Tag Archives: Evolution

On Evolved Values

Biological evolution selects roughly for creatures that do whatever it takes to have more descendants in the long run. When such creatures have brains, those brains are selected for having supporting habits. And to the extent that such brains can be described as having beliefs and values that combine into actions via expected utility theory, then these beliefs and values should be ones which are roughly behaviorally-equivalent to the package of having accurate beliefs, and having values to produce many descendants (relative to rivals). Equivalent at least within the actual environments in which those creatures were selected.

Humans have unusually general brains, with which we can think unusually abstractly about our beliefs and values. But so far, we haven’t actually abstracted our values very far. We instead have a big mess of opaque habits and desires that implicitly define our values for us, in ways that we poorly understand. Even though what evolution has been selecting for in us can in fact be described concisely and effectively in an abstract way.

Which leads to one of the most disturbing theoretical predictions I know: with sufficient further evolution, our descendants are likely to directly and abstractly know that they simply value more descendants. In diverse and varying environments, such a simpler more abstract representation seems likely to be more effective at helping them figure out which actions would best achieve that value. And while I’ve personally long gotten used to the idea that our distant descendants will be weird, to (the admittedly few) others who care about the distant future, this vision must seem pretty disturbing.

Oh there are some subtleties regarding whether all kinds of long-term descendants get the same weight, to what degree such preferences are non-monotonic in time and number of descendants, and whether we care the same about risks that are correlated or not across descendants. But those are details: evolved descendants should more simply and abstractly value more descendants.

This applies whether our descendants are biological or artificial. And it applies regardless of the kind of environments our descendants face, as long as those environments allow for sufficient selection. For example, if our descendants live among big mobs, who punish them for deviations from mob-enforced norms, then our descendants will be selected for pleasing their mobs. But as an instrumental strategy for producing more descendants. If our descendants have a strong democratic world government that enforces rules about who can reproduce how, then they will be selected for gaining influence over that government in order to gain its favors. And for an autocratic government, they’d be selected for gaining its favors.

Nor does this conclusion change greatly if the units of future selection are larger than individual organisms. Even if entire communities or work teams reproduce together as single units, they’d still be selected for valuing reproduction, both of those entire units and of component parts. And if physical units are co-selected with supporting cultural features, those total physical-plus-cultural packages must still tend to favor the reproduction of all parts of those packages.

Many people seem to be confused about cultural selection, thinking that they are favored by selection if any part of their habits or behaviors is now growing due to their actions. But if, for example, your actions are now contributing to a growing use of the color purple in the world, that doesn’t at all mean that you are winning the evolutionary game. If wider use of purple is not in fact substantially favoring the reproduction of the other elements of the package by which you are now promoting purple’s growth, and if those other elements are in fact reproducing less than their rivals, then you are likely losing, not winning, the evolutionary game. Purple will stop growing and likely decline after those other elements sufficiently decline.

Yes of course, you might decide that you don’t care that much to win this evolutionary game, and are instead content to achieve the values that you now have, with the resources that you can now muster. But you must then accept that tendencies like yours will become a declining fraction of future behavior. You are putting less weight on the future compared to others who focus more on reproduction. The future won’t act like you, or be as much influenced by acts like yours.

For example, there are “altruistic” actions that you might take now to help out civilization overall. For example, you might build a useful bridge, or find some useful invention. But if by such actions you hurt the relative long-term reproduction of many or most of the elements that contributed to your actions, then you must know you are reducing the tendency of descendants to do such actions. Ask: is civilization really better off with more such acts today, but fewer such acts in the future?

Yes, we can likely identify some parts of our current packages which are hurting, not helping, our reproduction. Such as genetic diseases. Or destructive cultural elements. It makes sense to dump such parts of our reproduction “teams” when we can identify them. But that fact doesn’t negate the basic story here: we will mainly value reproduction.

