romeostevensit

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People Will Listen

Reasonably you'd probably skip the first few doublings for something that low value, which changes the outcome significantly.

People Will Listen

Portfolio construction theory says optimal allocation to gold is generally not zero. Gold you can email seems clearly valuable. As for the heuristic that leads to thinking bitcoin is a good idea in general it was 'if you see a new mathematical result that enables a new/different kind of payment layer invest some time and money to understand it.'

The Case for Extreme Vaccine Effectiveness

Great post, I noticed I was confused about this the other day.

couldn't find anything about any other vaccines working this way (all or nothing).

I think it's fair for this hypothesis to hang out in the space given that previous vaccines weren't mRNA based.

Monastery and Throne

I think it's easy to denigrate (almost exclusive) trackers of social reality. But they are people who experienced conditioning that most of the variance in the outcomes they care about were controlled by social reality inputs. It makes sense that they constantly have to track changes in social reality because social reality is anti-inductive/adversarially optimizing out from under you constantly. Being able to ignore large swaths of social reality and thus bootstrap more permanent wins (since solutions in causal reality often stay wins) is something of a privilege. People mostly tracking causal reality also pay an often invisible cost related to having social reality treat you as a defector.

Specializing in Problems We Don't Understand

Agree. I like to split the empirical problems out using levels of abstraction:

Traversal problems: each experiment is expensive or it isn't clear how to generate a new experiment from old ones because of lack of visibility about controlled variables.

Representation space problems: the search space is too large, our experiments don't reliably screen off large portions of it. So we can't expect to converge in any reasonable time.

Intentional problems: we're not even clear on what we're trying to do or whether our representation of what we're trying to do matches the natural categories of the solution space such that we are even testing real things when we design the experiment.

Implementation problems: we can't build the tooling or control the variable we need to control even if we are pretty sure what it is. Measurement problems means we can't distinguish between important final or intermediate outcomes (eg error bars).

Convict Conditioning Book Review

not impossible per se though you'll note none of these people do a full range of motion. https://www.youtube.com/watch?v=4DEQhjiaNL4

Covid 4/9: Another Vaccine Passport Objection

These are helpful considerations to highlight, thank you.

Monastery and Throne

Notice how your examples are working class. The middle class or Venkat's aptly named clueless are maximally insulated from causal reality.

Affordances

One of the major helps for me was thinking in terms of number of potential leverage points for an intervention. If an intervention seems hard to me it is often the case that I am simply trying to lever on a bad spot and am unaware of other spots to place the lever.

Deliberate practice is often about building an intervention model that is more fine grained, which exposes more such points.

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