Things to Write About

An article backlog

Table of Contents

    The Optimization–Satisfaction Tradeoff. Or, perhaps, the Optimization–Contentment tradeoff, but that one isn't as nice, though contentment really illustrates the point better. Regardless, looking into research by Schwartz, Iyengar et al., and Diab et al. in the context of optimizing behavior and happiness, satisfaction, and contentment. Asking how we can balance these two. Perhaps including what David and I talked about in terms of “giving yourself something to be content with" – planning ahead.

    Habits, goals, and whether it is better to achieve them through discipline or through 'tricks' or 'systems'. Should we try to be really disciplined at, say, going to the gym every day? Or is it more practical to use 'tricks' like “go to the gym and do 1 push-up"? Are those tactics ultimately sustainable?

    Is it bad to replace passion with reason? The perils of relying on passion

    The impact of ambiguity. We know that ambiguity affects conditioning of learning. It seems that ambiguity affects habit development. I posit that ambiguity also affects the state of flow.

    Is living a good/enjoyable/fulfilling/comfortable life directly in opposition to wanting to live an impactful life? Some things:

    How do we get the most out of nonfiction books?

    Is it better to build tools that let people build great things or use tools to build great things?

    Loss-of-trust analysis

    The worst place to be is in the middle

    • Activation energy (graph?)
    • Sufficiency – 'good enough'

    Mental model: path-dependency

    Mental model: deliberate difficulty

    • See notes

    Guidelines for personal development articles

    • Give obvious advice non-obvious execution plans
    • Offer them a checklist.
      • Offer them a printable checklist.
        • Offer them a printable checklist that you send to them.

    Expectation theory

    Data over capital

    • There is the sense that data is more important than capital, especially with e.g. AI projects
    • Data is hard to obtain, capital is less of an issue
    • Kevin Kelly on Watson: "This kind of AI can be scaled up or down on demand. Because AI improves as people use it, Watson is always getting smarter; anything it learns in one instance can be immediately transferred to the others."
    • Chris Dixon on Waze: "The mapping startup Waze used data network effects to produce better maps than its vastly better capitalized competitors."
    • So, start something early. Gather as much data as possible. Then use that scarce, valuable resource to develop and train better systems.

    Technological externalities of economic rocketships

    • The rocketship of cellphones has produced cheaper components which has allowed VR to flourish (e.g. sensors)
    • The rocketship of gaming has made GPUs cheaper, and now viable for deep learning
    • Economic impacts inseparable from innovation. Is there a way we can anticipate upcoming cost reductions due to impending rocketships to take advantage of them?

    Antifragility / counterintuitivity

    • Reactance

    Models of viability

    • Self-sustaining
    • Benefactor
    • Side project (college student / nights and weekends / someone who is working who is building something on the side that doesn't need it to sustain their livelihood)