Fritjof Capra

©2015 Peter Fritz Walter. Some rights reserved.
Creative Commons Attribution 4.0 International License.

A New Scientific Understanding of Living Systems, New York: Anchor Books, 1997.

5 Stars

Review

The Web of Life is the book in which Fritjof Capra defined his approach to ecology, thereby making ecology, or deep ecology, a concept that is part of a new science paradigm, powerfully introduced and promoted by one of the most important science theorists of our times.

What is deep ecology and why do we need it? Capra writes:

Whereas the old paradigm is based on anthropocentric (human-centered) values, deep ecology is grounded in ecocentric (earth-centered) values. It is a worldview that acknowledges the inherent value of nonhuman life./11

Such a deep ecological ethics is urgently needed today, and especially in science, since most of what scientists do is not life-furthering and life-preserving but life-destroying. With physicists designing weapon systems that threaten to wipe out life on the planet, with chemists contaminating the global environment, with biologists releasing new and unknown types of microorganisms without knowing the consequences, with psychologists and other scientists torturing animals in the name of scientific progress — with all these activities going on, it seems most urgent to introduce ‘ecoethical’ standards into science./Id.

This book’s quest is enormous in that it requires modern science to fundamentally shift its regard upon nature, and upon living! Our regard upon nature has been conditioned by patriarchy since about five thousand years, and it’s a rather defensive, distorted, schizophrenic, and reductionist regard. Capra looked back in history and found amazing early intuitions and truths propagated by our great thinkers, poets and philosophers, such as for example Immanuel Kant, Johann Wolfgang von Goethe or William Blake.

The understanding of organic form also played an important role in the philosophy of Immanuel Kant, who is often considered the greatest of the modern philosophers. An idealist, Kant separated the phenomenal world from a world of ‘things-in-themselves.’ He believed that science could offer only mechanical explanations, but he affirmed that in areas where such explanations were inadequate, scientific knowledge needed to be supplemented by considering nature as being purposeful./21

Capra wondered why our science and technologies are so deeply hostile toward our globe, which we call Mother Earth after all, and so little caring for its preservation? He found conclusive answers in ancient traditions that fostered what we call today aGaia worldview, a respectful attitude toward the earth, the mother, the yin energy and generally female values:

The view of the Earth as being alive, of course, has a long tradition. Mythical images of the Earth Mother are among the oldest in human religious history. Gaia, the Earth Goddess, was revered as the supreme deity in early, pre-Hellenic Greece. Earlier still, from the Neolithic through the Bronze Ages, the societies of ‘Old Europe’ worshiped numerous female deities as incarnations of Mother Earth./22

This is how Capra, always grounded in common sense and meaningful retrospection, smoothly introduces the novice reader to the concept of systems research or thesystems view of life.

Post-matriarchal thought, which was naturally systemic, can be traced from theAtomistic Worldview (Democritus), over the Cartesian Worldview (Newton, La Mettrie, René Descartes) and Relativistic Worldview (Einstein, Planck, Heisenberg), to theSystemic Worldview (Bohm, Bateson, Grof, Capra, Laszlo, etc.) and the Holistic Worldview(Talbot, Goswami, McTaggart, etc.).

In all systems, we have to deal with different levels of complexity that are woven in each other, thus rendering it almost impossible to dissect parts of the system for closer research without disturbing the system. This means that, contrary to earlier vivisectionist science, we need to leave the system intact and focus our research onto the whole of it — which makes it all so complex, but this very complexity renders justice to nature!

As a result, we had to develop a new mathematics, which today is called the mathematics of complexity, in order to deal with the high complexity levels in living systems. This also means that our chief scientific method — deductive analysis — is inadequate for any inquiry in the functionality of living systems, because they are networks within networks and can only be grasped scientifically through understanding their properties.

According to the systems view, the essential properties of an organism, or living system, are properties of the whole, which none of the parts have. They arise from the interactions and relationships among the parts. These properties are destroyed when the system is dissected, either physically or theoretically, into isolated elements. Although we can discern individual parts in any system, these parts are not isolated, and the nature of the whole is always different from the mere sum of its parts. (…) The great shock of twentieth-century science has been that systems cannot be understood by analysis. The properties of the parts are not intrinsic properties but can be understood only within the context of the larger whole. Thus the relationship between the parts and the whole has been reversed./29

At each scale, under closer scrutiny, the nodes of the network reveal themselves as smaller networks. We tend to arrange these systems, all nesting within larger systems in a hierarchical scheme by placing the larger systems above the smaller ones in pyramid fashion. But this is a human projection. In nature there is no ‘above’ or ‘below’, and there are no hierarchies. There are only networks nesting within other networks./35

This means that living systems are not, as most of our governmental and societal organization, hierarchical, but network-based, and thus structured not vertically but horizontally by ‘neuronally’ linking segments to larger molecular structures that distribute information instantly over the whole of the network. You can also say that a living network is a system of ‘total information sharing’ where there is not one single molecule that is uninformed at any point in time and space.

The fact that horizontal networks are nested within other horizontal networks, while the different networks all possess a different level of complexity, makes research so intricate. This is inter alia why high-performance computers have greatly aided in developing systems theory. But the most revolutionary insight here is that our usual habit of dissecting parts from a whole for further scrutiny and scientific investigation does not work with living systems. Why is this so?

Ultimately — as quantum physics showed so dramatically — there are no parts at all. What we call a part is merely a pattern in an inseparable web of relationships. Therefore the shift from the parts to the whole can also be seen as a shift from objects to relationships./37

Hence, the whole of our approach to scientific investigation has to shift from an object-based to a relationship-based research approach when we deal with living systems. This requires researchers to change their inner setup which is exactly what quantum physics revealed to us, that is, the observer’s belief system will be reflected in the outcome of the research.

And there is one more crucial element in systems research that Capra explains and elucidates. It is what we already learnt within the revolutionary reframing of science by quantum physics, the fact namely that in approaching quantum reality, and organic behavior, we have to learn the mathematics of probability. What is probability? It is the approximation of behavior. Dealing with approximations means that we leave the certainty principle and venture into what Heisenberg called theuncertainty principle. Giving up certainty triggers fear. This fear was very vividly described by Max Planck and Werner Heisenberg when the paradigm began to shift and quantum physics slowly but definitely began to undermine traditional physics. When we abandon certainty, we begin to grasp the notion of approximation, and of probability, and accordingly, we will shift our mathematical constructs when we deal with open systems.

What makes it possible to turn the systems approach into a science is the discovery that there is approximate knowledge. This insight is crucial to all of modern science. The old paradigm is based on the Cartesian belief in the certainty of scientific knowledge. In the new paradigm it is recognized that all scientific concepts and theories are limited and approximate. Science can never provide any complete and definite understanding./41

Unlike closed systems, which settle into a state of thermal equilibrium, open systems maintain themselves far from equilibrium in this ‘steady state’ characterized by continual flow and change./48

Living systems are open systems, which means that their main characteristic ischange and flow, and not continuity and static behavior. And they are far from equilibrium, which is the single most revolutionary discovery of systems research. It means living systems are constantly struggling against decay, and decay means equilibrium. When we extrapolate this insight from organic systems into our metaphysical reality, we see that it applies also to human beings, and even to religions. When we are settled and satiated, we are not alive. This is what it all boils down to. So this profound insight from systems research may help us to survive in a state far from equilibrium, putting our assuredness or false assuredness away, to stay with a beginner’s mind, as it’s so wistfully expressed in Zen. Our universe is a basically patterned universe, so is human intelligence.

