Double Whammy - How ICT Projects are Fooled by Randomness and Screwed by Political Intent
Alexander Budzier, Bent Flyvbjerg

TL;DR
This paper analyzes how ICT project forecasts are distorted by optimism bias, delusion, deception, and Black Swan events, revealing the statistical distribution changes and the influence of political motives and randomness on project outcomes.
Contribution
It demonstrates the impact of psychological biases and randomness on ICT project performance, introducing a distribution-based framework to distinguish between political influence and outlier effects.
Findings
CDF shifts at two critical points indicating different effects
Black Swan projects contribute significant uncertainty
Performance deviations are linked to biases and randomness
Abstract
The cost-benefit analysis formulates the holy trinity of objectives of project management - cost, schedule, and benefits. As our previous research has shown, ICT projects deviate from their initial cost estimate by more than 10% in 8 out of 10 cases. Academic research has argued that Optimism Bias and Black Swan Blindness cause forecasts to fall short of actual costs. Firstly, optimism bias has been linked to effects of deception and delusion, which is caused by taking the inside-view and ignoring distributional information when making decisions. Secondly, we argued before that Black Swan Blindness makes decision-makers ignore outlying events even if decisions and judgements are based on the outside view. Using a sample of 1,471 ICT projects with a total value of USD 241 billion - we answer the question: Can we show the different effects of Normal Performance, Delusion, and Deception?…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Systems and Decision Making · Complex Systems and Time Series Analysis · Innovation Diffusion and Forecasting
