Groupthink and the wisdom of crowds

Information cascades

To link to: getting along, swarm sensing, voting systems, democracy, groupthink. 🏗 When do group decisions embody the wisdom of crowds and when groupthink? How do we tie the group consensus to reality rather than let the dynamics of signalling and simulacra dominate.

Diversity dividends

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, using causal analysis.)

Other random readings: Chris Dillow, diversity trumps ability.

The new Matthew Syed book (Syed 2020) (titled Rebel Ideas or Superteams depending where you are) apparently covers some of this material.

Social structure of knowledge

Vested interests, contrarians, consensus.

Scott Aaronson on “armchair epidemiology” uses the COVID-19 public communication fiasco as a lense on 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.

This resembles 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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