In this study, we investigate the problem of aggregating crowd opinions for decision making. The Wisdom of Crowds (WoC) theory explains how crowd opinions should be aggregated in order to improve the performance of decision making. Crowd independence and a weighting mechanism are two important factors to crowd wisdom. However, most existing crowd opinion aggregation methods fail to build a diff...

Distributing labeling tasks among hundreds or thousands of annotators is an increasingly important method for annotating large datasets. We present a method for estimating the underlying value (e.g. the class) of each image from (noisy) annotations provided by multiple annotators. Our method is based on a model of the image formation and annotation process. Each image has different characterist...

The use of collective intelligence applications in educational settings has been reported in the literature for almost two decades. These collective intelligence applications aggregate individual knowledge via different synchronous and asynchronous mode of communications and Web 2.0 applications which support learning, communication and collaboration activities, and create “Wisdom of the Crowds...

We consider a general model of the non-cooperative provision of a public good. Under very weak assumptions there will always exist a unique Nash equilibrium in our model. A small redistribution of wealth among the contributing consumers will not change the equilibrium amount of the public good. However, larger redistributions of wealth will change the set of contributors and thereby change the ...

Given a collection of images of a static scene taken by many different people, we identify and segment interesting objects. To solve this problem, we use the distribution of images in the collection along with a new field-of-view cue, which leverages the observation that people tend to take photos that frame an object of interest within the field of view. Hence, image features that appear toget...

DAGGRE was part of a program that began in 2010 as a multi-team forecasting challenge sponsored by IARPA. To harness the “wisdom of crowds”, researchers built an online combinatorial prediction market for predicting events of interest to the U.S. intelligence community. Prediction markets have a long history (See Rhode & Stumpf, 2004.), and their value is described at length by Surowiecki (2004...

Sentiment analysis has recently gained popularity in the financial domain thanks to its capability to predict the stock market based on the wisdom of the crowds. Nevertheless, current sentiment indicators are still silos that cannot be combined to get better insight about the mood of different communities. In this article we propose a Linked Data approach for modelling sentiment and emotions ab...

Collective intelligence, or the wisdom of crowds, refers to a phenomenon by which, under the right conditions, groups of individuals can render highly accurate judgments. This phenomenon has long played an important role in economics, where understanding the behavior of groups is often essential to explaining economic outcomes. More recently, political scientists have shown that trends in publi...

We empirically examine the impact of herding on the accuracy of consensus earnings forecasts from a crowd-based platform (Estimize.com). We monitor user information set by tracking user viewing activities and running experiments. We find that the more public information a user views, the less weight she puts on her private information. While this improves the accuracy of individual forecasts, i...

The „wisdom of the crowds‟ effect describes the finding that combining responses across a number of individuals in a group leads to aggregate performance that is as good as or better than the performance of the best individuals in the group. Here, we look at the wisdom of the crowds effect in the Minimum Spanning Tree Problem (MSTP). The MSTP is an optimization problem where observers must conn...