Transmemes

Redefining the meme and the replicator

Part 3 of 3: Transmemes

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Finding The Memes

Redefining the meme and the replicator

Part 2 of 3: Finding The Memes

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A New Model

Redefining the meme and the replicator

Part 1 of 3: A New Model

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Three answers to three problems with memes

In her influential 1999 book, The Meme Machine (chapter 5) Susan blackmore raised three important problems about memetics.  Each problem was titled as follow: "We cannot specify the unit of a meme", "We do not know the mechanism for copying and storing memes", "Memetic evolution is Lamarckian". These three problems are still largely relevant today, as progress with memetics is proving to be slow. However I think my proposed views on memetics, which I call the code model, could help answering or clearing up some of those points.

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Informational Memes

Objections to Daniel Dennett’s informational meme.

The concept of information seems to agree with the meme idea, and that is why many memeticists equate memes with information. This view is currently championed by Daniel Dennett himself and it is his own arguments that I want to scrutinise here. I myself also assumed that describing memes as information was a fair bet or at least that it would not hurt the meme idea. I came to discover how Dennett insists on describing memes (and genes) as information, to the point where this would be the only right way of describing memes, as opposed to codes for example. This got me thinking. Considering that I define memes as codes myself (link), I was compelled to try and find out whether the concept of information is a better way to describe memes or not. This is my attempt to better understand Dennett’s information and to find out whether information is really fit to serve as a model for memes.

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Redefining the meme and the replicator

These are the ideas I have presented during the last memelab.
I tried my best to condense and communicate my vision of memetics. I propose a new definition of both the replicator and the meme in the hope to make memetics a more testable and falsifiable science. I would love to hear your comments.

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Cultural Intelligence

I would like to try and explore briefly the nature of intelligence from the point of view of memetics. Still today, we struggle to define intelligence. It is quite telling to see how short and vague the definition of intelligence is in Wikipedia for example. Maybe a memetic approach to this question could be enlightening.
See article below.
You may also read this article on its own at this address: Cultural Intelligence

Cultural intelligence and more

A short reflection on the nature of intelligence

By Sylvain Magne

1. Introduction

I would like to try and explore briefly the nature of intelligence from the point of view of memetics. Still today, we struggle to define intelligence. It is quite telling to see how short and vague the definition of intelligence is in a popular place like Wikipedia for example. Maybe a memetic approach to this question could be enlightening.

Among the many proposed definitions of intelligence, my preference goes to intelligence defined as being the ability to solve problems. What I like about this definition is that it is broad, in the sense that it can apply to many organisms and then it makes intelligence somewhat quantifiable, with measurable problem solving goals. Indeed there are many kinds of problems to be solved and therefore there are many kinds of intelligence. Our intelligence, as with all living things, is a product of evolution, and through evolution we have acquired many problem solving abilities. Of all types of intelligence, humans have excelled at a particular one, cultural intelligence. I would like to show how culture is not just the product of our intelligence, but is also a creator of intelligence.

I am going to explore intelligence through a series of rather arbitrary types, but that I think relevant to my point.

2. Biological intelligence

Our first type of intelligence, I would suggest, is physiological intelligence. It is in the ability of our bodies to run themselves, to solve the amazingly complex problems of building organs, communicating between organs, processing energy, breathing and eating, defending our bodies against viruses and bacteria, etc. In essence, these tasks are so complex that nobody can fully understand them. Yet we perform these every day, perfectly oblivious to their inner workings. Our most advanced organ is arguably our brain but it is also the organ that we understand the least. Considering this is the place where our commonly understood intelligence is coming from, no wonder we cannot fully understand intelligence yet. We share physiological intelligence with all living things on this planet, and all species have their own tricks and ways of solving specific problems. Physiological intelligence is intelligence indeed, and the fact that it is a common gift we all have does not make it less impressively efficient.

The second type of intelligence is behavioral intelligence. Our bodies are meant to move and interact with a complex three dimensional world. We have an inbuilt intelligence that allows us to quickly learn how to best move inside our world. Our brain is the main organ responsible for our ability to achieve this. Our brains analyse the world through our senses, sight, smell, hearing, touch, taste, and can act upon the world via our muscle power. Thanks to behavioral intelligence, we can hunt, we can fight or flee, we can explore, we can reach, we can manipulate objects. Again, we share comparable levels of behavioral intelligence with many animals on this planet, and again this intelligence is of a very high quality. Even though we don’t seem to apply any conscious intelligence to see with our eyes, our brains make use of incredible amounts of intelligence to allow us to see.

Our third type of intelligence is our social intelligence. Not only can we interact with our environment, but we can also interact with each other in many complex ways. This intelligence allows us to work as communities and coordinate social structures that can benefit most of its members. Thanks to our social, emotional and psychological intelligence we can communicate with each other, work together, help each other, build together, protect each other, mate and care for each other. Again many species on our planet share comparable social intelligence. We are more aware of this type of intelligence because we actively improve it through our lives and our brains let us be very aware of it. Yet many aspects of it, if not most of it, is still happening on a subconscious level, out of our conscious reach.

