Five things you should know about cost overrun

Bent Flyvbjerg, Atif Ansar, Alexander Budzier, Søren Buhl, Chantal Cantarelli, Massimo Garbuio, Carsten Glenting, Mette Skamris Holm, Dan Lovallo, Daniel Lunn, Eric Molin, Arne Rønnest, Allison Stewart, Bert van Wee

Research output: Contribution to journalArticleScientificpeer-review

39 Citations (Scopus)
10 Downloads (Pure)

Abstract

This paper gives an overview of good and bad practice for understanding and curbing cost overrun in large capital investment projects, with a critique of Love and Ahiaga-Dagbui (2018) as point of departure. Good practice entails: (a) Consistent definition and measurement of overrun; in contrast to mixing inconsistent baselines, price levels, etc. (b) Data collection that includes all valid and reliable data; as opposed to including idiosyncratically sampled data, data with removed outliers, non-valid data from consultancies, etc. (c) Recognition that cost overrun is systemically fat-tailed; in contrast to understanding overrun in terms of error and randomness. (d) Acknowledgment that the root cause of cost overrun is behavioral bias; in contrast to explanations in terms of scope changes, complexity, etc. (e) De-biasing cost estimates with reference class forecasting or similar methods based in behavioral science; as opposed to conventional methods of estimation, with their century-long track record of inaccuracy and systemic bias. Bad practice is characterized by violating at least one of these five points. Love and Ahiaga-Dagbui violate all five. In so doing, they produce an exceptionally useful and comprehensive catalog of the many pitfalls that exist, and must be avoided, for properly understanding and curbing cost overrun.

Original languageEnglish
Pages (from-to)174-190
Number of pages17
JournalTransportation Research Part A: Policy and Practice
Volume118
DOIs
Publication statusPublished - 2018

Keywords

  • Agency
  • Behavioral science
  • Cost forecasting
  • Cost overrun
  • Cost underestimation
  • De-biasing
  • Deception
  • Delusion
  • Moral hazard
  • Optimism bias
  • Reference class forecasting
  • Root causes of cost overrun
  • Strategic misrepresentation

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