The Logic of the Mind

While Ludwig von Bertalanffy worked on his general systems theory, attempts to develop self-guiding and self-regulating machines led to an entirely new field of investigation that had a major impact on the further development of the systems view of life. Drawing from several disciplines, the new science represented a unified approach to problems of communication and control, involving a whole complex of novel ideas, which inspired Norbert Wiener to invent a special name for it—“cybernetics.” The word is derived from the Greek J^ybernetes (“steersman”), and Wiener defined cybernetics as the science of “control and communication in the animal and the machine .” 1

The Cyberneticists

Cybernetics soon became a powerful intellectual movement, which developed independently of organismic biology and general systems theory. 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. Their investigations led them to the

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concepts of feedback and self-regulation and then, later on, to selforganization.

This attention to patterns of organization, which was implicit in organismic biology and Gestalt psychology, became the explicit focus of cybernetics. Wiener, especially, recognized that the new notions of message, control, and feedback referred to patterns of organization—that is, to nonmaterial entities—that are crucial to a full scientific description of life. Later on Wiener 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 in 1950. “We are not stuff that abides, but patterns that perpetuate themselves.” 2

The cybernetics movement began during World War II, when a group of mathematicians, neuroscientists, and engineers— among them Norbert Wiener, John von Neumann, Claude Shannon, and Warren McCulloch—formed an informal network to pursue common scientific interests. 3 Their work was closely linked to military research that dealt with the problems of tracking and shooting down aircraft and was funded by the military, as was most subsequent research in cybernetics.

The first cyberneticists (as they would call themselves several years later) set themselves the challenge of discovering the neural mechanisms underlying mental phenomena and expressing them in explicit mathematical language. Thus while the organismic biologists were concerned with the material side of the Cartesian split, revolting against mechanism and exploring the nature of biological form, the cyberneticists turned to the mental side. Their intention from the beginning was to create an exact science of mind. 4 Although their approach was quite mechanistic, concentrating on patterns common to animals and machines, it involved many novel ideas that exerted a tremendous influence on subsequent systemic conceptions of mental phenomena. Indeed, the contemporary science of cognition, which offers a unified scientific conception of brain and mind, can be traced back directly to the pioneering years of cybernetics.

The conceptual framework of cybernetics was developed in a

series of legendary meetings in New York City, known as the Macy Conferences. 5 These meetings—especially the first one in 1946—were extremely stimulating, bringing together a unique group of highly creative people who engaged in intense interdisciplinary dialogues to explore new ideas and ways of thinking. The participants fell into two core groups. The first formed around the original cyberneticists and consisted of mathematicians, engineers, and neuroscientists. The other group consisted of scientists from the humanities who clustered around Gregory Bateson and Margaret Mead. From the first meeting on, the cyberneticists made great efforts to bridge the academic gap between themselves and the humanities.

Norbert Wiener was the dominant figure throughout the conference series, imbuing it with his enthusiasm for science and dazzling his fellow participants with the brilliance of his ideas and often irreverent approaches. According to many witnesses Wiener had the disconcerting tendency to fall asleep during discussions, and even to snore, apparently without losing track of what was being said. Upon waking up, he would immediately make detailed and penetrating comments or point out logical inconsistencies. He thoroughly enjoyed these discussions and his central role in them.

Wiener was not only a brilliant mathematician, he was also an articulate philosopher. (In fact, his degree from Harvard was in philosophy.) He was keenly interested in biology and appreciated the richness of natural, living systems. He looked beyond the mechanisms of communication and control to larger patterns of organization and tried to relate his ideas to a wide range of social and cultural issues.

John von Neumann was the second center of attraction at the Macy Conferences. A mathematical genius, he had written a classic treatise on quantum theory, was the originator of the theory of games, and became world famous as the inventor of the digital computer. Von Neumann had a powerful memory, and his mind worked with enormous speed. It was said of him that he could understand the essence of a mathematical problem almost instantly and that he would analyze any problem, mathematical or

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practical, so clearly and exhaustively that no further discussion was necessary.

At the Macy meetings von Neumann was fascinated by the processes of the human brain and saw the description of brain functioning in formal logical terms as the ultimate challenge of science. He had tremendous confidence in the power of logic and great faith in technology, and throughout his work he looked for universal logical structures of scientific knowledge.