3. Cultural intelligence

Our fourth type of intelligence is our cultural intelligence. Where we share most of the other types of intelligence with many other species, our cultural intelligence is comparatively vast and unique. Thanks to the development of our brains, we can learn complex languages and many other complex cultural traits and tricks. Thanks to our great memory capabilities, these cultural traits can be passed on through generations and can be accumulated to create very advanced cultural items. These cultural items have given humans so much intelligent power that humans could be said to be incomparably intelligent, on many levels. Humans can solve more problems than any other living species, but maybe even more importantly, whatever problems humans cannot solve now they may be able to solve at a later stage by evolving and improving their culture. Cultural intelligence evolves much faster than biological intelligence.

Biological evolution has made humans very intelligent and in many ways among the most intelligent species on the planet, but today, culture is really what makes humans superintelligent.

What is a human being without culture? No culture means no language, no know-how of any kind. An acultural human is doomed to be less intelligent than the average cultural human. That is because culture gives us many tools to solve many problems. The tools that we learn when growing up are the very thing that makes us significantly more intelligent. More than people may think. In fact, if a child has not had the opportunity to learn a language during the first two years of its life, the child will not be able to fully develop its cognitive powers. Biological evolution has given us means to develop a culture but if we don’t use this, we are nothing more than wild animals. By providing an education to children, by giving them cultural knowledge and understanding, we are literally developing their intelligence. Obviously, that is true only if the culture given is actually useful in that sense.

Culture is therefore not just something that humans created, it is also something that makes us what we are today. As generations succeed generations, culture evolves faster than our DNA can, much much faster. Our current evolution as human beings is driven more by our cultural evolution than anything else. As intelligent beings we change our culture, but through education, culture changes us. Culture outlives us, it is our legacy and it is the headmaster of our future minds.

The strange thing is that although we create culture, in many ways we understand it very little. We live and bathe in culture but we do not have a wide perspective on culture. The only promising model that we have today to understand culture is memetics. Memetics embodies our understanding that culture is a complex evolving system to which darwinian evolutionary theory applies. In order to truly understand culture we need to understand it in a darwinian context, and for that we need to develop memetics.

From the perspective of memetics, humans are the product of two replicators, the biological genes on one side and the cultural memes on the other. Where our genes build our bodies, memes build our cultural minds. Our brains come readily programmed with room for “cultural programming”. On one side one could look at the genetic brain as being both the hardware of our intelligence and the operating system for it. On the other side, culture writes programs inside our brains, the software. On one hand our brains come ready with pre-installed biological programs to learn by themselves about the environment through trial and error, and on the other hand culture comes with ready made tried and tested cultural programs that help us leap forward and acquire levels of intelligence one could not reach without it.

Today our brains are meme machines as much as they are gene machines. Every time we are exposed to memes they are sculpting our brains bit by bit. Our minds, the way we think, the things we know, the tricks we learned, the language we speak, the skills we perfected, all have been programmed by a mixture of experience and exposition to cultural memes. Without memes, you would not play or hear music, without memes, you would not speak, you would not read, there would be no movies to watch, no paintings, no art, no tools, no science, no history, no stories. Without memes, you wouldn’t know how to cook, you wouldn't know how to dance, you wouldn’t know how to take care of your health or your children, you wouldn’t know how to build a home. Without memes and culture to program our brains, we would be damn stupid.

4. Technological intelligence

Now, evolution doesn’t stop there.

There is a fifth type of intelligence which is growing rapidly, and that is technological intelligence. Technological intelligence is the result of problem solving artefacts, also known as tools. Since we created tools, tools allowed us to solve more problems, such as how to build homes or hunt more efficiently, etc. For a long time, tools were guided directly by our brains, as a sort of extension of our bodies. But now tools have become much more sophisticated. Tools have grown brains of their own with which they can achieve things that are simply impossible for our brains and bodies. Thanks to technology we fly, we cure diseases, we make complex calculations, we see the infinitely small and the infinitely large, we communicate through space and time, we accomplish feats comparable to magic.

This fantastic technological intelligence is the direct product of cultural intelligence, and the way it is going, it is bound to surpass our own intelligence in every way.

Where technological intelligence has not already surpassed us, it is catching up with our biological intelligence at an amazing rate. All of the types of intelligence I mentioned above are steadily being acquired by technological intelligence. Their physiological intelligence, for example, still looks rough compared to how complex body cells are, but in many ways, machines have already many great features. Where cells can create tissues, these tissues are also fragile, whereas machines are comparatively extremely strong. But where cells can mend tissues and defend themselves, machines are still unable to even start doing that. Where cells are tiny and working at the molecular level, machines are very cumbersome and rough. This said the future technologies such as nanotechnologies are looking to do just that, and even if it takes some time to get there, it’s only a matter of time, and in biological time, it’s just round the corner.

