Guanchun Wang's research while affiliated with Princeton University and other places
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Publications (5)
We analyze individual probabilistic predictions of state outcomes in the 2008 U.S. presidential election. Employing an original survey of more than 19,000 respondents, we find that partisans gave higher probabilities to their favored candidates, but this bias was reduced by education, numerical sophistication, and the level of Obama support in thei...
Probability forecasts in complex environments can benefit from combining the estimates of large groups of forecasters (“judges”). But aggregating multiple opinions raises several challenges. First, human judges are notoriously incoherent when their forecasts involve logically complex events. Second, individual judges may have specialized knowledge,...
The Coherent Approximation Principle (CAP) is a method for aggregating forecasts of probability from a group of judges by enforcing coherence with minimal adjustment. This paper explores two methods to further improve the forecasting accuracy within the CAP framework and proposes practical algorithms that implement them. These methods allow flexibi...
In this paper, practical algorithms for solving the probabilistic judgment aggregation problem are given. First, the scalable Coherent Approximation Principle (CAP) algorithm proposed by Predd, et al., and its computational savings gained through Successive Orthogonal Projection are explained. Implications of de Finetti's theorem in this situation...
This paper analyzes individual probabilistic predictions of state outcomes in the 2008 U.S. presidential election. Employing an original survey of more than 19,000 respondents, ours is the first study of electoral forecasting to involve multiple sub-national predictions and to incorporate the influence of respondents' home states. We relate a range...
Citations
... (Graefe, 2014;Lewis-Beck & Tien, 1999;Miller et al., 2012;Rothschild & Wolfers, 2013), and general elections in the United Kingdom (UK) (Lewis- Beck & Stegmaier, 2011;Murr, 2011Murr, , 2016. These studies suggest the existence of a "wisdom of the crowd," meaning that citizens possess a type of knowledge of everyday politics that is embedded in social networks and can hardly be captured by traditional polling methods. ...
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... However, when the number of judges and events is large, the reliability of the original estimates can vary considerably among judges and the group forecasts might demonstrate systematic bias. In [10], we addressed this issue by allowing the weighting of judges' forecasts by their credibility (i.e., assigning greater weight to potentially more credible judges). Here, we explore further methods that can improve the coherent aggregated forecasts. ...
... Predd et al. (2008) showed that coherence-weighted aggregation improved group forecast accuracy on sports and economic forecasts. Studies have since generalized the method to US presidential election forecasts (Wang et al., 2011), general-knowledge and forecasting questions (Fan et al., 2019;Karvetski et al., 2013), and Bayesian judgment tasks (Karvetski et al., 2020;Mandel et al., 2018). The findings ...
... Chen et al., 2003;Forsythe et al., 1992Forsythe et al., , 1999, markets appear to provide better estimates than any one individual can, especially in complex combinatorial prediction markets (Y. Chen & Pennock, 2010) where individuals make systematic errors (Wang et al., 2011). ...
