The Systems View of Life: A Unifying Vision

by Fritjof Capra

Thus the relationship between the parts and the whole has been reversed. In the systems approach, the properties of the parts can be understood only from the organization of the whole.

In other words, the 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.

As we shift our attention from macroscopic objects to atoms and subatomic particles, nature does not show us any isolated building blocks, but rather appears as a complex web of relationships between the various parts of a unified whole.

An electron is neither a particle nor a wave, but it may show particle-like aspects in some situations and wave-like aspects in others.

This means that neither the electron nor any other atomic “object” has any intrinsic properties independent of its environment. The properties it shows – particle-like or wave-like – will depend on the experimental situation – that is, on the apparatus it is forced to interact with.

Whenever we use classical terms – particle, wave, position, velocity – to describe atomic phenomena, we find that there are pairs of concepts, or aspects, which are interrelated and cannot be defined simultaneously in a precise way. The more we emphasize one aspect in our description, the more the other aspect becomes uncertain, and the precise relation between the two is given by the uncertainty principle.

At the subatomic level, matter does not exist with certainty at definite places, but rather shows “tendencies to exist,” and atomic events do not occur with certainty at definite times and in definite ways, but rather show “tendencies to occur.”

At the subatomic level, the solid material objects of classical physics dissolve into wave-like patterns of probabilities. These patterns, furthermore, do not represent probabilities of things, but rather of probabilities of interconnections.

As Bohr (1934, p. 57) explained: “Isolated material particles are abstractions, their properties being definable and observable only through their interaction with other systems.”

Subatomic particles, then, are not “things” but are interconnections among things, and these, in turn, are interconnections among other things, and so on. In quantum theory we never end up with any “things”; we always deal with interconnections.

According to the physicist Henry Stapp (quoted by Capra, 1982, p. 139), “An elementary particle is not an independently existing analyzable entity. It is, in essence, a set of relationships that reach outward to other things.”

The behavior of any part is determined by its nonlocal connections to the whole, and since we do not know these connections precisely, we have to replace the narrow classical notion of cause and effect by the wider concept of statistical causality.

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.

In the words of Werner Heisenberg (1958, p. 58), “What we observe is not nature itself, but nature exposed to our method of questioning.”

Atoms consist of particles, and these particles are not made of any material stuff. When we observe them we never see any substance; what we observe are dynamic patterns continually changing into one another – a continuous dance of energy.

Systems thinking means a shift of perception from material objects and structures to the nonmaterial processes and patterns of organization that represent the very essence of life.

In the systems view (b), we realize that the objects themselves are networks of relationships, embedded in larger networks. For the systems thinker, the relationships are primary. The boundaries (dotted lines) of the discernible patterns – the so-called “objects” – are secondary.

Thus, the perceptual shift from objects to relationships goes hand in hand with a change of methodology from measuring to mapping.

Systems thinking includes a shift of perspective from structures to processes.

Bogdanov distinguished three kinds of systems: organized complexes, where the whole is greater than the sum of its parts; disorganized complexes, where the whole is smaller than the sum of its parts; and neutral complexes, where the organizing and disorganizing activities cancel each other.

open systems that cannot be described by classical thermodynamics. He called such systems “open” because they need to feed on a continual flux of matter and energy from their environment to stay alive.

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.

defined cybernetics as the science of “control and communication in the animal and the machine.”

Wiener (1950, p. 96) expanded the concept of pattern, from the patterns of communication and control that are common to animals and machines to the general idea of pattern as a key characteristic of life. “We are but whirlpools in a river of ever-flowing water,” he wrote. “We are not stuff that abides, but patterns that perpetuate themselves.”

discover common principles of organization in that diversity – “the pattern which connects,” as he would put it many years

feedback as the essential mechanism of homeostasis, the self-regulation that allows living organisms to maintain themselves in a state of dynamic balance.

