Introduction

A Biomimetics approach to information and communication

The technical aspects of storing, indexing, sorting and searching information have reached a high level of sophistication. Billions of dollars have been invested in knowledge management projects, search engine algorithms and strategies. There seems little scope for further improvement or new ideas.

However, there is one area of information technology that is in its infancy: that of dealing with the vast differences in the way people think and the way people understand information. This is not a technical problem that can be solved by clever computer programming. The solutions will depend upon adopting appropriate strategies that take into account the way in which the human brain interprets and processes information.

Until the last decade of the twentieth century, the human brain was assumed to be similar in all people. The variations in structure and functioning were assumed to be aberrations, caused by genetic defects, disease or injury. It was only when brain imaging techniques became widespread and ubiquitous in a variety of different disciplines that it became apparent that differences in brain structure and functioning were not exceptions, but the rule. And, these differences are purposefully introduced as a strategic advantage by the evolutionary process.

Put simply: brain abnormality is a purposeful part of nature's design to increase the efficiency of human competitiveness and survival. It is not efficient if everyone thinks in the same way or has the same knowledge. Far better to have a variety of differences that can be melded together to deal with problems and contingencies. Nature has provided a host of different emotions that compel individuals to need each other and to work together in groups. In this way, cooperation and collaboration become the norm, which maximizes the advantages of having neural variation within a population.

Even within the same culture, people differ genetically from each other. They have different brain structures and different emotional biases. They have different educational and social backgrounds, different knowledge and experiences. They will have different needs and different uses for information. Finding a strategy that can cater for this full range of human variability is a challenge that now faces information technology.

For a solution, we turn to the new understandings about the functioning of the brain, which is only now beginning to emerge from contemporary neurological research.

Learning from biological systems

Computer technology has difficulty in dealing with overwhelming variety and an abundance of uncertainty and ambiguity. But, biological organisms have evolved to thrive in these conditions.

They employ ingenious strategies and highly efficient solutions that far exceed anything yet accomplished by human technology. We have much to gain by trying to understand how natural systems do this, so that we can copy their techniques.

Studying and copying biological systems is becoming a rapidly expanding field of new research. It has a name: Biomimetics, which is defined as follows:

Biomimetics: the study of biological mechanisms and processes for the purpose of abstracting the functions for use in technical applications. It is an enabling discipline, which looks towards nature for ideas that may be adapted and adopted for solving problems.

The most obvious place to look for biological inspiration in the field of information technology is the human brain. The wealth of new discoveries coming out of contemporary neuroscience research is crying out for application in knowledge management systems.

The decade of the brain

On July 17, 1990, President George Bush, through a Proclamation (6158), declared 1990-1999 as the 'Decade of the Brain'. This started a chain reaction throughout the world with most of the major economies investing millions of dollars into neuroscience institutes; initiating a plethora of research programmes to explore the structure and functioning of the human brain. In the US alone, more than 1000 new members are being added to the Society of Neuroscience every year.

This explosion of new interest in the brain was driven mainly by technological advances in brain imaging techniques, which allows scientists to see where activity is taking place in the brain during various kinds of conscious and unconscious mental processing.

By comparing the brain activities of normal people with those that have known neurological damage, or diagnosed mental deficiencies, it is possible to gain valuable insights into where different types of mental activity is taking place and which parts of the brain are involved. This work has transformed the way in which the workings of the brain are now being understood - particularly the functions relating to memory, perception and information processing.

The breakthrough in understanding

Brain imaging techniques are not concerned with the intricate details of neural circuitry. They look at higher levels of brain functioning - outlining the strategy the brain uses to solve problems.

One of the most important insights came out of the work on the olfactory system by Walter J. Freeman of the Neurophysiology Lab at the University of California, Berkeley, CA, USA. His imaging techniques revealed that millions of odor receptors in the nose were sending sensory information to a pea sized area in the brain known as the olfactory bulb. There, millions of neural signals give rise to a discreet number of patterns of neural activity, each of which comes into existence as a result of a particular odor.

It was further noticed that each of these different patterns activated different combinations of areas in the brain associated with memory and the generation of emotional and physiological responses.

The breakthrough came when the patterns of neural activity in the olfactory bulb were displayed on an oscilloscope. These patterns were recognized as being similar to the characteristic patterns observed in chaotic systems with attractor basins.

