Industry Influencing Collective Scientific Reasoning: A Bayesian, Agent-based Exploration

Recent work in Bayesian, agent-based modelling of scientific communities has employed the Bala-Goyal framework to study the mechanisms involved when industry influence applies the so-called 'Tobacco Strategy' to undermine collective inquiry. Motivated by limitations of these models, we propose an alternative based on a recently introduced framework for normative argument exchange across networks. We implement representations of two distinct types of industry influence: `Obfuscating' influence directs inquiry to experiments with low expected value of information. `Misleading' influence filters private research and only communicates misleading signals from the world. We explored the impacts of both strategies on the polarization \& mean error of, and flow of information through, social networks of scientists via computer simulations. We conclude that even against highly optimistic background assumptions, and in a less simplified model of inquiry and argumentation, industry influence poses a plausible threat to collective deliberation.