Sunday, 19 April 2015

Cultural infant mortality

In the organic realm, infant mortality is an important observed phenomenon, with an elevated infant mortality rate being observed in a wide range of species. The study of elevated infant mortality from an evolutionary perspective is part of what is known as Life history theory.

Several factors account for elevated infant mortality - for example:

  • The small size of infants makes them less able to store resources - and thus more vulnerable to resource fluctuations.
  • Some infants are widely dispersed but face patchy environments - where not all of them can thrive.
  • Infants are often produced in huge numbers - and there aren't enough resources available for them all to survive to adulthood.

In the 1930s, Ronald Fisher proposed a general concept that covers many of these ideas - known as reproductive value. Reproductive value varies over the lifespan of an organism, reflecting their future reproductive potential. Old organisms have low reproductive value (their expected lifespan is lower and their fertility is reduced) and often infants do as well - due to the kinds of factors mentioned above. Fisher proposed that mortality rates could reasonably be expected to be optimised by natural selection to be proportional to the inverse of reproductive value.

Reproductive value is a concept which is closely related to fitness. Like fitness is is quite a general concept. However, as with fitness it is worth distinguishing between actual reproductive value (measured after the fact) and expected reproductive value - which is calculated on the basis of some other predictive theory about how inherited traits and the environment combine to affect the performance of the organism.

Though the concept of reproductive value is very general it is also often vague. For example, it follows that if infants are produced in huge numbers in each generation they will outstrip their resources, their reproductive value will be low - and there will be high levels of infant mortality. However here, high infant mortality follows directly from high birth rates - and invoking the concept of reproductive value didn't really help very much. It also doesn't help to answer the question of why so many offspring are produced in the first place. Isn't mass infant death very wasteful? In the case of widely dispersed seeds facing a patchy environment, some waste seems inevitable. However, in other cases, natural selection between the offspring may play an important role - weeding out those organisms with deleterious mutations or bad gene combinations by making sure that they "fail fast".

This brings us to cultural infant mortality. Culture provides a new domain for life history theorists with many interesting examples. What can be learned? What can life history theory contribute?

First there are some similarities. As with DNA genes, some memes face a patchy environment. Most flyers are trampled into the ground; the street preacher's ranting mostly gets no further than the sidewalk - and so on. Memes are also mass produced in far greater numbers than can ever survive. Radio and TV signals are broadcast in all directions. Some make it into space, where they could survive for a long time, while most others quickly hit dirt and turn into heat. Start-up companies and IT projects also exhibit high rates of infant mortality.

Also as with genes, many memes are end-of-line copies - with a limited lifespan and a low chance of personal reproduction. Most artifacts are like this. They are the equivalent of somatic cells of cultural evolution - their primary purpose is to assist the reproductive memes in the factory that made them - via cultural kin selection. Life history theory treats these end-of-line copies a bit different from germ-line copies.

Unlike DNA genes, the memes in artifacts often don't have very flexible control over their associated life history variables. If your main strategy for persisting is to be hard and strong that doesn't result in very much flexibility regarding senescence rates. DNA has mastered regeneration - and can more flexibly allocate resources to maintenance processes over the course of a lifespan. Artifact regeneration is a thing - as some automobile owners can attest. However, many artifacts are hard for end users to repair and they often get trashed at the end of their natural lifespan.

Another thing that most memes are not very good at yet is growth. Without growth, infants are not small, and so suffer less from predation and mechanical insults. Of course, some cultural forms do grow. Cities, roads networks and telecommunication networks all grow. However, most artifacts don't really grow - and without growth there is much less scope for infant mortality. Many memes aren't good at growing today. However we are still near to the origin of cultural evolution and it seems reasonable to expect that this limitation will disappear once we have easy access to robust molecular manufacturing technology.

