FAMILIAR Part 4: Aligned Resources

One important reason why the Star Wars series of films has such a wide appeal is due to its story structure. George Lucas was inspired by Joseph Campbell's book, The Hero with a Thousand Faces, and deliberately applied Campbell's ideas into his storyline.

Campbell was studying comparative mythology and wanted to find out if there are common elements between major myths around the world that have lasted for thousands of years. He elucidated a fundamental structure which he called the "monomyth" or "the hero's journey" and summarized it like this:

A hero ventures forth from the world of common day into a region of supernatural wonder: fabulous forces are there encountered and a decisive victory is won: the hero comes back from this mysterious adventure with the power to bestow boons on his fellow man.

The monomyth is divided into three sections - "Departure", "Initiation" and "Return". Each of these sections has a set of characteristic stages, for example "Supernatural Aid" in Departure where the hero encounters an old wizard (Obi-Wan!) who provides him with special tools (Lightsabre!) and advice (Use the Force!) for the adventure ahead.

Only a few world myths contain all these stages, some of them only have a few stages and others have them in a different order. Campbell's monomyth is thus criticized for focusing on the similarities and glossing over the differences between the myths, and scholars have also questioned its usefulness and general validity.

Nonetheless the monomyth has been an influential tool for plot development; aside from Star Wars, popular movies like The Lion King and the Matrix series (possibly the Harry Potter series as well) have story structures that are modelled on the monomyth.

I won't go into further details of the monomyth here, but suffice to say that by using a comparative strategy, Campbell was able to create a common resource out of the dozens of diverse mythologies in the world. He recognized that it is impossible to do this based on any single myth.

Indeed, I would argue that in general single cases only represent data and not knowledge. Outside the context of mythology, even single cases that are firmly rooted in physical evidence cannot really enlighten us about the nature of our Universe; we can only learn about them, not from them.

In other words, they have descriptive but not prescriptive value.

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The power of comparative analysis was driven home in my mind very early in my graduate student career by my advisor.

During a genomics lecture he illustrated this by showing a single sequence from one species of animal. For example, here's part of the amino acid sequence of a human gene:

MYNMMETELKPPGPQQTSGGGGGNSTAAAAGGNQKNSPDRVKRPMNAFMVWSRGQRRKMAQEN...

Well, it's a string of letters. You can't learn much just by staring at it.

But when you do an alignment with homologous genes from many other species...






... important features immediately jump out at you.

The yellow blocks represent regions that are completely identical over hundreds of millions of years of evolution - it's a good bet that those regions are functionally crucial. Blue and green blocks are identical only among some species, while white areas exhibit high variability.

Therefore, you can see regions of similarities as well as regions of differences. Regions that are common to mammals, or just to rodents, or unique to one species which may reflect functions that are only relevant to those group of animals.

This is knowledge.

Whether it is science or history, information derived from a single case is only descriptive of the case itself - in order to understand fundamental principles, produce testable predictions or to "give advice" to other people, you must have data from more than one case. With an increasing number of aligned cases comes a more accurate and more refined knowledge of the subject matter.

Hence the "analogy" aspect of FAMILIAR - knowledge obtained by comparing the features of complex systems and aligning them into a structured resource, not only at the same organizational level, but also across organizational levels.

I am aware that argument from analogy is a logical fallacy, but that does not preclude the use of an "analogy machine" like FAMILIAR to start the investigation by generating hypotheses and enabling cross-discipline visualization.

Having a systematic way to align single cases into a common resource allows people to see both the similarities and differences between the cases. Where the cases are too different in key areas to be effectively compared, proposed models can be rejected as uninformative. Where cases have striking similarities over numerous key characteristics, there is compelling support for a fundamental structure among them.

However, human knowledge is wildly varible in format. How is it possible to align diverse forms of knowledge into one common resource?

Stay tuned for the next post on the FAMILIAR Core.

FAMILIAR Part 3: General System Theory

Ludwig von Bertalanffy was an Austrian-born biologist who was a major figure in the development of the systems theory.