Where behavioral intelligence is concerned, again there are things machines do far better than us, like moving at high speed, flying, carrying weights, etc, but then, as surprising as it may be, no machine can walk like a human can. After many years of research such seemingly simple behaviour is a real challenge for machines. But again progress is being made, we are seeing the first machines walking up stairs, or running in fields like a horse would do. Again, machines are picking up speed and catching up fast. Another great hurdle for machines is vision. In order to move well, machines need to see well. The complexity of this task is enormous but again every day sees new progress on this front, and where we only see a limited portion of the light spectrum, machines will see a lot more, in more detail, in many more dimensions.

Social intelligence is something still very new to technology. Social interactions between machines and humans is something we’re only just starting to explore. The most blatant progress is made by smartphones. These little techno pets that we carry around can hear us and “understand” our vocal instructions. They can also talk back, recognise our facial expressions and more. Machines, it could be said, are slowly becoming social machines, but there is much room for improvement still.

Finally, cultural intelligence in machines is practically inexistent. Humans are still today the main creators of cultural content and only on occasions are machines allowed to learn from each other, teach each other or exchange “techno cultural” items. Despite that, it is already in the air and work is being done in that direction. We are looking at creating machines which could learn on their own, pass that information onto other machines and come up with creative solutions to previously unknown problems. This is leading towards what we commonly call artificial intelligence.

As you can see, technological intelligence is growing fast, in all directions, and it may be relevant to try and anticipate the effects of such rapid growth.

5. Conclusion

Intelligence in my view is the result of the evolution of problem solving capabilities. Through the ages, natural selection has allowed for many biological types of intelligence to emerge. Then something radically new happened and cultural intelligence emerged in humans. Culture evolved at a comparatively much faster rate, making us more intelligent but also giving rise today to yet another new type of intelligence which is technological intelligence. All of these types of intelligence are the byproducts of underlying evolutionary processes. Darwin’s theory of evolution has given us a tool to understand biological evolution and now we need a new tool to understand cultural and technological evolution. We need them because today things are moving and changing so fast that the problems that we will face now and in the future are coming to us faster and harder. We need, somehow, to see them coming. Our best chance at understanding the dynamics of cultural and technological changes is memetics.

Memelab Autumn 2014

So I had the pleasure of joining the latest session of Susan Blackmore's Memelab which took place on the first and second of November 2014.

It was a fun, inspirational and enlightening experience. The idea of the Memelab is very simple. It is a rather informal gathering of people interested in discussing the subject of memetics. There were seven of us this time, coming from various backgrounds and different parts of the world. The only rule to this event was a schedule planned so that each participant had a chance to lead the discussion one way or another for an hour. 

This time, Memelab took place in Bristol:

Alan Winfield hosted this session in his own house and made everyone feel at home. It made for a charming weekend indeed.

We discussed many subjects during this session. Here are a few I can remember:

• Martin was interested in understanding how advertising fatigue may occur and if we develop some kind of immunity to memes in advertising.
• Rachel wanted to explore how simple drawings get affected by successive hand copying.
• Susan, was asking what should be done to inform the world about the fast approaching reign of technology.
• Alan introduced his work on ethical robots.
• Andrew introduced us to his PHD work on memetics and religion.
• And myself I tried to emphasise the fact that we find it hard, still today, to define memes.

I enormously enjoyed this event and very much look forward to renewing this experience.

Here is the list of participants:

Susan Blackmore

http://www.susanblackmore.co.uk/

Alan Winfield

http://alanwinfield.blogspot.ch/

Martin Farncombe

www.practicalmemetics.com

Rachel Cohen

www.chinesewhispers.org.uk

Andrew Atkinson

http://independent.academia.edu/AndrewAtkinson1

Marina Strinkovsky

http://notazerosumgame.blogspot.co.uk/

Sylvain Magne

http://memelogic.blogspot.co.uk/

The entity

Finally getting into the details of redefining the replicator. Here we will define Richard Dawkin's "entity" as a code. Comments are very welcome.

You can also view the article below more comfortably with this link : The entity

The entity.

All is code.

By Sylvain Magne

In this chapter, I want to show that the “entity” that R. Dawkins refers to, can be understood as a code, a kind of computer program. And this, whether we are talking about actual computer programs, or genes or cultural traits. I will first remind us that the theory of evolution itself is an algorithm, a kind of code. I will then show how we can rigorously look at the universe and understand it as a pile of codes, by taking what I call the code’s eye view on the universe. Once R. Dawkins’ entity is understood as a code we can finally start describing this elusive “entity” in more practical details.

1. Evolution is an algorithm

As a matter of fact, the theory of evolution and the concept of replicator are models based on algorithms and mathematics. Mathematics are fundamental to any science indeed but algorithms and computer programs become more and more important in modeling our understanding of the universe. It turns out that algorithms are more universal than one may think at first.

1. Definition of algorithm

An algorithm consists simply of a series of well defined step by step operations. The concept of algorithm is central to any computing process. Quite simply, every computer program is an algorithm or made of algorithms. But algorithms are also part of our everyday life. A recipe is a form of algorithm as well, and so is a guide to build a piece of furniture or the rules of a board game. These algorithms tell you how to go about executing a specific task with a simple set of rules. Algorithms can also be found in nature. Within the living cells, the replication of DNA molecules follows a very precise step by step algorithm.

