This dissertation is a compilation of several studies that are united by their relevance to probabilistic judgment aggregation. In the face of complex and uncertain events, panels of judges are frequently consulted to provide probabilistic forecasts, and aggregation of such estimates in groups often yield better results than could have been made by any single judge. The 2008 presidential election presented a good opportunity to gather a large set of probabilistic forecasts to study the "Wisdom of the Crowd" effect. Regression models are developed to analyze how variables affect the forecasting accuracy and wishful thinking bias. A new algorithm, which efficiently enforces probabilistic coherence in the forecasts, while weighing judges by their credibility, is proposed and shown to improve forecasting accuracy over simple aggregation methods such as linear averaging. Comparisons with poll aggregators and prediction markets are made to further demonstrate the prediction power of the weighted coherent algorithm. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.]