High infant mortality is often regarded as a bad thing. However from the perspective of Darwinian processes, high infant mortality has some desirable aspects. If something is going to fail it is often best if it fails fast. Investments in components that are going to fail are often bad investments: it is better to spend the resources on something that is not going to fail. For many long-lived organisms there's a high-intensity selection process around the time of conception: gamete selection. More failures can occur during gestation and around birth. Rather than lamenting these failures, Darwinism suggests that we should regard them more as part of a natural process of weeding out the weak and unfit before they can do more serious damage to a family's resources.

Monday, 13 April 2015

Crack propagation in slow motion

On my first positional inheritance page, I used an illustration of a lighting strike in slow motion - as an illustration of the concept.

Here's a similar video of glass breaking in slow motion:

The video illustrates that cracks propagate from locations other than the branching tips. Some distance behind the tip of the crack is still a possible source of new (usually transverse) cracks. In a Darwinian model of splitting and recombining individuals, the entities that are evolving are crack tips - but this video illustrates that the notion of a crack tip has to be significantly extended in space if it is to result in a good quality model.

The resulting shatter pattern appears to be fairly heavily reticulated. It looks like a network - rather than a simple tree. However, appearances can be deceptive. If you look at the slow motion evolution, each crack forms from an existing crack - and there's a strict parent-offspring relationship that holds everywhere. In this case, the pattern of cracks forms a genuine family tree - something you might not guess at if looking at the resulting static fracture pattern.

Sunday, 12 April 2015

Abby Rabinowitz - a brief history of the meme

Abby Rabinowitz has recently written a brief history of the meme concept:

The Meme as Meme Why do things go viral, and should we care?

Some effort appears to have gone into the article: for one thing, Susan Blackmore, Daniel Dennett and James Gleick were apparently interviewed for it.

Abby seems to have some rather critical comments, though:

Yet, the very breadth of the concept makes it difficult to approach memes from the perspective of serious, observation-based science. In the analogy to genes, memes have inevitably disappointed. As Dawkins himself wrote, memes, as entities, are more vague than genes, where alleles compete to hold the same “chromosomal slots.” Unlike genes, memes are not directly observable and have high rates of mutation. Also, no one seems to be sure if memes exist. On the phone, Blackmore told me “the one good reason” memetics might not be a science: “There has been no example of where some scientific discovery has been made using meme theory, that couldn’t have been made any other way.”
Vagueness was, essentially one of John Maynard Smith's criticisms. IMO, he put it more eloquently, so I'll quote him:

The explanatory power of evolutionary theory rests largely on three assumptions: that mutation is non-adaptive, that acquired characters are not inherited, and that inheritance is Mendelian—that is, it is atomic, and we inherit the atoms, or genes, equally from our two parents, and from no one else. In the cultural analogy, none of these things is true. This must severely limit the ability of a theory of cultural inheritance to say what can happen and, more importantly, what cannot happen.
I've previously replied to this here, saying:

let's assume for a moment that his conclusion is true - and that it is harder to make predictions with cultural evolution than it is with biological evolution.

So what? Theories of cultural evolution are not in competition with theories of biological evolution - they compete with other theories of cultural change that are less inspired by Darwinism.

To expand on this, a theory making vague predictions doesn't make it bad. The issue is whether it does better than competing theories. Similarly, a ten-day whether forecast is going to have some sizeable error bars. That doesn't mean that it isn't the best quality forecast available. Nor does it mean that you should not heed its predictions.

I don't mean to grant the thesis that theories of cultural evolution really are vague (compared to the organic realm). That seems like a difficult claim to test - because you need to compare similar theories in the two realm - and what counts as 'similar' theories seems pretty subjective. I expect the jury will remain out on this issue for some time to come.

As for scientific discoveries that could not have been made without the meme concept - that seems like an unreasonable request to me. Science is Turing complete. Unless you destroy its foundations, prohibiting the use of scientific terminology or theories doesn't create a show-stopping situation. As with patents, there's usually some sort of work around.

The Copernican revolution is a good example of this. Without the concept of a heliocentric model, geocentric models still made accurate predictions. These models were ugly - but they worked and were consistent with the data. This historical episode illustrates how science can stagger on - even with a restricted set of models that excludes the most parsimonious ones.

