Abstract
Valuations from “prediction markets” reveal expectations about the likelihood of events. “Conditional prediction markets” reveal expectations conditional on other events occurring. For example, in 1996, the Iowa Electronic Markets (IEM) ran markets to predict the chances that different candidates would become the Republican Presidential nominee. Other concurrent IEM markets predicted the vote shares that each party would receive conditional on the Republican nominee chosen. Here, using these markets as examples, we show how such markets could be used for decision support. In this example, Republicans could have inferred that Dole was a weak candidate and that his nomination would result in a Clinton victory. This is only one example of the widespread potential for using specific decision support markets.
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Berg, J.E., Rietz, T.A. Prediction Markets as Decision Support Systems. Information Systems Frontiers 5, 79–93 (2003). https://doi.org/10.1023/A:1022002107255
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DOI: https://doi.org/10.1023/A:1022002107255