Clear-cut and concise, with ample examples to delve deeper if you so choose. Still, I think it could be further distilled from 19 Marxism’s to 13:
THINKING STRAIGHT
1) Consider extremes (e.g. Person A and B take 2 and 3 hours to paint a room, respectively. Why not 2.5 if they collab? Consider if just A is painting… if B helps it must be shorter, no?)
2) Simplify (e.g To decide between PhD in Economics or Policy, do you want to study narrow problems precisely or broad problems imprecisely? e.g to train neural networks for AGI, test on a simple task any human could do but that requires extreme skill to do without specific training, e.g. complex models whose results are a mystery are not useful, but simple models that can be translated into intuitive insights are, e.g. science of hitting a baseball vs “Hands, Hips, Head”)
UNCERTAINTY
3) The world is more uncertain than you think so think probabilistically about the world (e.g. FiveThirtyEight gave Trump 29% chance of winning the 2016 election yet everyone was shocked. Distinguish between Risk, Uncertainty, and Ignorance on the known/unknown States of World & Probabilities matrix, and follow these steps: understand subjectivity of most real-world probability, assess probability, update when new info arises. Practice with low-stakes e.g. what is the probability the cashier asks for your ID. Update your prior to posterior if they check the person in front of you)
4) Uncertainty causes you to favor the status quo. This can cause you to pass up high-upside options. (e.g During early days of COVID, some in medical field argued for status quo of no treatment until proven with RCT. But given long time for RCT, high # of patients, and high death rate, Maryaline Catillon, a hospital director in France, argued for less rigorous observational studies to inform treatment in the interim). The Niagara Falls example: You need to transport 100 objects over Niagara Falls analogy: Bucket A worked 70/100 times, Bucket B worked 1/2 times. Tempting to use Bucket A. But you should use Bucket B for several more times to learn more about its performance, and then switch back to A if appropriate. Same in evaluating whether to invest time and effort into a project. Projects with uncertain outcomes, or areas that have never been explored, are more interesting, and potentially higher gain.
DECISIONS
5) Good decisions can have poor outcomes (e.g. expected cost of buying iPhone insurance is higher vs not buying for most people, but it still hurts when you drop your phone).
6) Some decisions have high probability of bad outcome (e.g any child welfare policy has a high probability that some children will still be abused or neglected. As a result, child welfare directors too often have short terms which results in unstable leadership for state agencies, which makes things worse for children and families.)
7) Errors of commission should be weighted the same as errors of omission (e.g selling $20K Amazon stock in 1998 vs. not buying $20K Amazon stock in 1998 should be weighted equally, but omission bias leads most to weight errors of commission higher)
8) Don’t be limited by options in front of you (e.g. don’t stick with the statin that gives you sweaty palms when there are tens of other options out there that may not have the side effect. I actually think this is a poor example and more illustrative of the Niagara Falls effect. Instead, consider that there are options you don’t know about at all yet - seek them out before evaluating next steps.)
9) Info only valuable if it can change your decision (e.g. the Data Team conundrum! e.g. doctor thought Richard’s mother either had appendicitis or a tumor and wanted to keep her overnight to learn more. But either way they’d operate, so Richard pushed to operate immediately. They did, and found a leaky appendix and peritonitis - waiting to operate would have been extremely dangerous.)
UNDERSTANDING POLICY
10) Long division is the most important tool for policy analysis (i.e. basic benefit per unit of cost calculation).
11) Elasticities are a powerful tool for understanding many important things in life (e.g. “do you prefer to spend your time at work, with family, or on personal projects” is a bad question. Instead think about the value of your last (marginal) work hour
12) Heterogeneity in the population explains many phenomena (e.g. Stanford University president Hennessy argued that early admissions are fair bc average SAT score is 210 points higher for early admission. But actually need to compare lowest SAT score for early admission against highest score denied in general admission.)
13) Capitalize on complementaries (e.g. running a tennis camp - you need to scale instructors in line with tennis courts). This is the same as The Goal bottleneck concept.