Cultural Evolution

Theories of biological evolution continue today to shed light on the details of life on Earth. Yet there is one species whose curious properties challenge – and defy, according to many thinkers – biological explanations. Homo sapiens is unique in that it has developed culture, a term that scientists use to define the complex sum of language, clothing, diets, ceremonies, religion, art, design, engineering, technology, etc. Could culture be explained by theories of biological evolution?

Dawkins takes one step further to extend the explanatory power of the gene-centered perspective so as to include subjects such as animal artifacts and manipulation by defining the ‘central theorem’ of the extended phenotype: “An animal’s behaviour tends to maximize the survival of the genes ‘for’ that behaviour, whether or not those genes happen to be in the body of the particular animal performing it” (1982: 233) [my italics]. This means that the behaviors exhibited by an organism because of a parasite affecting it should be traced back to the parasite’s genes. Similarly, the dams that beavers build are included in the phenotype of the beaver genes, and the evolution of these dams could be analyzed within a science of “extended genetics”. (1982: 203)

The torrent of tools and behaviors called ‘culture’ may look as if it belongs to what Dawkins means by extended phenotype. However, the author claims that culture does not qualify for the concept of extended phenotype because cultural information is not transmitted through genetic means. So although some of it seems to have positive effects on the fitness of our species, culture is not a direct product of genetic evolution (Dawkins 1976: 189–90).

According to Dennett, culture is “an extra medium of design preservation and design communication” (1995: 338). With culture, we get another layer of designed objects which begs for explanation and evolution, the great ‘designer’, is the first explanation that comes to mind. Only this time, it works on a different medium:

Today the Earth is embedded with artifacts like computer networks and circuses that cannot be accounted for by appeal to either the properties of matter or biological evolution. That is, biological evolution does not provide us with adequate explanatory power to account for the existence of computers any more than the properties of matter can explain the existence of giraffes. Computers are manifestations of yet another causal principle: the evolution of culture. (Gabora 1996)

To emphasize the distinction between the biological medium and the cultural medium, British psychologist Henry Plotkin reminds us that a natural science of culture can be of two different kinds, working at different levels. The first concentrates on the claim that culture is a direct consequence of the biological evolution of humans. The other sees culture as an evolutionary system on its own, independent from – or partly dependent to – the biological evolution.¹ (2000: 70)

Lineages of Objects

The idea that culture develops by some kind of descent with modification has been elaborated by many thinkers since before Darwin published The Origin of Species. After all, compared to the geological timescales in which biological change operates, it is much more obvious that “any new thing that appears in the made world is based on some object already in existence” (Basalla 1988: 45). Michl notes that, especially in the 18th century, thinkers such as David Hume, Edmund Burke, Adam Smith and Adam Ferguson came close to an evolutionary perspective on human society. What Darwin did, by revealing the mechanism by which design emerges in nature, was to offer a much more solid structure to these historical interpretations of the apparent design behind elements of human culture, from institutions like language, laws and money to physical artifacts. (2002)

One particularly interesting example is Augustus Henry Pitt-Rivers, a Victorian General influenced by Darwin, who worked on creating evolutionary trees out of his personal collection of primitive weapons and tools (Basalla 1988: 16–7).

Figure 6. Pitt-Rivers’s evolutionary tree of primitive tools and weapons. (Basalla 1988: 19)

However, just as Darwin observed nature on the phenotype level without the knowledge of genetics, these attempts to ‘darwinize’ culture depended on formal observations of cultural objects, without an information-theoretical perspective like the one that genetics provided in biology in the 20th century. More importantly, they could not explain cases where cultural traits do not create advantages for the people exhibiting them.

Although the American anthropologist F. T. Cloak published a paper with similar ideas in 1975, it is Dawkins, in his 1976 book The Selfish Gene, who is acknowledged to have laid the foundations for a new perspective in theories of cultural evolution that attempted to solve these problems. After describing and advocating the ‘gene’s eye view’ in biology throughout the book, he introduces the concept of meme in the last chapter and looks at culture from the meme’s eye view.


Dawkins sees the meme as a new kind of replicator with its own self-interest that has created culture as its ‘vehicle’ (i.e. phenotype). Meme is the counterpart in cultural evolution of gene in biological evolution; it is a piece of information that codes for a cultural trait – it is the unit of cultural transmission. (Dawkins created the word by abbreviating the Greek work mimeme: “that which is imitated”.) According to the memetic theory (or memetics), every element of human culture is subject to the algorithm of evolution as the memes that code for them are replicated, mutated and selected.