Von Neumann and Wiener had much in common. 6 Both were admired as mathematical geniuses, and their influence on society was far stronger than that of other mathematicians of their generation. They both trusted their subconscious minds. Like many poets and artists, they had the habit of sleeping with pencil and paper near their beds and made use of the imagery of their dreams in their work. However, these two pioneers of cybernetics differed significantly in their approach to science. Whereas von Neumann looked for control, for a program, Wiener appreciated the richness of natural patterns and sought a comprehensive conceptual synthesis.

In keeping with these characteristics, Wiener stayed away from people with political power, whereas von Neumann felt very comfortable in their company. At the Macy Conferences their different attitudes toward power, and especially toward military power, was the source of growing friction, which eventually led to a complete break. Whereas von Neumann remained a military consultant throughout his career, specializing in the application of computers to weapons systems, Wiener ended his military work shortly after the first Macy meeting. “I do not expect to publish any future work of mine,” he wrote at the end of 1946, “which may do damage in the hands of irresponsible militarists.” 7

Norbert Wiener had a strong influence on Gregory Bateson, with whom he had a very good rapport throughout the Macy Conferences. Bateson’s mind, like Wiener’s, roamed freely across disciplines, challenging the basic assumptions and methods of several sciences by searching for general patterns and powerful universal abstractions. Bateson thought of himself primarily as a biologist and considered the many fields he became involved in_

anthropology, epistemology, psychiatry, and others—as branches of biology. The great passion he brought to science embraced the full diversity of phenomena associated with life, and his main aim was to discover common principles of organization in that diversity—“the pattern which connects,” as he would put it many years later. 8 At the cybernetics conferences Bateson and Wiener both searched for comprehensive, holistic descriptions while being careful to remain within the boundaries of science. In so doing, they created a systems approach to a broad range of phenomena.

His dialogues with Wiener and the other cyberneticists had a lasting impact on Bateson’s subsequent work. He pioneered the application of systems thinking to family therapy, developed a cybernetic model of alcoholism, and authored the double-bind theory of schizophrenia, which had a major impact on the work of R. D. Laing and many other psychiatrists. However, Bateson’s most important contribution to science and philosophy may have been the concept of mind, based on cybernetic principles, which he developed during the 1960s. This revolutionary work opened the door to understanding the nature of mind as a systems phenomenon and became the first successful attempt in science to overcome the Cartesian division between mind and body. 9

The series of ten Macy Conferences was chaired by Warren McCulloch, professor of psychiatry and physiology at the University of Illinois, who had a solid reputation in brain research and made sure that the challenge of reaching a new understanding of mind and brain remained at the center of the dialogues.

The pioneering years of cybernetics resulted in an impressive series of concrete achievements, in addition to the lasting impact on systems thinking as a whole, and it is amazing that most of the novel ideas and theories were discussed, at least in their outlines, at the very first meeting. 10 The first conference began with an extensive description of digital computers (which had not yet been built) by John von Neumann, followed by von Neumann’s persuasive presentation of analogies between the computer and the brain. The basis of these analogies, which were to dominate the cyberneticists’ view of cognition for the subsequent three decades, was

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the use of mathematical logic to understand brain functioning, one of the outstanding achievements of cybernetics.

Von Neumann’s presentations were followed by Norbert Wiener’s detailed discussion of the central idea of his work, the concept of feedback. Wiener then introduced a cluster of new ideas, which coalesced over the years into information theory and communication theory. Gregory Bateson and Margaret Mead concluded the presentations with a review of the conceptual framework of the social sciences, which they considered inadequate and in need of basic theoretical work inspired by the new cybernetic concepts.

Feedback

All the major achievements of cybernetics originated in comparisons between organisms and machines—in other words, in mech-

Figure 4-1

Circular causality of a feedback loop.

anistic models of living systems. However, the cybernetic machines are very different from Descartes’s clockworks. The crucial difference is embodied in Norbert Wiener’s concept of feedback and is expressed in the very meaning of “cybernetics.” 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 (see figure 4-1). The consequence of this arrangement is that the first link (“input”) is affected by the last (“output”), which results in self-regulation of the entire system, as the initial effect is modified each time

it travels around the cycle. Feedback, in Wiener’s words, is the “control of a machine on the basis of its actual performance rather than its expected performance.” 11 In a broader sense feedback has come to mean the conveying of information about the outcome of any process or activity to its source.

