A New, Improved, Completely Whacky Theory of Evolution
This blog posts presents some really weird, speculative science, that I take with multiple proverbial salt-grains ... but, well, wouldn't it be funky if it were true?
The idea came to mind in the context of a conversation with my old friend Allan Combs, with whom I co-edit the online journal Dynamical Psychology.
It basically concerns the potential synergy between two apparently radically different lines of thinking:
Morphic Fields
The basic idea of a morphic field is that, in this universe, patterns tend to continue -- even when there's not any obvious causal mechanism for it. So that, for instance, if you teach thousands of rats worldwide a certain trick, then afterwards it will be easier for additional rats to learn that trick, even though the additional rats have not communicated with the prior one.
Sheldrake and others have gathered a bunch of evidence in favor of this claim. Some say that it's fraudulent or somehow subtly methodologically flawed. It might be. But after my recent foray into studying Ed May's work on precognition, and other references from Damien Broderick's heartily-recommended book Outside the Gates of Science (see my previous blog posts on psi), I'm becoming even more willing than usual to listen to data even when it goes against prevailing ideas.
Regarding morphic fields on the whole, as with psi, I'm still undecided, but interested. The morphic field idea certainly fits naturally with my philosophy that "the domain of pattern is primary, not the domain of spacetime"
Estimation of Distribution Algorithms
EDA's, on the other hand, are a nifty computer science idea aimed at accelerating artificial evolution (that occurs within software processes)
Evolutionary algorithms are a technique in computer science in which, if you want to find/create a certain object satisfying a certain criterion, you interpret the criterion as a "fitness function" and then simulate an "artificial evolution process" to try to evolve objects better and better satisfying the criterion. A population of candidate objects is generated at random, and then, progressively, evolving objects are crossed-over and mutated with each other. The fittest are chosen for further survival, crossover and mutation; the rest are discarded.
Google "genetic algorithms" and "genetic programming" if this is novel to you.
This approach has been used to do a lot of practical stuff -- in my own work, for example, I've evolved classification rules predicting who has cancer or who doesn't based on their genetic data (see Biomind); evolved little programs controlling virtual agents in virtual worlds to carry out particular tasks (see Novamente); etc. (though in both of those cases, we have recently moved beyond standard evolutionary algorithms to use EDA's ... see below...)
EDA's mix evolutionary algorithms with probabilistic modeling. If you want to find/create an object satisfying a certain criterion, you generate a bunch of candidates -- and then, instead of letting them cross over and mutate, you do some probability theory and figure out the patterns distinguishing the fit ones from the unfit ones. Then you generate new babies, new candidates, from this probability distribution -- throw them into the evolving population; lather, rinse, repeat.
It's as if, instead of all this sexual mating bullcrap, the Federal gov't made an index of all our DNA, then did a statistical study of which combinations of genes tended to lead to "fit" individuals, then created new individuals based on this statistical information. Then these new individuals, as they grow up and live, give more statistical data to throw into the probability distribution, etc. (I'd argue that this kind of eugenics is actually a plausible future, if I didn't think that other technological/scientific developments were so likely to render it irrelevant.)
Martin Pelikan's recent book presents the idea quite well, for a technical computer science audience.
Moshe Looks' PhD thesis presents some ideas I co-developed regarding applying EDA's to automated program learning.
There is by now a lot of mathematical/computational evidence that EDA's can solve optimization problems that are "deceptive" (hence very difficult to solve) for pure evolutionary learning. To put it in simple terms, there are many broad classes of fitness functions for which pure neo-Darwinist evolution seems prone to run into dead ends, but for which EDA style evolution can jump out of the dead ends.
Morphic Fields + EDA's = ??
Anyway -- now how do these two ideas fit together?
What occurred to Allan Combs and myself in an email exchange (originating from Allan reading about EDA's in my book The Hidden Pattern) is:
If you assume the morphic field hypothesis is true, then the idea that the morphic field can serve as the "probability distribution" for an EDA (allowing EDA-like accelerated evolution) follows almost immediately...
How might this work?
One argument goes as follows.
Many aspects of evolving systems are underdetermined by their underlying genetics, and arise via self-organization (coupled to the environment and initiated via genetics). A great example is the fetal and early-infancy brain, as analyzed in detail by Edelman (in Neural Darwinism and other writings) and others. Let's take this example as a "paradigm case" for discussion.