But what are patterns? Capra explains the importance of pattern when he explores the meaning of self-organization, which is one major characteristic of living systems. In order to scientifically explain pattern we need to change or for the least upgrade our basic toolset of scientific investigation. Capra explains:

To understand the phenomenon of self-organization, we first need to understand the importance of pattern. The idea of a pattern of organization — a configuration of relationships characteristic of a particular system — became the explicit focus of systems thinking in cybernetics and has been a crucial concept ever since. From the systems point of view, the understanding of life begins with the understanding of pattern./80

In the study of structure we measure and weigh things. Patterns, however, cannot be measured or weighed; they must be mapped. To understand a pattern we must map a configuration of relationships. In other words, structure involves quantities, while pattern involves qualities./81

The systems view of life really involves a radical change in our scientific thinking because traditional science was quantity-based and measure-oriented, while systemic science is quality-based and relationship-oriented.

Capra exemplifies this truth by looking at the properties involved in the scientific focus of both static and systemic science theory. In this context, we should look at feedback loops as an important self-regulatory function in living systems. This is important because without feedback loops, living systems could not be self-organizing. Capra explains:

Systemic properties are properties of pattern. What is destroyed when a living organism is dissected is its pattern. The components are still there, but the configuration of relationships among them — the pattern — is destroyed, and thus the organism dies./81

Because networks of communication may generate feedback loops, they may acquire the ability to regulate themselves. For example, a community that maintains an active network of communication will learn from its mistakes, because the consequences of a mistake will spread through the network and return to the / source along feedback loops. Thus the community can correct its mistakes, regulate itself, and organize itself. Indeed, self-organization has emerged as perhaps the central concept in the systems view of life, and like the concepts of feedback and self-regulation, it is linked closely to networks. The pattern of life, we might say, is a network pattern capable of self-organization. This is a simple definition, yet it is based on recent discoveries at the very forefront of science./82–83

Another central point in this book is Capra’s focus upon the intrinsic quality of living systems as nonlinear systems that require, to be understood, an equally nonlinearmathematical approach. One early realization of mathematical nonlinearity was the introduction of the fractal in mathematics. Capra explains:

The great fascination exerted by chaos theory and fractal geometry on people in all disciplines — from scientists to managers to artists — may indeed be a hopeful sign that the isolation of mathematics is ending. Today the new mathematics of complexity is / making more and more people realize that mathematics is much more than dry formulas; that the understanding of pattern is crucial to understand the living world around us; and that all questions of pattern, order, and complexity are essentially mathematical./152–153

After having elucidated that systems research involves a process-based scientific approach rather than an object-based one, Capra presents the perhaps most important research topic in this book: the reinvestigation of cognition based on the insights from systems research. Capra pursues:

The identification of mind, or cognition, with the process of life is a radically new idea in science, but it is also one of the deepest and most archaic intuitions of humanity. In ancient times the rational human mind was seen as merely one aspect of the immaterial soul, or spirit./264

In fact, the whole debate about information processing, vividly criticized in the early writings of think tank Edward de Bono, and the even larger debate about cybernetics make it all clear that cognition is currently in a process of reevaluation:

The computer model of cognition was finally subjected to serious questioning in the 1970’s when the concept of self-organization emerged. (…) These observations suggested a shift of focus — from symbols to connectivity, from local rules to global coherence, from information processing to the emergent properties of neural networks./266

In my scientific exploration of emotions, I have revisited our scientific grasp of emotions, as it was coined within a fragmented and reductionist science paradigm. Fritjof Capra comprehensively explains that emotions are not singular elements but coherently organized within a patterned system in which cognition and response are intertwined in a self-regulatory and organic whole:

The range of interactions a living system can have with its environment defines its ‘cognitive domain’. Emotions are an integral part of this domain. For example, when we respond to an insult by getting angry, that entire pattern of physiological processes — a red face, faster breathing, trembling, and so on — is part of cognition. In fact, recent research strongly indicates that there is an emotional coloring to every cognitive act./269

The most important fact that systems theory teaches us about cognition is that it does not work like a computer processes information. Information processing, already declared years ago ‘an obsession of modern science’ by Edward de Bono, is quite a misnomer because our brain does not ‘process’ information as a computer does.

A computer processes information, which means that it manipulates symbols based on certain rules. The symbols are distinct elements fed into the computer from outside, and during the information processing there is no change in the structure of the machine. The physical structure of the computer is fixed, determined by its design and construction. The nervous system of a living organism … interacts with its environment by continually modulating its structure, so that at any moment its physical / structure is a record of previous structural changes. The nervous system does not process information from the outside world but, on the contrary, brings forth a world in the process of cognition./274–275

Capra then answers to the debate about artificial intelligence and the myths it creates in the minds of masses of people:

A lot of confusion is caused by the fact that computer scientists use words such as intelligence, memory, and language to describe computers, thus implying that these expressions refer to the human phenomena we know well from experience. This is a serious misunderstanding. For example, the very essence of intelligence is to act appropriately when a problem is not clearly defined and solutions are not evident. Intelligent human behavior in such situations is based on common sense, accumulated from lived experience. Common sense, however, is not available to computers because of their blindness of abstraction and the intrinsic limitations / of formal operations, and therefore it is impossible to program computers to be intelligent./275–276

Real intelligence is human, and original, not mechanical and artificial! True intelligence is contextual, as language is. No computer can understand meaning. A rat’s intelligence is a million times closer to that of man than that of the most powerful and sophisticated computer. Capra notes:

The reason is that language is embedded in a web of social and cultural conventions that provides an unspoken context of meaning. We understand this context because it is common sense to us, but a computer cannot be programmed with common sense and therefore does not understand language./276

Mind is not a thing but a process — the process of cognition, which is identified with the process of life. The brain is a specific structure through which this process operates. Thus the relationship between mind and brain is one between process and structure./278

Now, let us look at what sustainability means in systems research. A system is sustainable when it’s not only functional but also well integrated in a greater continuum so that it has a good prognosis for survival, for continuity. Capra writes:

Partnership is an essential characteristic of sustainable communities. The cyclical exchanges of energy and resources in an ecosystem are sustained by pervasive cooperation. Indeed, we have seen that since the creation of the first nucleated cells over two billion years ago, life on Earth has proceeded through ever more intricate arrangements of cooperation and coevolution. Partnership — the tendency to associate, establish links, live inside one another, and cooperate — is one of the hallmarks of life./301

Partnership and cooperation were indeed alien words under patriarchy but they were imbedded in pre-patriarchal cultures, such as the Minoan Civilization, and thus what we get today is a return to the sources.