Chaotic systems and attractor basins are a technical subject in their own right, but essentially, what it means is that an interconnected network of neurons can receive a multitude of different inputs but will settle into only a limited number of steady states. These states are called attractors and the variations of inputs that settle into one of these steady states are said to fall within its basin of attraction.

In other words, the network of neurons in the olfactory bulb becomes a pattern detector, identifying specific patterns in the millions of signals coming from the receptor cells in the nose. Each pattern activating a particular combination of neurons within the bulb (the particulr neurons activated when the network of neurons is in one of the attractor states).

As the neurons in each separate attractor are connected to different combinations of other neural networks in the brain, the olfactory bulb can be thought of as a multiple position switch - which will activate different combinations of neural networks according to which receptors in the nose are activated by different odors.

Brain imaging techniques then found similar pattern detecting networks in other parts of the brain known to be dealing with auditory and visual sensory inputs.

It was soon realized that much of the brain consists of similar dynamic structures - where attractors are functioning as complex switches to activate other dynamic networks with attractor basins -forming a network of controllable connections to centers of emotion and hormone control units.

The brain is now seen as a complex, inter-linked, dynamic system that is continuously re-configuring its activity in response to learned patterns derived from its sensory inputs. There is no hierarchical storage, as we might understand it in terms of database technology. There is no sorting, searching and locating. Instead, combinations of neural networks are instantly brought into play in response to sensory inputs - such that their interactivity creates what we would describe as our perceptions of the world we are interacting with.

The inference of this research is that our perceptions of the world are not reflecting a true, literal reality, but an internally created reality - an artificial fabrication, constructed on the fly under the influence of memories, past experiences, emotions and biases in our genetic makeup.

How the brain learns

Significantly, when Walter Freeman was experimenting with different odors, during his work on the olfactory system, he found that when a subject was exposed to a novel odor, a new pattern of neural activity emerged (a new attractor basin manifested). This pattern could be 'taught' to activate specific areas of the brain according to whether or not the odor was associated with pleasant or unpleasant experiences.

It is only a short step from this to understanding how auditory inputs can create attractor states in auditory neural networks and become associated with particular words. Each of these 'word' attractor states can be arranged - through a learning process - to activate specific areas of the brain that are associated with particular emotional responses, visual images and relevant attractor basins of other words.

Such a process also comes into play when we read words in a text document. The light stimulated signals from the cones in the retinas of our eyes activate different attractor basins, which we learn to associate with the attractors of particular auditory words.

Brain imaging techniques show this complexity in action. And, it reveals much more. It shows how emotional moods, hormone levels, genetic variation, pharmaceutical drugs and a host of other factors can greatly influence this brain activity. It shows how different people will interpret and react differently to the same information. It shows how people interpret and react differently to the same information at different times according to mood, context and situation.

Interestingly, brain imaging can reveal similarities or differences in the combinations of neural areas that are brought into play by different cultural groups when they are presented with identical information.

Ramifications for knowledge management

This knowledge, of the physical activity in the brain as it perceives and processing information, has profound ramifications for knowledge management systems. It gives substance to the wealth of empirical evidence that people differ markedly in how they understand information and the knowledge they gain from it. An insightful, inspirational document to one person may seem a nonsensical collection of buzzwords to another. Contradictory conclusions can be drawn from the same tracts of text when read by people who have different moods or have different background experiences.

Given this situation, who can be relied on to evaluate the content of a paper? Whose views are best, when it comes to classification and categorization?

The problems are compounded by the knowledge that the brain doesn't absorb information in a literal sense. Brain imaging has shown that all inputs to the brain are lost during the neural processing procedures. The brain constructs its own version of the information it receives, using elements drawn from memory to build selected elements of the information it perceives into its own unique conceptual framework.

This can be likened to an artist showing somebody a picture they have drawn and then for that person to cut it up into pieces and add some of the pieces to a picture they have created themselves.

Two, well documented, examples illustrate this problem. Firstly, it has been shown that people in a depressed state of mind seem to process only negative content. Conversely, people in an euphoric state of mind process only positive information.