2. Evolution, a recursive algorithm

Algorithms can be “run” once or many consecutive times. By brushing one’s teeth everyday we are using a recursive algorithm. Recursive algorithms are simply algorithms that are typically run again and again. For example, when creating a puff pastry one needs to flatten and fold the dough many times over, thus repeating a simple algorithm in order to create the thin layers of the pastry. Learning a poem by heart through repeated reading and recitation is also a recursive algorithm. With regard to evolution, it is through generations and generations that the genes get selected by natural selection, undergoing the same recurring selective process. Evolution is therefore the result of recursive algorithms.

3. A replicating recursive algorithm

The particularity of the evolutionary algorithm is that it is circular, by the fact that it applies to itself, making copies of itself, the copies then make more copies of themselves and so on allowing for a continued process that can potentially go on forever. This way, genes make copies of themselves through reproduction into new generations that will, in turn, make copies of themselves, thus allowing for populations to grow and spread. Evolution is a rather simple algorithm that anyone can grasp easily. There is nothing difficult to it. What can be more difficult to grasp is the effect, on the long run, of recursive algorithms, especially when errors and accidents happen along the way, which is the case in nature. Life has been going on for a long time and the algorithm of evolution has had the leisure to run many times indeed. A lot can happen in that time, so much that it can lead to the complexity of life that we know today.

All of this to say that evolution is an algorithm and that the algorithm’s perspective may well be the best angle to study evolution. So let’s try, before we take the point of view of the replicator, to take the point of view of algorithms.

2. All is code

As it turns out, algorithms are more than “just algorithms”. They’re not just something that sometimes happens to be an algorithm. Indeed, if one looks at the universe from a certain point of view, then everything is algorithm. Or so mathematics say.

Algorithms are nothing else than mathematical objects. Mathematics is the best and only real tool that we have to model the world around us. Mathematics are fascinating for the fact that they allow us to model the world in such simple and elegant ways. A simple mathematical formula can explain and predict the movements of planets with perfect accuracy and scientists find it very efficient to look at the world and make sense of it through mathematics. Evolution is no exception.

Now there are two slightly different angles that one can use to look at the world. Since the boom of computers and their ability to run algorithms we have had a rather new perspective on the world which, in some cases can be easier to grasp. For example, one equation will not be enough to model the formation of galaxies but by using computers and complex algorithms we can simulate the creation of galaxies with our desktop computers. This new approach would simply be impossible and out of reach without the use of computers. Algorithms help us understand the workings of such complex systems as galaxies, the weather, evolution and many complex chaotic systems.

However, the relevance of algorithms goes even further. In fact it has been shown that the relationship between mathematics and computer algorithms is more fundamental than expected. The French mathematician and neuroscientist Jean-Louis Krivine has changed the way one can look at the universe in a rather revolutionary way.

4. Mathematics' equivalence

Jean-Louis Krivine has helped greatly to demonstrate mathematically the equivalence between maths and a particular programming language called Lambda Calculus. Strictly, for the purpose of this argument, it doesn't really matter to know what the Lambda Calculus is. What is important is to understand what the equivalence means. Basically, J.L.K.’s equivalence means that for all mathematical theorems there exists a computer program that is equivalent, and reciprocally for every computer program there exists an equivalent mathematical object. As a result, there is a strict equivalence between maths and computer programs, in other words, maths are computer programs and computer programs are maths.

5. The universe is made of codes

What this fact implies is that where we may understand things mathematically, we can also understand them as programs. Science has shown us how we can look at the world and understand it in mathematical terms, in other words, with equations. What J.L.K.’s equivalence tells us, is that we can just as well and rigorously change our perspective and look at the universe as if it were a computer. The laws of gravity, quantum mechanics, and every scientific laws are indeed equivalent to computer programs with this particular fact that the programer and the computer are one and the same, the universe itself. In other words the universe is a giant computing system constantly firing lines of codes.
Now, it’s all very well but why would it be interesting for memetics to look at the universe as a computer? Why take the point of view of the code?

6. Why take the code's eye view?

The point of this is to show that R. Dawkins’ “entity” can be understood as an algorithm, or a code, simply because, as we have just seen, anything in the universe can be understood as a code. The good thing about this is that, unlike the elusive “entity”, the code is something that we can hope to study and understand. We can now try and see what we can learn about replicators from the fact that they are codes and not just vague “entities”. We can now hope to lift the misunderstanding that stems from the mysterious “entity”.

Also depending on what subject scientists are studying it may be easier to take one view or another. When it comes to the movements of planets, a purely mathematical approach may be simpler, but when it comes to chaotic systems such as the weather, algorithms are more practical. It is easy to see that, when studying genes, the code’s eye view may come more naturally, simply because genes are codes and because of the complexity and chaotic nature of it all.

