The Web of Life


The Web of LifeA New Scientific Understanding of Living Systems, New York: Anchor Books, 1997

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 a Gaia 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 the systems view of life.

Post-matriarchal thought, which was naturally systemic, can be traced from the Atomistic Worldview (Democritus), over the Cartesian Worldview (Newton, La Mettrie, René Descartes) and Relativistic Worldview (Einstein, Planck, Heisenberg), to the Systemic 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 the uncertainty 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 is change 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 nonlinear mathematical 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.

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