Refactoring science

Computer programmers have a proverb that goes:

Replace repetitive expressions by calls to a common function

This is a type of operation known as "refactoring". Refactoring - for any non-programmers in the audience - involves reorganising code without changing its function. I think what's currently happening with memetics is a similar type of operation involving refactoring science.

A very common type of refactoring operation involves identifying two pieces of code that perform similar functions and replacing them with calls to a common subroutine. Separate pieces of code that perform similar tasks can arise in many ways. Similar code could be developed independently by different developers. Or it could be duplicated from a shared source and modified for a new purpose.

In science we see essentially the same thing: models are developed independently, turn out to have essentially the same dynamics and then need combining.

A classic recent example of this involves kin selection and group selection. While originally conceived as very different processes, many modern formulations have turned out to be different ways of expressing the same types of dynamics. Group selection and kin selection have turned out to be close synonyms.

Organic evolution and cultural evolution are currently getting the same treatment - in that "universal" models are being developed that cover both cases.

The motivation for refactoring is normally that it prevents duplicated maintenance work. When maintenance effort needs duplicating, it costs more to perform. The branches involved can gradually get further out of step with each other as time passes. This introduces incompatibilities and merging the branches can become increasingly expensive as time passes.

As with refactoring in computer science, the original routines do not need to be performing exactly the same function as one another. Even if they are doing a similar job it often pays to combine them. Sometimes the differences are represented as different parameters. Sometimes they are "lambda functions". Sometimes the differing functionality is encapsulated in pluggable modules.

That's the role that genetics and memetics play in evolutionary theory. They are pluggable modules that are accepted as parameters to a more general evolutionary theory.

That it clearly proposes this refactoring operation is one of the unique features of memetics. It neatly partitions the required changes when adapting evolutionary theory to cover culture into:

  • Changes to evolutionary theory it make it more general;
  • The creation of encapsulated theories of genetics and memetics;

I think that some of the debates over memetics are illuminated by this comparison with refactoring - at least for those with a background in computer science. When refactoring, there are often team members that say features should be being worked on instead. Sometimes the objection that refactoring will introduce bugs is made. Others point to the cost of the refactoring operation. Some say that the code isn't that similar after all, and shouldn't be combined. Some say it's too late to make the change at this stage, and we should learn to live with the old design.

I think we see many of the same objections being made by those involved in evolving modern evolutionary theory. However, this does seem like a pretty attractive refactoring to me. It is worth bearing in mind that science is forever. We should strive to make our models clean and beautiful - for the sake of those that come after us.

Why does this commonality between computer programming and science exist? I think that's a fairly easy one: both science and computer programs involve building and maintaining models of the world - and that's enough to explain the commonality.

Thursday, 9 April 2015

Daniel Dennett: Intelligent design

Here's the video. One of the main themes here is cultural evolution.

Sunday, 5 April 2015

Steven Pinker's closing straw man attack

Steven Pinker concluded his 2012 article attacking cultural evolution with the following "straw man" attack:

No one could be more sympathetic to the application of evolutionary biology to human affairs than I am, and I have made use of many of its tools. But group selection and memetics have been unhelpful, and even evolutionary psychology in its totality can take us only so far. That is because human cultural change is driven by ideas. In the case of language, they are the lexical and grammatical analyses by which listeners make sense of the speech of others; in the case of violence, they are ideologies by which people justify their collective actions, such as religions, Marxism, nationalism, utilitarianism, enlightenment humanism, romantic militarism, and many others. If you reduce these ideas to simple tokens that are spread by contagion or multiply at different rates, and don't considering how their content affects the beliefs and desires of human protagonists, you will end up with a seriously incomplete understanding of cultural change.

It is true that if you reduce ideas to simple tokens that are spread by contagion or multiply at different rates, and don't consider how their content affects their human hosts, you will end up with a seriously incomplete understanding of cultural change. The problem is that nobody ever advocated developing a complete understanding of cultural change by doing that in the first place. This is just a ridiculous straw man concocted by Steven Pinker. He doesn't bother supporting it by any references - because he has none.

