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Cited by 494

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

From Nobel Prize to Project Management: Getting Risks Right

Open access
Flyvbjerg, Bent·2006·Project Management Journal

Abstract

A major source of risk in project management is inaccurate forecasts of project costs, demand, and other impacts. The paper presents a promising new approach to mitigating such risk based on theories of decision-making under uncertainty, which won the 2002 Nobel Prize in economics. First, the paper documents inaccuracy and risk in project management. Second, it explains inaccuracy in terms of optimism bias and strategic misrepresentation. Third, the theoretical basis is presented for a promising new method called “reference class forecasting,” which achieves accuracy by basing forecasts on actual performance in a reference class of comparable projects and thereby bypassing both optimism bias and strategic misrepresentation. Fourth, the paper presents the first instance of practical reference class forecasting, which concerns cost forecasts for large transportation infrastructure projects. Finally, potentials for and barriers to reference class forecasting are assessed.

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Metadata

Authors
Flyvbjerg, B.
Venue
Project Management Journal
Published
2006-08
Volume
37
Issue
3
Pages
5-15
ISSN
  • 1938-9507 (ISSN-L)
  • 8756-9728
OpenAlex
W2139984108
Cited by
494
References
1
FWCI
7.67
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: From Nobel Prize to Project Management: Getting Risks Right. https://app.cochat.ai/papers/10.1177/875697280603700302
The paper
Flyvbjerg, B. (2006). From Nobel Prize to Project Management: Getting Risks Right. Project Management Journal. https://doi.org/10.1177/875697280603700302

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.