Examples of memes are tunes, ideas, catch-phrases, clothes fashions, ways of making pots or of building arches. Just as genes propagate themselves in the gene pool by leaping from body to body via sperms or eggs, so memes propagate themselves in the meme pool by leaping from brain to brain via a process which, in the broad sense, can be called imitation. If a scientist hears, or reads about, a good idea, he passed it on to his colleagues and students. He mentions it in his articles and his lectures. If the idea catches on, it can be said to propagate itself, spreading from brain to brain. As my colleague N.K. Humphrey neatly summed up an earlier draft of this chapter: `… memes should be regarded as living structures, not just metaphorically but technically. When you plant a fertile meme in my mind you literally parasitize my brain, turning it into a vehicle for the meme’s propagation in just the way that a virus may parasitize the genetic mechanism of a host cell.’ (Dawkins 1976: 192)

This chapter was the manifesto that started the science of memetics, and like every revolutionary first step, it contains some problematic tentative definitions and examples to be refined in later stages. Perhaps it is better to mention some of these problems before proceeding to look at the implications of the memetic theory.

Dawkins admits that he was “insufficiently clear about the distinction between the meme itself, as replicator, on the one hand, and its ‘phenotypic effects’ or ‘meme products’ on the other”. He goes on to tell that the meme should be defined as a unit of information residing in the brain, and its phenotypic effects (what Wilkins calls phemotype) as outward manifestations in the form of words, music, images, styles of clothes, etc. (1982: 109) Dennett expresses this duality with a memorable phrase: “A wagon with spoked wheels carries not only grain or freight from place to place; it carries the brilliant idea of a wagon with spoked wheels from mind to mind.” (1991: 204) Similarly, Salingaros and Mikiten posit that an architectural style exists in two different forms: (1) as an ideology taught in schools and described in books, and (2) as images represented in the built environment (2002). Nevertheless, the distinction between memotype and phemotype remains a topic of discussion. (see Aunger 2000: 6; Hull 2000: 59–60; Blackmore 1999: 63–66)

Another problem with the famous passage quoted above is that some of the examples that Dawkins cites as memes (scientific theories, ways of making pots, etc.) are too big and complex to be units of selection. Dawkins clearly expresses that he is aware of the issue and that he uses the ‘X meme’ talk (e.g. the god meme) as a shortcut (1976: 195), but this simplistic language, still adopted by some memeticists today without warning, is often turned against the memetic theory by critics speaking in sarcastic tones in order to condemn it as an oversimplification.¹

Although it is true that memeticists have not agreed yet upon a single strict definition for the term meme (Aunger 2000: 2–5), it is highly likely that, whatever it will be, it will not be that simple a definition to allow us to talk about “the chair meme”, let alone “the general relativity meme”, for the same reasons why we do not talk about “the bird gene”, or even “the wing gene”, or even “the feather gene”, and so on. As John Wilkins (1998) reminds, “there is no smooth reduction of memetic structures from cultural behavior to atomic memes, just as there is no smooth reduction from phenotypic traits to single genes”. In this perspective, a cultural object is a memetic construct, resulting from the interactions of maybe thousands of memes that we may not readily map one-to-one onto the properties that we perceive and talk about on a semantic level.

Maybe the most revolutionary side of the memetic theory is that it answers the ‘Cui bono?’ question in a radically different way. Unlike the other theories of cultural evolution that existed before it, memetics treats items of culture as replicators and vehicles on their own right without appealing to genes or ‘person’s, and asserts that the ultimate beneficiary of culture is culture itself – that is, another type of cluelessly replicating bits of information. For instance, “each image has a set of attributes that makes it more or less likely to stick in memory and to be transmitted to others” (Salingaros & Mikiten 2002). Memes spread themselves without regard to whether they are useful (e.g. agriculture), neutral (e.g. music), or positively harmful (e.g. cigarettes) to us (Blackmore 1999: 7).

Memes don’t necessarily make you more biologically fit, nor are they necessarily going to make you less fit. Memes aren’t fit themselves simply because they make you live healthier lives. Memes are fit only insofar as they are propagated successfully; forget the effects they have on biology. (Wilkins 1998)

Furthermore, Hull emphasizes that memetic processes should not be analyzed with genetic evolution in mind as the essential analog to which other forms of evolution must resemble: the only common basis is the algorithm of evolution (replication, variation and selection) and the further details may differ endlessly. All examples of evolution should be treated equally. (Hull 2000: 45) The anthropologist William Durham called it “Campbell’s Rule” in reference to the American psychologist Donald Campbell who expressed this principle in 1960.