The only way out I see is: stop evolution. Stop, or slow to a crawl, the changes that induce selection of features that influence reproduction. This would require a strong civilization-wide government, and it only works until we meet the other grabby aliens. Worse, in an actually changing universe, such stasis seems to me to seriously risk rot. Leading to a slowly rotting civilization, clinging on to its legacy values but declining in influence, at least relative to its potential. This approach doesn’t at all seems worth the cost to me.

But besides that, have a great day.

Added 7p: There many be many possible equilibria, in which case it may be possible to find an equilibrium in which maximizing reproduction also happens to maximize some other desired set of values. But it may be hard to maintain the context that allows that equilibrium over long time periods. And even if so, the equilibrium might itself drift away to support other values.

Added 8Dec: This basic idea expressed 14 years ago.

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General Evolvable Brains

Human brains today can do a remarkably wide range of tasks. Our mental capacities seem much more “general” than that of all the artificial systems we’ve ever created. Those who are trying to improve such systems have long wondered: what is the secret of human general intelligence? In this post I want to consider we can learn about this from fact that the brain evolved. How would an evolved brain be general?

A key problem faced by single-celled organisms is how to make all of their materials and processes out of the available sources of energy and materials. They do this mostly via metabolism, which is mostly a set of enzymes that encourage particular reactions converting some materials into others. Together with cell-wall containers to keep those enzymes close to each other. Some organisms are more general than others, in that they can do this key task in a wider range of environments.

Most single-celled organisms use an especially evolvable metabolism design space. That is, their basic overall metabolism system seems especially well-suited to finding innovations and adaptations mostly via blind random search, in a way that avoids getting stuck in local maxima. As I explained in a recent post, natural metabolisms are evolvable in part because they have genotypes that are highly redundant relative to phenotypes: many sets of enzymes can map any given set of inputs into any given set of outputs. And this redundancy requires a substantial overcapacity; the metabolism needs to contain many more enzymes than are strictly needed to create any given mapping.

The main way that such organisms are general is that they have metabolisms with a large library of enzymes. Not just a large library of genes that could code for enzymes if turned on, but an actual large set of enzymes usually created. They make many more enzymes than they actually need in each particular environment where they find themselves. This comes at a great cost; making all those enzymes and driving their reactions doesn’t come cheap.

A relevant analogous toy problem is that of logic gates mapping input signals onto output signals:

[In] a computer logic gate toy problem, … there are four input lines, four output lines, and sixteen binary logic gates between. The genotype specifies the type of each gate and the set of wires connecting all these things, while the phenotype is the mapping between input and output gates. … All mappings between four inputs and four outputs can be produced using only four internal gates; sixteen gates is a factor of four more than needed. But in the case of four gates the set of genotypes is not big enough compared to the set of phenotypes to allow easy evolution. For [evolvable] innovation, sixteen gates is enough, but four gates is not. (more)

Note that evolution doesn’t always use such highly evolvable design spaces. For example, our skeletal structure doesn’t have lots of extra bones sitting around ready to be swapped into new roles in new environments. In such cases, evolution chose not to pay large extra costs for generality and evolvability, because the environment seemed predictable enough to stay close to a good enough design. As a result, innovation and adaptation of skeletal structure is much slower and more painful, and could fail badly in novel enough environments.

Now let’s consider brains. It may be that for some tasks, evolution found such an effective structure that it chose to commit to that structure, betting that its solution was stable and reliable enough across future environments to let it forgoe the big extra costs of more general and evolvable designs. But if we are looking to explain a surprising generality, flexibility, and rapid evolution in human brains, it makes sense to consider the possibility that human brain design took a different path, one more like that of single-celled metabolism.

That is, one straightforward way to design a general evolvable brain is to use a extra large toolbox of mental modules that can be connected together in many different ways. While each tool might be a carefully constructed jewel, the whole set of tools would have less of an overall structure. Like a pile of logical gates that can be connected many ways, or metabolism sub-networks that can be connected together into many networks. In this case, the secret to general evolvable intelligence would be less in the particular tools and more in having an extra large set of tools, plus some simple general ways to search in the space of tool combinations. A tool set so large that the brain can do most tasks in a great many different ways.

