No 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. Five Key Questions about Cost Overrun Cost overrun in large capital investment projects can be hugely damaging, incurring outsize losses on investors and tax payers, compromising chief executives and their organizations, and even leading to bankruptcy (Flyvbjerg et al. 2009, Flyvbjerg and Budzier 2011). Accordingly, cost overrun receives substantial attention in both the professional literature and popular media. Yet it is not always clear how cost overrun is defined, why it happens, and how to best 1 All authors have co-authored or authored publications based on the data, theories, and methods commented on by Love and Ahiaga-Dagbui (2018).

No abstract

No abstract

Background: Aboriginal people in Australia experience the highest prevalence of diabetes in the country, an excess of preventable complications and early death. There is increasing evidence demonstrating the importance of healthcare systems for improvement of chronic illness care. The aims of this study were to assess the status of systems for chronic illness care in Aboriginal community health centres, and to explore whether more developed systems were associated with better quality of diabetes care.