Quotes

  • There are solutions to the major problems of our time, some of them even simple. But they require a radical shift in our perceptions, our thinking, our values. And, indeed, we are now at the beginning of such a fundamental change of worldview in science and society, a change of paradigms as radical as the Copernican revolution. But this realization has not yet dawned on most of our political leaders. The recognition that a profound change of perception and thinking is needed if we are to survive has not yet reached most of our corporate leaders, either, or the administrators and professors of our large universities. /4
  • Whereas the old paradigm is based on anthropocentric (human-centered) values, deep ecology is grounded in ecocentric (earth-centered) values. It is a worldview that acknowledges the inherent value of nonhuman life. /11
  • Such a deep ecological ethics is urgently needed today, and especially in science, since most of what scientists do is not life-furthering and life-preserving but life-destroying. With physicists designing weapon systems that threaten to wipe out life on the planet, with chemists contaminating the global environment, with biologists releasing new and unknown types of microorganisms without knowing the consequences, with psychologists and other scientists torturing animals in the name of scientific progress — with all these activities going on, it seems most urgent to introduce ‘ecoethical’ standards into science. /11
  • The emphasis on the parts has been called mechanistic, reductionist, or atomistic; the emphasis on the whole holistic, organismic, or ecological. In twentieth-century science the holistic perspective has become known as ‘systemic’ and the way of thinking it implies as ‘systems thinking’. /17
  • At the dawn of Western philosophy and science, the Pythagoreans distinguished ‘number’, or pattern, from substance, or matter, viewing it as something that limits matter and gives it shape. As Gregory Bateson put it: — The argument took the shape of ‘Do you ask what it’s made of — earth fire, water, etc.?’ Or do you ask, ‘What is its pattern?’ Pythagoreans stood for inquiring into pattern rather than inquiring into substance. /18
  • William Blake, the great mystical poet and painter who exerted a strong influence on English Romanticism, was a passionate critic of Newton. He summarized his critique in these celebrated lines: — May God us keep from single vision and Newton’s sleep. /21
  • ‘Each creature’, wrote Goethe, ‘is but a patterned gradation (Schattierung) of one great harmonious whole.’ /21
  • The Romantic artists were concerned mainly with a qualitative understanding of patterns, and therefore they placed great emphasis on explaining the basic properties of life in terms of visualized forms. Goethe, in particular, felt that visual perception was the door to understanding organic form. /21
  • The understanding of organic form also played an important role in the philosophy of Immanuel Kant, who is often considered the greatest of the modern philosophers. An idealist, Kant separated the phenomenal world from a world of ‘things-in-themselves’. He believed that science could offer only mechanical explanations, but he affirmed that in areas where such explanations were inadequate, scientific knowledge needed to be supplemented by considering nature as being purposeful./21
  • The view of the Earth as being alive, of course, has a long tradition. Mythical images of the Earth Mother are among the oldest in human religious history. Gaia, the Earth Goddess, was revered as the supreme deity in early, pre-Hellenic Greece. Earlier still, from the Neolithic through the Bronze Ages, the societies of ‘Old Europe’ worshiped numerous female deities as incarnations of Mother Earth. /22
  • The German embryologist Hans Driesch initiated the opposition to mechanistic biology at the turn of the century with his pioneering experiments on sea urchin eggs, which led him to formulate the first theory of vitalism. When Driesch destroyed one of the cells of an embryo at the very early two-celled stage, the remaining cell developed not into half a sea urchin, but into a complete but smaller organism. Similarly, complete smaller organisms developed after the destruction of two or three cells in four-celled embryos. Driesch realized that his sea urchins eggs had done what a machine could never do: they had regenerated wholes from some of their parts. /26
  • This is, in fact, the root meaning of the word ‘system’, which derives from the Greek synhistanai (‘to place together’). To understand things systemically literally means to put them into a context, to establish the nature of their relationships. /27
  • Indeed, an outstanding property of all life is its tendency to form multileveled structures of systems within systems. Each of these forms a whole with respect to its parts while at the same time being a part of a larger whole. Thus cells combine to form tissues, tissues to form organs, and organs to form organisms. These in turn exist within social systems and ecosystems. Throughout the living world we find living systems nesting within other living systems. /28
  • What the early systems thinkers recognized very clearly is the existence of different levels of complexity with different kinds of laws operating at each level. Indeed, the concept of ‘organized complexity’ became the very subject of the systems approach. At each level of complexity the observed phenomena exhibit properties that do not exist at the lower level. For example, the concept of temperature, which is central to thermodynamics, is meaningless at the level of individual atoms, where the laws of quantum theory operate. Similarly, the taste of sugar is not present in the carbon, hydrogen, and oxygen atoms that constitute its components. In the early 1920’s the philosopher C.D. Broad coined the / term ‘emergent properties’ for those properties that emerge at a certain level of complexity but do not exist at lower levels. /28–29
  • According to the systems view, the essential properties of an organism, or living system, are properties of the whole, which none of the parts have. They arise from the interactions and relationships among the parts. These properties are destroyed when the system is dissected, either physically or theoretically, into isolated elements. Although we can discern individual parts in any system, these parts are not isolated, and the nature of the whole is always different from the mere sum of its parts. /29
  • The great shock of twentieth-century science has been that systems cannot be understood by analysis. The properties of the parts are not intrinsic properties but can be understood only within the context of the larger whole. Thus the relationship between the parts and the whole has been reversed. /29
  • Analysis means taking something part in order to understand it; systems thinking means putting it into the context of a larger whole. /30
  • In the formalism of quantum theory these relationships are expressed in terms of probabilities, and the probabilities are determined by the dynamics of the whole system. Whereas in classical mechanics the properties and behavior of the parts determine those of the whole, the situation is reversed in quantum mechanics; it is the whole that determines the behavior of the parts. /31
  • At the turn of the century, the philosopher Christian von Ehrenfels was the first to use Gestalt in the sense of an irreducible perceptual pattern, which sparked the school of Gestalt psychology. Ehrenfels characterized a gestalt by asserting that the whole is more than the sum of its parts, which would become the key formula of systems thinkers later on. /31
  • The notion of pattern was always implicit in the writings of the Gestalt psychologists, who often used the analogy of a musical theme that can be played in different keys without losing its essential features. /32
  • [T]he web of life consists of networks within networks. At each scale, under closer scrutiny, the nodes of the network reveal themselves as smaller networks. We tend to arrange these systems, all nesting within larger systems, in a hierarchical scheme by placing the larger systems above the smaller ones in pyramid fashion. But this is a human projection. In nature there is no ‘above’ or ‘below’, and there are no hierarchies. There are only networks nesting within other networks. /35
  • Ultimately — as quantum physics showed so dramatically — there are no parts at all. What we call a part if merely a pattern in an inseparable web of relationships. Therefore the shift from the parts to the whole can also be seen as a shift from objects to relationships. /37
  • For thousands of years Western scientists and philosophers have used the metaphor of knowledge as a building, together with many other architectural metaphors derived from it. We speak of fundamental laws, fundamental principles, basic building blocks, and the like, and we assert that the edifice of science must be built on firm foundations. /38
