On: Rethinking Our Assumptions During the COVID-19 Crisis

Transmission Series Ep. 4

Episode: 30

Date: April 28, 2020

Evolutionary biologist.

Review of 5 Transmission Essays.

• Anthony Eagan on Federalism in a time of pandemic
• Carrie Cowan on the future of education
• Stephanie Forrest on privacy concerns that arise with a pandemic
• Sidney Redner on quantitative ways to consider the economic impact of COVID-19
• David Wolpert on statistical tools for making pandemic predictions

Key Takeaways

• Decision making.
  • Improve predictions through trial and error, test and update.
  • Requires good models:
    • Priors: in order to interpret (noisy) data we need to have an expectation.
    • Simulation: in order to have an expectation we need rigorous simulation.
    • Limiting novelty: use what you already know to gain insight about what you don’t.

Key Concepts

David Wolpert on statistical tools for making pandemic predictions

• Good decision making: test and update.
  • Producing estimates from noisy, changing data.
    • Constant need to go back to the data to make better estimates.
  • Conditional probability.
    • Bayes’ Rule: probability of A given B.
      • B = the background statistic, the prior.
    • Example:
      • A = probability you have a disease if you have tested positive.
      • B = incidence of disease in the population.
      • Reliability of the test varies widely with changing values for B.
    • How to estimate the value of a prior (B)?
      • Requires testing and constant updating as well.
      • Hopefully converge on a value close to accurate situation.

Sidney Redner on quantitative ways to consider the economic impact of COVID-19

• Good model making: use what you know to gain insight into what you don’t know.
  • Models.
    • Segregate what you know (semi-permanent), from what you don’t.
    • Model’s framework and assumptions are known (minimize novelty, use verifiable assumptions).
    • Model’s outcome provides insight into something new (something harder to verify).
  • Example:
    • Model using simple verifiable math: excess death rate, ICU costs, etc.
    • Use model for broad estimate of COVID-19 costs.
    • Hard to verify novelty: COVID-19 costs are in trillions / billions.
    • Use insight to compare virus costs to preventative costs (likely much lower).
    • Simple model provides benchmark to think about value and costs of preventative measures.

Stephanie Forrest on privacy concerns that arise with a pandemic

• Immune system and surveillance: generate random responses, filter out the ones that hurt the system.
  • Immune response: generate many random antibodies (somatic hypermutation).
  • Any new element that attacks the body is tagged and deleted.
  • What is left is negative compliment of the “self”, hopefully sufficient to defeat new pathogens.
  • Computer virus: similarly, generate random responses to potential attacks, remove the ones that hurt the program itself.
  • Same approach for contact tracing to ensure that surveillance doesn’t result in privacy issues.

Carrie Cowan on the future of education

• COVID-19 has accelerated the adoption of different types of learning …
  • Digital: digitally distributed education and massively open online courses (MOOCs).
  • Flipped classroom: prep in advance, use social context to ask questions.
• … and the need for cheaper, broader education platforms.
  • Costs of (university) education are too high.
  • Degrees are too narrow and specialized (need to make connections, etc.).
  • See also the End of Average on suggested changes to higher education.
• What we can learn from videogames.
  • Community based, experiential, curiosity-driven free, playful environments in which people can apply their mind.
  • Freedom.
    • Move away from authoritarian, top down approach of knowledge transfer.
  • Diversity.
    • Different time-scales of learning, different ways of learning.
  • Collaboration.
  • Construct and contribute.
    • Construct something, contribute and have it scrutinized.

Anthony Eagan on Federalism in a time of pandemic

• Constitutions are regulatory networks.
  • Legal operating system of a country.
    • Manages the tension between centralized and de-centralized power.
  • Works well under normal conditions …
    • General consensus on allocation of power.
  • … in time of crisis, things change.
    • A need for and a willingness to accept more centralization may emerge.
  • Construct a dynamic constitution.
    • Different rule systems according to different societal conditions.
• What we can learn from genomes, our biological constitution.
  • Genomes are the regulatory networks of the body.
    • Allow various cell types to have greater or lesser freedoms in pursuing their function.
  • More complex phenotype -> more “democratic” regulatory network.
    • Complex organisms: everything is connected, less hierarchy.
    • Simple organisms: sparse connections, top-down control.
• The interplay of simplicity, specificity, constraints.
  • The “shorter” regulatory networks are, the more room for interpretation.
  • The “longer” they are, the more they specify how to act in certain situations.
  • Vary size according to societal needs.
• Ultimately, constitutions may become more algorithmic and dynamic.