Imagine someone saying that if you reduce parasites to genes that multiply at different rates and don't consider how they affect their hosts, you will end up with a seriously incomplete understanding of disease. That would be pretty ridiculous. Nobody ever advocated attempting to understand disease in this way in the first place. This is not a criticism of genes or genetics, it's a misunderstanding of what these concepts mean and how they are applied.

Yes, there are people using "bean-counting" techniques on genes and memes - in population genetics and population memetics. But these folk are not fooled into thinking that frequencies are everything. Frequency analysis is just a tool.

Steven Pinker's closing criticism is a straw man attack. If that's what he thinks memetics is about, it reflects poorly on his understanding of the subject. This puts him in a poor position to offer criticisms - though he doesn't seem to realise this.

Jerry Coyne revisits his objections to memetics

Jerry Coyne revisits his objections to memetics in a recent blog post:

The reasons for spread of memetic traits, I think, are so varied that they differ profoundly and incompatibly with the spread of “genetic” traits via natural selection, which has only one pathway: a trait spreads when it enhances the number of copies of the genes that produce it. In other words, you can reverse-engineer a Darwinian trait by studying how it affects reproduction, but you could never do that with “memetic” traits like music, words, the use of forks, and so on. Each one spreads by a unique pathway, compelled by unique forces.

Supposedly genetics has one pathway: enhancing gene reproduction - while memetics does not. However, in memetics, memes spread by enhancing meme reproduction (and longevity and fidelity, just like in genetics). The claim that this is somehow dissimilar from genetics seems unsubstantiated to me.

Yes, aerodynamics influences aeroplane memes, structure affects girder memes and principles from chemistry affect solar panels. However, similarly, aerodynamics influences gird genes, structure affects tree genes and principles from chemistry affect photosynthesis in plant leaves. There are similar types of "unique forces" in both organic and cultural evolution.

Jerry goes on to revisit the objection that saying that cultural items spread "because they are memes" is an empty tautology - that what we really want to know is the actual factors that make a cultural item spread, and this gets us into varied territory that lacks general principles.

However, the same objection was made against genetics. It was claimed that "survival of the fittest" was just an empty tautology. The classical response to this is to say that here "fittest" should be interpreted as meaning "expected reproductive success on the basis of the actual or expected phenotype". The exact same response applies in memetics too. Scientists have a range of theories about which memes spread and which do not.

Coyne apparently doubts the applicability of evolutionary theory to culture. However this is now well established and there's a large literature on the topic. I've read a lot of this literature, but I see no evidence that Coyne has got any further than "The Meme Machine". Coyne simply isn't familiar with the literature of the subject he is discussing. Others have also pointed this out.

Another issue is that Coyne seems to want memetics to explain why memes do better than others. However it isn't the job of genetics to explain why some genes do better than others. Instead, genetics addresses the details of how mutation and recombination operate. IMO, memetics has the same basic remit in the cultural realm: to explain how cultural mutation and recombination operate. Why some memes do better than others is the result of a multitude of ecological factors - just as is true with genes.

I think Jerry's technical objections to memetics fail. There aren't any credible technical objections to memetics - it's a perfectly sensible approach to studying cultural evolution. My impression is that this has dawned on many of those who study cultural evolution. Many of these folk have apparently given up attempting to find technical flaws in memetics. The most prominent meme critics these days are a bunch of non-experts who frankly don't know what they are talking about.

Saturday, 28 March 2015

Selection is simple, general and explains goodness of fit

The concept of "selection" is at the heart of evolutionary theory. Some people assign that role to natural selection - but the main difference between selection and natural selection seems to be the idea that selection also includes artificial selection. However, the whole idea that selection by man is somehow "unnatural" doesn't deserve much in the way of scientific respect.