  • In the new systems thinking, the metaphor of knowledge as a building is being replaced by that of the network. As we perceive reality as a network of relationships, our descriptions, too, form an interconnected network of concepts and models in which there are no foundations. (…) When this approach is applied to science as a whole, it implies that physics can no longer be seen as the most fundamental level of science. Since there are no foundations in the network, the phenomena described by physics are not any more fundamental than those described by, say, biology or psychology. They belong to different systems levels, but none of those levels is any more fundamental than the others. /39
  • The new paradigm implies that epistemology — understanding of the process of knowing — has to be included explicitly in the description of natural phenomena. /40
  • When we draw a picture of a tree, most of us will not draw the roots. Yet the roots of a tree are often as expansive as the parts we see. In a forest, moreover, the roots of all trees are interconnected and form a dense underground network in which there are no precise boundaries between individual trees. In short, what we cal a tree depends on our perceptions. It depends, as we say in science, on our methods of observation and measurement. In the words of Heisenberg: ‘What we observe is not nature itself, but nature exposed to our method of questioning.’ Thus systems thinking involves a shift from objective to ‘epistemic’ science, to a framework in which epistemology — ‘the method of questioning’ — becomes an integral part of scientific theories. /40
  • The criteria of systems thinking described in this brief summary are all interdependent. Nature is seen as an interconnected web of relationships, in which the identification of specific patterns as ‘objects’ depends on the human observer and the process of knowing. This web of relationships is described in terms of a / corresponding network of concepts and models, none of which is any more fundamental than the others. /40–41
  • What makes it possible to turn the systems approach into a science is the discovery that there is approximate knowledge. This insight is crucial to all of modern science. The old paradigm is based on the Cartesian belief in the certainty of scientific knowledge. In the new paradigm it is recognized that all scientific concepts and theories are limited and approximate. Science can never provide any complete and definite understanding. /41
  • Systems thinking is always process thinking. /42
  • Unlike closed systems, which settle into a state of thermal equilibrium, open systems maintain themselves far from equilibrium in this ‘steady state’ characterized by continual flow and change. Bertalanffy coined the German term Fliessgleichgewicht (‘flowing balance’) to describe such a state of dynamic balance. He recognized clearly that classical thermodynamics, which deals with closed systems at or near equilibrium, is inappropriate to describe open systems in steady states far from equilibrium. /48
  • Bertalanffy correctly identified the characteristics of the steady state as those of the process of metabolism, which led him to postulate self-regulation as another key property of open systems. This idea was refined by Prigogine thirty years later in terms of the self-organization of ‘dissipative structures’. /49
  • The cyberneticists were neither biologists nor ecologists; they were mathematicians, neuroscientists, social scientists, and engineers. They were concerned with a different level of description, concentrating on patterns of communication, especially in closed loops and networks. /51
  • A feedback loop is a circular arrangement of causally connected elements, in which an initial cause propagates around the links of the loop, so that each element has an effect on the next, until the last ‘feeds back’ the effect into the first element of the cycle. /56
  • Like the Cartesian model of the body as a clockwork, that of the brain as a computer was very useful at first, providing an exciting framework for a new scientific understanding of cognition and leading to many fresh avenues of research. By the mid-1960s, however, the original model, which encouraged the exploration of its own limitations and the discussion of alternatives, had hardened into a dogma, as so often happens in science. During the subsequent decade almost all of neurobiology was dominated by the information-processing perspective, whose origins and underlying assumptions were hardly ever questioned anymore. /67
  • Recent developments in cognitive science have made it clear that human intelligence is utterly different from machine, or ‘artificial’, intelligence. The human nervous system does not process any information (in the sense of discrete elements existing ready-made in the outside world, to be picked up by the cognitive system), but interacts with the environment by continually modulating its structure. Moreover, neuroscientists have discovered strong evidence that human intelligence, human memory, and human decisions are never completely rational but are always colored by emotions, as we all know from experience. /68
  • Our thinking is always accompanied by bodily sensations and processes. Even if we often tend to suppress these, we always think also with our body; and since computers do not have such a body, truly human problems will always be foreign to their intelligence. /68
  • Increasingly, all forms of culture are being subordinated to technology, and technological innovation, rather / than the increase in human well-being, has become synonymous with progress. The spiritual impoverishment and loss of cultural diversity through excessive use of computers is especially serious in the field of education. (…) The use of computers in school is based on the now outdated view of human beings as information processors, which continually reinforces erroneous mechanistic concepts of thinking, knowledge, and communication. Information is presented as the basis of thinking, whereas in reality the human mind thinks with ideas, not with information. /70
  • In the computer model of cognition, knowledge is seen as context and value free, based on abstract data. But all meaningful knowledge is contextual knowledge, and much of it is tacit and experiential. Similarly, language is seen as a conduit through which ‘objective’ information is communicated. In reality, … language is metaphoric, conveying tacit understandings shared within a culture. /70
  • Critical arguments had been presented already during the pioneering phase of cybernetics. For example, it was argued that in actual brains there are no rules; there is no central logical processor, and information is not stored locally. Brains seem to operate on the basis of massive connectivity, storing information distributively and manifesting a self-organizing capacity that is nowhere to be found in computers. However, these alternative ideas were eclipsed in favor of the dominant computational view, until they reemerged thirty years later during the 1970s, when systems thinkers became fascinated by a new phenomenon with an evocative name — self organization. /71
  • T[he] triumph of molecular biology resulted in the widespread belief that all biological functions can be explained in terms of molecular structures and mechanisms. Thus most biologists have become fervent reductionists, concerned with molecular details. Molecular biology, originally a small branch of the life sciences, has now become a pervasive and exclusive way of thinking that has led to a severe distortion of biological research. /77
  • It could be argued … that the understanding of living organisms as energetically open but organizationally closed systems, the recognition of feedback as the essential mechanism of homeostasis, and the cybernetic models of neural processes — to name just three examples that were well established at the time — represented major advances in the scientific understanding of life. /78
  • The cyberneticists concentrated on nonlinear phenomena like feedback loops and neural networks, and they had the beginnings of a corresponding nonlinear mathematics, but the real breakthrough came several decades later and was linked closely to the development of a new generation of powerful computers. /79
  • To understand the phenomenon of self-organization, we first need to understand the importance of pattern. The idea of a pattern of organization — a configuration of relationships characteristic of a particular system — became the explicit focus of systems thinking in cybernetics and has been a crucial concept ever since. From the systems point of view, the understanding of life begins with the understanding of pattern. /80
  • In the study of structure we measure and weigh things. Patterns, however, cannot be measured or weighed; they must be mapped. To understand a pattern we must map a configuration of relationships. In other words, structure involves quantities, while pattern involves qualities. /81
  • Systemic properties are properties of pattern. What is destroyed when a living organism is dissected is its pattern. The components are still there, but the configuration of relationships among them — the pattern — is destroyed, and thus the organism dies. /81
