Topic
Collective intelligence
About: Collective intelligence is a research topic. Over the lifetime, 3039 publications have been published within this topic receiving 57630 citations.
Papers published on a yearly basis
Papers
TL;DR: A multitype epistemology is begun which admits both the pre- and subconscious modes of human knowing and, reframing the concept of the cognizing individual, the collective knowledge of social groups, to help managers discover their place in the firm as a dynamic knowledge-based activity system.
Abstract: Knowledge is too problematic a concept to make the task of building a dynamic knowledge-based theory of the firm easy. We must also distinguish the theory from the resource-based and evolutionary views. The paper begins with a multitype epistemology which admits both the pre- and subconscious modes of human knowing and, reframing the concept of the cognizing individual, the collective knowledge of social groups. While both Nelson and Winter, and Nonaka and Takeuchi, successfully sketch theories of the dynamic interactions of these types of organizational knowledge, neither indicates how they are to be contained. Callon and Latour suggest knowledge itself is dynamic and contained within actor networks, so moving us from knowledge as a resource toward knowledge as a process. To simplify this approach, we revisit sociotechnical systems theory, adopt three heuristics from the social constructionist literature, and make a distinction between the systemic and component attributes of the actor network. The result is a very different mode of theorizing, less an objective statement about the nature of firms ‘out there’ than a tool to help managers discover their place in the firm as a dynamic knowledge-based activity system.
4,664 citations
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TL;DR: The background and state-of-the-art of big data are reviewed, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid, as well as related technologies.
Abstract: In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.
2,703 citations
National Center for Atmospheric Research1, Pacific Northwest National Laboratory2, University of Toronto3, Los Alamos National Laboratory4, Cornell University5, University of Wisconsin-Madison6, Lawrence Livermore National Laboratory7, Lawrence Berkeley National Laboratory8, Oak Ridge National Laboratory9, University of Calgary10
TL;DR: The Community Earth System Model (CESM) as discussed by the authors is a community tool used to investigate a diverse set of Earth system interactions across multiple time and space scales, including biogeochemical cycles, a variety of atmospheric chemistry options, the Greenland Ice Sheet, and an atmosphere that extends to the lower thermosphere.
Abstract: The Community Earth System Model (CESM) is a flexible and extensible community tool used to investigate a diverse set of Earth system interactions across multiple time and space scales. This global coupled model significantly extends its predecessor, the Community Climate System Model, by incorporating new Earth system simulation capabilities. These comprise the ability to simulate biogeochemical cycles, including those of carbon and nitrogen, a variety of atmospheric chemistry options, the Greenland Ice Sheet, and an atmosphere that extends to the lower thermosphere. These and other new model capabilities are enabling investigations into a wide range of pressing scientific questions, providing new foresight into possible future climates and increasing our collective knowledge about the behavior and interactions of the Earth system. Simulations with numerous configurations of the CESM have been provided to phase 5 of the Coupled Model Intercomparison Project (CMIP5) and are being analyzed by the broad com...
2,639 citations
TL;DR: A psychometric methodology for quantifying a factor termed “collective intelligence” (c), which reflects how well groups perform on a similarly diverse set of group problem-solving tasks, and finds converging evidence of a general collective intelligence factor that explains a group’s performance on a wide variety of tasks.
Abstract: Psychologists have repeatedly shown that a single statistical factor—often called “general intelligence”— emerges from the correlations among people's performance on a wide variety of cognitive tasks. But no one has systematically examined whether a similar kind of “collective intelligence” exists for groups of people. In two studies with 699 individuals, working in groups of two to five, we find converging evidence of a general collective intelligence factor that explains a group's performance on a wide variety of tasks. This “c factor” is not strongly correlated with the average or maximum individual intelligence of group members but is correlated with the average social sensitivity of group members, the equality in distribution of conversational turn-taking, and the proportion of females in the group. As research, management, and many other kinds of tasks are increasingly accomplished by groups—both those working face-to-face and "virtually"(1‐3)—it is becoming even more important to understand the determinants of group
2,566 citations
TL;DR: An introduction to crowdsourcing is provided, both its theoretical grounding and exemplar cases, taking care to distinguish crowdsourcing from open source production.
Abstract: Crowdsourcing is an online, distributed problem-solving and production model that has emerged in recent years. Notable examples of the model include Threadless, iStockphoto, InnoCentive, the Goldcorp Challenge, and user-generated advertising contests. This article provides an introduction to crowdsourcing, both its theoretical grounding and exemplar cases, taking care to distinguish crowdsourcing from open source production. This article also explores the possibilities for the model, its potential to exploit a crowd of innovators, and its potential for use beyond forprofit sectors. Finally, this article proposes an agenda for research into crowdsourcing.
2,270 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2025 | 69 |
| 2024 | 104 |
| 2023 | 111 |
| 2022 | 123 |
| 2021 | 166 |
| 2020 | 190 |