The definition of "selection" in a scientific context is the subject of some controversy. To give an example, Hull, Langman and Glenn (1999) define 'selection' as follows:

we define selection as repeated cycles of replication, variation and environmental interaction so structured that environmental interaction causes replication to be differential.

For me, such definitions are problematical. "Selection" is an ordinary English word, which means "a choice from alternatives". Choosing from alternatives strikes me as being a simple and sensible scientific concept. However, the idea of structured "repeated cycles of replication, variation and environmental interaction" is long-winded and complex. I generally favour simple scientific concepts on basic grounds of parsimony.

It is quite common for definitions of natural selection to be very specific. For example, they often mention "populations" or "inherited traits". Being overly specific is a common problem in science. Scientists favour general theories that explain lots of data over more specific ones that don't explain so much. However while inductive inference proceeds from specific examples to general theories, it sometimes takes a while for it to get there. Selection takes place between alternatives. The term "population" carries with it a lot of irrelevant and unnecessary baggage. As for selection involving "inherited traits" - that excludes selection that acts on traits that are not inherited. Yet much the same theory applies to these traits. Defining selection - or natural selection - as involving "inheritance" is a narrow and blinkered approach to the topic.

The idea of choosing from alternatives is really a very general one. If you think about it, practically any form of change can be described as being the result of a selection between different types of possible change. Selection is often contrasted with mutation - but even mutations can be described as a form of choice - a choice between different possible types of mutation. In evolutionary biology selection of which organisms die, which organisms reproduce and which organisms are chosen as mates are considered to be important forms of selection - while selection of which mutations take place and which do not is not generally considered to be a form of "selection" at all. The "selection" criteria for mutations is often assumed to be pretty trivial: mutations are chosen "at random".

Does the generality of the concept of selection make the concept any less scientific? If practically any change can be explained in terms of selection, then isn't invoking selection as an explanation vacuous? Not really: scientific explanations usually say what type of entities are being selected. An explanation invoking selection on organisms can be perfectly scientific and testable. Specifying the entities being selected in a theory rules out a wide range of other selective explanations. Also, an explanation in terms of selection is always possible - but it might not be likely: other explanations might be more parsimonious. Compare with information. You can describe anything in terms of information. However we still have useful theories associated with information. Being very general is not necessarily a fatal flaw.

In biology, selection is the primary explanation of adaptation. However, goodness of fit is invoked in other areas of science - and very often selection is involved in its creation. For example, observation selection explains goodness of fit between observers and the universe they observe.

There's a grand thesis associated with selection in which selection explains most goodness of fit. Gary Cziko and Donald Campbell are among the proponents of this view. For example, they describe learning strategies as either "instructional" or "selectionist" and then argue that what looks like instructional learning often turns out to be selectionist. This view also has its critics - who describe the act of labeling "instructional" learning as being really "selectionist" as obtuse. For example, Henry Plotkin raises this objection in Evolutionary Worlds Without End.

My sympathies lie with Cziko and Campbell on this issue - though I have some issues with their presentation. It's better to have a broad concept of selection - and this leads naturally to a grand unified theory of selection in which selection explains most goodness of fit. The idea means that selection must be involved in all template copying. For example, the "goodness of fit" between a footprint in the sand and the corresponding foot must be explicable in terms of selection acting against the grains of sand that were displaced. This is a somewhat counter-intuitive use of the term "selection" - but it perfectly possible to imagine the foot as the selecting agent and the grains of sand as being the selectees.

The existence of goodness of fit usually means that - at some stage a selector has acted on alternative choices - and those remaining 'fit' the selector. Of course goodness of fit by chance is always a possibility. However it is a possibility that is usually easily quantified and rejected. Almost all goodness of fit in nature can be attributed to selection.

Sunday, 22 March 2015

Fitness landscapes and positional inheritance

Fitness landscapes are a common way of visualizing the relationship between variables associated with an organism and fitness. The height of the landscape represents fitness and the domain over which the fitness landscape is defined is composed of other variables associated with the organism. Most frequently the information associated with heritable elements is used - but other variables affecting fitness could be included as well - such as environmental factors.