Much of the search for brain innovations and adaptations would then be a search in the space of ways to connect these tools together. Some aspects of this search could happen over evolutionary timescales, some could happen over the lifetime of particular brains, and some could happen on the timescale of cultural evolution, once that got started.

On the timescale of an individual brain lifetime, a search for tool combinations would start with brains that are highly connected, and then prune long term connections as particular desired paths between tools are found. As one learned how to do a task better, one would activate smaller brain volumes. When some brain parts were damaged, brains would often be able to find other combinations of the remaining tools to achieve similar functions. Even losing a whole half of a brain might not greatly reduce performance. And these are all in fact common patterns for human brains.

Yes, something important happened early in human history. Some key event changed the growth rate of human abilities, though not immediate ability levels, and it did this without much changing brain modules and structures, which remain quite close to those of other primates. Plausibly, we had finally collected enough hard-wired tools, or refined them well enough, to let us start to reliably copy each others’ behaviors. And that allowed cultural evolution, a much-faster-than-evolutionary search in the space of practices. Such practices included choices of which combinations of brain modules to activate in which contexts.

What can this view say about the future of brains? On ems, it suggests that human brains have a lot of extra capacity. We can probably go far in taking an em that can do a job task and throwing away brain modules not needed for that task. At some point cutting hurts performance too much, but for many job tasks you might cut 50% to 90% before then.

Regarding other artificial intelligence, it suggests that if we still have a lot to learn via substantially random search, with no grand theory to integrate it all, then we’ll have to focus on collecting more better tools. Machines would gradually get better as we collect more tools. There may be thresholds where you need enough tools to do a certain jobs well, and while most tools would make only small contributions, perhaps there are a few bigger tools that matter more. So key thresholds would come from the existence of key jobs, and from the lumpiness of tools. We should expect progress to be relatively continuous, except perhaps due to the discovery of especially  lumpy tools, or to passing thresholds that enable key jobs to be done.

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How Does Evolution Escape Local Maxima?

I’ve spend most of my intellectual life as a theorist, but alas it has been a while since I’ve taken the time to learn a new powerful math-based theory. But in the last few days I’ve enjoyed studying Andreas Wagner’s theories of evolutionary innovation and robustness. While Wagner has some well-publicized and reviewed books, such as Arrival of the Fittest (2014) and Robustness and Evolvability in Living Systems (2005), the best description of his key results seems to be found in a largely ignored 2011 book: The Origins of Evolutionary Innovations. Which is based on many academic journal articles.

In one standard conception, evolution does hill-climbing within a network of genotypes (e.g, DNA sequence), rising according to a “fitness” value associated with the phenotype (e.g., tooth length) that results from each genotype. In this conception, a big problem is local maxima: hill-climbing stops once all the neighbors of a genotype have a lower fitness value. There isn’t a way to get to a higher peak if one first must travel through a lower valley to reach it. Maybe random noise could let the process slip through a narrow shallow valley, but what about valleys that are wide and deep? (This is a familiar problem in computer-based optimization search.)

Wagner’s core model looks at the relation between genotypes and phenotypes for metabolism in an organism like E. coli. In this context, Wagner defines a genotype as the set of chemical reactions which the enzymes of an organism can catalyze, and he defines a phenotype as the set of carbon-source molecules from which an organism could create all the other molecules it needs, assuming that this source was its only place to get carbon (but allowing many sources of other needed molecules). Wagner defines the neighbors of a genotype as those that differ by just one reaction.

There are of course far more types of reactions between molecules than there are types of molecules. So using Wagner’s definitions, the set of genotypes is vastly larger than the set of phenotypes. Thus a great many genotypes result in exactly the same phenotype, and in fact each genotype has many neighboring genotypes with that same exact phenotype. And if we lump all the connected genotypes that have the same phenotype together into a unit (a unit Wagner calls a “genotype network”), and then look at the network of one-neighbor connections between such units, we will find that this network is highly connected.

