We are interested in the design of prediction markets, or markets where price discovery is the designer’s end goal and trading is a means to that end.

For example, suppose a policymaker seeks a forecast of the likelihood of an avian flu outbreak in 2008. He may float a security paying $1 if and only if an outbreak actually occurs in 2008, hoping to attract traders willing to speculate on the outcome. With sufficient liquidity, traders will converge to a consensus price reflecting their collective information about the value of the security, which in this case corresponds to the market’s estimate of the probability of outbreak.

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Traditionally, market makers are human decision makers seeking to earn a profit.

In contrast, a prediction market designer may well subsidize a market maker that expects to lose some money, in return for improving trader incentives, liquidity, and price discovery. The market maker’s loss can be seen as the cost of gathering information for more accurate forecasts. Of course, the market operator cannot afford to lose arbitrary amounts of money; typically he or she will want to set a maximum bound on the market maker’s loss, ensuring that no matter what happens, the loss cannot exceed the bound.