All three articles in my dissertation gather information from individuals, analyze it, and aggregate that information into forecasts of upcoming events. The motivation is to make forecasts more efficient (accurate and timely), more versatile (provide the most useful information for each stakeholder), and more economically efficient (equally or more efficient and versatile for less time and/or money). The first article looks at prediction markets and polls and concludes that prediction market-based forecasts are more efficient. The two methods, polling versus prediction markets, vary in four key ways: sample selection (a random sample of representative group versus a self-selected group), question type (intention versus expectation), aggregation method (average versus weighted by money, a proxy for confidence), and incentive (not incentive compatible versus incentive compatible). The second article isolates the second aspect of that list by comparing the efficiency of forecasts created by polling the respondents on their expectations versus intentions. Expectation-based forecasts are more efficient, even using non-random samples for the expectation. Asking the expectation question to one respondent is the equivalent of asking several respondents the intention question. Further, the expectation question helps adjust the sample to be more representative of the target group. The third article tests a new interactive web-based interface that captures both “best estimate” point-estimates and probability distributions from non-experts. In contrast to standard methods of directly asking respondents to state their confidence, using my method, which induces the respondents to reveal confidence, there is a sizable and statically significant positive relationship between confidence and the accuracy of individual-level expectations. This positive correlation between confidence and accuracy can be utilized to create confidence-weighted aggregated forecasts that are more efficient than the standard “consensus forecasts.”