowd wisdom and other available information to make better March Madness picks? And do you have any tips on how to be a good Twitter-assisted public speaker?  Leave a comment, please, and let us know.

My answer to his first question:

- I would have looked at BetFair and HubDub, in addition to NewsFutures.

PS: Prediction markets are included in the mix —along with wikis, blogs, social networking software, RSS, links, search, and tags.

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Andrew McAfee’s post reveals this:

Prediction markets were (sic) very much like stock markets. They contained securities, each of which had a price. [...]

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Not sure why they used the past tense.

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Prediction markets are in fact event derivative markets.

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Papers from Robin Hanson, Justin Wolfers, Eric Zitzewitz, Koleman Strumpf, etc., are free to download.

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Via George Tziralis, of Ask Markets.

Previously: Do Google’s enterprise prediction markets work?

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Via our good friend George Tziralis of AskMarkets

[*] Original verbatim is this: I can’t think of a single reason not to deploy predictive markets.

Any market is somewhat “predictive”. Our prediction markets are specially designed for easy trading and easy interpretation.