Brain imaging has revealed that this doesn't happen only with conscious, cognitive functions: it also happens at a sub conscious level, affecting moods and behavior. Seeing where activity is taking place in the brain explains why different people get can different meanings from the same document - and why a person can get different meanings at different times according to their moods and the conscious or unconscious context in which they are reading.

Similarly, the perceived accuracy and importance of information is known to be greatly influenced by a reader's perception of the person providing the information. Brain imaging shows how such prior knowledge can make differences to the way information is processed - reflected in changes to the neural circuits that are called into play.

Another important observation - borne out by brain imaging techniques - is the way in which people use heuristic strategies when reading information.

In a world where there is infinitesimally more information available than it is possible to read, people will quickly scan through documents looking for clues as to whether or not to make the effort to absorb the content. Documents are often only partially read and often reading will be aborted altogether on account of a single phrase that conflicts with previous knowledge. This heuristic reading strategy occurs not only at a conscious level, but also at a subconscious level, causing a reader to be unaware that information content is being distorted or lost.

Memes and concepts

The knowledge that information is absorbed in selected, isolated chunks adds further complexity to knowledge management systems. Documents cannot be treated as self-contained wholes. They must be viewed as a collection of ideas that may or may not be viewed in the same context as the author intends.

Richard Dawson, in his 1976 book "The Selfish Gene", introduced the concept of memes. These are ideas that are contained within words or phrases. Like biological genes, memes can spread through a population, replicate and mutate. Just as the physical form of a human can be said to be the product of a set of genes, so an informational document can be said to be a product of the memes it contains.

In the new understanding of the brain, memes take the form of attractor basins. When a meme is evoked, the attractor will activate all the neural networks that relate to that meme. Emotions, mental images and cognitive structures are brought into consciousness and perhaps other memes brought into play.

Note: the word meme is itself a meme, which encapsulates the concept of the way in which ideas are transmitted from person to person, and across generations, through the use of language.

Advancing technology and sophisticated business techniques are continuously creating new ideas and concepts. These memes are often represented by new words or old words given extended meanings - giving rise to new atractor basins in the brain.

Although this is an effective and efficient means of communicating ideas and concepts, it is fraught with problems. Outside of the particular groups where meme words are created, the words can have different meanings or associations. Meme words can also be used by people who have misunderstood the original idea and put them to use in a different context.

Used in papers where meanings are not fully defined, meme words can cause misunderstandings and gaps in knowledge or reasoning. A word representing a highly sophisticated idea to one person can give a completely different meaning to another or even be dismissed as a pretentious buzzword.

When, and where, to use word memes can be highly problematic for any kind of knowledge management system. It may be even more of a problem when communicating across different cultures.

Bounded rationality

The brief explanations above paint a rough picture of the way in which recent work in neurology is helping us to understand how humans absorb and process information. It provides solid evidence that every individual has a uniquely limited and biased perception of the world.

Brain imaging has shown how information doesn't only give rise to emotions, it also shows that emotions, prior to perception, can determine how information is perceived, understood and absorbed. This is why it is so easy for people to differ over the value and relevance of content.

It is virtually impossible for anyone to have absolute and true knowledge because the human brain cannot possibly accommodate all there is to know about a subject, particularly in large, evolving information spaces that are subject to competitive initiatives and continuous new discoveries.

This practical limitation to the amount of knowledge a person can possess is known as "bounded rationality". It means that every person can act and respond only according to what they know - and this knowledge is likely to be biased according to their emotions and particular genetic makeup.

As all knowledge entered into a knowledge management system is authored, judged, sorted and selected by people with bounded rationalities, it can never be proved to be truly authoritative.

Abnormality is a good thing

The more that is learned about how the brain processes information the less likely it seems that this knowledge can be of much use in creating a computer system that can deal with information intelligently. But, this begs the question, "How does the brain deal intelligently with the information it is receiving?".

The answer comes not from examining the processes that go on in a single brain, but by considering a community of brains interacting with each other.

Twentieth century psychology was based mainly around the idea that there is some kind of 'normal' brain that has 'normal' functions. Any deviation from these normalities was considered abnormal. The greater the deviation from the mean, the greater the abnormality considered to be present in the individual.