If there is a morphic field, then it would store the patterns that occurred most often in brain-moments. The brains that survived longest would get to imprint their long-lasting patterns most heavily on the morphic field. So, the morphic field would contain a pattern P, with a probability proportional to the occurrence of P in recently living brains ... meaning that occurrence of P in the morphogenetic field would correspond roughly to the fitness of organisms containing P.
Then, when young brains were self-organizing, they would be most likely to get imprinted with the morphic-field patterns corresponding to the most-fit recent brains....
So, if one assumes a probabilistically-weighted morphic field (with the weight of a pattern proportional to the number of times it's presented) then one arrives at the conclusion that evolution uses an EDA ...
Interesting to think that the mathematical power of EDA's might underly some of the power of biological evolution!
The Role of Symbiosis?
In computer science there are other approaches than EDAs for jumping out of evolutionary-programming dead ends, though -- one is symbiosis and its potential to explore spaces of forms more efficiently than pure evolution. See e.g. Richard Watson's book from a couple year back --
Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity
on the Gradualist Framework of Evolution
and, also, Google "symbiogenesis." (Marginally relevantly, I wrote a bit about Schwemmler's ideas on symbiogenesis and cancer , a while back.)
But of course, symbiosis and morphic fields are not contradictory notions.
Hypothetically, morphic fields could play a role in helping organisms to find the right symbiotic combinations...
But How Could It Be True?
How the morphic fields would work in terms of physics is a whole other question. I don't know. No one does.
As I emphasized in my posts on psi earlier this year, it's important not to reject data just because one lacks a good theory to explain it.
I do have some interesting speculations to propound, though (I bet you suspected as much ;-). I'll put these off till another blog post ... but if you want a clue of my direction of thinking, mull a bit on
http://www.physics.gatech.edu/schatz/clocks.html
The idea came to mind in the context of a conversation with my old friend Allan Combs, with whom I co-edit the online journal Dynamical Psychology.
It basically concerns the potential synergy between two apparently radically different lines of thinking:
- Rupert Sheldrake's idea of "morphic fields"
- The notion of EDA's (Estimation of Distribution Algorithms) in computer science
Morphic Fields
The basic idea of a morphic field is that, in this universe, patterns tend to continue -- even when there's not any obvious causal mechanism for it. So that, for instance, if you teach thousands of rats worldwide a certain trick, then afterwards it will be easier for additional rats to learn that trick, even though the additional rats have not communicated with the prior one.
Sheldrake and others have gathered a bunch of evidence in favor of this claim. Some say that it's fraudulent or somehow subtly methodologically flawed. It might be. But after my recent foray into studying Ed May's work on precognition, and other references from Damien Broderick's heartily-recommended book Outside the Gates of Science (see my previous blog posts on psi), I'm becoming even more willing than usual to listen to data even when it goes against prevailing ideas.
Regarding morphic fields on the whole, as with psi, I'm still undecided, but interested. The morphic field idea certainly fits naturally with my philosophy that "the domain of pattern is primary, not the domain of spacetime"
Estimation of Distribution Algorithms
EDA's, on the other hand, are a nifty computer science idea aimed at accelerating artificial evolution (that occurs within software processes)
Evolutionary algorithms are a technique in computer science in which, if you want to find/create a certain object satisfying a certain criterion, you interpret the criterion as a "fitness function" and then simulate an "artificial evolution process" to try to evolve objects better and better satisfying the criterion. A population of candidate objects is generated at random, and then, progressively, evolving objects are crossed-over and mutated with each other. The fittest are chosen for further survival, crossover and mutation; the rest are discarded.
Google "genetic algorithms" and "genetic programming" if this is novel to you.
This approach has been used to do a lot of practical stuff -- in my own work, for example, I've evolved classification rules predicting who has cancer or who doesn't based on their genetic data (see Biomind); evolved little programs controlling virtual agents in virtual worlds to carry out particular tasks (see Novamente); etc. (though in both of those cases, we have recently moved beyond standard evolutionary algorithms to use EDA's ... see below...)
EDA's mix evolutionary algorithms with probabilistic modeling. If you want to find/create an object satisfying a certain criterion, you generate a bunch of candidates -- and then, instead of letting them cross over and mutate, you do some probability theory and figure out the patterns distinguishing the fit ones from the unfit ones. Then you generate new babies, new candidates, from this probability distribution -- throw them into the evolving population; lather, rinse, repeat.
