Five things you should know about cost overrun
Bent Flyvbjerg (University of Oxford)
Atif Ansar (University of Oxford)
Alexander Budzier (University of Oxford)
Søren Buhl (Aalborg University)
Chantal Cantarelli (University of Sheffield)
Massimo Garbuio (University of Sydney)
Carsten Glenting (Viegand Maagøe A/S)
Mette Skamris Holm (Aalborg Municipality)
Dan Lovallo (University of Sydney)
Daniel Lunn (University of Oxford)
Eric Molin (TU Delft - Technology, Policy and Management)
Arne Rønnest (Esrum Kloster and Møllegård)
Allison Stewart (Infrastructure Victoria)
Bert van Wee (TU Delft - Technology, Policy and Management)
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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.