Computational Properties of Prediction Markets



Computational Properties of Prediction Markets

How does the crowd get its wisdom?


Prediction markets use bets on the outcomes of events and have been quite successful in harnessing the “Wisdom of Crowds,” giving mostly accurate predictions both in popular events (Oscars, sporting events elections, sporting events) as well as many corporations using them internally to predict sales and the implications of new features. These tools have become so valuable that the Commodity Futures Trading Commission has recently asked for public comments for regulations of these event contracts.

How do these markets aggregate information so well? We can create computational models of these markets to determine what kinds of knowledge these markets can help us understand. Research in this direction will help us develop contracts that will maximize the accuracy and usefulness of the results while limiting problems of manipulability and limited volume.


A typical market, such as found on, will have a security such as 2016.OLYMPICS.NTH.AMERICA that will pay off $100 if 2016 Summer Olympics are in North America and $0 otherwise. One can show that a risk-neutral user who believes the probability of this even is say 33% would buy this security if the price were below 33 and sell (possibly short) this security if the price is above 33. One could thus use the price as an aggregate predection of the probability that the event will occur. Considerable analysis of past markets have show the surprisingly accurate value of these predictions. If you rounded off the predictions, Intrade accurate predicted the outcome of every state in the 2004 electoral college and the 2008 senate races.

There markets aggregate disparate information over its many users to compute these probabilities similar to parallel and distributed computation mechanisms. Thus theoretical computer scientists can help develop accurate models of these processes that would lead to the development of securities that will improve both the quality and quantity of information received, help limit the effects of market manipulability and give accurate results even if there is low liquidity in the markets.

Contributors and Credits

Lance Fortnow


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