Wiener’s original example of the steersman is one of the simplest examples of a feedback loop (see figure 4-2). When the boat deviates from the preset course—say, to the right—the steersman assesses the deviation and then countersteers by moving the rudder to the left. This decreases the boat’s deviation, perhaps even to the point of moving through the correct position and then deviating to the left. At some time during this movement the steersman makes a new assessment of the boat’s deviation, countersteers accordingly, assesses the deviation again, and so on. Thus he relies on continual feedback to keep the boat on course, its actual trajectory oscillating around the preset direction. The skill of steering a boat consists in keeping these oscillations as smooth as possible.

Assessing Deviation

A similar feedback mechanism is in play when we ride a bicycle. At first, when we learn to do so, we find it difficult to monitor the feedback from the continual changes of balance and to steer

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the bicycle accordingly. Thus a beginner’s front wheel tends to oscillate strongly. But as our expertise increases, our brain monitors, evaluates, and responds to the feedback automatically, and the oscillations of the front wheel smooth out into a straight line.

Self-regulating machines involving feedback loops existed long before cybernetics. The centrifugal governor of a steam engine, invented by James Watt in the late eighteenth century, is a classic example, and the first thermostats were invented even earlier. 12 The engineers who designed these early feedback devices described their operations and pictured their mechanical components in design sketches, but they never recognized the pattern of circular causality embedded in them. In the nineteenth century the famous physicist James Clerk Maxwell wrote a formal mathematical analysis of the steam governor without ever mentioning the underlying loop concept. Another century had to go by before the connection between feedback and circular causality was recognized. At that time, during the pioneering phase of cybernetics, machines involving feedback loops became a central focus of engineering and have been known as “cybernetic machines” ever since.

The first detailed discussion of feedback loops appeared in a paper by Norbert Wiener, Julian Bigelow, and Arturo Rosen- blueth, published in 1943 and titled “Behavior, Purpose, and Teleology.” 13 In this pioneering article the authors not only introduced the idea of circular causality as the logical pattern underlying the engineering concept of feedback, but also applied it for the first time to model the behavior of living organisms. Taking a strictly behaviorist stance, they argued that the behavior of any machine or organism involving self-regulation through feedback could be called “purposeful,” since it is behavior directed toward a goal. They illustrated their model of such goal-directed behavior with numerous examples—a cat catching a mouse, a dog following a trail, a person lifting a glass from a table, and so on—analyzing them in terms of the underlying circular feedback patterns.

Wiener and his colleagues also recognized feedback as the essential mechanism of homeostasis, the self-regulation that allows

living organisms to maintain themselves in a state of dynamic balance. When Walter Cannon introduced the concept of homeostasis a decade earlier in his influential book The Wisdom of the Body, u he gave detailed descriptions of many self-regulatory metabolic processes but never explicitly identified the closed causal loops embodied in them. Thus the concept of the feedback loop introduced by the cyberneticists led to new perceptions of the many self-regulatory processes characteristic of life. Today we understand that feedback loops are ubiquitous in the living world, because they are a special feature of the nonlinear network patterns that are characteristic of living systems.

Figure 4-3

Positive and negative causa! links.

The cyberneticists distinguished between two kinds of feedback—self-balancing (or “negative”) and self-reinforcing (or “positive”) feedback. Examples of the latter are the commonly known runaway effects, or vicious circles, in which the initial effect continues to be amplified as it travels repeatedly around the loop.

Since the technical meanings of “negative” and “positive” in this context can easily give rise to confusion, it may be worthwhile to explain them in more detail. 15 A causal influence from A to B is defined as positive if a change in A produces a change in B in

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the same direction—for example, an increase of B if A increases and a decrease if A decreases. The causal link is defined as negative if B changes in the opposite direction, decreasing if A increases and increasing if A decreases.