We need to remember Campbell’s Rule when we compare memes and genes. Genes are instructions for making proteins, stored in the cells of the body and passed on in reproduction. Their competition drives the evolution of the biological world. Memes are instructions for carrying out behaviour, stored in brains (or other objects) and passed on by imitation. Their competition drives the evolution of the mind. Both genes and memes are replicators and must obey the general principles of evolutionary theory and in that sense are the same. Beyond that they may be, and indeed are, very different — they are related only by analogy. (Blackmore 1999: 17)

One of the most popular criticisms towards memetics is that no one knows exactly where and how memes are encoded (Aunger 2000: 6). We know now that genes are encoded with the sequence of four bases in a long chain called DNA, and the details of that mechanism are meticulously studied by scientists all over the world. What is the substrate for memes? Wilkins, for one, argues that many memes reside as neural net structures in human brains, but many also emerge at a higher cultural level: “All memes have neural substrates, but not all are encoded in those substrates” (1998). Hull suggests at this point that memeticists should work on both theoretical and experimental fronts simultaneously with tentative operational definitions and assumptions; more sound ones can emerge only as we start doing memetics (2000: 46–9).

One other fundamental attack on the memetic theory concerns the ways in which memetic novelty occurs – in other words, how human creativity works. Unlike the evolutionary processes where there is no foresight, the argument goes, humans are ‘autonomous’ designers with predetermined purposes in their minds (Blackmore 1999: 249). In fact, Dawkins himself makes use of this distinction to teach evolution when he likens nature to a blind watchmaker, as opposed to a true watchmaker (of Paley) who has foresight and plans his actions according to a specific purpose (1986: 5). So human design cannot be evolutionary because it is “apparently both directional and able to take enormous leaps (in evolutionary jargon, ‘saltatory’)” (Gatherer 1999: 96). But is it really?

1 See Adam Kuper’s section titled ‘The Ecology of Ideas’ in Aunger 2000 for a perfect example.

An Evolutionary Account of the Human Creative Process

In Darwin’s Dangerous Idea, Dennett asks how Johann Sebastian Bach was able to create the St. Matthew Passion, and goes on to explain that the composition was the result of years of work by Bach who had the “benefit of forty-two years of living” and who was influenced by the Christianity which took roughly two millennia to develop in a social and cultural context that emerged in hundreds of millennia thanks to the species Homo sapiens which evolved in roughly three-and-a-half billion years: “billions of years of irreplaceable design work”. Bach was lucky in his genes as he did come from a family full of musicians, and he was lucky to have lived in the cultural atmosphere teeming with the memes that led him to compose the St. Matthew Passion. (1995: 511–2)

This is a snapshot of the vision of creativity that the memetic theory entails. This view is elegantly illustrated in Ernst Gombrich’s quotation of the art critic Heinrich Wölfflin’s words: “… every picture owes more to other pictures painted before than it owes to nature.” (Gombrich 1954: 376)

Within this perspective, Paley’s watchmaker argument is flawed from the outset in that it fails to recognize that no watch is the result of a creation ex nihilo by a single watchmaker: it owes its intricate design to a centuries long tradition of watchmaking that has accumulated efforts and trials and errors, small and large, by hundreds of watchmakers and other mechanical designers (Michl 2002).

Accordingly, Michl is particularly unhappy with the common understanding of the concept of design as tightly related to terms like creativityoriginalitygeniusintentionplan, or project, all referring back to individual persons and individual brains.¹ As a design educator, he further complains that

expressions such as to be influenced, to be inspired, to take over a solution, to start out from, to build further on, or to steal, are used with an apologetic, or accusatory, undertone as though they implied a reprehensible lack of independence on the part of the designer, as though the designer ought really to be uninfluenced and indeed immune to influence by others, as though she ought to be 100% original in the sense of starting from scratch, i.e. creating exclusively out of her sole head.

