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Friday, November 2, 2012

Crowdsourcing and Predictive Markets – The Wisdom Of The Crowd

There are two fascinating developments in the world of innovation and prediction, and both use the ‘wisdom’ of large, random groups of people who either predict outcomes or generate innovative ideas or solutions.  The first are called Prediction Markets which work by presenting a problem to large numbers of people and asking them to bet on the result.   

A classic example is that of ‘Galton’s ox’, a seminal study in which Sir Francis Galton noted down the estimates of 800 or so entrants in a competition to guess the weight of an ox. He found that the average (mean) estimate of the crowd was almost exactly correct. Similar accuracy was reproduced in classic experiments in which students were asked to guess the number of jelly beans in a jar or the weight of a range of objects (Leighton Vaughn Williams, The Cleverness of Crowds)

James Surowiecki in his book The Wisdom of Crowds tells the story of the game show "Who Wants to be a Millionaire" in which a contestant could ask an expert for help with a question or ask the audience. The experts were right 65 percent of the time, and the audience was right 91 percent of the time. (Betting vs. Polls, www.uncleguidosfacts.com)

This concept has led researchers to ask whether such a model could be used for more serious issues, such as predicting election results.

Betting is the best example of how random individuals can predict better than experts.  The first case is that of Iowa’s Electronic Market (IEM) where random individuals are invited to bet on a particular result, for example a political campaign.  They must actually bet money or they can take the more conservative play money option.  These betting results are compared to expert pundit predictions and/or the results from polls.  The markets consistently outperformed the pundits and pollsters. (Betting vs. Polls)

Betting on American elections was permitted up until recently, and Rohde and Strumpf studied election results from 1868 to 1940 and compared betting predictions with actual outcomes:

The market did a remarkable job forecasting elections in an era before scientific polling. In only one case did the candidate clearly favored in the betting a month before Election Day lose, and even state-specific forecasts were quite accurate. This performance compares favorably with that of the Iowa Electronic Market (currently the only legal venue for election betting in the U.S.). Second, the market was fairly efficient,despite the limited information of participants and attempts to manipulate the odds by political parties and newspapers (Betting vs. Polls)

Other observers have concluded that the scope for using prediction markets is limitless:

“Prediction markets in general perform exceedingly well compared to individual forecasts. In his article on prediction markets, Philip O'Connor writes: "In fact, studies of prediction markets have found that the market price does a better job of predicting future events than all but a tiny percentage of individual guesses. (Michael Rozeff on LewRockwell.com)

The idea behind predictive markets is simple – there is enough collective wisdom in large crowds to correctly estimate, predict, and forecast.  The Washington Post used to provide NFL predictions by staff sports writers only, but now features the ‘betting’ of readers who almost always are correct in picking a winner and who come very close in predicting the point spread.  This is ‘targeted’ betting in which only sports fans participate, so the accuracy even in such a small sample is as high as it would be if the sample were random but very large.

The second important development in using non-technical ‘crowds’ to generate information is called ‘Crowdsourcing’:

Crowdsourcing is a distributed problem-solving and production model. In the classic use of the term, problems are broadcast to an unknown group of solvers in the form of an open call for solutions. Users—also known as the crowd—submit solutions. Solutions are then owned by the entity that broadcast the problem in the first place—the crowdsourcer. The contributor of the solution is, in some cases, compensated either monetarily, with prizes, or with recognition. (Wikipedia)

The idea was developed by Jeff Howe, explored in his seminal book entitled Crowdsourcing: Why the Power of the Crowd is Driving American Business (2008), and reviewed by Kathleen Smith:.

Several years ago, Proctor and Gamble first began to [contract] the solving its most difficult problems to InnoCentive. Specializing in technical problem solving, InnoCentive offered a new and innovative model for finding talent: anyone could send in solutions to the problems that the company posted. The winning solution would be selected only on the basis of its merit, and only then would the solver’s identity be revealed, and only then would he or she be paid for the labor of research and development. (“The New Road to Meritocracy”, Open Letters Monthly, 2008)

The results were impressive, and other firms began to take up the idea.  Now, only a few years after the publication of the book, crowdsourcing has become a staple of American business.

Howe not only touts the genius of his approach from a management perspective, but from a political one – crowdsourcing is the ultimate way to match democracy with business:

Crowdsourcing has the capacity to form a sort of perfect meritocracy. Gone are pedigree, race, gender, age and qualification. What remains is the quality of the work itself. In stripping away all considerations outside quality, crowdsourcing operates under the most optimistic of assumptions: that each one of us possesses a far broader, more complex range of talents than we can currently express within current economic structures. (Kathleen Smith)

The more diverse the crowd, suggests Howe, the more likely a solution will be found:

Given the proper conditions, diversity will trump ability in the case of a crowdcasting network. There’s a very simple reason for this: the ultimate success of one solution is not diminished by the number of unsuccessful solutions…. As more people apply more diverse sets of problem-solving methods—no matter how harebrained—the odds that someone will crack the nut can’t help but go up. (Kathleen Smith)

Crowdsourcing has been used successfully for simple marketing problems (T-shirt design) or complex scientific ones (electromagnetics). 

The reason why crowdsourcing is now possible? Howe says:

…a renaissance of amateurism, the emergence of the open source software movement, the increasing availability of the tools of production, and finally the rise of vibrant online communities organized according to people’s interests—have made crowdsourcing not only possible, but inevitable.

So, why not engage the American public in issues of politics, economics, or foreign policy?  We might be better off.


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