Biology Reference
In-Depth Information
each have a 50% chance of occurring, and trader 2 believes that events A
and C each have a 50% chance of occurring. On the basis of their privileged
knowledge, trader 1 pursues contracts based on events B and C, and trader 2
pursues contracts based on events A and C. Competition drives the price of
contract C higher, while the lack of competition drives the price of contracts
A and B toward zero. Thus, the prices investors are willing to pay for the
contracts will reveal the actual outcome.
In general, prediction markets work because they: (1) aggregate informa-
tion from all participants, each of whom has different information about
the issue in question; (2) provide incentives that encourage participants to
reveal their knowledge in their trades; (3) provide feedback to participants—
through market prices, traders learn about the beliefs of other traders and
are motivated to collect more information; and (4) allow traders to share their
knowledge anonymously, thereby, encouraging traders to signal information
through the market that they might not state publicly.
8.3.2 requirements for Prediction Markets
Prediction markets are commonly compared to surveys since both tools
ask questions of participants, and both are designed to aggregate informa-
tion. However, the number of potential applications for prediction markets
is smaller than for surveys. First, unlike surveys, a successful application of
a prediction market requires both uncertainty about an outcome and differ-
ing opinions about the outcome probabilities. If all participants have the same
information and the same opinions, then no one will trade, no prices will be
generated, and no information will be aggregated. Second, also unlike surveys,
prediction markets need to be based on an outcome that can be verified after
the fact. Consider, for example, the question of the reemergence of SARS. A
feasible prediction market contract might be based on a question such as “Will
at least one case of human-to-human transmission of SARS occur in Hong
Kong by January of 2012, as documented by the World Health Organization?”
A contract based on a question such as “Will there be several unreported cases
of SARS in Hong Kong by January of 2012?” would not be successful. The out-
come of the event is inherently unobservable, so ultimate liquidation of the
contracts would be in dispute and traders would have no reason to buy or sell
those contracts. Surveys, on the other hand, could ask either question.
There are several other important considerations for successful predic-
tion markets. First, successful markets require data from diverse sources.
Specifically, traders must have different information and opinions. Thus, for
a local seasonal influenza market, the goal would be to enlist a diverse group
of traders from a variety of healthcare professions and geographical locations.
For example, physicians, nurses, pharmacists, clinical microbiologists, teach-
ers, administrators, and public health practitioners all have some knowledge
about influenza activity in their communities. Second, an active trading
base is needed. We know from both theory and experience that increasing