Now that we know that entities can be understood as algorithms or programs or codes, we should hope to use our “code goggles” and reinterpret the concepts of genes and memes. If we know better what a code is, we’ll know better what genes and memes are, right?

So do we actually understand what algorithms and codes are? Again, we’ll have to agree on what a code is exactly in order to be as clear as possible on the nature of replicators. I would like to be sure that we have defined the core concepts of the replicator as accurately as possible. It would be no use replacing the word “entity” with the word “code” if one could not define the latter precisely. In the next chapter we will try and define the codes the best we can.

3. The code

I have already used several words to talk about codes, such as algorithm and program. I will use the word code instead of algorithm or program for it is less specific to the realm of computer science and applies in practice to more fields. Now the question is; what is a code exactly?

Unfortunately, there is no universal definition of the code, at least none that I know of. This means that we may need to offer our own definition. There are for sure many uses for the word “code” in a wide variety of cultural fields. For example, we speak of highway code, dress code, secret code, penal code, code of honour, bar code, zip code, genetic code, computer code, code of conduct, etc.. the list goes on. On top of that there are many synonyms of the word "code" such as "rule", "law", "theorem", "principle", "policy", “directive”, "program", "algorithm", "recipe", "method", "procedure", etc.
Here, I wish to use the word code to talk about all and any of those types of code. Remember that at the end of the day, all is code. So In order to agree on the meaning of code and for the purpose of better defining the concept of replicator it is important to define the code as precisely as possible. Yet, what I am looking for here is the most general definition possible, a universal definition of code that would be central to any type of code and still be very precise.

In order to be coherent I will start by considering the computer code. After all, this is the code that was shown to be equivalent to mathematical objects and therefore most universal. Given a computer code, what could we say about that code?

Quite simply, a computer code could be said to be a set of instructions. Indeed, all there is in a computer code are series of commands written in computer language for a computer to read and execute, nothing more nothing less than instructions.

By definition, the very existence of a computer code implies that there should be a computer to read that code. If there were no computer to read the code in the first place, there could be no code for it. What else than a computer can read a computer code? This also implies that the computer code is written in a specific language. There are many computer languages and if the language used is not right then the code is unreadable. Also, let’s not forget that computer codes don’t float up in the air, they need a disc or some kind of memory device to be stored on.

All of these elements, such as a storage device, a computer and a compatible language are necessary elements for the code to exist and make sense. They are in fact inseparable. If one of those parts were to be missing, the code would be meaningless. But that’s not all. There is one last piece that is important. The computer which is reading the code needs to be able to perform actions accordingly to the instructions of the code. If the computer could not do that it would be exactly as if it weren’t actually reading the code, it would be as if it were blind, and therefore it would actually simply fail at being the reader of the code. By necessity, the reader needs to be able to perform actions, the very actions that the code commands.
I believe there is no need for anything more other than these conditions to be true for the code to exist. All these parts are necessary and sufficient for a code to be.

I also believe that these elements are necessary for all types of codes, computer codes and others. Take the genetic code for instance. Its medium is made of chains of nucleotides, the reading of the code is done by the cell hosting the genetic material and acting as a computer, the genes need to be written in proper genetic terms to be able to instruct the cell correctly, and finally the cell needs to be able to read and act accordingly to the code. In the same way as the computer code, the genetic code would be lost if any of the reader, the medium or the language were missing or faulty. And again the same could be said of a cultural element such as a recipe for example. Its medium would be the paper and ink it is written on, the reader would be the cook and the language would have to be one which the cook can read so he can cook. And then again, if any of these elements were to be missing the recipe would be unusable.

So, the code, no matter its nature, is part of a necessary group of five elements and cannot work without this group. So again, this group contains:

• The code itself which contains the instructions
• The medium that carries the code
• The language shared by the code and the reader
• The reader which reads the code
• The actions carried out by the reader

As a result we could offer a definition of the code as follows:

• A code is a set of instructions which are stored on a specific medium, written in a compatible language and executable by a reader through actions.

Again we may need to go a little further and make sure we define each of these elements as clearly as possible. What more could be said of the code, the medium, the language, the reader and the actions?

7. Medium

The medium is intimately linked to the code. So much that in some cases we could confuse the code and the medium, and it may be necessary to really look closely to work out which is what. I will elaborate this point in the Code section further down.
The medium allows two things: the storage of the code, of course, but also its transportation, and different media will have different properties. The medium will thus confer to the code its specs such as its physical size, it’s weight, its robustness and longevity, its precision, its accessibility, its manoeuvrability, etc.
This means that codes’ fitness will be very dependent on their media’s properties.
Examples of media are magnetic or optical discs for computer programs, DNA molecules for the genetic code, sound waves for words, etc.
Through evolution, the natural biological media such as DNA or RNA have many great advantages. For instance they are extremely small in size, robust, and of digital type. These are great characteristics for a medium to have. Comparatively, sound waves are a poor medium as they are short lived, large in size and analog. The trend with technology is clearly towards better, stronger, faster, smaller and generally more reliable media. This trend makes perfect sense from an evolutionary perspective.

