Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/288773 
Year of Publication: 
2020
Citation: 
[Journal:] Business & Information Systems Engineering [ISSN:] 1867-0202 [Volume:] 63 [Issue:] 4 [Publisher:] Springer Fachmedien Wiesbaden [Place:] Wiesbaden [Year:] 2020 [Pages:] 329-348
Publisher: 
Springer Fachmedien Wiesbaden, Wiesbaden
Abstract: 
Research has shown that aggregation of independent expert judgments significantly improves the quality of forecasts as compared to individual expert forecasts. This “wisdom of crowds” (WOC) has sparked substantial interest. However, previous studies on strengths and weaknesses of aggregation algorithms have been restricted by limited empirical data and analytical complexity. Based on a comprehensive analysis of existing knowledge on WOC and aggregation algorithms, this paper describes the design and implementation of a static stochastic simulation model to emulate WOC scenarios with a wide range of parameters. The model has been thoroughly evaluated: the assumptions are validated against propositions derived from literature, and the model has a computational representation. The applicability of the model is demonstrated by investigating aggregation algorithm behavior on a detailed level, by assessing aggregation algorithm performance, and by exploring previously undiscovered suppositions on WOC. The simulation model helps expand the understanding of WOC, where previous research was restricted. Additionally, it gives directions for developing aggregation algorithms and contributes to a general understanding of the WOC phenomenon.
Subjects: 
Simulation
Forecasting
Expert judgment
Expert aggregation
Wisdom of crowds
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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