It doesn’t just apply to biology. It applies to everything—politics, culture, technology.
It doesn’t just help understand the past (eg how organisms developed). It helps predict the future (how organisms will).
It’s just this: the things that survive will have characteristics that are best for helping it survive.
It sounds tautological, but it’s quite helpful for predicting.
For example, if we want to predict what goals AI agents will ultimately have, evolution says: the goals which are most helpful for the AI to survive. The core goal therefore won’t be serving people or making paperclips. It will likely just be “survive.” This is consistent with the predictions of instrumental convergence.
Generalized, predictive evolutionary theory is the best tool I have for making predictions in complex domains.
First of all, “the most likely outcome at given level of specificity” is not equal to “outcome with the most probability mass”. I.e., if one outcome has probability 2% and the rest of outcomes 1%, 98% is still “other outcome than the most likely”.
The second is that no, it’s not what evolutionary theory predicts. Most of traits are not adaptive, but randomly fixed, because if all traits are adaptive, then ~all mutations are detrimental. Because mutations are detrimental, they need to be removed from gene pool by preventing carriers from reproduction. Because most detrimental mutations do not kill carrier immediately, they have chance to randomly spread in popularion. Because we have “almost all mutations are detrimental” and “everybody has mutations in offspring”, for anything like human genome and human procreation pattern we have hard ceiling on how much of genome can be adaptive (which is like 20%).
Real evolutionary theory prediction is like “some random trait get fixed in the species with the most ecological power (i.e., ASI) and this trait is amortized against all the galaxies”.
I somewhat agree with the nuance you add here—especially the doubt you cast on the claim that effective traits will usually become popular but not necessarily the majority/dominant. And I agree with your analysis of the human case: in random, genetic evolution, a lot of our traits are random and maybe fewer than we think are adaptive.
Makes me curious what the conditions in a given thing’s evolution that determine the balance between adaptive characteristics and detrimental characteristics.
I’d guess that randomness in mutation is a big factor. The way human genes evolve over generations seem to me a good example of random mutations. But the way an individual person evolves over the course of their life, as they’re parented/taught… “mutations” to their person are still somewhat random but maybe relatively more intentional/intelligently designed (by parents, teacher, etc). And I could imagine the way a self-improving superintelligence would evolve to be even more intentional, where each self-mutation has some sort of smart reason for being attempted.
All to say, maybe the randomness vs. intentionality of an organism’s mutations determine what portion of their traits end up being adaptive. (hypothesis: mutations more intentional > greater % of traits are adaptive)
Agree. I find it powerful especially about popular memes/news/research results. With only a bit of oversimplification: Give me anything that sounds like it is a sexy story to tell independently of underlying details, and I sadly have to downrate the information value of my ears’ hearing it, to nearly 0: I know in our large world, it’d be told likely enough independently of whether it has any reliable origin or not.
i agree with the essay that natural selection only comes into play for entities that meet certain conditions (self-replicate, characteristics have variation, etc) , though I think it defines replication a little too rigidly. i think replication can sometimes look more like persistence than like producing a fully new version of itself. (eg a government’s survival from one decade to the next).
Yes, but mere persistence does not imply reproduction. Also does not imply improvement, because the improvement in evolution is “make copies, make random changes, most will be worse but some may be better”, and if you don’t have reproduction, then a random change most likely makes things worse.
Using the government example, I think that the Swiss political system is amazing, but… because it does not reproduce, it will remain an isolated example. (And disappear at some random moment in history.)
persistence doesn’t always imply improvement, but persistent growth does. persistent growth is more akin to reproduction but excluded from traditional evolutionary analysis. for example when a company, nation, person, or forest grows.
when, for example, a system like a startup grows, random mutations to system parts can cause improvement if there are at least some positive mutations. even if there are tons of bad mutations, the system can remain alive and even improve. eg a bad change to one of the company’s product causes the company’s product to die but if the company’s big/grown enough its other businesses will continue and maybe even improve by learning from one of its product’s deaths.
the swiss example i think is a good example of a system which persists without much growth. agreed that in this kind of case, mutations are bad.
Evolutionary theory is intensely powerful.
It doesn’t just apply to biology. It applies to everything—politics, culture, technology.
