SciCast is a research project run by George Mason University to forecast the outcomes of key issues in science and technology. SciCast is based on the idea that the collective wisdom of an informed and diverse group is often more accurate at forecasting the outcome of events than that of one individual expert.
SciCast topics include agriculture, biology and medicine, chemistry, computational sciences, energy, engineered technologies, global change, information systems, mathematics, physics, science and technology business, social sciences, space sciences and transportation’¦
How SciCast works (Credit: George Mason University)
SciCast is a community-driven initiative. Participants write their own questions in a publishing tool called Spark and participate in a process to get those questions published. SciCast participants can make forecasts at any time about any published question. Unlike a survey, participants can change their forecast at any time to account for new information. In this way, SciCast is a real-time indicator of what our participants think is going to happen.
Technically, SciCast is a (combinatorial) prediction market. Prediction markets can be used to forecast the outcome of a wide variety of topics and are used today in large corporations and governments to understand the likelihood of meeting key performance metrics, quantify risks that may jeopardize operations, and better understand industry trends.
After the answer to a question is known and made public, participants who answered correctly will be be rewarded and move up on our leaderboard. The more correct forecasts a participant makes, the more influence they’ ll have in other forecasts. Power-users may choose to see the market-style interface and manage their own points, at their own risk. Others may prefer the survey-style interface, where they update their beliefs and let the system manage their points by making edits or trades for them. Participants can mix modes freely.
Unlike other forecasting sites, SciCast can create relationships between forecast questions that may have an influence on each other. For example, it may ask a question about the volume of sea ice in the Arctic in a given month. It may also ask a question about average temperature in this same locale or other influencing metrics. SciCast will learn from its participants how strong of a relationship these questions have to each other and will adjust their outcomes accordingly. So, if participants raise the forecasted average Arctic temperature, SciCast will instantly adjust forecasts for the corresponding level of Arctic sea ice, according to the correlations made by previous forecasters.
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