The Wisdom of Crowds

Summary

The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, published in 2004, is a book written by James Surowiecki about the aggregation of information in groups, resulting in decisions that, he argues, are often better than could have been made by any single member of the group. The book presents numerous case studies and anecdotes to illustrate its argument, and touches on several fields, primarily economics and psychology. The opening anecdote relates Francis Galton's surprise that the crowd at a county fair accurately guessed the weight of an ox when their individual guesses were averaged (the average was closer to the ox's true butchered weight than the estimates of most crowd members). The book relates to diverse collections of independently deciding individuals, rather than crowd psychology as traditionally understood. Its central thesis, that a diverse collection of independently deciding individua

Official source

https://en.wikipedia.org/wiki/The_Wisdom_of_Crowds

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (6)

Related publications

Related people (2)

Related people

Related units

Related concepts (3)

Related concepts

Related courses (1)

This is a collection of lectures on "structured innovation systems," codified approaches to stimulating and managing the process of innovation. Some of the systems to be covered may be Design Thinking, Open Innovation, Crowdsourcing, Lean Innovation, and other structured innovation systems.

Related courses

Related lectures (2)

Related lectures

Crowdsourcing has been widely established as a means to enable human computation at large-scale, in particular for tasks that require manual labelling of large sets of data items. Answers obtained from heterogeneous crowd workers are aggregated to obtain a robust result. However, existing methods for answer aggregation are designed for \emph{discrete} tasks, where answers are given as a single label per item. In this paper, we consider \emph{partial-agreement} tasks that are common in many applications such as image tagging and document annotation, where items are assigned sets of labels. Common approaches for the aggregation of partial-agreement answers either (i) reduce the problem to several instances of an aggregation problem for discrete tasks or (ii) consider each label independently. Going beyond the state-of-the-art, we propose a novel Bayesian nonparametric model to aggregate the partial-agreement answers in a generic way. This model enables us to compute the consensus of partially-sound and partially-complete worker answers, while taking into account mutual relationships in labels and different answer sets. We also show how this model is instantiated for incremental learning, incorporating new answers from crowd workers as they arrive. An evaluation of our method using real-world datasets reveals that it consistently outperforms the state-of-the-art in terms of precision, recall, and robustness against faulty workers and data sparsity.

2018

Crowdsourcing has been widely established as a means to enable human computation at large-scale, in particular for tasks that require manual labelling of large sets of data items. Answers obtained from heterogeneous crowd workers are aggregated to obtain a robust result. However, existing methods for answer aggregation are designed for \emph{discrete} tasks, where answers are given as a single label per item. In this paper, we consider \emph{partial-agreement} tasks that are common in many applications such as image tagging and document annotation, where items are assigned sets of labels. Going beyond the state-of-the-art, we propose a novel Bayesian nonparametric model to aggregate the partial-agreement answers in a generic way. This model enables us to compute the consensus of partially-sound and partially-complete worker answers, while taking into account mutual relations in labels and different answer sets. An evaluation of our method using real-world datasets reveals that it consistently outperforms the state-of-the-art in terms of precision, recall, and scalability.

IEEE2018

DISTINGUISHING “CROWDED” ORGANIZATIONS FROM GROUPS AND COMMUNITIES: IS THREE A CROWD?

Christopher Tucci, Gianluigi Viscusi

In conventional wisdom on crowdsourcing, the number of people define the crowd and maximizing this number is often assumed to be the goal of any crowdsourcing exercise. However, we propose that there are structural characteristics of the crowd that might be more important than the sheer number of participants. These characteristics include (1) growth rate and its attractiveness to the members, (2) the equality among members, (3) the density within provisional boundaries, (4) the goal orientation of the crowd, and (5) the "seriality" of the interactions between members of the crowd. We then propose a typology that may allow managers to position their companies’ initiatives among four strategic types: crowd crystals, online communities, closed crowd, and open crowd driven innovation. We show that incumbent companies may prefer a closed and controlled access to the crowd, limiting the potential for gaining results and insights from fully open crowd-driven innovation initiatives. Consequently, we argue that the effects on industries and organizations by open crowds are still to be explored, possibly via the mechanisms of entrepreneurs exploiting open crowds as new entrants, but also for the configuration of industries such as, e.g., finance, pharmaceuticals, or even the public sector where the value created usually comes from interpretation issues and exploratory problem solving.

SSRN2015