Meet professor Thomas W. Malone (on the right), from the MITs Center for Collective Intelligence.

No Gravatar

Photo Credit: Neely Steinberg

Tom Malone

MIT&#8217-s Center for Collective Intelligence

PS: The guy on the computer is Jason Carver.

Collective Prediction – Combining human and machine intelligence in prediction economies

No Gravatar

MIT&#8217-s CCI:

Think of a domain in which you would like accurate predictions of future events: sales volumes for a company&#8217-s products, outcomes of sporting events or military conflicts, crime rates by neighborhood, terrorist actions, new products introduced by a company&#8217-s competitors, or the clinical outcomes of different medical treatments someone you know might receive for cancer.

Now imagine a network of humans and computers that makes predictions in this domain&#8211-not perfectly, but better than was possible before. And imagine that these predictions get better and better over time as the network learns from its own experience. We propose to do some of the essential research needed to help create such networks.

A number of researchers have recently developed prediction markets in which participants buy and sell predictions about uncertain future events and are paid only if their predictions are correct. Such prediction markets have been found to be surprisingly accurate in a wide range of situations (including forecasting product sales and US Presidential elections).

We propose to build on this previous work to develop prediction economies &#8212- networks of people and computers paid (either in currency or points) for accurate predictions about future events. A prediction economy can include (a) one or more prediction markets (b) markets for various other kinds of information relevant to the events being predicted, and (c) markets for services by people (such as image analysis) or by machines (such as multiple regression, machine learning, and data mining).

Importantly, both people and their automated agents will be allowed to participate in any part of the economy. For instance, automated agents can do &#8220-program trading&#8221- in two related prediction markets whenever they see inconsistent prices in the two markets. In this way, prediction economies provide a flexible new approach to integrating human and machine expertise: People have an incentive to create new automated agents whenever they can codify useful expertise algorithmically, and they have an incentive to participate in markets directly when they can do a better job than the existing automated agents. But when people can&#8217-t improve on what the automated agents are already doing, then they have no incentive to intervene.

Drawing on theories in organization science, computer science, cognitive science, and economics, this work will develop new forecasting and collaboration tools that blend human and machine capabilities to more accurately forecast risks and opportunities, thus helping to build more agile systems in many domains.

Hummm&#8230- More meta than the prediction markets? Is that possible?&#8230- Will their proposal fly? Humm&#8230-

Read the previous blog posts by Chris F. Masse:

  • Ratted by his bank, sex-addict New York governor Eliot Spitzer (alias “Client 9”) resigns.
  • BBC’s coverage of politics is dull like taxes, death and German sausages.
  • Never talk when you can nod, and never nod when you can wink, and never write an e-mail because it’s death. You’re giving prosecutors all the evidence we need.
  • Is Justin Wolfers a libertarian? Probably not.
  • The information technology that caught Eliot Spitzer
  • Eric Zitzewitz’s 10 minutes of fame
  • Fun with conditional probabilities

MIT Center for Collective Intelligence – Play-money prediction exchange

No Gravatar

Yesterday, I blogged about the MIT CCI&#8217-s collective book project, &#8220-We Are Smarter Than Me&#8220-, which will be presented today at a live web cast (at lunch time, EST).

I completely overlooked that the MIT CCI is launching a play-money prediction exchange. The topics are CCI self-centric and thus totally uninteresting.


What is a &#8220-Prediction Tool&#8221-?

The Prediction Tool on this site is based on the idea of prediction markets. &#8220-Prediction markets are speculative markets created for the purpose of making predictions. Assets are created whose final cash value is tied to a particular event or parameter (e.g., Will there be at least 10,000 registered community members by March 31, 2007?). The current market prices can then be interpreted as predictions of the probability of the event or the expected value of the parameter. Other names for prediction markets include information markets, decision markets, idea futures, and virtual markets.&#8221- (Source: Wikipedia)

OK I get it, sort of, but what does that mean?

We have made a set of predictions about the success of the &#8220-We&#8221- community. You get to buy and sell stock in these predictions based on how likely you think they are to come true. If the prediction turns out to be true, the stock will pay out $100 per share. If it turns out not to be true, the stock will pay out $0 per share.

The hope is that through trading stocks back and forth, the market value of the stocks will eventually closely match the probability of each event coming true.

My Question: Does anybody know which software/design the MIT CCI is using here?

The Answer (added October 25): Shared Insights runs the MIT CCI&#8217-s play-money prediction exchange with the software provided by Consensus Point.