8. Reader and Actions

The reader is the code’s best friend. Codes and readers are meant to be together like a specific key is meant for a specific keyhole. The reader is quite simply the device that reads the code. By reading I mean that it understands the code enough to act accordingly to the instructions provided by the code. If the reader cannot follow the instructions with the right actions then it may as well be blind.

To qualify as a reader, this reader must be able to read at least two different codes and must be able to act in at least two distinct ways accordingly to these codes. A reader may act in many various ways by transforming the environment or by transforming itself. Its actions may also consist in writing more code or erasing code. That’s what readers do, they read codes and transform the world.

Although a reader needs to be a proper “actor” there is one particular action which consists in doing nothing. I believe that this act of “doing nothing” can be regarded as an action as well, as long as there is at least one other action possible that does something (anything except for nothing) and which can be differentiated from the “doing nothing” action. These are the two minimum actions that any reader should be capable of: “doing something” and “doing nothing”. This said, pretty much anything can work as a reader for one code or another, even a piece of rock or a single elementary particle can act as a reader to another rock or particle, simply by interacting or not interacting.

The reader includes everything that is directly involved in generating the actions, however large this reader may need to be. It could be as large as being everything that the code is not. This said, although it may seem necessary to include the sun as part of a reader when the sun is providing the main energy source for that reader, it may be more convenient to consider a smaller range for the reader and limit this range to a human body or a laptop computer for example. But it may be necessary in some cases to consider larger readers, particularly when considering larger timescales.

Every reader has a point of entry, a point in space and time where the code is read. This has some importance because the point of entry will have characteristics that the code will need to comply with. This point of entry will confer characteristics to both the reader and the code, just like the medium confers characteristics to the code. A gene needs to find its way into a cell, a sound wave needs to find its way into an ear, and a computer disc needs to find its way into a disk drive. This constraint is actually a good way to find the code. If we ask the “question where is the code?” then one simply need to find the entry point and we should find it there.

Other point of limitation are the actions a reader can achieve. Not all readers are equal and their range of skills vary. A code can only expect of a reader what that reader can do. The nature of the actions of a reader may be just about anything one can think of and again it may consist in modifying the environment as well as modifying itself.

Now if the universe is made of codes then what are readers exactly? Aren’t they supposed to be codes as well?

Yes indeed, readers are codes too. Anything in the universe could be regarded as a code that could be read by some reader. Readers are just codes that can read other codes and readers contain codes that can be read by other readers. It’s all a matter of perspective. A computer programme such as your web browser is a code and this program can also read other codes which are web pages, these web pages can read other codes such as flash files, and so on. Other example, flowers are the readers of their own genes and the bees read the flowers to choose the ones they will harvest.

So the existence of readers is a matter of perspective. Readers and codes exist relatively to one another. All is code but some codes can be the readers of other codes. We could imagine readers that are readers of each other or codes of each other. It’s all relative.

9. Language

To identify and decipher a code, the reader and the code need to share the same language. Without this, the code remains indecipherable and therefore inaccessible.

All languages have specific characteristics. For instance, any language is made of terms, bits of code, just like words make up our own language and genes make up the genetic language. The terms of a language are those specific terms that are irreducible ie the terms that cannot be cut into smaller meaningful pieces. It consists of, on one hand the alphabet, the elementary blocks of the language such as letters, GTCA molecules for genes or “1” and “0” for computer language, and on the other hand a set of predefined combinations or words such as “tree” or “cat” or, in the case of genes, codons. A code is therefore a subset of the language, a specific series of terms. Those terms are derived solely from all the original terms of the language, just like a sentence is made of words from the dictionary. A random set of terms such as “mlCw e8T4t-3n2” will most likely be meaningless and therefore may not constitute a rightful code. Furthermore, languages have rules that define how the terms can be arranged in order for them to make sense. These rules of arrangement are known as grammatical rules. As a consequence, a code needs to respect two things in order to be readable. It needs to use proper terms of the language, i.e. the right words, and it needs to sequence those terms properly according to the grammatical rules.

It is important to notice that it is the reader that sets the rules. For the code to be read it is up to the code to fit the rules of the reader. This means that the meaning of a code is relative to the reader. For example the English word “we”, if heard by a French ear, will be understood as “oui”, meaning “yes” in French. One same code will have different meanings relatively to the reader. This relative aspect is of great importance to my efforts of redefining the replicator, but we will come back to it later.

Here again, languages are not all equal. Just like computer languages have evolved to allow programmers more freedom or ease when programming, any language will offer different possibilities to the code and may influence its fitness.

10. Code

As mentioned earlier, it can be tricky sometime to make the difference between the code and its medium.

