ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2021
When we can trust computers (and when we can't).
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- Uncertainty Quantification forChemical reviews · 2026Review
- Modeling Heterogeneous Catalysis Using Quantum Computers: An Academic and Industry Perspective.Journal of chemical information and modeling · 2025Review
- Validation of a computational model of bone conduction sound reception in mysticetes.PloS one · 2025Article
- Artificial Intelligence Must Be Made More Scientific.Journal of chemical information and modeling · 2024Article
- Multiscale co-simulation design pattern for neuroscience applications.Frontiers in neuroinformatics · 2024Article
- Developing image analysis methods for digital pathology.The Journal of pathology · 2022Review
- When Do We Need Massive Computations to Perform Detailed COVID-19 Simulations?Advanced theory and simulations · 2022Article
- A matter of trust: Learning lessons about causality will make qAOPs credible.Computational toxicology (Amsterdam, Netherlands) · 2022Review
- Pandemic drugs at pandemic speed: infrastructure for accelerating COVID-19 drug discovery with hybrid machine learning- and physics-based simulations on high-performance computers.Interface focus · 2021Article
- Uncertainty quantification in classical molecular dynamics.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Using a Digital Twin of an Electrical Stimulation Device to Monitor and Control the Electrical Stimulation of CellsFrontiers in bioengineering and biotechnology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
With the relentless rise of computer power, there is a widespread expectation that computers can solve the most pressing problems of science, and even more besides. We explore the limits of computational modelling and conclude that, in the domains of science and engineering which are relatively simple and firmly grounded in theory, these methods are indeed powerful. Even so, the availability of code, data and documentation, along with a range of techniques for validation, verification and uncertainty quantification, are essential for building trust in computer-generated findings. When it comes to complex systems in domains of science that are less firmly grounded in theory, notably biology and medicine, to say nothing of the social sciences and humanities, computers can create the illusion of objectivity, not least because the rise of big data and machine-learning pose new challenges to reproducibility, while lacking true explanatory power. We also discuss important aspects of the natural world which cannot be solved by digital means. In the long term, renewed emphasis on analogue methods will be necessary to temper the excessive faith currently placed in digital computation. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.