  • There is something else to life, something nonmaterial and irreducible — a pattern of organization. /81
  • The structure of the human brain is enormously complex. It contains about 10 billion nerve cells (neurons), which are interlinked in a vast network through 1,000 billion junctions (synapses). The whole brain can be divided into subsections, or subnetworks, which communicate with each other in network fashion. All this results in intricate patterns of intertwined webs, networks nesting within larger networks. /82
  • Because networks of communication may generate feedback loops, they may acquire the ability to regulate themselves. For example, a community that maintains an active network of communication will learn from its mistakes, because the consequences of a mistake will spread through the network and return to the / source along feedback loops. Thus the community can correct its mistakes, regulate itself, and organize itself. Indeed, self-organization has emerged as perhaps the central concept in the systems view of life, and like the concepts of feedback and self-regulation, it is linked closely to networks. The pattern of life, we might say, is a network pattern capable of self-organization. This is a simple definition, yet it is based on recent discoveries at the very forefront of science. /83
  • Hypercycles turn out to be not only remarkably stable, but also capable of self-replication and of correcting replication error, which means that they can conserve and transmit complex information. (…) The lesson to be learned here seems to be that the roots of life reach down into the realm of nonliving matter. /94
  • Lovelock recognized the Earth’s atmosphere as an open system, far from equilibrium, characterized by a constant flow of energy and matter. /102
  • The new mathematics … is one of relationships and patterns. It is qualitative rather than quantitative and thus embodies the shift of emphasis that is characteristic of systems thinking — from objects to relationships, from quantity to quality, from substance to pattern. /113
  • Nonlinear phenomena dominate much more of the inanimate world than we had thought, and they are an essential aspect of the network patterns of living systems. Dynamical systems theory is the first mathematics that enables scientists to deal with the full complexity of these nonlinear phenomena. /123
  • The exploration of nonlinear systems over the past decades has had a profound impact on science as a whole, as it has forced us to reevaluate some very basic notions about the relationships between a mathematical model and the phenomena it describes. One of those notions concerns our understanding of simplicity and complexity./123
  • The behavior of chaotic systems is not merely random but shows a deeper level of patterned order./123
  • In nonlinear systems … small changes may have dramatic effects because they may be amplified repeatedly by self-reinforcing feedback. Such nonlinear feedback processes are the basis of the instabilities and the sudden emergence of new forms of order that are so characteristic of self-organization. /124
  • The Mandelbrot set is a storehouse of patterns of infinite detail and variations. Strictly speaking, it is not self-similar because it not only repeats the same patterns over and over again, including small replicas of the entire set, but also contains elements from an infinite number of Julia sets! It is thus a ‘superfractal’ of inconceivable complexity. /151
  • T[he] isolation of mathematics is a striking sign of our intellectual fragmentation and as such is a relatively recent phenomenon. Throughout the centuries many of the great mathematicians made outstanding contributions to other fields as well. In the eleventh / century the Persian poet Omar Khyyám, who is world renowned as the author of the Rubáiyát, also wrote a pioneering book on algebra and served as the official astronomer at the caliph’s court. Descartes, the founder of modern philosophy, was a brilliant mathematician and also practiced medicine. Both inventors of the differential calculus, Newton and Leibniz, were active in many fields besides mathematics. Newton was a ‘natural philosopher’ who made fundamental contributions to virtually all branches of science that were known at his time, in addition to studying alchemy, theology, and history. Leibniz is known primarily as a philosopher, but he was also the founder of symbolic logic and was active as a diplomat and historian during most of his life. The great mathematician Gauss was also a physicist and astronomer, and he invented several useful instruments, including the electric telegraph. /151–152
  • The great fascination exerted by chaos theory and fractal geometry on people in all disciplines — from scientists to managers to artists — may indeed be a hopeful sign that the isolation of mathematics is ending. Today the new mathematics of complexity is / making more and more people realize that mathematics is much more than dry formulas; that the understanding of pattern is crucial to understand the living world around us; and that all questions of pattern, order, and complexity are essentially mathematical. /152–153
  • From Pythagoras to Aristotle, to Goethe, and to the organismic biologists, there is a continuous intellectual tradition that struggles with the understanding of pattern, realizing that it is crucial to the understanding of living form. /158
  • The understanding of pattern, then, will be of crucial importance to the scientific understanding of life ./158
  • T[he] striking property of living systems suggests process as a third for a comprehensive description of the nature of life. The process of life is the activity involved in the continual embodiment of the system’s pattern of organization. Thus the / process criterion is the link between pattern and structure. /168–169
  • The pattern of organization can be recognized only if it is embodied in a physical structure, and in living systems this embodiment is an ongoing process. Thus structure and process are inextricably linked. One could say that the three criteria — pattern, structure, and process — are three different but inseparable perspectives on the phenomenon of life. /160
  • To find out whether a particular system — a crystal, a virus, a cell, or the planet Earth — is alive, all we need to do is to find out whether the pattern of organization is that of an autopoietic network. If it is, we are dealing with a living system; if it is not, the system is nonliving. /161
  • Autopoiesis and cognition are two different aspects of the same phenomenon of life. In the new theory all living systems are cognitive systems, and cognition always implies the existence of an autopoietic network. /161
  • Autopoiesis, or ‘self-making’, is a network pattern in which the function of each component is to participate in the production or transformation of other components of the network. In this way the network continually makes itself. It is produced by its components and in turn produces those components. /162
  • Since all components of an autopoietic network are produced by other components in the network, the entire system is organizationally closed, even though it is open with regard to the flow of energy and matter. This organizational closure implies that a living system is self-organizing in the sense that its order and behavior are not imposed by the environment but are established by the system itself. In other words, living systems are autonomous. This does not mean that they are isolated from their environment. On the contrary, they interact with the environment through a continual exchange of energy and matter. /167
  • Autopoiesis, then, is seen as the pattern underlying the phenomenon of self-organization, or autonomy, that is so characteristic of all living systems. /168
  • [A]utopoietic networks must continually regenerate themselves to maintain their organization. This, of course, is a well-known characteristic of life. /168
  • According to the theory of living systems, mind is not a thing but a process — the very process of life. In other words, the organizing activity of living systems, at all levels of life, is mental activity. The interactions of a living organism — plant, animal, or human — with its environment are cognitive, or mental interactions. Thus life and cognition become inseparably connected. Mind — or, more accurately, mental process — is immanent in matter at all levels of life. /172
  • The central insight of the Santiago theory is the same as Bateson’s — the identification of cognition, the process of knowing, with the process of life. /174
  • The new concept of cognition, the process of knowing, is thus much broader than that of thinking. It involves perception, emotion, and action — the entire process of life. In the human realm cognition also includes language, conceptual thinking, and all the other attributes of human consciousness. The general concept, however, is much broader and does not necessarily involve thinking. /175
  • The Santiago theory provides … the first coherent scientific framework that really overcomes the Cartesian split. Mind and matter no longer appear to belong to two separate categories but are seen as representing merely different aspects, or dimensions, of the same phenomenon of life. /175