If the code is not the medium, then what is the code? If the ink on the paper is not the code then what is? The code is something rather intangible for it is not in the matter itself but in the arrangement of that matter. For example, in the case of text, the code is in the particular arrangement of the ink, more precisely in the geometric shapes of the letters. When drawing a circle, the circle is not in the ink but in the arrangement of that ink, an arrangement that resembles a circle. It is interesting to note that where letters are two dimensional arrangements of matter, words are one dimensional sequences of letters. The geometric shapes of letters are abstract objects, and this is true of all codes, all codes are abstract objects. The same goes for DNA, quite obviously. The code of genes is not the molecules themselves, but their arrangement, or to be more precise, their sequence. For good reasons, genes are described as sequences of nucleotides. And again, the same goes for computers in very much the same way. Computer programs are indeed written as sequences of elementary “bits” which can be stored on many various types of media. Codes often come in one dimensional sequences of bits. Such is the case for sounds, genes and computer programs. But they can also come in multiple dimensional forms. Such is the case of images. Images are typically two dimensional but can also be three dimensional. Also, everyday objects such as a door key for example can carry three dimensional codes. Food involves usually even higher number of dimensions, putting together shapes, colours, taste, smell and textures. Computers can potentially handle codes of much higher amount of dimensions.

A consequence of this is that one can’t exactly see or touch a code. One can only read a code by interacting with its medium, by seeing or touching its medium or by other means of reading. The procedure of reading is what will turn the code into meaning.

11. Coding systems

I call a coding system, any system containing codes, media and readers. Our universe is a coding system obviously but computers and brains can be regarded as coding systems on their own. Coding systems can be complex and contain sub coding systems themselves. For example computer run programmes that can run sub programmes with their own codes and so on. Also our brains have their own internal workings but are also part of a larger social coding system that exists only within groups of people.

It can be interesting to study how coding systems may arise by chance, like life arose on earth.

4. Conclusion

I hope I managed to show clearly that the world can be understood from the point of view of the code. Thus showing that a replicator is itself a code and consequently holds the characteristics of any code. This important fact will now help us understand better what replicators are. We have defined Richard Dawkin’s “entity” as a code and it is time now to tackle the meaning of “copy”.

Comments on Robert Boyd and Peter J. Richerson's article

 I wish to make comments on Robert Boyd and Peter J. Richerson's article:

“Memes: Universal acid or better mousetrap.”

Published in the following book :

Darwinizing Culture.

The status of memetics as a science.

Edited by Robert Aunger.

Foreword by Daniel Dennett.

With contributions from:

Robert Aunger, Susan Blackmore, Maurice Bloch, Robert Boyd, Rosaria Conte, David L.Hull, Adam Kuper, Kevin Laland, John Odling-Smee, Henry Plotkin, Peter J. Richerson, Dan Sperber.

In this instance, once again I would like to show how Boyd and Richerson misunderstand the very concept of replicator, and consequently offer a critique of memetics which is not as constructive as it could have been. 

Furthermore, I want to challenge Boyd and Richerson suggestion that population thinking is a better way to looking at cultural evolution.

1/

Boyd and Richerson first introduce replicators as “material objects that are faithfully copied”. It certainly isn't Richard Dawkin's view who described replicators as “any entity in the universe of which copies are made”. Indeed an “entity” is not necessarily a “material object” and by limiting their horizon of possibilities, Boyd and Richerson limit their own understanding of memetics. We will see later how this leads to confusion.

Further down in the article Boyd and Richerson make another mistake about replicators and write:

In The Extended Phenotype, Richard Dawkins (1982) argues that the cumulative evolution of complex adaptations requires what he calls replicators, things in the physical world that produce copies of themselves, and have the three additional properties:

1. Fidelity. The copying must be sufficiently accurate so that even after a long chain of copies the replicator remains almost unchanged.
   
2. Fecundity. At least some varieties of the replicator must be capable of generating more than one copy of themselves.
   
3. Longevity. Replicators must survive long enough to affect their own rate of replication.

From reading this it appears that Boyd and Richerson didn't understand Richard Dawkin's definitions.

First of all, fidelity means that replicators remain unchanged and not “almost” unchanged. A replicator that changes is not a replicator. If an instance of a replicator mutates then it is a whole new potential replicator that appears. This new replicator is instantly in competition for survival against the very replicator from which it originated. Richard's point about fidelity is that it needs to be not 100% perfect so as to allow mutations to happen, new replicators to appear, and evolution to occur by selecting the best replicators.

Secondly, technically, all replicators do not need to generate more than one copy of themselves. What Richard meant to say is that the higher the fecundity the higher the chances of survival. In other words, natural selection will usually (but not always) favour higher fecundity.

Finally, replicators obviously live long enough to affect their own replication rate otherwise they wouldn't replicate at all! Again, what Richard meant here is that the longer a replicator lives the more chances it has to have an opportunity to get copied.  

It is very unfortunate that Boyd and Richerson misrepresent Richard's ideas in such a way. Because of this misunderstanding they then criticise memetics for the wrong reasons.

2/

Now let's move on to Boyd and Richerson's argument in favour of population thinking. They write:

In this chapter we want to convince you that population thinking, not natural selection, is the key to conceptualising culture in terms of material causes. This argument is based on three well-established facts.

1. There is persistent cultural variation among human groups. Any explanation of human behaviour must account for how this variation arises and how it is maintained.
   