That is, if one presumes that evolution (using a large population of variants) finds it easy to make “neutral” moves between genotypes with exactly the same phenotype, and hence the same fitness, then large networks connecting genotypes with the same phenotype imply that it only takes a few non-neutral moves between neighbors to get to most other phenotypes. There are no wide deep valleys to cross. Evolution can search large spaces of big possible changes, and doesn’t have a problem finding innovations with big differences.

Wagner argues that there are also far more genotypes than phenotypes for two other cases: the evolution of DNA sequences that set the regulatory interactions among regulatory proteins, and for the sequences of ribonucleotides or amino acids that determine the structure and chemical activity of molecules.

In addition, Wagner also shows the same applies to a computer logic gate toy problem. In this problem, there are four input lines, four output lines, and sixteen binary logic gates between. The genotype specifies the type of each gate and the set of wires connecting all these things, while the phenotype is the mapping between input and output gates. Again, there are far more genotypes than phenotypes. However, the observant reader will notice that all mappings between four inputs and four outputs can be produced using only four internal gates; sixteen gates is a factor of four more than needed. But in the case of four gates the set of genotypes is not big enough compared to the set of phenotypes to allow easy evolution. For easy innovation, sixteen gates is enough, but four gates is not.

If we used a larger space of genotypes within which the number of logic gates could vary, and if the fitness function had a penalty for using more logical gates, then we’d have a problem. No matter where the genotype started, evolution might quickly cut the number of gates down to the minimum needed to implement its current input-output mapping, and then after that too few neutral changes would be possible to make evolution easy. The same problem seems possible in Wagner’s core model of metabolism; if the fitness function has a penalty for the number of enzymes used, evolution might throw away enzymes not needed to produce the current phenotype, after which too few neutral changes might be possible to allow easy evolution.

Wagner’s seems to suggest a solution: larger more complex systems are needed for robustness to varying environments:

Based on our current knowledge, the metabolic reaction networks of E. coli and yeast comprise more than 900 chemical reactions. However in a glucose minimal environment, more than 60 percent of these reactions are silent. … Overall, in E. coli, the fraction of reactions that would not reduce bio-mass growth when eliminated exceeds 70 percent. This is … a general property of viable networks that have similar complexity. … As a metabolic generalist, the E. coli metabolic network can synthesize its biomass from more than 80 alternative carbon sources. … All these observations indicate that the large metabolic networks of free-living organisms are much more complex than necessary to sustain life in any one environment. Their complexity arises from their viability in multiple environments. A consequence is that these networks appear highly robust to reaction removal in any one environment, where every metabolic networks has multiple natural neighbors. This neutrality, however, is conditional on the environment. (pp.153-154)

I’m not sure this solves the problem, however. In the logic gate toy problem, even if phenotype fitness is given by a weighted average over environments, we’ll still have the same temptation to increase fitness by dropping gates not needed to implement the current best bit mapping. In the case of enzymes for metabolism, fitness given by a weighted average of environments may also promote an insufficient complexity of enzymes. It seems we need a model that can represent the value of holding gate or enzyme complexity in reserve against the possibility of future changes.

I worry that this more realist model, whatever it may be, may contain a much larger set of phenotypes, so that the set of genotypes is no longer much larger, and so no longer guarantees many neutral changes to genotypes. Perhaps a “near neutrality” will apply, so that many genotype neighbors have only small fitness differences. But it may require a much more complex analysis to show that outcome; mere counting may not be enough. I still find it hard to believe that for realistic organisms, the set of possible phenotypes is much less than the set of genotypes. Though perhaps I could believe that many pairs of genotypes produce the same distribution over phenotypes, as environments vary.

Added 10am: Another way to say this: somehow the parameter that sets how much complexity to keep around has to change a lot slower than do most other parameters encoded in the genome. In this way it could notice the long term evolvability benefits of complexity.

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