For example, in the feedback loop representing the steering of a boat, redrawn in figure 4-3, the link between “assessing deviation” and “countersteering” is positive—the greater the deviation from the preset course, the greater the amount of countersteering. The next link, however, is negative—the more the countersteering increases, the sharper the deviation will decrease. Finally, the last link is again positive. As the deviation decreases, its newly assessed value will be smaller than that previously assessed. The point to remember is that the labels “+” and “-” do not refer to an increase or decrease of value, but rather to the relative direction of change of the elements being linked—equal direction for “+” and opposite direction for

Figure 4-4

Centrifugal governor.

The reason why these labels are so convenient is that they lead to a very simple rule for determining the overall character of the feedback loop. It will be self-balancing (“negative”) if it contains

an odd number of negative links and self-reinforcing (“positive”) if it contains an even number of negative links. 16 In our example there is only one negative link; so the entire loop is negative, or self-balancing. Feedback loops are frequently composed of both positive and negative causal links, and their overall character is easily determined simply by counting the number of negative links around the loop.

The examples of steering a boat and riding a bicycle are ideally suited to illustrate the feedback concept, because they refer to well-known human experiences and are thus understood immediately. To illustrate the same principles with a mechanical device for self-regulation, Wiener and his colleagues often used one of the earliest and simplest examples of feedback engineering, the centrifugal governor of a steam engine (see figure 4-4). It consists of a rotating spindle with two weights (“flyballs”) attached to it in such a way that they move apart, driven by the centrifugal force, when the speed of the rotation increases. The governor sits on top of the steam engine’s cylinder, and the weights are connected with a piston, which cuts off the steam as they move apart. The pressure of the steam drives the engine, which drives a flywheel. The flywheel, in turn, drives the governor, and thus the loop of cause and effect is closed.

The feedback sequence is easily read off from the loop diagram drawn in figure 4-5. An increase in the speed of the engine increases the rotation of the governor. This increases the distance between the weights, which cuts down the steam supply. As the steam supply decreases, the speed of the engine decreases as well; the rotation of the governor slows down; the weights move closer together; steam supply increases; the engine speeds up again; and so on. The only negative link in the loop is the one between “distance between weights” and “steam supply,” and therefore the entire feedback loop is negative, or self-balancing.

From the beginning of cybernetics, Norbert Wiener was aware that feedback is an important concept for modeling not only living organisms but also social systems. Thus he wrote in Cybernetics:

Speed of Engine

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H-

Steam Supply

Rotation of Governor

Weights

Figure 4-5

Feedback loop for centrifugal governor.

It is certainly true that the social system is an organization like the individual, that is bound together by a system of communication, and that it has a dynamics in which circular processes of a feedback nature play an important role. 17

It was the discovery of feedback as a general pattern of life, applicable to organisms and social systems, which got Gregory Bateson and Margaret Mead so excited about cybernetics. As social scientists they had observed many examples of circular causality implicit in social phenomena, and during the Macy meetings the dynamics of these phenomena were made explicit in a coherent unifying pattern.

Throughout the history of the social sciences numerous metaphors have been used to describe self-regulatory processes in social life. The best known, perhaps, are the “invisible hand” regulating the market in the economic theory of Adam Smith, the “checks and balances” of the U.S. Constitution, and the interplay of thesis and antithesis in the dialectic of Hegel and Marx. 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. 18

If the circular logical pattern of self-balancing feedback was not recognized before cybernetics, that of self-reinforcing feedback

had been known for hundreds of years in common parlance as a “vicious circle.” The expressive metaphor describes a bad situation leading to its own worsening through a circular sequence of events. Perhaps the circular nature of such self-reinforcing, “runaway” feedback loops was recognized explicitly much earlier, because their effect is much more dramatic than the self-balancing of the negative feedback loops that are so widespread in the living world.

There are other common metaphors to describe self-reinforcing feedback phenomena. 19 The “self-fulfilling prophecy,” in which originally unfounded fears lead to actions that make the fears come true, and the “bandwagon effect”—the tendency of a cause to gain support simply because of its growing number of adherents—are two well-known examples.