2. Culture is information stored in the human brain's. Every human culture contains vast amounts of information. Important components of this information are stored in human brain.
   
3. Culture is derived. The psychological mechanisms that allow culture to be transmitted arose in the course of hominid evolution. Culture is not simply a by-product of intelligence and social life.

They then develop each point in the following chapters.

In the first Chapter Boyd and Richerson advocate the group theory of evolution. Valid points  against this view of evolution have been made before and I am not going to repeat them here. All I want to say here is that however seductive and intuitive the group theory may seem it nevertheless lacks any strong theoretical model in the sense that in group theory no one knows what is being selected. I believe that group theory may be a convenient tool for studying some evolutionary patterns but gives no real explanation for how evolution occurs. Also, I don't think it will apply any better to cultural evolution than it does to biological evolution.

In the second chapter, Boyd and Richerson argue that the majority of culture is stored in our brains, rather than in artefacts for example. They also argue that culture is not stored in our genes, or in other words, that genes have very little role to play in the differences that we can see in different cultures. The points Boyd and Richerson make are very good I find. The only issue with this lies in the definition of culture. Indeed culture lacks a clear definition today and any attempt at studying it begs the question “what is culture?”. If we were to to transpose the idea of culture to genes, what would you call the “genetic culture”? Is it the genotype? Is it the phenotypes? Is it the living cells and bodies? Is it all of it? Or is the question irrelevant? 

In my view, if I may share it at this point, we should maybe forget about culture for a second and think about culture from a replicator point of view, where there are only two things: the replicator and its phenotype. In my view, which I argue in my articles, what's in our brains are only the phenotypes of the memes and the memes exist only momentarily through the various media that we use to communicate them, such as light waves, sound waves, etc. If you want to know more, visit my online articles :

http://memelogic.blogspot.ch/2012/10/a-new-meme-theory-introduction.html

In the third chapter, Boyd and Richerson introduce the concept of local enhancement. They explain how a mother monkey can take its baby monkey to places where the baby monkey will learn new tricks, not through looking at his mother, but through his own exploration. Therefore there is a kind of inherited behaviour linked to the habits of the parent without having children directly copying them.

I find it to be a very interesting point, but unfortunately, from the replicator's perspective there is no reason to see anything cultural here. There are no replicators involved in this process, Boyd and Richerson say it themselves, but the fact that they nonetheless consider this as being cultural material is, in my view, highly doubtful. If you consider local enhancement as being able to create any “cultural material” then suddenly an awful lot of biological behaviours become cultural too. Is the fact that salmon goes back to the place where it was born cultural? Is the fact that the next generations of plants grow in the same place cultural? There is just as much local enhancement here and yet you wouldn't call this cultural.

In my opinion, Boyd and Richerson are very confused about the nature of culture. They see culture where it is not, where biological processes are enough to explain the observed behaviours without needing to introduce a hypothetical culture.

Boyd and Richerson go on to try and show that there can be no replicators inside our brains because there is no way to prove that the way one brain learns a behaviour is the same as the way another brain learns it. I perfectly agree with their position here and they make their point very well. The problem here is though, that they conclude that culture may not need replicators to exist. All I want to say here is that the fact that memes are not in brains does not mean that memes don't exist. I go back to my own ideas here which are that memes are actually travelling on the media that we use to communicate, and that our cultural brain structures are the phenotypes of these memes.

Boyd and Richerson then go even further and attempt to give their “coup de grâce” to the meme idea. They try to show that the copying process is too weak to allow the existence of memes. For example, in the pronunciation of certain sounds, we never copy exactly the sound of our parents or others, we don't necessarily either find the average pronunciation, but we create our own version that fits best us and our environment. Therefore, according to them there is no replicator here but there is nonetheless a cultural transmission and evolution taking place.

The problem of the fidelity in the copying process of memes is a big and serious issue that they do well to bring up. Dan Sperber also points the finger at this problem in his own article.

I myself advocate that memetics suffers from a serious lack of definition and it comes down to  two central questions. These are the two questions that Richard Dawkins left unanswered for us in his own definition of the replicator:

A replicator may be defined as any entity in the universe of which copies are made.

( Richard Dawkins 1976 )

And the real questions that need answering are:

What is a copy ?

What is the entity being copied ?

That is the very reason why I offer my own definitions in my articles to try and be as precise and accurate in order to have meaningful tools to develop memetics. One of the key elements to defining a true copy is to define a reference, a point from which two entities can be compared. For this purpose I introduced the concept of reader. In my theory, the reader and the replicators are part of a necessary system, together with the environment. The concept of reader is nothing magical and is a rather simple idea which efficiently helps answering the questions of copying fidelity, and others. The concept of reader makes the concept of copy a relativistic concept instead of an absolute concept as it is usually understood. The definition of reader comes together with a better definition of the replicator.

I invite you to read my articles on the subject here :
http://memelogic.blogspot.ch/2012/10/a-new-meme-theory-introduction.html

As always, comments are very welcome.