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LIT-1341: How to Beat Science and Influence People: Policymakers and Propaganda in Epistemic Networks

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StatusActive — read 2026-10-21 (NOTE-1038) in full, from the authors' arXiv manuscript (1801.01239v2); worth reading for its model of the tobacco strategy, in which selective sharing of genuine results and honest research published selectively mislead a Bayesian audience while the scientists converge on the truth (THEORY-855), for the conditions under which this works, and for the finding that 'fair' reporting slows the audience. Read it knowing that its policy makers update on forwarded results as if they were unselected, that every result is a simulation of a stylized bandit model averaged over runs in which the scientists reached the truth, and that why the smallest studies serve biased production best is a sufficient-statistic fact the paper does not state (THEORY-845).
Version2
Tagsepistemology, network-science, philosophy-of-science
Cited as “paper” byNOTE-1038 — Weatherall, O'Connor and Bruner — How to beat science and influence people
Cited as “source” byTHEORY-845 — In Weatherall, O'Connor and Bruner's network model a policy maker's credence depends on the evidence it has received only through the total excess of successes over failures of the new action, so a propagandist's effect is the excess of failures it adds; for the same pulls, running single-trial studies and publishing the failures adds at least as large an excess as any other division into studies, which is why biased production does best with the smallest studies, THEORY-855 — In Bala–Goyal network models of unbiased scientists testing a slightly better new action, a propagandist who forwards to Bayesian policy makers only those real results that favour the worse action can leave the policy makers near-certain of the false theory while the scientists converge on the true one; the effect weakens as the policy makers hear more scientists, as the better action's advantage grows and as each study gets larger

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Weatherall, J. O., O’Connor, C. & Bruner, J. P. (2020). How to Beat Science and Influence People: Policymakers and Propaganda in Epistemic Networks. The British Journal for the Philosophy of Science 71(4): 1157–1186. doi:10.1093/bjps/axy062. Preprint: arXiv:1801.01239 (4 January 2018).

Read in full (NOTE-1038).

Why this is here

A25 placed it as prior art for influence through selected evidence, beside LIT-744 (Holman & Bruner): “CLAIM-330’s mathematical instability result remains valid, but its application to strategic epistemic influence should be positioned alongside—not as a replacement for—existing network epistemology.” A26 §4 put it at the level of control over evidence. Bears on CLAIM-330 and CLAIM-404, and sits with the record’s network-epistemology readings (LIT-733, LIT-724, LIT-751, THEORY-143).

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