Collective Intelligence Unit

The Collective Intelligence Unit (CIU) studies collective intelligence (CI) in international private and public organizations. The aim is to examine how collective intelligence and “wisdom of crowds” can benefit adaptive decision-making processes for the purpose of increasing the competitive advantage of organizations.


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Our approach to the study of collective intelligence

Collective intelligence strongly contributes to the shift of knowledge and power from the individual to the collective. The study of collective intelligence is essentially the study of the synergies among: 1) web-based information 2) software platforms for information aggregation and hardware, and 3) crowds (both experts and novices) for decision-making. 

The studies of the unit explore and exploit different software aggregation mechanisms to capture collective intelligence for adaptive decision-making, such as crowdsourcing for predictions, crowdsourcing for innovation, ideation and creativity, and crowdsourcing for skills. Additionally, the unit integrates Big Data and AI in their studies using Hadoop and MapReduce platforms. 


CIU cooperates with private and public organizations on co-financed research projects to explore and exploit collective intelligence for sustainable competitiveness. Research agendas are: 

To examine how employees and other crowds’ insights can favor decision-making processes for international management. 

To study how leadership, team characteristics and organizational structures hinder and create opportunities for collective intelligence behavior and practices.

To understand the value of crowd predictions of performance-based measures (KPIs), product and service development, strategic scenarios, communication strategies, climate change, corruption and terror, accidents, incidents and fuzzy events.

To understand the value of crowdsourcing for innovation and creativity.

To examine the integration of firms’ existing forecasting models integrating crowd predictions as adjustments mechanisms to increase predictive power of the forecasting model. 

To explore the integration of crowd predictions and Big Data to develop new prediction models.

To explore predictability, knowledge and diversity of crowds.

To apply human computation and laboratory experiments to study predictive capabilities of individuals.


You are welcome to contact us for an introductory meeting on how we can collaborate with you on Collective Intelligence research projects.

Please contact Carina Antonia Hallin.



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cCarina Antonia Hallin
Head of Unit
Assistant Professor, Ph.D.

Mobile: +45 27 84 33 10


Daiana Fobian Nielsen
Project Leader
The Industry Foundation Project
lLuca Brügger
Project Leader
The Financial Sector Project 
cCecilie Olesen
Research Assistant
The Industry Foundation Project
flFrederik Kjøller Larsen
Student Researcher
The Financial Sector Project 

bBrian Christopher Poll
Student Researcher
The Industry Foundation Project
julian jensen portraitJulian Johannes Umbhau Jensen
Student Researcher
The Financial Sector Project 
StinaStina Slott Rasmussen
Student Researcher
The Industry Foundation Project

Kristine Pontoppidankristine-pontoppidan-100x100px.jpg
External Lecturer in Crowdsourcing/crowdfunding
Project Leader SME Crowdsourcing Lab

eElena Ercolani 
Student Researcher
The Industry Foundation Project


Anne Sofie Lind
Research Affiliated
The Copenhagen Airport/Mærsk Training Project 




The page was last edited by: Department of International Economics and Management // 01/18/2018