PROJECT 2019-2023

Open Government:  A study of Collective Intelligence and Artificial intelligence for Policy-Making

The project is funded by Slagelse Municipality and headed by the Collective Intelligence Research Group (CIG) at the IT University of Copenhagen. The total project sum amounts to DKK 2.5 million. 

Aims of the project:

The research purpose of the project is to explore the benefits and limitations of the usage of collective intelligence and AI for policy-making to evaluate and compare with other international participatory democracy platforms.

Contact team:

Carina Antonia Hallin: caah@itu.dk

Nino Javakhishvili-Larsen: njav@itu.dk

Work in progress: 

Aminpour, P., Gray, S., Paulson, L., Hallin, C.A. et al. Becoming Intelligent about Collective Intelligence for Public Policy.

Boucher, S., Hallin, C.A., and Paulson, L. Smarter Together: 50 Case Studies on Collective Intelligence for Public Policy. 

PROJECT 2017-2020

Crowd Predictions From The Frontline

The project was funded by the Danish Industry Foundation and headed by Dr. Carina Antonia Hallin and Project Manager Brian Christopher Pollstergaard. The total project sum amounts to DKK 3.2 million. 

Aims of the project:

Applying new collective intelligence technologies, this project theoretically and empirically studies crowd predictions as a decision tool. This is done through firm, specifically designed prediction software that collects updated information from the frontline in Danish multinational corporations, enabling management to create a better basis for proactive decisions in global markets.

You can find all final project reports here. 

Collaborators:

The Danish Industry Foundation. 

Lego Group A/S, Radiometer A/S, Grønbech & Sønner ApS, Arburg ApS, and Lik ApS. 

Work in progress:

Hallin, C.A. On the Outlook for Weak Signals: Harnessing Collective Intelligence for Strategic Issue Management. 

Hallin, C.A. Evidence of Collective Intelligence in Organizations.

Hallin, C.A. A Typology of “The Predictive Wisdom” of Stakeholders for Decision-Making. 

Publications:

Xin Li; Torben Juul Andersen; Carina Antonia Hallin / A Zhong-Yong Perspective on Balancing the Top-down and Bottom-up Processes in Strategy-making In: Cross Cultural and Strategic Management, 17.6.2019

Torben Juul Andersen; Carina Antonia Hallin; Kjeld Fredens /Et netværk af hjerner: Tænk med dine medarbejdere og led med succes. København: Gyldendal 2018, 208 p.

Carina Antonia Hallin; Torben Juul Andersen; Sigbjørn Tveterås / Harnessing the Frontline Employee Sensing of Capabilities for Decision Support In: Decision Support Systems, Vol. 97, 5.2017, p. 104-112

Torben Juul Andersen; Carina Antonia Hallin / Global Strategic Responsiveness: Exploiting Frontline Information in the Adaptive Multinational Enterprise. Abingdon: Routledge 2017, 168 p. (Strategy Matters, No. 1)

Carina Antonia Hallin / Aggregating Predictions of Operational Uncertainties from the Frontline: A New Proactive Risk Management Practice. In: The Routledge Companion to Strategic Risk Management. ed. /Torben Juul Andersen. Abingdon: Routledge 2016, p. 487-500 (Routledge Companions in Business, Management and Accounting)

Torben Juul Andersen; Carina Antonia Hallin / The Adaptive Organization and Fast-slow Systems In: Oxford Research Encyclopedias: Business and Management: A Community of Experts. ed. /Ramon J. Aldag. New York: Oxford University Press 2016

PROJECT 2016-2019

A Study of Household Credits, Debts and Savings and Regional GDP:

Advancing predictability of Fluctuations Integrating Big Data and Crowdsourcing of Predictions in the Financial Sector.

The project was funded by the research program Nordic Finance and the Good Society, Center for Corporate Governance, Copenhagen Business School. The project was headed by Dr. Carina Antonia Hallin and computer scientist Dr. Oded Koren, Shenkar College of Engineering and Design, Israel.

