Discussion of the value of a mathematical model for the dissemination of propaganda

A couple people pointed me to this article, “How to Beat Science and Influence People: Policy Makers and Propaganda in Epistemic Networks,” by James Weatherall, Cailin O’Connor, and Justin Bruner, also featured in this news article. Their paper begins:

In their recent book Merchants of Doubt [New York:Bloomsbury 2010], Naomi Oreskes and Erik Conway describe the “tobacco strategy”, which was used by the tobacco industry to influence policy makers regarding the health risks of tobacco products. The strategy involved two parts, consisting of (1) promoting and sharing independent research supporting the industry’s preferred position and (2) funding additional research, but selectively publishing the results. We introduce a model of the Tobacco Strategy, and use it to argue that both prongs of the strategy can be extremely effective—even when policy makers rationally update on all evidence available to them. As we elaborate, this model helps illustrate the conditions under which the Tobacco Strategy is particularly successful. In addition, we show how journalists engaged in ‘fair’ reporting can inadvertently mimic the effects of industry on public belief.

This is an important topic and I like the general principle but I wasn’t so clear what was gained from the mathematical model, beyond the qualitative description and discussion. I asked the authors, and O’Connor replied:

There are a few reasons we think a model is useful here. 1) The models can help us verify that the tobacco strategy might have indeed played the type of role that Oreskes and Conway claim it played. For example, the models show that in principle something as simple as sharing the spurious results of real, independent scientists might be able to prevent the public from figuring out that smoking was dangerous. 2) They can help us neatly identify causal dependencies in cases like this, which is especially useful in figuring out which conditions make it harder or easier for propagandists. For instance, we see from the models that larger scientific communities might be a bad thing in some cases, because the extra researchers are potential sources of spurious results. This is not initially obvious. 3) By dint of making these causal dependencies clear, they help us identify interventions that might help protect the public from industry propaganda.

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