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THEORY-1174: Industry can bias a scientific community toward a worse treatment without corrupting any individual, by selectively funding researchers whose methods already favour its product, provided researchers differ in method and both influence and the training of newcomers follow productivity; an independent funder that discounts industry-funded work counteracts this, and one that funds on productivity alone makes it worse

2 min read

Properties5
StatusProposed
Tagsphilosophy-of-science, epistemology, network-science
SourceLIT-744 — Experimentation by Industrial Selection
ExtendsTHEORY-146 — In Holman and Bruner's bandit model of honest Bayesian researchers whose beliefs funding cannot touch, who differ in method and whose influence and training of newcomers follow productivity, industry funding of those whose methods already favour its drug lowers, in the runs their figures report, the share of the community on the better treatment, and has no effect without methodological diversity; at the one setting reported, an independent funder that counts industry-funded trials toward its merit threshold lowers that share further, and one that excludes them raises it
Bears onQUESTION-315 — Does industrial selection, industry funding of researchers whose methods already favour its product, produce industry-favourable consensus in real research fields through those researchers' productivity and their training of newcomers, rather than through funded researchers changing their methods or results, and does an independent funder that discounts industry-funded work counteract it?

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Source

Holman & Bruner (2017), Experimentation by Industrial Selection, LIT-744, read in full in NOTE-574. What its model and figures showed is THEORY-146, which this entry extends.

The claim

The paper names a route by which industry biases science that is not corruption: industrial selection. Where researchers honestly differ in how they measure success, funding those whose measure already favours a product makes them more productive, more influential with their peers, and more likely to train the next generation, so the community drifts toward their method while no one’s judgment changes (NOTE-574, C1–C2). Without methodological diversity it does not happen (C3). An independent funder that rewards productivity counting industry-funded work makes it worse, and one that discounts that work counteracts it (C4). The authors offer the antiarrhythmic drug disaster as a real case of the mechanism, read from a secondary history (C6).

The evidence is one bandit model at a few parameter settings, with the funder comparison at one setting, and one historical case. The claim is about real research communities.

What would settle it

The promote_when, which is QUESTION-315: evidence from real fields that traces methods through funding and training lineages, and comparisons of funders whose merit assessments do or do not count industry-funded work. More simulations of the same model family, and case histories read through the model, cannot settle it.

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