Fang Fang () (College of Business Administration, California State University at San Marcos) Maxwell Stinchcombe (Department of Economics, the University of Texas at Austin) Andrew Whinston (Department of IROM, the University of Texas at Austin)
Abstract
We designed a platform with a betting mechanism for eliciting costly, dispersed information of different quality. Our objective is to elicit both dispersed information and the precision of the information so as to efficiently weight dispersed information to produce reliable forecasts. After deciding to incur the costs to acquire information, participants will report their forecasts and decide the size of their bets to indicate precision. Our mechanism selects those with relatively precise information at lower costs. We also discuss the implementation issues of our mechanism and the implication to fulfil the task of crowd sourcing.
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