Monday, 11 May 2015

Brent Jesiek's history of memetics

In 2013 I called for a history of cultural evolution. Something like a more fleshed-out version of my own Memetic Timeline.

However at that stage I hadn't seen Brent Jesiek's 110 page MSc thesis. This presents a comprehensive history of memetics. It mostly concentrates on the period from 1975 to 2003. It is available free of charge online - to ResearchGate members.

Brent Jesiek's history is comprehensive and impressive. It's a history of memetics - rather than a history of cultural evolution - focusing heavily on those thinkers that dealt with the possibility of there being cultural equivalents of genes. This rules out much of the work done in academia on cultural evolution - much of which is still very confused and muddled about this point.

Unfortunately, this focus leaves out much of interest - and some of what it puts in its place is not too interesting. For example, there's quite a large section devoted to the efforts of Aaron Lynch. Alas, Lynch's book on memetics was pretty terrible. Paul Marsden's account of how bad it was is of much better quality. Also, Susan Blackomre doesn't get much space in this history - which doesn't seem very fair, given the scale of her efforts.

Anyway, despite some misplaced emphasis, Brent Jesiek's history is an essential guide to the history of memetics. It's great to have such a resource available online.

Saturday, 9 May 2015

Cultural evolution vs history

Some wondered why we call it "cultural evolution" - rather than "history".

In 2012, Steven Pinker asked:

Does Cultural Group Selection Add Anything to Conventional History?

In 2015, Richard Lewontin asked:

Why do you use cultural evolution instead of cultural history? Why evolution instead of history?

To me these are odd questions to ask - but I think there are reasonable answers:

  • The term "history" has traditionally referred to human evolution after the invention of writing. By contrast, cultural evolution goes back many millions of years and also applies to non-human animals.

  • The term "evolution" conjures up Darwin's famous explanation of how evolution operates. The term "history" fails to do this. The association is appropriate.

  • History has traditionally been studied as part of the humanities. The humanities have historically been characterized by poor quality scientific traditions. In particular, historians widely rejected theory, picturing theories as preconceptions which could distort the facts. As a result, history increasingly turned into a fact-gathering exercise. This is, of course, not a scientific approach to the topic. As a result, many scientists don't want to associate themselves with historians. The historians dirtied their own nest, and many scientists don't want to be tainted by their stench.

We have the terms "evolution" as well as "natural history". They don't mean exactly the same thing. "Evolution" traditionally refers to change - whereas "history" can cover both change and stasis. Also, "evolution" has stronger connotations of gradual change.

Wednesday, 6 May 2015

Cultural evolution: evolutionary frontier

I've talked in the past about Cultural evolution's scientific lag. At first glance it might seem as though cultural evolution is a scientific backwater. There are few conferences or journals. Representation at universities is very patchy. Funding is poor. Sympathetic colleagues are hard to find and progress has been depressingly slow. This doesn't seem like a very attractive package to a budding young scientist. So: what is the attraction?

Though in one sense it is true that theories of cultural evolution lag behind their organic counterparts, in another respect cultural evolution is on the leading edge of evolution itself. If you look at most recent significant changes in the world, many of them involve cultural evolution. For example, memes - more than genes - are responsible for space travel, computers and the internet. Cultural evolution is on the cutting edge.

Cultural evolution is also on the leading edge of evolutionary theory. Organic evolution is a done deal - and has been for over a hundred years. There, researchers are mostly putting the icing on an existing cake. Cultural evolution is where the real action is. It is where new researchers can make an impact and make important discoveries.

Cultural evolution is of enormous social and political significance. A proper scientific understanding of how culture evolves is critical for making good policy decisions. Cultural evolution is much too important to be left to cultural anthropologists, who have failed to get to grips with the topic for over a hundred years and seem to suffer from poor scientific literacy.

Lastly, the role of cultural evolution looks set to become ever more important as time passes. In particular, memetic algorithms - which emulate cultural evolution - look set to play a critical role in the development of machine intelligence. Memetic algorithms and memetic programming are similar to genetic algorithms and genetic programming - only they are inspired by cultural evolution rather than organic evolution. Machines, like their human inventors before them, look set to harness the power of cultural coevolution - in order to attain the rapid rate of change which will fuel their future expansion and prosperity. The "code rush" as some call it. It is on.