In this post, we will apply the idea of fitness landscapes to simple systems involving positional inheritance. Hopefully this will help to illustrate how the concept of 'fitness' applies to these kinds of system. To create a plot of fitness, we have to say what we mean by fitness. Fitness is a notoriously overloaded and slippery idea in biology - as was once explained in a book chapter titled "An Agony in Five Fits". Here, we won't proscribe any particular definition of fitness, but rather will show how to apply some common definitions of fitness.

The first aspect of measuring fitness is to define what entities you are measuring the fitness of. If there are multiple types of organism in a system, you have to say which one you are interested in tracking the fitness of. In simple systems involving positional inheritance this decision is often relatively simple: since there's only one main candidate entity. For example, with lightning strikes tracking the reproducing tips of the lightning are the obvious candidate. With stream systems, the branching tips of the streams themselves would be the most obvious object of study. With propagating cracks, the crack tips would be the object of study. With diffusion-limited aggregation, the available aggregation points would be what was tracked. In many of these cases, the precision of the available measuring instruments may be a factor in deciding exactly what entities are tracked.

Having selected the entities to be measured, the next thing to do is to decide how to measure fitness. Although there are many fitness metrics used for different purposes, we can categorize them in a few main ways. Fitness metrics can be:

  • Relative or absolute - depending on whether you are interested in relative success or absolute results;
  • Expected or actual - actual fitness measure growth rates while expected fitness can be calculated in advance;
  • Short or long term - the time horizon affects fitness measurements: offspring don't always result in grandchildren;
  • Generational or per unit of time - measuring growth in generational time units can sometime be useful.
These all apply to positional inheritance systems - though generational measures of fitness are not much use there. It's like using generational fitness metrics with bacteria. Bacteria are mortal and do have lifespans - but their lifespans are not much like the human three score years and ten. It's more a case that the bacteria live until they have a fatal accident - and the frequency of bacterial deaths are heavily determined by environmental factors. Thus, the lifespan of a bacterium is not usually a particularly useful or interesting figure. Using generational fitness with simple positional inheritance systems is a lot like this.

A fitness landscape is usually a plot of fitness over gene space. The peaks illustrate where well-adapted organisms are likely to be found. The roughness of the fitness landscape influences whether and how quickly evolving organisms will be able to find the peaks.

With simple positional inheritance systems, the 'genes' in question are positions - since position is one of the main things that is inherited in these systems. So the domain of the fitness landscape plot is usually simple two or three dimensional space. Fitness measures how likely branching or splitting is to take place at points in that space. Since reproduction typically requires resources, fitness can be reasonably expected to be correlated with resource availability.

It is common for fitness landscapes to change over time. As the environment changes, different genetic combinations are favored - and the fitness landscape shifts dynamically. With simple positional inheritance systems fitness landscapes tend to change in a predictable manner - the highest peaks tend to be systematically eroded. Because reproduction requires resources, takes place where resources are plentiful and depletes local resources, resource-rich areas will be systematically exploited and eliminated.

Fitness landscapes only track the parameters specified in their domain. If other factors affect fitness, the calculated fitnesses will not be accurate if these are omitted. For example, it is common to leave environmental factors out of fitness landscape plots.This can result in a lack of realism. With simple positional inheritance systems, fitness can also depend on more than positional factors. For example, consider a spreading fire. The reproduction rate of flames will be heavily influenced by positional factors - such as the local availability of combustible material. However other factors can also affect the rate of flame reproduction - such as the wind direction and the temperature - these are often a function of time as well as position. If there are more factors you can add them to the domain of the fitness landscape - but then you get a more complex plot in a higher dimensional space - which might not be so easy to make use of.


Saturday, 21 March 2015

Richard Dawkins on memes, Oxford, 2014

The blurb reads: An extract from Richard Dawkin's open Q&A session at the Oxford Union on 18th February 2014.