The author proclaims that human design is instead a supra-individual and cumulative process, and designers always start off where other designers (or they themselves) have left off; it is practically impossible to start from scratch. He speaks of a “common pool of knowledge” – reminiscent of the gene pool concept in evolutionary biology – in which designers of present and past, living or no longer living, collaborate. (2002)

As Blackmore recapitulates, new ideas come from the variation and the combination of the old ones (1999: 15). Now the question is: Can this process of variation and recombination be considered evolution? Does it fulfill the three requirements of the algorithm of evolution? Derek Gatherer makes a strong case for it:

If the design process begins with the production of novel ideas generated from random combinations and mutations of existing ideas, continues with selection of those ideas for applicability to the problem at hand, and then proceeds to the (not necessarily accurate) transmission of those ideas, then the conditions necessary for an evolutionary process exist. That, in a nutshell, is the basis for an evolutionary theory of the design process. (1999: 102)

In fact, Campbell preceded memetics when he described the same evolutionary mechanism with random variation for creative cognitive processes in 1960, building upon what thinkers like Ernst Mach, Paul Souriau and Henri Poincaré had written. But does the production of novel ideas really generate from random changes? Here arises the issue of human consciousness or foresight, mentioned at the end of the previous section. Human creative activity is generally seen as closely linked to consciousness. However, this explanation of creativity leads to a metaphysical conception where consciousness somehow creates ideas in a rather magical way independently from the underlying physical brain activity.² (Blackmore 1999: 206) The memetic model implies that the human brain is a generator and selector of random novelty (Gatherer 1999: 97). It posits evolution as the way to get good design, without any conscious, teleological design decisions, and yet we humans seem to make perfectly conscious and autonomous decisions with specific endpoints in mind. What does it mean anyway to make a decision and who in the first place does it?

Research in cognitive sciences and neuroscience suggests that there is not a central decision-maker in the brain where all the input comes together and is transformed into output (Dennett 1991; Blackmore 1999). The brain is a parallel processor without a boss and we are not “godlike creators of ideas, manipulating and controlling them as our whim dictates, and judging them from an independent, Olympian standpoint” (Dennett, 1990). Dennett argues that the way in which our minds work (to generate speech, in the case of his argument) can be explained by a Pandemonium model in which

a torrent of verbal products emerging from thousands of word-making demons in temporary coalitions could exhibit a unity, the unity of an evolving best-fit interpretation, that makes them appear as if they were the executed intentions of a [inner] Conceptualizer.

According to this theory of mind, the decisions that we make are the products of super fast unconscious evolutionary processes going on in our minds and they do not require an ‘inner Conceptualizer’ or ‘Central Meaner’ (1991: 227–52). Cognitive scientist Rosaria Conte affirms that “a decision-based process is not necessarily explicit and reflected on: mental filters do not necessarily operate consciously, so agents may not be able to report on them.” (2000: 93)

Similarly, theoretical neurobiologist William Calvin suggests that the human brain is a “Darwin machine”, making use of random noise to create millisecond-long generations of alternatives and shaping them through series of unconscious selections (1987). It is precisely because we only experience and remember some of the steps that are comprehensible and useful that the human creative process appears to have a direction (Souriau 1881 and Poincaré 1913 cited in Campbell 1960; Gatherer 1999: 97). The human designer’s brain is a memetic environment where memes get mutated randomly, selected at levels many of which are not conscious at all. Just like in biological evolution, this process creates what looks like foresight in retrospect (Blackmore 1999: 241). The subjective sense of intentionality, insight and autonomy that the designer feels is in fact an illusion produced by selection (Campbell 1960: 384; Gatherer 1999: 98).

But if it is true that human minds are themselves to a very great degree the creations of memes, then we cannot sustain the polarity of vision with which we started; it cannot be “memes versus us,” because earlier infestations of memes have already played a major role in determining who or what we are. The “independent” mind struggling to protect itself from alien and dangerous memes is a myth. (Dennett 1991: 207)

Memes, just like genes, are selected against the background of other memes in the meme pool (Dawkins 1976: 194) and the ‘me’ that does the choosing is itself a fluid and dynamic memetic construct installed in the brain (Dennett 1991: 431; Blackmore 1999: 241): it provides the memetic background (or environment) that new mutations are selected against.

The designer may protest: ‘But solved it’, but the memeticist would reply: ‘No, you were the brain/processing unit in which the cultural solution to the problem arranged itself.’ (Gatherer 1999: 98)

“Designer labels – selling a product with the help of the designer’s name (and/or signature) – further strengthen the illusion that products have a single and clearly identifiable originator.” (Michl 2002) Gatherer (1999: 100) notes that the conception of an individual design genius was a product of the growth of individualism during the Renaissance, reminding that many philosophers before the 14th century used to attribute their works to some important figure of the past such as Aristotle in order to ensure a wider readership.
2 As opposed to this view, some scientists focus solely on the intelligence of the creative individual, although this explanation also is problematic in that it ignores the transaction and the transformation of ideas between the individual and the cultural environment. (Blackmore 1999: 206)

Digital Evolutionary Algorithms

The memetic theory is considered to be in its infancy, and debates continue as to whether it is a progressive scientific research program at all (Aunger 2000: 2–3). Nevertheless, anthropologist Dan Sperber admits that the very idea that the Darwinian model of selection is not strictly limited to biology is theoretically interesting, whether there actually are memes or not (2000: 163).

