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.
- Bayes’ Rule: probability of A given B.
- Producing estimates from noisy, changing data.
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.
- Models.
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.
- Legal operating system of a country.
- 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.
- Genomes are the regulatory networks of the body.
- 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.