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A Computational Model of Crowds for Collective Intelligence
Beedie School of Business Simon Fraser University, Vancouver.ORCID iD: 0000-0002-0568-7767
Modelling of Complex Social Systems Program - Simon Fraser University.
Computing Science - Simon Fraser University.
2014 (English)Report (Refereed)
Abstract [en]

Can Crowds serve as useful allies in policy design? How do non-expert Crowds perform relative to experts in the assessment of policy measures? Does the geographic location of non-expert Crowds, with relevance to the policy context, alter the performance of non-experts Crowds in the assessment of policy measures? In this work, we investigate these questions by undertaking experiments designed to replicate expert policy assessments with non-expert Crowds recruited from Virtual Labor Markets. We use a set of ninety-six climate change adaptation policy measures previously evaluated by experts in the Netherlands as our control condition to conduct experiments using two discrete sets of non-expert Crowds recruited from Virtual Labor Markets. We vary the composition of our non-expert Crowds along two conditions: participants recruited from a geographical location directly relevant to the policy context and participants recruited at-large. We discuss our research methods in detail and provide the findings of our experiments.

Place, publisher, year, edition, pages
MIT Center for Collective Intelligence , 2014. , p. 4
National Category
Business Administration
Research subject
Industrial Marketing
Identifiers
URN: urn:nbn:se:ltu:diva-23663Local ID: 7e4c22f6-04ee-46d0-9cd1-bdef9a78b64bOAI: oai:DiVA.org:ltu-23663DiVA, id: diva2:996712
Note
Upprättat; 2014; Bibliografisk uppgift: Available at SSRN; 20151207 (andbra)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2025-10-21Bibliographically approved

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Prpic, John

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