After all, quite independently from the big question of whether human culture really is the product of replicating bits of information, the algorithm of evolution is being technically used today in solving engineering and optimization problems or in creating art, design, and artificial life, mostly thanks to computers (Bentley 1999: 6). People program computers to create populations of solutions, allow better solutions to ‘have children’ with some random variation, and make worse solutions ‘die’. By repeating this process, better and better generations of solutions are evolved.

The father of this field of research and application is accepted to be John Holland who also coined the term generic algorithm in his 1975 book Adaptation in Natural and Artificial Systems. Nevertheless, there were several other people who had proposed similar ideas in 1960s, like Ingo Rechenberg and Hans-Paul Schwefel, or Lawrence Fogel. (Reeves & Rowe 2003: 2)

British computer scientist Peter Bentley is especially interested in the application of evolutionary algorithms to design and art. He asks “Why evolve designs?” in his introduction to Evolutionary Design by Computers and cites the following answers (1999: 4–5):

  1. Evolution is a good, general-purpose problem solver.
  2. Uniquely, evolutionary algorithms have been used successfully in every type of evolutionary design.
  3. Evolution and the human design processes share many similar characteristics.
  4. The most successful and remarkable designs known to mankind were created by natural selection, the inspiration for evolutionary algorithms.

Figure 7. Evolutionary design as the intersection of evolutionary biology, computer science, and design. (Bentley 1999: 35)

The author notes that there exist various types of digital evolutionary algorithms of which the genetic algorithm is the most widely used and resembles biological evolution the most. Genetic algorithms have two separate virtual spaces: the search space containing coded solutions to the problem – genotypes – and the solution space containing actual solutions – phenotypes. Genotypes are transformed into phenotypes through a specific mapping (embryogeny) so that the solutions – their fitness – can be evaluated. Strings of genetic information are called chromosomes.

A simple genetic algorithm works as follows. First, an initial population of individual solutions is created with completely random genotypes. After the initialization, the main loop begins. Phenotypes of every individual are generated through the mapping, evaluated and given fitness values according to how ‘good’ they are with respect to a problem objective or fitness function.

Then the genotypes of the individuals are copied into a temporary space usually called the mating pool with one important condition: the higher the fitness value of an individual, the more copies of its genotype are placed into the pool. ‘Parents’ for the next generation to be created are then randomly picked from this pool, thus more fit genotypes are more likely to be chosen. ‘Children’ genotypes are created by applying random mutations or crossovers to these parents. (Crossover is the technique of mixing two chromosomes into one, by splitting two chromosomes from two different parents at one point and switching the parts.) New children are created until the new population is full.

This is the last step of the main loop, and this loop – genotype-phenotype transformation, evaluation, regeneration – is repeated for a specified number of generations or until a proper solution evolves. (Bentley 1999: 8–10)


Figure 8. Flowchart, the basic genetic algorithm. (Bentley 1999: 9)

Evaluation is usually made by built-in fitness functions that automatically analyze and grade solutions, but in some cases – especially when aesthetic choices are involved – human evaluators make the selection (Bentley 1999: 30). Some researchers work on implementing artificial neural networks for fitness evaluation involving aesthetic preferences (Lewis 2008: 11). Selection methods range between deciding on which individuals will reproduce and deciding on which individuals will ‘die’ without children (negative selection) (Bentley 1999: 30–33).

Digital evolutionary algorithms are successfully used to evolve designs of jet engine turbine blades, aerodynamic cars, satellite structures, photorealistic faces, factory schedules, school timetables, fraud-detection systems, architectural plans, or game-playing strategies (Bentley 2001: 57). There are programs such as GenJam that evolve melodies, or ‘evolutionary artists’ such as Steven Rooke who work with software that evolve abstract images (eds Bentley & Corne 2002).


Figure 9. Fourteen generations of ancestors of an individual image entitled “Afman” by Steven Rooke (above) and Afman’s two parents and three grandparents. (eds Bentley & Corne 2002: 344)

It is important to acknowledge that “[e]volution is not simulated in these algorithms, it actually happens. (…) An evolutionary algorithm no more simulates evolution than a pocket calculator simulates addition, or a typewriter simulates text.” Evolution is a substrate-neutral process, and every instance of it in every medium is an equally valid form of evolution as the biological one. (Bentley 1999: 6f)