Around the mid-20th century he was concerned about the overemphasis on the reductionistic approach and the resulting fragmentation of science. In 1968 he published his book General System Theory where he wrote:

A consequence of the existence of general system properties is the appearance of structural similarities or isomorphisms in different fields. There are correspondences in the principles that govern the behaviour of entities that are, intrinsically, widely different. To take a simple example, an exponential law of growth applies to certain bacterial cells, to populations of bacteria, of animals or humans, and to the progress of scientific research measured by the number of publications in genetics or science in general.

System isomorphisms also appear in problems which are recalcitrant to quantitative analysis but are nevertheless of great intrinsic interest. There are, for example, isomorphies between biological systems and 'epiorganisms' like animal communities and human societies.

It seems therefore that a general system theory of systems would be a useful tool providing, on the one hand, models that can be used in, and transferred to, different fields, and safeguarding, on the other hand, from vague analogies which often have marred the progress in these fields.

Bertalanffy, together with some of his contemporaries, noted that regulation via feedback loops allow a system to maintain stability (today this field of study is called cybernetics).

He was also interested in the apparent contradiction between the 2nd law of thermodynamics and the increase in organizational complexity of living systems during embryo development and evolution. He proposed an idea that resolves this:

According to the second principle of thermodynamics, the general trend of events in physical nature is towards states of maximum disorder and levelling down of differences, with the so-called heat death of the universe as the final outlook, when all energy is degraded into evenly distributed heat of low temperature, and the world process comes to a stop.

In contrast, the living world shows, in embryonic development and in evolution, a transition towards higher order, heterogeneity, and organization.

But on the basis of the theory of open systems, the apparent contradiction between entropy and evolution disappears. In all irreversible processes, entropy must increase. Therefore, the change of entropy in closed systems is always positive; order is continually destroyed.

In open systems, however, we have not only production of entropy due to irreversible processes, but also import of entropy which may well be negative. This is the case in the living organism which imports complex molecules high in free energy. Thus, living systems, maintaining themselves in a steady state, can avoid the increase of entropy, and may even develop towards states of increased order and organization.

Bertalanffy's concept of an open system is usually illustrated like this:















Input refers to the stimuli and imported materials from the external environment, throughput refers to the processes within the system, and output refers to the resulting response or exported materials.

To emphasize the importance of feedback control, this version is also used:














Bertalanffy's open system model inspired biologist James Grier Miller to examine the applicability of systems theory to living systems.

Miller published Living Systems Theory in 1978, expounding his general theory about the existence of all living systems, their structure, interaction, behavior and development. He proposed that living systems must contain 20 critical subsystems which he later arranged into 8 nested hierarchical levels:















Miller noted that:

All nature is a continuum. The endless complexity of life is organized into patterns which repeat themselves—theme and variations—at each level of system. These similarities and differences are proper concerns for science. From the ceaseless streaming of protoplasm to the many-vectored activities of supranational systems, there are continuous flows through living systems as they maintain their highly organized steady states.

His observation of recursive patterns is likely to be inspired by the work of Benoît Mandebrot, the mathematician who founded the field of fractal geometry. Mandebrot observed that many objects in nature exhibited self-similarity and scale invariance - parts that are made up of smaller scale versions of the overall shape.



















Interestingly, neither Bertalanffy nor Miller made any big impact on the field of biology itself, which in the wake of monumental discoveries of DNA and the central dogma, has remained firmly rooted in the reductionist paradigm.

General System Theory has become influential mainly in information science and cybernetics, whereas Living Systems Theory is more commonly read in sociology.

That is why as a neuroscience undergrad I had never heard of these guys and was completely unaware of systems theory, even though I regularly lamented with one of my fellow students about the lack of a systems interpretation of neuronal behaviour.

I first read Bertalanffy in 2005 when I picked up his General System Theory from the NUS library during the height of the popularity of the buzzword "Systems Biology". I wanted to know all this "systems" talk really meant, apart from expensive, shiny new high-throughput liquid handling robots.

I found his book to be quite repetitive but I was impressed by his open systems model, which I now call the "Bertalanffy Box". Excitedly, I wrote an article about it which was published in a student's magazine (GSS Journal 2005).