Evolutionary biology takes a different view. Evolution needs variation to cope with variety, unpredictability and uncertainty in the environment. It would be disastrous if humans evolved with identical, normal brains. It needs some that are biased towards optimism so that some people will take risks and produce progress. It needs other brains that are biased towards pessimism so as to maintain stability. It needs some brains that are inclined towards leadership and responsibility and other brains inclined towards following. It needs some brains inclined towards curiosity, other brains satisfied with the status quo. It needs brains that concentrate on detail and other brains that concentrate on higher levels of organization.

The human brain hasn't evolved to optimize the fitness of the individual, it has evolved to optimize the fitness of a group. It isn't an advantage for everyone to think the same way or have the same emotional responses. Evolutionary strategy requires a group to be able to judge situations in a variety of different ways to evoke a variety of responses. It doesn't care if some of the conclusions people come up with are wrong, all it is concerned with is that at least some of them come up with the right solutions.

To evolution, the presence of variation and abnormality is an essential part of a long term strategy for survival.

Cultural differences can be beneficial

Seen in this light, cultural differences become blurred against the background of dealing with the differences between people in general. Finding solutions that take into account the differences between people within the same culture would automatically include the differences across cultures.

However, because evolutionary biological strategy seems to indicate that it is not beneficial for everyone to think in exactly the same way, it may be preferable to encourage and promote difference. This suggests that cultural differences should be promoted, rather than compensated for.

On top of all the other problems associated with knowledge management, this idea would seem to lead to complete chaos - but it is this kind of chaos in which biological systems seem to thrive.

Stigmergy - Nature's design strategy

Biological systems thriving on chaos may seem a difficult concept to come to terms with. But, if we understand how this happens, we can adopt a similar strategy for systems of knowledge management.

Let's consider some of the reasons people need information. They need it to be able to be aware of changes and developments in their respective fields and have knowledge of changes in other areas that might affect their goals. They need the kind of information that allows them to become aware of new openings and new opportunities. They need to know what their competitors are doing. This is a huge and highly volatile information space and will need a solution that can cope with the dynamics of constant unpredictable change.

Fortunately, this is a much studied area in evolutionary biology and there are some excellent models to work from. The most basic of these models is the concept of stigmergy. This word is a meme that encapsulates the basic model of evolutionary progress.

Stigmergy was originally discovered and named about fifty years ago by Pierre-Paul Grasse, a French biologist studying ants and termites. He was intrigued to learn how these virtually brainless creatures could create highly sophisticated messaging systems and build extremely complex architectural structures. It was a mystifying puzzle that nobody had ever been able to explain.

What he uncovered defied rational explanation. There were no plans, organization or control built into the brains or genes of the ants. The ants weren't even communicating with each other. The sophisticated frameworks and complex structures were emerging spontaneously.

The understanding eventually came about through knowledge gained in the study of the self-organizing characteristics of complex systems.

Complexity arises in stigmergic systems because individuals interact not with each other but with a common environment. They interact with the environment by making changes to it. These changes affect the way further changes are made. This gives rise to a positive feedback effect, where information feeds upon information (much the same effect as when conversations can take unpredictable directions according to the way people respond to each other's comments). The general idea being that an environment is being changed by individuals, who see what is there already and add to it with a view to making improvements.

Such random activity would seem to lead to nothing other than a disorganized, chaotic growth of information. But, if there is a mechanism in place that can quickly eliminate any changes that degrade the system and promote any changes that are beneficial, self-organization and increased efficiency will emerge. This is the strategy that biological organisms use - which we know as evolution.

Nature selects for changes that improve fitness to survive and reproduce. In human commercial systems, selectivity is determined by the ability of changes to provide more value than it costs to produce (otherwise they will run out of money and die).

It is this concept that explains how the World Wide Web has self-organized to become increasingly more efficient and complex without any central planning or overall control. It explains how any complex environment - in biology, business, technological and scientific fields - becomes increasingly organized and efficient.

The essential characteristic of this model is that the evolutionary progress is not planned or predetermined. All possibilities for change are allowed to happen, but a selective mechanism is used to sort out the good from the bad. In this way, continuous improvement is selected from continuous change; self-organization emerges - and in a direction that leads to greater and greater efficiency.

Information technology can greatly benefit from an understanding of the way in which the human brain has evolved to function as part of a collaborative system. But, this requires a conceptual framework that is completely alien to the worlds of conventional business and computer methods and and techniques.