An Experiment on Prediction Markets in Science
Citation: Almenberg J, Kittlitz K, Pfeiffer T (
An Experiment on Prediction Markets in Science
Johan Almenberg 0
Ken Kittlitz 0
Thomas Pfeiffer 0
Joel M. Schnur, George Mason University, United States of America
0 1 Stockholm School of Economics, Stockholm, Sweden , 2 Consensus Point, Nashville , Tennessee, United States of America, 3 Program for Evolutionary Dynamics, Harvard University , Cambridge, Massachusetts , United States of America
Prediction markets are powerful forecasting tools. They have the potential to aggregate private information, to generate and disseminate a consensus among the market participants, and to provide incentives for information acquisition. These market functionalities can be very valuable for scientific research. Here, we report an experiment that examines the compatibility of prediction markets with the current practice of scientific publication. We investigated three settings. In the first setting, different pieces of information were disclosed to the public during the experiment. In the second setting, participants received private information. In the third setting, each piece of information was private at first, but was subsequently disclosed to the public. An automated, subsidizing market maker provided additional incentives for trading and mitigated liquidity problems. We find that the third setting combines the advantages of the first and second settings. Market performance was as good as in the setting with public information, and better than in the setting with private information. In contrast to the first setting, participants could benefit from information advantages. Thus the publication of information does not detract from the functionality of prediction markets. We conclude that for integrating prediction markets into the practice of scientific research it is of advantage to use subsidizing market makers, and to keep markets aligned with current publication practice.
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A prediction market is a marketplace for contracts whose payoffs
depend on the outcome of a future event. In a well-functioning
market, contract prices can be interpreted as forecasts about the
outcome of the event, derived from the beliefs of all market
participants. Contract types can be designed to elicit various aspects
of the probability distribution associated with an event [1]. One
popular contract type, for example, pays $1 if a specific outcome is
realized, and $0 otherwise. The price of such a contract can be
interpreted as the predicted probability of that outcome occurring.
In practice, prediction markets facilitate trading by generating
standardized contract rules, and are typically organized so that the
market forecast is salient and easily interpreted.
To the extent that market prices can be interpreted as collective
forecast, prediction markets disseminate, or broadcast,
information. Although the mapping from individual beliefs to market
prices is potentially complicated because individuals may differ in
their risk aversion and in the availability of funds for betting [2,3],
in practice prediction markets have been found to generate good
predictions for events ranging from product sales and horse races
to presidential elections [4,5]. By making collective forecasts
available to a broader public, the dissemination property of
prediction markets has the potential to generate social utility.
Prediction markets can also facilitate more complex information
processing tasks. If different market participants have different,
complementary pieces of private information, prediction markets
have the potential to aggregate this information. Aggregation of
dispersed information means that the market prediction is close to
the forecast of a hypothetical trader in possession of all the
information. A market that aggregates all available information is
said to display strong efficiency [6,7]. The information aggregation
property is illustrated by an example from Plott (1988) [8]: Suppose
an event has three mutually exclusive outcomes, X, Y, and Z. The
payoff of a contract depends on which outcome is realized. Half of
the traders in the market are informed that the outcome will not be
X, and the other half is informed that the outcome will not be Y. A
market that is able to aggregate this information will forecast that the
outcome will be Z. This prediction differs from simply averaging the
traders initial beliefs. Information aggregation requires that traders
learn from the market. Laboratory experiments suggest markets can
accomplish information aggregation tasks reasonably well, although
the details of the process are not fully understood [8,9].
Because making reliable predictions is a key objective in science,
prediction markets offer potential benefits to scientific research [10,11].
Dissemination and aggregation properties of markets might be valuable
because knowledge in scientific research is often highly decentralized.
When settling a research problem, this may lead to diverging op (...truncated)