  • In the Santiago theory the relationship between mind and brain is simple and clear. Descartes’ characterization of mind as ‘the thinking thing’ (res cogitans) is finally abandoned. Mind is not a thing but a process — the process of cognition, which is identified with the process of life. The brain is a specific structure through which this process operates. The relationship between mind and brain, therefore, is one between process and structure. /175
  • The brain is, of course, not the only structure through which / the process of cognition operates. The entire dissipative structure of the organism participates in the process of cognition, whether or not the organism has a brain and a higher nervous system. /175–176
  • [A]ll organisms in an ecosystem produce wastes, but what is waste for one species is food for another, so that wastes are continually recycled and the ecosystem as a whole generally remains without waste. /177
  • By blending water and minerals from below with sunlight and CO2 from above, green plants link the earth and the sky. We tend to believe that plants grow out of the soil, but in fact most of their substance comes from the air. The bulk of the cellulose and the other organic compounds produced through photosynthesis consists of heavy carbon and oxygen atoms, which plants take directly from the air in the form of CO2. Thus the weight of a wooden log comes almost entirely from the air. When we burn a log in a fireplace, oxygen and carbon combine once more into CO2, and in the light and heat of the fire we recover part of the solar energy that went into making the wood. /178
  • Prigogine realized that classical thermodynamics, the first science of complexity, is inappropriate to describe systems far from equilibrium because of the linear nature of its mathematical structure. /181
  • Farther away from equilibrium, the fluxes are stronger, entropy production increases, and the system no longer tends toward equilibrium. On the contrary, it may encounter instabilities leading to new forms of order that move the system farther and farther away from the equilibrium state. In other words, far from equilibrium, dissipative structures may develop into forms of ever-increasing complexity. /181
  • Far from equilibrium, the system’s flow processes are interlinked through multiple feedback loops, and the corresponding mathematical equations are nonlinear. The farther a dissipative structure is from equilibrium, the greater is its complexity and the higher is the degree of nonlinearity in the mathematical equations describing it. (…) Nonlinear equations usually have more than one solution; the higher the nonlinearity, the greater the number of solutions. This means that new situations may emerge at any moment. Mathematically speaking, the system encounters a bifurcation point in such a case, at which it may branch off into an entirely new state. /182
  • Near equilibrium we find repetitive phenomena and universal laws. As we move away from equilibrium, we move from the universal to the unique, toward richness and variety. /182
  • In a Newtonian world there would be no chemistry and no life. /184
  • T[he] new perception of order and disorder represents an inversion of traditional scientific views. According to the classical view, for which physics was the principal source of concepts and metaphors, order is associated with equilibrium, as, for example, in crystals and other static structures, and disorder with non-equilibrium situations, such as turbulence. In the new science of complexity, which takes its inspiration from the web of life, we learn that nonequilibrium is a source of order. The turbulent flows of water and air, while appearing chaotic, are really highly organized, exhibiting complex patterns of vortices dividing and subdividing again at smaller and smaller scales. In living systems the order arising from nonequilibrium is far more evident, being manifest in the richness, diversity, and beauty of life all around us. Throughout the living world chaos is transformed into order. /190
  • The conceptual shift implied in Prigogine’s theory involves several closely related ideas. The description of dissipative structures that exist far from equilibrium requires a nonlinear mathematical formalism, capable of modeling multiple interlinked feedback loops. In living organisms these are catalytic loops (that is, nonlinear, irreversible chemical processes), which lead to instabilities through repeated self-amplifying feedback. When a dissipative structure reaches such a point of instability, called a bifurcation point, an element of indeterminacy enters into the theory. At the bifurcation point the system’s behavior is inherently unpredictable. In particular, new structures of higher order and complexity may emerge spontaneously. Thus self-organization, the spontaneous emergence of order, results from the combined effects of non-equilibrium, irreversibility, feedback loops, and instability. /192
  • Instead of being a machine, nature at large turns out to be more like human nature — unpredictable, sensitive to the surrounding world, influenced by small fluctuations. Accordingly the appropriate way of approaching nature to learn about her complexity and beauty is not through domination and control, but through respect, cooperation, and dialogue. /193
  • In the deterministic world of Newton there is no history and no creativity. In the living world of dissipative structures history plays an important role, the future is uncertain, and thus uncertainty is at the heart of creativity. /193
  • Many of these cyclical changes occur much faster than one / would imagine. For example. Our pancreas replaces most of its cells every twenty-four hours, the cells of our stomach lining are reproduced every three days, our white blood cells are renewed in ten days, and 98 percent of the protein in our brain is turned over in less than one month. Even more amazing, our skin replaces its cells at the rate of one hundred thousand cells per minute. In fact, most of the dust in our homes consists of dead skin cells. /219
  • Kicking a stone and kicking a dog are two very different stories, as Gregory Bateson was fond of pointing out. The stone will react to the kick according to a linear chain of cause and effect. Its behavior can be calculated by applying the basic laws of Newtonian mechanics. The dog will respond with structural changes according to its own nature and (nonlinear) pattern of organization. The resulting behavior is generally unpredictable. /219
  • Like Prigogine’s theory of dissipative structures, the theory of Autopoiesis shows that creativity — the generation of configurations that are constantly new — is a key property of all living systems. A special form of this creativity is the generation of diversity through reproduction, from simple cell division to the highly complex dance of sexual reproduction. For most living organisms ontogeny is not a linear path of development but a cycle, and reproduction is a vital step in that cycle. /221
  • Rather than seeing evolution as the result of random mutations and natural selection, we are beginning to recognize the creative unfolding of life in forms of ever-increasing diversity and complexity as an inherent characteristic of all living systems. Although mutation and natural selection are still acknowledged as important aspects of biological evolution, the central focus is on creativity, on life’s constant reaching out into novelty. /222
  • Fast bacteria can divide about every twenty minutes, so that in principle several billion individual bacteria can be generated from a single cell in less than a day. /228
  • Bacteria are able to adapt to environmental changes in a few years, where larger organisms would need thousands of years of evolutionary adaptation. /229
  • In other worlds, all bacteria are part of a single microscopic web of life. /230
  • The most striking evidence for evolution through symbiosis is presented by the so-called mitochondria, the ‘powerhouses’ inside most nucleated cells. These vital parts of all animal and plant cells, which carry out cellular respiration, contain their own genetic material and reproduce independently and at different times from the rest of the cell. /231
  • The evolutionary unfolding of life over billions of years is a breathtaking story. Driven by the creativity inherent in all living systems, expressed through three distinct avenues — mutations, the trading of genes, and symbioses — and honed by natural selection, the planet’s living patina expanded and intensified in forms of ever-increasing diversity. /232
  • [A]bout 3.5 billion years ago, the first autopoietic bacterial cells were born, and the evolution of life began. /236
  • Perhaps the most important task was to develop a variety of new metabolic pathways for extracting food and energy from the environment. One of the first bacterial inventions was fermentation — the breaking down of sugars and conversion into ATP molecules, the ‘energy carriers’ that fuel all cellular processes. This innovation allowed the fermenting bacteria to live off chemicals in the earth, in mud and water, protected from the harsh sunlight. /236