It's as if, instead of all this sexual mating bullcrap, the Federal gov't made an index of all our DNA, then did a statistical study of which combinations of genes tended to lead to "fit" individuals, then created new individuals based on this statistical information. Then these new individuals, as they grow up and live, give more statistical data to throw into the probability distribution, etc. (I'd argue that this kind of eugenics is actually a plausible future, if I didn't think that other technological/scientific developments were so likely to render it irrelevant.)
Martin Pelikan's recent book presents the idea quite well, for a technical computer science audience.
Moshe Looks' PhD thesis presents some ideas I co-developed regarding applying EDA's to automated program learning.
There is by now a lot of mathematical/computational evidence that EDA's can solve optimization problems that are "deceptive" (hence very difficult to solve) for pure evolutionary learning. To put it in simple terms, there are many broad classes of fitness functions for which pure neo-Darwinist evolution seems prone to run into dead ends, but for which EDA style evolution can jump out of the dead ends.
Morphic Fields + EDA's = ??
Anyway -- now how do these two ideas fit together?
What occurred to Allan Combs and myself in an email exchange (originating from Allan reading about EDA's in my book The Hidden Pattern) is:
If you assume the morphic field hypothesis is true, then the idea that the morphic field can serve as the "probability distribution" for an EDA (allowing EDA-like accelerated evolution) follows almost immediately...
How might this work?
One argument goes as follows.
Many aspects of evolving systems are underdetermined by their underlying genetics, and arise via self-organization (coupled to the environment and initiated via genetics). A great example is the fetal and early-infancy brain, as analyzed in detail by Edelman (in Neural Darwinism and other writings) and others. Let's take this example as a "paradigm case" for discussion.
If there is a morphic field, then it would store the patterns that occurred most often in brain-moments. The brains that survived longest would get to imprint their long-lasting patterns most heavily on the morphic field. So, the morphic field would contain a pattern P, with a probability proportional to the occurrence of P in recently living brains ... meaning that occurrence of P in the morphogenetic field would correspond roughly to the fitness of organisms containing P.
Then, when young brains were self-organizing, they would be most likely to get imprinted with the morphic-field patterns corresponding to the most-fit recent brains....
So, if one assumes a probabilistically-weighted morphic field (with the weight of a pattern proportional to the number of times it's presented) then one arrives at the conclusion that evolution uses an EDA ...
Interesting to think that the mathematical power of EDA's might underly some of the power of biological evolution!
The Role of Symbiosis?
In computer science there are other approaches than EDAs for jumping out of evolutionary-programming dead ends, though -- one is symbiosis and its potential to explore spaces of forms more efficiently than pure evolution. See e.g. Richard Watson's book from a couple year back --
Compositional Evolution: The Impact of Sex, Symbiosis, and Modularity
on the Gradualist Framework of Evolution
and, also, Google "symbiogenesis." (Marginally relevantly, I wrote a bit about Schwemmler's ideas on symbiogenesis and cancer , a while back.)
But of course, symbiosis and morphic fields are not contradictory notions.
Hypothetically, morphic fields could play a role in helping organisms to find the right symbiotic combinations...
But How Could It Be True?
How the morphic fields would work in terms of physics is a whole other question. I don't know. No one does.
As I emphasized in my posts on psi earlier this year, it's important not to reject data just because one lacks a good theory to explain it.
I do have some interesting speculations to propound, though (I bet you suspected as much ;-). I'll put these off till another blog post ... but if you want a clue of my direction of thinking, mull a bit on
http://www.physics.gatech.edu/schatz/clocks.html
posted by Ben at 9:56 AM
7 Comments:
The main problem with Morphic Field Theory is, that the universe - at the fundamental level - does not care nor even "know" about the meaning of information stored in brains, in exactly the same way that your CoreDuo CPU doesn't know about the stuff you see in a browser window. Morphic Fields require a god-like intelligence that creates, coordinates and projects meaning in biological organisms and as such is no different than, say, Christian religion.
Sadly, I feel the sometimes purposely misleading language of physicists is party to blame for the percieved scientific potential of such ideas, because they use concepts like the infamous "Observer" when talking about interactions with matter where there really shouldn't be any humanization going on.
Udo:
Certainly the morphic field theory (which may or may not be true) does NOT intrinsically require any kind of centralized observer or god-like intelligence.