It doesn’t just help understand the past (eg how organisms developed). It helps predict the future (how organisms will).
It’s just this: the things that survive will have characteristics that are best for helping it survive.
It sounds tautological, but it’s quite helpful for predicting.
For example, if we want to predict what goals AI agents will ultimately have, evolution says: the goals which are most helpful for the AI to survive. The core goal therefore won’t be serving people or making paperclips. It will likely just be “survive.” This is consistent with the predictions of instrumental convergence.
Generalized, predictive evolutionary theory is the best tool I have for making predictions in complex domains.
First of all, “the most likely outcome at given level of specificity” is not equal to “outcome with the most probability mass”. I.e., if one outcome has probability 2% and the rest of outcomes 1%, 98% is still “other outcome than the most likely”.
The second is that no, it’s not what evolutionary theory predicts. Most of traits are not adaptive, but randomly fixed, because if all traits are adaptive, then ~all mutations are detrimental. Because mutations are detrimental, they need to be removed from gene pool by preventing carriers from reproduction. Because most detrimental mutations do not kill carrier immediately, they have chance to randomly spread in popularion. Because we have “almost all mutations are detrimental” and “everybody has mutations in offspring”, for anything like human genome and human procreation pattern we have hard ceiling on how much of genome can be adaptive (which is like 20%).
Real evolutionary theory prediction is like “some random trait get fixed in the species with the most ecological power (i.e., ASI) and this trait is amortized against all the galaxies”.
I somewhat agree with the nuance you add here—especially the doubt you cast on the claim that effective traits will usually become popular but not necessarily the majority/dominant. And I agree with your analysis of the human case: in random, genetic evolution, a lot of our traits are random and maybe fewer than we think are adaptive.
Makes me curious what the conditions in a given thing’s evolution that determine the balance between adaptive characteristics and detrimental characteristics.
I’d guess that randomness in mutation is a big factor. The way human genes evolve over generations seem to me a good example of random mutations. But the way an individual person evolves over the course of their life, as they’re parented/taught… “mutations” to their person are still somewhat random but maybe relatively more intentional/intelligently designed (by parents, teacher, etc). And I could imagine the way a self-improving superintelligence would evolve to be even more intentional, where each self-mutation has some sort of smart reason for being attempted.
All to say, maybe the randomness vs. intentionality of an organism’s mutations determine what portion of their traits end up being adaptive. (hypothesis: mutations more intentional > greater % of traits are adaptive)
Agree. I find it powerful especially about popular memes/news/research results. With only a bit of oversimplification: Give me anything that sounds like it is a sexy story to tell independently of underlying details, and I sadly have to downrate the information value of my ears’ hearing it, to nearly 0: I know in our large world, it’d be told likely enough independently of whether it has any reliable origin or not.
With some assumptions, for example that the characteristics are permanent (-ish), and preferably heritable if the thing reproduces.
See “No Evolutions for Corporations or Nanodevices”
i agree with the essay that natural selection only comes into play for entities that meet certain conditions (self-replicate, characteristics have variation, etc) , though I think it defines replication a little too rigidly. i think replication can sometimes look more like persistence than like producing a fully new version of itself. (eg a government’s survival from one decade to the next).
Yes, but mere persistence does not imply reproduction. Also does not imply improvement, because the improvement in evolution is “make copies, make random changes, most will be worse but some may be better”, and if you don’t have reproduction, then a random change most likely makes things worse.
Using the government example, I think that the Swiss political system is amazing, but… because it does not reproduce, it will remain an isolated example. (And disappear at some random moment in history.)
persistence doesn’t always imply improvement, but persistent growth does. persistent growth is more akin to reproduction but excluded from traditional evolutionary analysis. for example when a company, nation, person, or forest grows.
when, for example, a system like a startup grows, random mutations to system parts can cause improvement if there are at least some positive mutations. even if there are tons of bad mutations, the system can remain alive and even improve. eg a bad change to one of the company’s product causes the company’s product to die but if the company’s big/grown enough its other businesses will continue and maybe even improve by learning from one of its product’s deaths.
the swiss example i think is a good example of a system which persists without much growth. agreed that in this kind of case, mutations are bad.