  • During subsequent stages of evolution, the microorganisms formed alliances and coevolved with plants and animals, and today our environment is so interwoven with bacteria that it is almost impossible to say where the inanimate world ends and life begins. We tend to associate bacteria with disease, but they are also vital for our survival, as they are for the survival of all animals and plants. /239
  • The recognition of symbiosis as a major evolutionary force has profound philosophical implications. All larger organisms, including ourselves, are living testimonies to the fact that destructive practices do not work in the long run. In the end the aggressors always destroy themselves, making way for others who know how to cooperate and get along. Life is much less a competitive struggle for survival than the triumph of cooperation and creativity. Indeed, since the creation of the first nucleated cells, evolution has proceeded through ever more intricate arrangements of cooperation and coevolution. /243
  • Like so many other life processes, rapid motion was invented by bacteria. The fastest member of the microcosm is a tiny, hairlike creature called spirochete (‘coiled hair’), also known as the ‘corkscrew bacterium’, which spirals in rapid motion. By attaching themselves symbiotically to larger cells, the rapidly moving corkscrew bacteria gave those cells the tremendous advantages of locomotion — the ability to avoid danger and seek out food. Over time the corkscrew bacteria progressively lost their distinct traits and evolved into the well-known ‘cell whips’ — flagellae, cilia, and the like — that propel a wide variety of nucleated cells with undulating or whipping motions. /244
  • As a scientific hypothesis the concept of symbiogenesis — the creation of new forms of life through the merging of different species — is barely thirty years old. But as a cultural myth the idea seems to be as old as humanity itself. Religious epics, legends, fairy tales, and other mythical stories around the world are full of / fantastic creatures — sphinxes, mermaids, griffons, centaurs, and more — born from the blending of two or more species. Like the new eukaryotic cells, these creatures are made of components that are entirely familiar, but their combinations are novel and startling. /244–245
  • Depictions of these hybrid beings are often frightening, but many of them, curiously, are seen as bearers of good fortune. For example, the god Ganesha, who has a human body with an elephant head, is one of the most revered deities in India, worshiped as a symbol of good luck and a helper in overcoming obstacles. Somehow the collective human unconscious seems to have known from ancient times that long-term symbioses are profoundly beneficial for all life. /245
  • As the specialization of cells continued in larger and more complex forms of life, the capability of self-repair and regeneration diminished progressively. Flatworms, polyps and starfish can regenerate almost their entire bodies from small fractions; lizards, salamanders, crabs, lobsters, and many insects are still able to grow back lost organs or limbs; but in higher animals regeneration is limited to renewing tissues in the healing of injuries. /246
  • Among the many multicellular organizations that evolved out of tightly knit communities of microorganisms, three — plants, fungi, and animals — have been so successful in reproducing, diversifying, and expanding, and expanding over the Earth that they are classified by biologists as ‘kingdoms’, the broadest category of living organisms. All in all there are five of these kingdoms — bacteria (microorganisms without cell nuclei), protists (microorganisms with nucleated cells), plants, fungi, and animals. Each of the kingdoms is divided into a hierarchy of subcategories, or taxa, beginning with phylum and ending with genus and species. /247
  • In the emerging theory of living systems mind is not a thing, but a process. It is cognition, the process of knowing, and it is identified with the process of life itself./264
  • The identification of mind, or cognition, with the process of life is a radically new idea in science, but it is also one of the deepest and most archaic intuitions of humanity. In ancient times the rational human mind was seen as merely one aspect of the immaterial soul, or spirit. /264
  • [T]he etymological roots of ‘soul’ and ‘spirit’ mean breath in many antique languages. The words for ‘soul’ in Sanskrit (atman), Greek (pneuma), and Latin (anima) all mean ‘breath’. The same is true of the world for ‘spirit’ in Latin (spiritus), in Greek (psyche), and in Hebrew (ruah). /264
  • The computer model of cognition was finally subjected to serious questioning in the 1970’s when the concept of self-organization emerged. (…) These observations suggested a shift of focus — from symbols to connectivity, from local rules to global coherence, from information processing to the emergent properties of neural networks. /266
  • The range of interactions a living system can have with its environment defines its ‘cognitive domain.’ Emotions are an integral part of this domain. For example, when we respond to an insult by getting angry, that entire pattern of physiological processes — a red face, faster breathing, trembling, and so on — is part of cognition. In fact, recent research strongly indicates that there is an emotional coloring to every cognitive act. /269
  • According to the Santiago theory, cognition is not a representation of an independent, pregiven world, but rather a bringing forth of a world. What is brought forth by a particular organism in the process of living is not the world but a world, one that is always dependent upon the organism’s structure. Since individual organisms within a species have more or less the same structure, they bring forth similar worlds. We humans, moreover, share an abstract world of language and thought through which we bring forth our world together. /270
  • A computer processes information, which means that it manipulates symbols based on certain rules. The symbols are distinct elements fed into the computer from outside, and during the information processing there is no change in the structure of the machine. The physical structure of the computer is fixed, determined by its design and construction. The nervous system of a living organism … interacts with its environment by continually modulating its structure, so that at any moment its physical / structure is a record of previous structural changes. The nervous system does not process information from the outside world but, on the contrary, brings forth a world in the process of cognition. /274–275
  • A lot of confusion is caused by the fact that computer scientists use words such as ‘intelligence,’ ‘memory,’ and ‘language’ to describe computers, thus implying that these expressions refer to the human phenomena we know well from experience. This is a serious misunderstanding. For example, the very essence of intelligence is to act appropriately when a problem is not clearly defined and solutions are not evident. Intelligent human behavior in such situations is based on common sense, accumulated from lived experience. Common sense, however, is not available to computers because of their blindness of abstraction and the intrinsic limitations / of formal operations, and therefore it is impossible to program computers to be intelligent. /275–276
  • The reason is that language is embedded in a web of social and cultural conventions that provides an unspoken context of meaning. We understand this context because it is common sense to us, but a computer cannot be programmed with common sense and therefore does not understand language. /276
  • Mind is not a thing but a process — the process of cognition, which is identified with the process of life. The brain is a specific structure through which this process operates. Thus the relationship between mind and brain is one between process and structure. /278
  • Recent research has shown that under normal conditions the antibodies circulating in the body bind to many (if not all) types of cells, including themselves. The entire system looks much more like a network, more like people talking to each other, than soldiers out looking for an enemy. Gradually immunologists have been forced to shift their perception from an immune system to an immune network. /279
  • Rather than merely reacting against foreign agents, the immune system serves the important function of regulating the organism’s cellular and molecular repertoire. As Francisco Varela and immunologist Antonio Coutinho explain, ‘The mutual dance between immune system and body … allows the body to have a changing and plastic identity throughout its life and its multiple encounters.’ /280
  • When immunologists inject large amounts of a foreign agent into the body, as they do in standard animal experiments, the immune system reacts with the massive defense response described in the classical theory. However, as Varela and Coutinho point out, this is a highly contrived laboratory situation. In its natural surroundings an animal does not receive large amounts of harmful substances. The small amounts that do enter its body are incorporated naturally into the ongoing regulatory activities of its immune network. /280