It just requires some kind of non-local connectedness -- vaguely analogous to (and possibly related to) though probably different from the kind involved in quantum mechanics; and potentially related to the kind involved in nonlinear resonance phenomena.
For sure, I could formulate a mathematical model of the universe in which morphic resonance exists but there is no god-like mind.
Your confidence about the operation of the universe at a fundamental level seems misplaced, given that we don't even have a consistent, universal theory of physics explaining all available physics data ... and given that the capability of physics to explain chemistry, biology and so forth is at this point largely a promissory note.
I have a math PhD and studied plenty of physics in university, and am not so naive as to be misled by polysemy of words like "observer", "relativity" and "energy" and so forth.
As in my comments on psi, I think the important thing is to grapple with Sheldrake's data and related data others have gathered. Rejecting data based on one's theoretical hypotheses needs to be done with great care, and I don't see you exercising any such care in your response.
I concede that my knowledge of the universe at the basic level may at best be described as hopelessly incomplete, as is anybody else's. I also feel that perhaps I didn't get my original point across, so I apologize.
I don't reject the data. I reject a simplistic interpretation of this data that is bordering on shamanism.
But maybe you can make a convincing argument on why brains (and the way they perceive and organize reality) are supposed to be different from the rest of all chemical and physical systems?
This is not an attack on you or your education or your intellectual achievements, there is no need to get all defensive. As a scientist I believe Morphic Fields are a supernatural explanation and do not constitute a scientificly valid framework for explaining anything, though.
Udo:
It's not obvious that, if the morphic field idea is correct, it applies only at the level of biological organisms. Conceivably it could apply at the level of chemical synthesis as well.
Also, it's not terribly surprising if different physical properties emerge at different levels of organization in complex systems, is it?
I do not claim to have a physics theory of morphic fields ... I'm just not ruling out that one might exist ... and I'm perplexed by the data of Sheldrake, May and others pointing in the direction that such phenomena might conceivably be real...
I don't think there is anything shamanistic or religious in my thinking or writing on these topics. I am non-religious, at times anti-religious. I just don't take for granted that our current scientific theories of the universe are anywhere near complete.
I am really not getting the point "morphic fields==animism/supernaturalism". The hypothesis of morphic fields simply states that spatial and temporal information about natural phenomena somehow is "stored" in the environment and later is re-used by phenomena of the same category of the information stored. (Say, crystal growth.) No consciousness, no intelligence involved.
Well, we know that holograms can store complex tridimensional information about an object in a medium, in a purely analogic way, which is not very different in essence from what a morphic field would do - except that holograms are a well-known phenomenon while morphic fields are a hypothesis. So, since we don't think that holograms are the work of spirits or demons, why should we think that of morphic fields?
You make some very good points. However, there are several things I just can't get on board with:
Holograms, for example, are explainable. It's a process that's based on scientific theories that are interconnected and mutually supporting. At least large parts of them can be explained and validated, they can be used to make predictions about the world as we see it and they also provide layered explanations on how stuff actually works.
Morphic Field Theory is just a collection of postulations. Of course, most theories start out like that. So here are some points that struck me as dubious when it comes to the actual content of the theory:
Essentially, this is a supposed mechanism for the transfer of information. Looking at the complexity of, say, brains and at the stunning range of individual differences, how can data be transferred from one brain to another without some intelligence sorting it all out?
How can a non-intelligent mechanism determine what molecules and neuronal connections are relevant for this? How can this mechanism even determine what information is encoded in those molecules and neurons? How are neurochemicals so fundamentally different from all the other matter in the universe? And if they are not, why can't we observe this morphic interaction in a laboratory with everyday-chemicals?
Then, when it comes to actually write this data back into other organisms: how exactly does that work? Is electric charge being transferred somehow? Are molecules altered or even created? Where does that energy come from? How does this mechanism know what structures to alter?
Don't get me wrong, those objections certainly don't mean that Morphic Fields can't be real. I just can't see the inner workings of this mechanism come together without an intelligent entity that determines what structures constitute "meaning" in reality and which ones don't. The scientific view in this regard is based on the notion that all matter adheres to the same set of rules, which totally breaks under Morphic Field Theory.
PS: I get that Morphic Fields could be (and possibly would have to be) applied not only to organisms. But show me an experiment that proves Morphic Fields in ordinary matter. At least with the complexity of biological beings, we have something to speculate about ;-)
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