Tuesday, 5 May 2015

Tim Lewens: Cultural Evolution: Conceptual Challenges

Tim Lewens has a book on cultural evolution coming out later this year: Cultural Evolution: Conceptual Challenges

His 2004 book - Organisms and Artifacts: Design in Nature and Elsewhere - also related to the topic.

I have generally dismissed Tim Lewens in the past as a feeble-minded meme critic who doesn't know what he is talking about. However the blurb to this book weakly suggests that he is in the process coming round to a sympathetic understanding of cultural evolution. Or maybe not, we will see. Here is the blurb:

Tim Lewens aims to understand what it means to take an evolutionary approach to cultural change, and why it is that this approach is often treated with suspicion. Convinced of the exceptional power of natural selection, many thinkers - typically working in biological anthropology, cognitive psychology, and evolutionary biology - have suggested it should be freed from the confines of biology, and applied to cultural change in humans and other animals. At the same time, others - typically with backgrounds in disciplines like social anthropology and history - have been just as vocal in dismissing the evolutionary approach to culture. What drives these disputes over Darwinism in the social sciences?

While making a case for the value of evolutionary thinking for students of culture, Lewens shows why the concerns of sceptics should not dismissed as mere prejudice, confusion, or ignorance. Indeed, confusions about what evolutionary approaches entail are propagated by their proponents, as well as by their detractors. By taking seriously the problems faced by these approaches to culture, Lewens shows how such approaches can be better formulated, where their most significant limitations lie, and how the tools of cultural evolutionary thinking might become more widely accepted.

Monday, 4 May 2015

Sylvain Magne: What is a meme?

Here's Sylvain Magne - with a 20 minute presentation on the definition of the term "meme".

There's a transcript here.

I'm pleased to see discussion of the topic. However, since space and time are limited, I'll mostly confine my comments to the points where there is disagreement.

I think this topic is best explored using the infrastructure and terminology of information theory. Information theory has useful concepts that formalize this topic - such as the idea of Shannon mutual information - which is useful for formalizes the notion of copying. This article suffers from failing to build on this previous work.

Sylvain defends the notion of a "replicator" - which has proved to be a controversial term. The concept of as replicator was originally promoted by Richard Dawkins - with the admirable aim of enlarging the scope of evolutionary theory beyond the realm of DNA genes. However it has also resulted in much misunderstanding, confusion and criticism. Though for many, it's a foundational concept for memetics, I've generally been quite critical of the replicator terminology.

The biggest problem is that the etymology of "replicator" implies high-fidelity copying - whereas most models of evolutionary processes accept the copying fidelity as a parameter - and do not insist that copying be high fidelity.

I think that the best way to defend the notion of a "replicator" is to abandon the notion of high fidelity copying. That's the approach I take with "repology". This makes "replicator" into a misnomer - but this is still the best option for those wanting to keep the terminology.

Sylvain presents a defense of the "replicator" concept that preserves its notion of high-fidelity copying. His defense hinges on the concept of a "reader". Sylvain's "reader" is a system which identified whether two copies are identical or not. Sylvain gives the example of key copying to illustrate the concept. The lock acts as a reader and determines whether keys are functionally identical or not. Certainly in many evolving systems there are "readers". These typically perform error correction and detection. DNA copying features physical systems which act as readers. The same is true for must cultural systems which copy words. However, for other systems, it is not obvious that a "reader" exists. When ants copy each others' pheremone trails, there's no system deciding whether the behaviours are identical copies or not. Nature often doesn't care much about whether copies are identical or not. It sometimes cares about similarity - but that's a bit different. Rather than dividing the world of copies into those that are identical and those that are not, it is usually better to consider identity to be the extreme end of a continuum of varying levels of similarity.

Scientists sometimes care whether two systems are identical or not. However nature doesn't insist on the critera they use - and different scientists may use different criteria. A geneticist might treat genes with the same base pair sequences as being identical - while someone studying proteins might have a different idea about what 'identical' means in this case.

The concepts of "replicators" and "readers" may seem attractive when dealing with digital genes and memes - but they seem more like added complication when dealing with more general versions of evolutionary theory - where high-fidelity copying is not necessarily present. There, the concept of imperfect copying seems simpler. Variable-fidelity copying makes the concept of a "replicator" functionally redundant. The concept of "similarity" is broader and richer than the idea that copies are either identical or they are not.

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.