Here is an exerpt:

Actually, Systems Biology is not strictly a new idea. Some aspects of this perspective can be traced as far back as Plato! The modern synthesis of its fundamental concepts, however, first appeared in the General System Theory (GST) proposed by biologist Ludwig von Bertalanffy in the late 1940s. At that time, Bertalanffy was disturbed by what he saw as the overspecialization and fragmentation of science. He felt that the standard reductionistic approach to science was driving scientists to obsess over tiny details that may not advance the understanding of the big picture. His solution was to find some common patterns of organization in nature which could help unify the sciences and even the humanities together.

In a nutshell, the GST is a holistic theory that describes a complex system by examining the interactions between its components, rather than by analyzing the detailed structure of each component. Gesalt psychologists say that “the whole is greater than the sum of its parts,” an illustration which is also valid for the GST. In the context of biology, Bertalanffy described living organisms as “open systems” that interacts comprehensively with their environment. Next, he recognized that complex systems have emergent properties that cannot be predicted by knowing the properties of its components. In addition, he observed that such a system can also exert control over its components, such as in homeostasis, by using feedback loops.

Although I liked the simplicity of the Bertalanffy box, I found it to be incomplete and aesthetically displeasing. It is only focused on one particular system and has no scaleable aspects, thus it cannot take into account the inputs that might have arrived from another organization level, and ignores the outputs that will affect another organization level.

While recuperating from an illness in a hospital, I decided to improve the Bertalanffy box by integrating Bertalanffy's idea with Mandebrot's (I hadn't read Miller yet), and this is the result:















The "extended" Bertalanffy box (which I called a "leaky" open system at that time) features a system that is affected by both internal and external inputs and contributes outputs to both internal and external states. Thus, it is embedded in an organization level with constant interactions with lower, adjacent and higher levels.

Now I had a repeatable unit that is scaleable and conceptually self-similar.

To illustrate the self-similar aspect in a more visually striking manner, I designed this diagram about a year ago:















Here you can see a complex system opened to reveal the interactions in its intrinsic environment. It is made up of complex systems which are in turn made of smaller complex systems. With this simple repeating unit, you can model a system of any amount of complexity.

I hope I have clearly explained the "Fractal" aspect of FAMILIAR.

But what about the "Analog" part?

Stay tuned for my next post on heroes' journeys and multispecies alignments.

FAMILIAR Part 2: Why No Runaway Complexity?

Long time Fresh Brainz readers probably know that my undergrad training was not in molecular biology, but in neuroscience.

My interest in systems science started about 10 years ago when I learnt a bizarre fact about neurons - that the transmission of neural impulses was a probabilistic process.

The firing of a neuronal action potential is not perfectly reliable because it depends on a complex interplay of input signals such as EPSPs, IPSPs and internal states such as the refractory period.

Due to the inherent unpredictability of any single neuron, vertebrates have to rely on a large number of neurons in each nerve in order to convey a reliable signal to other parts of the body.

This struck me as something that is particularly odd.

If you can't even trust one neuron to do its job, how can you trust a thousand of them?!??

Why won't they simply misfire all over the place and garble the signal?

The nervous system has often been compared with human technology such as computers, but you'd be barking mad to try design a computer using millions of components that are not 100% reliable.

These questions perplexed me during my undergrad years and also in my first job as a research assistant in neuroscience. What was even more puzzling then was the realization that other researchers around me simply took this fact for granted - nobody would explain to me why a bunch of unreliable parts could suddenly make a reliable system.

As I started grad school in 2004, I noticed that a similar situation occurs in cell biology. In an unfinished article entitled "Brief thoughts on the Inception of Systems" I wrote:

A cell is an amazing mixed bag of biochemical processes, some of them quite straightforward, others so convoluted that Occam’s razor would not find its mark there.

Unlike the oft used analogy of a factory, each cell is made up of components that do not fit together clearly like a clock. For example, many proteins have multiple roles across several different pathways. From cell to cell, proteins can have different functions depending on where and when it is expressed.

The compounded variability from the probabilistic performance of each intracellular player should become so large so as to make an integrated system impossible.

What I am saying is, if one wanted to make a reliable machine to fulfill a very specific function, one would not deliberately use components with variable and probabilistic characteristics. But yet cells do exists, and are quite stable and reliable. How can this be?

How indeed?

Let me illustrate this problem with a graph.















