Evidence map›Paper›PMID 40309464›Full record

ArticlePNAS nexus2025

Explanations for failures in designed and evolved systems.

Randolph M Nesse, Jay B Labov, Guru Madhavan

Abstract read
In one paragraph

Article in PNAS nexus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Randolph M NesseCenter for Evolution and Medicine, Arizona State University, Tempe, AZ 85287, USA.ORCID https://orcid.org/0000-0003-1768-0949
Jay B LabovNational Academy of Engineering, Washington, DC 20001, USA.ORCID https://orcid.org/0009-0002-9099-9032
Guru MadhavanNational Academy of Engineering, Washington, DC 20001, USA.ORCID https://orcid.org/0000-0003-1294-3455

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Engineers have long studied the origins of design features that make machines prone to failure, but biologists have only recently begun investigating why organisms have traits that make them susceptible to disease. This article compares explanations for vulnerability to failure in machines with explanations for traits that make bodies vulnerable to disease. Some global explanations are relevant for both: design deficiencies, corrupted plans, assembly variations, incorrect operating environment, and trade-offs. These similarities suggest that a common framework for failure analysis could be valuable. However, a closer look at each of the 10 global categories reveals fundamental differences: machines are built to match an ideal blueprint, while species have no perfect genome or form. Design trade-offs in machines involve balancing multiple factors such as performance, robustness, and costs, while biological trade-offs maximize only gene transmission, often at the expense of health and lifespan. Detailed consideration of these and other differences reveals how the metaphor of body as a designed machine fosters tacit creationism that misrepresents the nature of organically complex systems.

Indexed as

diseaseengineeringevolutionary medicinefailure analysistacit creationism

Identifiers

PMID40309464
PMCPMC12041745

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.