An Experiment on Prediction Markets in Science

PLOS ONE, Dec 2009

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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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. - 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)


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Johan Almenberg, Ken Kittlitz, Thomas Pfeiffer. An Experiment on Prediction Markets in Science, PLOS ONE, 2009, Volume 4, Issue 12, DOI: 10.1371/journal.pone.0008500