The importance of the feedback concept in social science has been analyzed in great detail by Richardson (1992), who points out that throughout the history of the social sciences, numerous metaphors have been used to describe self-regulatory processes in social life.

best known, perhaps, are the “invisible hand” regulating the market in the economic theory of Adam Smith (see Section 3.2.2), the “checks and balances” of the US Constitution, and the interplay of thesis and antithesis in the dialectic of Hegel and Marx (see Section 3.3.2). The phenomena described by these models and metaphors all imply circular patterns of causality that can be represented by feedback loops, but none of their authors made that fact explicit.

that living systems are energetically open while being – in today's terminology – organizationally closed: “Cybernetics might…be defined,” wrote Ashby (1952, p. 4), “as the study of systems that are open to energy but closed to information and control – systems that are ‘information-tight’.”

there has been a tension between two perspectives – the study of matter and the study of form – throughout the history of Western science and philosophy. The study of matter begins with the question, “What is it made of?”; the study of form asks, “What is its pattern?”

The study of pattern is crucial to the understanding of living systems because systemic properties, as we discussed in Section 4.1.3, arise from a configuration of ordered relationships. Systemic properties are properties of a pattern. What is destroyed when a living organism is dissected is its pattern. The components are still there, but the configuration of relationships between them – the pattern – is destroyed, and thus the organism dies.

As the early systems thinkers discovered, the most important property of this pattern of organization, common to all living systems, is that it is a network pattern.

The first and most obvious property of any network is its nonlinearity – it goes in all directions. Thus the relationships in a network pattern are nonlinear relationships.

Consequently, most scientists and engineers came to believe that virtually all natural phenomena could be described by linear equations. “As the world was a clockwork for the eighteenth century,” Ian Stewart (2002, p. 83) observes, “it was a linear world for the nineteenth and most of the twentieth century.”

The decisive change over the last three decades has been to recognize that nature, as Stewart puts it, is “relentlessly nonlinear.”

On the other hand, complex and seemingly chaotic behavior can give rise to ordered structures, to subtle and beautiful patterns. In fact, in chaos theory the term “chaos” has acquired a new technical meaning. The behavior of chaotic systems only appears to be random but in reality shows a deeper level of patterned order.

Another important property of nonlinear equations, which has been very disturbing to scientists, is that exact prediction is often impossible, even though the equations may be strictly deterministic. We shall see that this striking feature of nonlinearity has brought about an important shift of emphasis from quantitative to qualitative analysis.

By showing that simple deterministic equations of motion can produce unbelievable complexity that defies all attempts at prediction, Poincaré challenged the very foundations of Newtonian mechanics.

A complex system, typically, will move differently in the beginning, depending on how it starts off, but then will settle down to a characteristic long-term behavior, represented by its attractor. Metaphorically speaking, the trajectory is “attracted” to this pattern whatever its starting point may have been.

In complexity theory, an attractor is a mathematical representation of a dynamic (the system's long-term behavior) that is intrinsic to the system.

To their great surprise, these researchers discovered that there are a very limited number of different attractors. Their shapes can be classified topologically, and the general dynamic properties of a system can be deduced from the shape of its attractor.

There are three basic types of attractors: point attractors, corresponding to systems reaching a stable equilibrium; periodic attractors, corresponding to periodic oscillations; and so-called “strange attractors,” corresponding to chaotic systems.

One striking fact about strange attractors is that they tend to be of very low dimensionality, even in a high-dimensional phase space.

With the help of strange attractors a distinction can be made between mere randomness, or “noise,” and chaos. Chaotic behavior is deterministic and patterned, and strange attractors allow us to transform the seemingly random data into distinct visible shapes.

a simple set of nonlinear equations can generate enormously complex behavior.

However, this does not mean that chaos theory is not capable of any predictions. We can still make very accurate predictions, but they concern the qualitative features of the system's behavior rather than the precise values of its variables at a particular time.

Whereas conventional mathematics deals with quantities and formulas, nonlinear dynamics deals with qualities and patterns.

The qualitative analysis of a dynamic system, then, consists in identifying the system's attractors and basins of attraction, and classifying them in terms of their topological characteristics. The result is a dynamical picture of the entire system, called the “phase portrait.”

Smale used the term “structurally stable” to describe such systems, in which small changes in the equations leave the basic character of the phase portrait unchanged.

Such systems are said to be structurally unstable, and the critical points of instability are called “bifurcation points,” because they are points in the system's evolution where a fork suddenly appears and the system branches off in a new direction. Mathematically, bifurcation points mark sudden changes in the system's phase portrait.