When the number of components in a system is small, the total number of interactions is limited and predictability of component behaviour is high.

This is why we can play games like pool and snooker - we can tell where the target balls will end up.

As the number of components increase, the total number of interactions increase exponentially to such an extent that it quickly becomes impossible to know exactly what will happen.

Imagine a pool table with thousands of balls.

In addition, the probabilistic behaviour of each component makes this problem far, far worse.

Imagine a pool table with thousands of unbalanced balls!

By this additive concept, it should be impossible for any limited sentient being to comprehend much of the Universe, since it consists of trillions upon trillions of probabilistic subatomic particles in constant interaction.

But here lies the trick...















In some cases, the increasing number of components start to exhibit emergent properties, forming a complex system. The whole system itself becomes reliable enough to be a "component" (or module) of another larger system.

Each successive complex system then occupies a higher organization level in a hierarchical structure, so that the total number of "component" interactions at the higher organization level never gets out of hand.

This why we can predict the trajectory of a cannonball with such accuracy, even though we can never predict the exact locations of all the component electrons in a cannonball.

There is no runaway complexity because complexity appears "fold in" on itself with each successive organization level.

But how exactly does a complex system make itself reliable and predictable?

Stay tuned for my next article about Ludwig von Bertalanffy and his General System Theory.

FAMILIAR: Unity of Knowledge

It is with great reluctance that I reveal my new model of organizing and creating new knowledge, called FAMILIAR (Fractal-Analog Method of Integrating Limitless Information into Aligned Resources), partly because I had planned to refine this idea further so that I can publish it properly in a book, and partly because I'm not best friends with humanity right now and I don't want my ideas to fall into the hands of the people I hate.

As it currently stands, the model has some holes and is fairly useless, but when decked with sufficient relevant data it has some potentially powerful implications.

This is an exclusive privilege for Fresh Brainz readers only - please do not pass this knowledge to scumbag bankers, dickhead politicians and fuckfaced lawyers because they can turn this idea into a weapon for controlling everyone.

Many thanks.

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"The most incomprehensible thing about the world is that it is at all comprehensible." - Albert Einstein

Indeed, considering its complexity and vastness, the most mysterious thing about the Universe is the fact we can even begin to understand it.

Strangely enough, the reality is that the vast majority of living systems are routinely capable of coping with the staggering complexity of the Universe. People and microbes alike experience perfectly happy lives without needing to know the existence of quarks or quasars.

The key to this is the ability to prioritize and react only to a few immediately relevant aspects of the total complexity - to differentiate between knowledge and data.

Scientifically, "knowledge" comprises a collection of data (or fact) and an explanatory structure (or theory) that organizes the data into a meaningful whole. While data constitutes an important aspect of knowledge, by itself it is not knowledge. In fact, data that cannot yet be aligned into any coherent explanatory structure will usually be regarded as noise.

It doesn't take a genius to perceive the difference between knowledge and noise; the tiniest single-celled organism instinctively ignores most of the stimuli it receives from its environment and responds to only some of them that pose an immediate threat or benefit.

Likewise, scientific knowledge doesn't directly mimick the full complexity of a given system, but brutally simplifies it into some basic principles that are comprehensible and useful to the human mind, thus allowing testable predictions and technologies to be produced.

This simplification process is often accused by opponents of rational inquiry to be fatally flawed because it is unavoidably tentative and incomplete.

How can anyone claim to understand the whole Universe if one has only examined an infinitesimal fraction of it?

Here at Fresh Brainz, we think that such a feat is possible once you appreciate the crucial distinction between knowledge and data, and understand the core structure of systems that reiterate themselves over and over again, from the subatomic world, through the intricacies of cells, organisms and societies to the interactions of galactic clusters.

I should emphasize again that as limited beings we can never hope to collect every last bit of data about the Universe, but we can achieve an increasingly complete knowledge about the Universe.

FAMILIAR is a simple, scaleable model that seeks to organize and unite all aspects of human knowledge so that we can learn about the core structure of systems and in doing so, gradually approach a complete understanding of our Universe.

Stay tuned for the next post about runaway complexity.


Would you like to know more?
- Prologue to FAMILIAR: Redundancy Redundancy