To link to: getting along, swarm sensing, voting systems, democracy, groupthink. 🏗 When do group decisions embody the wisdom of crowds and when groupthink?
Possibly diversity and tolerance is not just an intrinsic moral good, but may pay literal dividends in terms of avoiding groupthink and being ore effective etc. What are the conditions for this happy state?
Does diversity help attain wisdom? Sometimes, it seems. Scott Page calls this the diversity dividend. Quantifying when and how it works is of interest to me.
Practically, see cultivating diversity.
McKinsey report, Vivian Hunt, Dennis Layton, and Sara Prince: Why diversity matters:
While correlation does not equal causation (greater gender and ethnic diversity in corporate leadership doesn’t automatically translate into more profit), the correlation does indicate that when companies commit themselves to diverse leadership, they are more successful.
(They could possibly have done better than that mealy-mouthed correlation phrasing if they wanted, via causal analysis.)
Other random readings: Chris Dillow, diversity trumps ability.
The new Matthew Syed book (titled Rebel Ideas or Superteams depending where you are) apparently covers some of this material (Syed 2020).
Scott Aaronson on “armchair epidemiology” takes the Corona virus public communication fiasco to wonder about societal collective knowledge and science and the role of contrarians. Connection to red queen signal dynamics should be apparent. The comment threads in that post meander around this topic at length.
Scott Alexander, Contrarians, Crackpots, and Consensus tries to crack this one open with an ontology.
I think a lot of things are getting obscured by the term “scientific establishment” or “scientific consensus”. Imagine a pyramid with the following levels from top to bottom:
FIRST, specialist researchers in a field…
SECOND, non-specialist researchers in a broader field…
THIRD, the organs and administrators of a field who help set guidelines…
FOURTH, science journalism, meaning everyone from the science reporters at the New York Times to the guys writing books with titles like The Antidepressant Wars to random bloggers…
ALSO FOURTH IN A DIFFERENT COLUMN OF THE PYRAMID BECAUSE THIS IS A HYBRID GREEK PYRAMID THAT HAS COLUMNS, “fieldworkers”, aka the professionals we charge with putting the research into practice. … FIFTH, the general public.
A lot of these issues make a lot more sense in terms of different theories going on at the same time on different levels of the pyramid. I get the impression that in the 1990s, the specialist researchers, the non-specialist researchers, and the organs and administrators were all pretty responsible about saying that the serotonin theory was just a theory and only represented one facet of the multifaceted disease of depression. Science journalists and prescribing psychiatrists were less responsible about this, and so the general public may well have ended up with an inaccurate picture.
There is another pyramid of fashionable disagreement that he mentions, the Intellectual Hipsters and Meta-Contrarianism pyramid.
Rex Douglass’s How to be Curious Instead of Contrarian About COVID-19 dive into the Richard Epstein contrarian piece about COVID-19 response as a case study in how to disagree productively.
How should non-epidemiologists publicly discuss COVID-19 data and models? When leaders and citizens are especially sensitive to signals on public health, what is our intellectual responsibility to defer to the analysis of more expert speakers? I argue that our responsibility during crisis is the same as it was before; to do good work, to the best of our abilities, with the scientific principles of curiosity and honesty. Alternative shorthands like ‘staying in your lane’ are a poor decision rule for sorting good work from bad, and they ignore the very messy process that underlies real-world scientific inquiry. Lane-keeping is a poor way to learn and become a better consumer of expert findings, and gate-keeping is a missed opportunity to provide the public goods of feedback and review. To demonstrate the point, this note provides a detailed review of a recent piece “Coronavirus Perspective” (Epstein 2020a). By applying and illustrating data science principles point for point to this non-epidemiological take on epidemiological questions, it is hoped that the reader will take away not why they should avoid working on new topics but rather how they should approach those topics in an honest, curious, and rigorous way.
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