Aims of the project:

To explore who the smart crowds for the economy are and study collective intelligence as a means to forecast the use of credits, debts, and savings at the regional level in Denmark. Moreover, the study investigates the links between forecasts of such financial variables and the Regional Gross Domestic Product.

Collaborators:

Dr. Oded Koren is a full faculty member in the Department of Industrial Engineering and Management at Shenkar College of Engineering, Design and Art in Israel. His research interests are in the areas of open-source development domains, Big Data, AI-related aspects, and mobile applications.

Dr. Nir Perel is a senior faculty member in the Department of Industrial Engineering and Management at Shenkar College of Engineering, Design and Art in Israel. His research interests include operations research modeling, queuing theory, and statistical analysis. Lately, his interests are focused on AI and open-source development.

Sigbjørn Tveterås is a Professor in Applied Economics in the Department of Industrial Economics, Risk and Planning at the University of Stavanger, Norway. His main research field is Applied Microeconometrics. Most of his published research articles focus on applied issues related to modelling, prediction models, demand, pricing, and market structures.

Work in progress: 

Hallin, C.A., Tveteraas, S., Koren, O., and Perel, N. Identify Smart Crowds for the Economy.

Koren, O., Koren, M., and Hallin, C.A. Classifier Clustering Procedure in Big Data Platforms. 

Publications:

Koren, O., Hallin, C.A., Perel, N., & Bendet, D. (2019). Decision-Making Enhancement in a Big Data Environment: Application of the K-Means Algorithm to Mixed Data, Journal of Artificial Intelligence and Soft Computing Research, 9(4), 293-302. doi: https://doi.org/10.2478/jaiscr-2019-0010

Hallin, C. A., Jensen, J. J. U., Koren, O., Perel, N., & Tveterås, S. (2019). Testing Smart Crowds for the Economy. In A. Monroy-Hernández, & M. Valentine (Eds.), Proceedings of the ACM Collective Intelligence 2019. New York: Association for Computing Machinery.

Koren, O., Hallin, C.A., Perel, N., & Bendet, D. (2019). Enhancement of the K-Means Algorithm for Mixed Data in Big Data Platforms: Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1. doi: https://doi.org/10.1007/978-3-030-01054-6_71

PROJECT 2016-2017

A Study of Collective Intelligence Behavior and Practices by the Largest Companies in Denmark

The project was headed by Dr. Antonia Hallin and Project Manager Julian Umbhau Jensen.

Aims of the project:

To investigate the state of knowledge and applications of collective intelligence and crowdsourcing methods amongst decision-makers in Denmark’s largest companies across different industries. 

Collaborators:

Flemming Binderup Gammelgaard is a Ph.D. candidate in crowdsourcing and fundraising. He is co-founder of Danish Crowdsourcing and project manager for the platform ‘VIA Connect’. 

Work in progress:

Gammelgaard, F.B. and Hallin, C.A. Barriers to Crowdsourcing in Large Organizations.

Hallin, C.A. and Gammelgaard, F.B. The adoption of Collective Intelligence Behavior and Practices in Strategy-Making. 

Hallin, C.A. Collective Intelligence: An Emergent Discipline in Management.

Publications:

Flemming Binderup Gammelgaard; Carina Antonia Hallin/ Changing the Innovation Game: Crowdsourcing in Incumbent Firms. Paper presented at the 6th Collective Intelligence, 2018.

Carina Antonia Hallin/ The State of Collective Intelligence Behavior and Practices of Danish Corporations Paper presented at the 5th Collective Intelligence Conference, 2017.

Carina Antonia Hallin; Julian J. U. Jensen / Kollektiv intelligens: Adfærd og praksis i Danmarks største virksomheder. Frederiksberg: Department of International Economics and Management, Copenhagen Business School 2017, 76 p. (Forskningsrapport CBS-INT, No. 1/2017).