Extra: Key COVID-19 Decisions with John Robb



In this short extra episode, Jim talks to John Robb about network decision making & consensus vs dissent dynamics, COVID as a decision making test, herd immunity vs social distancing, key population dynamics, John’s big tech solution & its main roadblocks, reflections on UBI & stimulus, the value of simple solutions, the failed US political response & potential fixes, regional compacts & local responses, managing the backside of the curve, broad testing feasibility, and more.

John is an author, inventor, entrepreneur, technology analyst, astro engineer, and military pilot. He’s started numerous successful technology companies, including one in the financial sector that sold for $295 million and one that pioneered the software we currently see in use at Facebook and Twitter. John’s insight on technology and governance has appeared on the BBC, Fox News, National Public Radio, CNBC, The EconomistThe New York Times, The Wall Street Journal, and BusinessWeek.

John served as a pilot in a tier-one counter-terrorism unit that worked alongside Delta and Seal Team 6. He wrote the book Brave New War on the future of national security, and has advised the Joint Chiefs of Staff, NSA, DoD, CIA, and the House Armed Services Committee.


One thought on “Extra: Key COVID-19 Decisions with John Robb

  1. Jim,

    This caught my attention. On the topic of digital contact tracing (~21:00), you said:

    “That’s the key thing about the dance, on the back side of this curve. You don’t need data from 100 percent of the people; you need statistically valid sampling data. On a population at the state-level, 3-4 million, you may need a few thousand people providing high-fidelity data to give you sense of the native incidence rate and have the powers-that-be be able to respond forcibly to a local flare-up.”

    Seems to me that you’re describing a spec/implementation that’s not currently in use or development, so i want to ask you to elaborate on your thinking.

    Quick summary of current specs/implementations (see also Ada Lovelace Institute report, p.25):
    — Proximity-based tracing: Singapore, PEPP-PT (Germany++), DP3T (Austria++), NHS, MIT, Apple/Google
    — Location-based tracing: South Korea, Israel, Taiwan
    — Current proximity ones are all voluntary; location ones are all mandatory (as far as i know).
    — Analysis of proximity-based tracing is that it requires 60+ percent adoption to be effective. (For example: https://twitter.com/cynddl/status/1254391597158072320, writing about hammer phase, rather than dance.)

    Questions:
    — Are you describing a voluntary, location-based approach? That does not exist, as far as i know.
    — What accounts for your low figure on required rate of adoption? Dance-phase conditions, versus hammer-phase? An aggregate view on the data, to assist manual tracing of individual cases? And/or..?
    — Or have i misunderstood?

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