  • Defensive immune activity is very important, but in the new view it is a secondary effect of the much more central cognitive activity of the immune system, which maintains the body’s molecular identity ./281
  • The nervous system, consisting of the brain and of a network of nerve cells throughout the body, is the seat of memory, thought, and emotion. The endocrine system, consisting of the glands and the hormones, is the body’s main regulatory system, controlling and integrating various bodily functions. The immune system, consisting of the spleen, the bone marrow, the lymph nodes, and the immune cells circulating through the body, is the body’s defense system, responsible for tissue integrity and controlling wound healing and tissue-repair mechanisms. In accord with this separation the three systems are studied in three separate disciplines — neuroscience, endocrinology, and immunology. However, the recent peptide research has shown in dramatic ways that these conceptual separations are merely historical artifacts that can no longer be maintained. According to Candace Pert, the three systems must be seen as forming a single psychosomatic network. /282
  • Peptides are the biochemical manifestation of emotions; they play a crucial role in the coordinating activities of the immune system; they interlink and integrate mental, emotional, and biological activities. /283
  • The entire group of sixty to seventy peptides may constitute a universal biochemical language of emotions. /284
  • Traditionally neuroscientists have associated emotions with specific areas in the brain, notably the limbic system. This is indeed correct. The limbic system turns out to be highly enriched with peptides. However, it is not the only part of the body where peptide receptors are concentrated. For example, the entire intestine is lined with peptide receptors. This is why we have ‘gut feelings’. We literally feel our emotions in our gut. /284
  • The uniqueness of being human lies in our ability to continually weave the linguistic network in which we are embedded. To be human is to exist in language. In language we coordinate our behavior, and together in language we bring forth our world. /290
  • From the perspective of the Santiago theory, the currently fashionable attempts to explain human consciousness in terms of quantum effects in the brain or other neurophysiological processes are all bound to fail. Self-awareness and the unfolding of our inner world of concepts and ideas are not only inaccessible to explanations in terms of physics and chemistry; they cannot even be understood through the biology or psychology of a single organism. According to Maturana, we can understand human consciousness only through language and the whole social context in which it is embedded. As its Latin root — conscire (‘knowing together’) — might indicate, consciousness is essentially a social phenomenon./291
  • Over the centuries the word maya — one of the most important terms in Indian philosophy — changed its meaning. From the creative power of Brahman it came to signify the psychological state of anybody under the spell of the magic play. As long as we confuse the material forms of the play with objective reality, without perceiving the unity of Brahman underlying all these forms, we are under the spell of maya. /291
  • The fact that neural circuits tend to oscillate rhythmically is well-known to neuroscientists, and recent research has shown that these oscillations are not restricted to the cerebral cortex but occur at various levels in the nervous system. /293
  • According to Varela, the primary conscious experience, common to all higher vertebrates, is not located in a specific part of the brain, nor can it be identified in terms of specific neural structures. It is the manifestation of a particular cognitive process — a transient synchronization of diverse, rhythmically oscillating neural circuits. /293
  • The power of abstract thinking has led us to treat the natural environment — the web of life — as if it consisted of separate parts, to be exploited by different interest groups. Moreover, we have extended this fragmented view to our human society, dividing it into different nations, races, religious and political groups. The belief that all these fragments — in ourselves, in our environment, and in our society — are really separate has alienated us from nature and from our fellow human beings and thus has diminished us. To regain our full humanity, we have to regain our experience of connectedness with the entire web of life. This reconnecting, religio in Latin, is the very essence of the spiritual grounding of deep ecology. /296
  • Ecosystems differ from individual organisms in that they are largely (but not completely) closed systems with respect to the flow of matter, while being open with respect to the flow of energy. /299
  • The 1991 war in the Persian Gulf, for / example, which killed hundreds of thousands, impoverished millions, and caused unprecedented environmental disasters, had its roots to a large extent in the misguided energy policies of the Reagan and Bush administrations. /299–300
  • To describe solar energy as economically efficient assumes that the costs of energy production are counted honestly. This is not the case in most of today’s market economies. The so-called free market does not provide consumers with proper information, because the social and environmental costs of production are not part of current economic models. These costs are labeled ‘external’ variables by corporate and government economics, because they do not fit into their theoretical framework. /300
  • Corporate economists treat as free commodities not only the air, water, and soil, but also the delicate web of social relations, which is severely affected by continuing economic expansion. Private profits are being made at public costs in the deterioration of the environment and general quality of life, and at the expense of future generations. The marketplace simply gives us the wrong information. There is a lack of feedback, and basic ecological literacy tells us that such a system is not sustainable. /300
  • One of the most effective ways to change the situation would be an ecological tax reform. Such a tax reform would be strictly revenue neutral, shifting the tax burden from income taxes to ‘eco-taxes.’ /300
  • Partnership is an essential characteristic of sustainable communities. The cyclical exchanges of energy and resources in an ecosystem are sustained by pervasive cooperation. Indeed, we have seen that since the creation of the first nucleated cells over two billion years ago, life on Earth has proceeded through ever more intricate arrangements of cooperation and coevolution. Partnership — the tendency to associate, establish links, live inside one another, and cooperate — is one of the hallmarks of life. /301
  • The flexibility of an ecosystem is a consequence of its multiple feedback loops, which tend to bring the system back into balance whenever there is a deviation from the norm, due to changing environmental conditions. For example, if an unusually warm summer results in increased growth of algae in a lake, some species of fish feeding on these algae may flourish and breed more, so that their numbers increase and they begin to deplete the algae. Once their major source of food is reduced, the fish will begin to die out. As the fish population drops, the algae will recover and expand again. In this way the original disturbance generates a fluctuation around a feedback loop, which eventually brings the fish/algae system back into balance. /302
  • All ecological fluctuations take place between tolerance limits. There is always the danger that the whole system will collapse when the fluctuation goes beyond those limits and the system can no longer compensate for it. The same is true for human communities. Lack of flexibility manifests itself as stress. In particular, stress will occur when one or more variables of the system are pushed to their extreme values, which induces increased rigidity throughout the system. Temporary stress is an essential aspect of life, but prolonged stress is harmful and destructive to the system. These / considerations lead to the important realization that managing a social system — a company, a city, or an economy — means finding the optimal values for the system’s variables. If one tries to maximize any single variable instead of optimizing it, this will invariably lead to the destruction of the system as a whole. /302–303
  • A diverse ecosystem will also be resilient, because it contains many species with overlapping ecological functions that can partially replace one another. /303
  • Diversity means many different relationships, many different approaches to the same problem. A diverse community is a resilient community, capable of adapting to changing situations. /303

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