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vgr's profile
Venkatesh Rao
Venkatesh Rao
Venkatesh Rao
@vgr

Tweets

Venkatesh Rao

@vgr

This is my conversational account. For my work follow @ribbonfarm, @breaking_smart, @artofgig. Tweets are 90% vacuous views, apathetically held. Mediocritopian.

Los Angeles, CA
venkateshrao.com
Joined August 2007

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    Venkatesh Rao‏ @vgr Mar 2
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    1/ Been thinking about non-ergodicity and path dependency due to provocations from @doriantaylor and @TaylorPearsonMe among others. Trying to come up with the simplest toy example I think clarifies the basic question. Here’s what I came up with...

    11:04 AM - 2 Mar 2020
    • 7 Retweets
    • 34 Likes
    • ifrit Leon T K 오메르 Pamela J. Hobart Stian Håklev (侯爽) 𝕸𝖆𝖗𝖈 𝕬𝖗𝖇𝖔𝖓𝖊𝖘 💻🖥📱🏖 dark mutualist Rick Sanchez
    3 replies 7 retweets 34 likes
      1. New conversation
      2. Venkatesh Rao‏ @vgr Mar 2
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        2/ Let’s call it the Perez Problem, after Carlota Perez. It’s the problem of how a complex society deploys complex new technology requiring a degree of coordination and convention, like say the internet (a protocol convention) or vaccines.

        1 reply 2 retweets 5 likes
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      3. Venkatesh Rao‏ @vgr Mar 2
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        3/ An invention X has occurred in a society of 1000 people that can be deployed in 3 ways, A, B, and C. Each way has a learning curve attached, with an unknown fatality rate that declines to 0 over time. Eg if you go all-in on A, there will be fatalities F_A on deployment path.

        1 reply 2 retweets 2 likes
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      4. Venkatesh Rao‏ @vgr Mar 2
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        4/ Due to network effects, each path A is more efficient at larger scales of deployment, but the efficiency is *uncertain* and not a deterministic function of efficiency at lower scales. So it could be that A is most efficient when 500 people use it, B at 750, but C at 1000.

        1 reply 2 retweets 3 likes
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      5. Venkatesh Rao‏ @vgr Mar 2
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        5/ So efficiency for method A, at scale n, and time t is E(A, n, t). It is a function of time on its own learning curve only. Assume no cross-learning.

        1 reply 2 retweets 2 likes
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      6. Venkatesh Rao‏ @vgr Mar 2
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        6/ Worse, the fatality rates F_A, F_B, F_C are also uncertain functions of deployment scale n, and time t. But you have only 1 timeline to work with. What do you do?

        1 reply 1 retweet 2 likes
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      7. Venkatesh Rao‏ @vgr Mar 2
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        7/ Pure small-scale testing won’t work. You can’t divide population into 3 groups of 333 people each to pick lowest fatality/highest efficiency for a test period T because it doesn’t predict efficiency/fatality at n=1000, and worse, people are gonna die during the test phase

        1 reply 2 retweets 2 likes
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      8. Venkatesh Rao‏ @vgr Mar 2
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        8/ OTOH, if you go all-in in series, ABC, BAC, CAB, CBA, BAC, BCA will all have different fatality profiles and each will have previous stages capping the maximium effectiveness at full scale, since people are dying along the way.

        2 replies 2 retweets 1 like
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      9. Venkatesh Rao‏ @vgr Mar 2
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        9/ Point is... there’s no way to test your way out of this bind because the indeterminacy in scaling efficiency and fatality rates with n from 0-1000 fundamentally limits the information input available for your problem.

        1 reply 2 retweets 3 likes
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      10. Venkatesh Rao‏ @vgr Mar 2
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        10/ Under the tightest conditions, basically nothing is possible I think. Series or parallel, the only way to land in the most efficient future with the most people left alive to enjoy it is to get lucky somehow.

        3 replies 2 retweets 3 likes
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      11. Venkatesh Rao‏ @vgr Mar 2
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        11/ You need to loosen the constraints to make non-brute-force learning possible and pick the best future with better than random chance. Three mitigations help: the efficiency scaling is predictable, the fatality scaling is predictable, cross learning is possible.

        1 reply 2 retweets 3 likes
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      12. Venkatesh Rao‏ @vgr Mar 2
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        12/ The problem strikes me as a sort of restless multi-armed bandit problem with non-independent, non-stationary arms. These are known to be horribly intractable. AFAIK the Gittins index approach doesn’t work. You can’t sample the arms and shift from explore to exploit easily.

        1 reply 2 retweets 5 likes
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      13. Venkatesh Rao‏ @vgr Mar 2
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        13/ In practice the problem is not that tightly constrained and all 3 mitigations are available to a degree. Also, it’s rarely 3 static futures, and the fatality rate is rarely a big fraction of test population. So early experimentation leads to iterative refinement of options.

        1 reply 1 retweet 2 likes
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      14. Venkatesh Rao‏ @vgr Mar 2
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        14/ So societies have a deployment path that looks like a fractal generated from (Parallel —> Refactor Options —> Series) across space and time

        1 reply 2 retweets 2 likes
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      15. Venkatesh Rao‏ @vgr Mar 2
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        15/ The key to the practical solution is to guess the scaling phase transition points correctly across fractal levels, so you can switch between parallel/series gears and refactor options at the right time. The “series” option looks like “exploit” locally in time/space.

        2 replies 3 retweets 4 likes
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      16. Venkatesh Rao‏ @vgr Mar 4
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        16/ Throwing in some links that inspired this line of thinking. First @TaylorPearsonMe Big Little Idea Called Ergodicityhttps://taylorpearson.me/ergodicity/ 

        1 reply 2 retweets 2 likes
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      17. Venkatesh Rao‏ @vgr Mar 4
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        17/ Ole Peters 2019 Nature article which seems to have caused this current surge of interest, "The ergodicity problem in economics"https://www.nature.com/articles/s41567-019-0732-0 …

        1 reply 0 retweets 2 likes
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      18. Venkatesh Rao‏ @vgr Mar 4
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        Wright Meets Markowitz, via @mengwong http://research.economics.unsw.edu.au/vpanchenko/papers/WriteMeetsMarkowitz.pdf …

        1 reply 0 retweets 2 likes
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      19. Venkatesh Rao‏ @vgr Mar 4
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        Founder effect: non-ergodicity in nature https://en.wikipedia.org/wiki/Founder_effect …

        1 reply 0 retweets 2 likes
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      20. End of conversation

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