In spite of the extensive knowledge of self-reinforcing feedback in common folk wisdom, it played hardly any role during the first P h ase of cybernetics. The cyberneticists around Norbert Wiener acknowledged the existence of runaway feedback phenomena but did not study them any further. Instead they concentrated on the self-regulatory, homeostatic processes in living organisms. Indeed, purely self-reinforcing feedback phenomena are rare in nature, as they are usually balanced by negative feedback loops constraining their runaway tendencies.

In an ecosystem, for example, every species has the potential of undergoing an exponential population growth, but these tendencies are kept in check by various balancing interactions within the system. Exponential runaways will appear only when the ecosystem is severely disturbed. Then some plants will turn into “weeds,” some animals become “pests,” and other species will be exterminated, and thus the balance of the whole system will be threatened.

During the 1960s anthropologist and cyberneticist Magoroh Maruyama took up the study of self-reinforcing, or “deviation- amplifying” feedback processes in a widely read article, titled “The Second Cybernetics.” 20 He introduced the feedback diagrams with “+” and “—” labels attached to their causal links, and he used this convenient notation for a detailed analysis of the

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interplay of negative and positive feedback processes in biological and social phenomena. In doing so, he linked the feedback concept of cybernetics with the notion of “mutual causality,” which had been developed by social scientists in the meantime, and thus contributed significantly to the influence of cybernetic principles on social thought. 21

From the point of view of the history of systems thinking, one of the most important aspects of the cyberneticists’ extensive studies of feedback loops is the recognition that they depict patterns of organization. The circular causality in a feedback loop does not imply that the elements in the corresponding physical system are arranged in a circle. Feedback loops are abstract patterns of relationships embedded in physical structures or in the activities of living organisms. For the first time in the history of systems thinking, the cyberneticists clearly distinguished the pattern of organization of a system from its physical structure—a distinction that is crucial in the contemporary theory of living systems. 22

Information Theory

An important part of cybernetics was the theory of information developed by Norbert Wiener and Claude Shannon in the late 1940s. It originated in Shannon’s attempts at the Bell Telephone Laboratories to define and measure amounts of information transmitted through telegraph and telephone lines in order to estimate efficiencies and establish a basis for charging for messages.

The term information” is used in information theory in a highly technical sense, which is quite different from our everyday use of the word and has nothing to do with meaning. This has resulted in endless confusion. According to Heinz von Foerster, a regular participant in the Macy Conferences and editor of the written proceedings, the whole problem is based on a very unfortunate linguistic error—the confusion between “information” and “signal,” which led the cyberneticists to call their theory a theory of information rather than a theory of signals. 23

Information theory, then, is concerned mainly with the problem of how to get a message, coded as a signal, through a noisy chan-

nel. However, Norbert Wiener also emphasized the fact that such a coded message is essentially a pattern of organization, and by drawing an analogy between such patterns of communication and the patterns of organization in organisms, he further prepared the ground for thinking about living systems in terms of patterns.

Cybernetics of the Brain

During the 1950s and 1960s Ross Ashby became the leading theorist of the cybernetics movement. Like McCulloch, Ashby was a neurologist by training, but he went much further than McCulloch in exploring the nervous system and constructing cybernetic models of neural processes. In his book Design for a Brain, Ashby attempted to explain in purely mechanistic and deterministic terms the brain’s unique adaptive behavior, capacity for memory, and other patterns of brain functioning. “It will be assumed,” he wrote, “that a machine or an animal behaved in a certain way at a certain moment because its physical and chemical nature at that moment allowed no other action.” 24

It is evident that Ashby was much more Cartesian in his approach to cybernetics than Norbert Wiener, who made a clear distinction between a mechanistic model and the nonmechanistic living system it represents. “When I compare the living organism with ... a machine,” wrote Wiener, “I do not for a moment mean that the specific physical, chemical, and spiritual processes of life as we ordinarily know it are the same as those of life-imitating machines.” 25

In spite of his strictly mechanistic outlook, Ross Ashby advanced the fledgling discipline of cognitive science considerably with his detailed analyses of sophisticated cybernetic models of neural processes. In particular he clearly recognized that living systems are energetically open while being—in today’s terminology—organizationally closed: “Cybernetics might ... be defined,” wrote Ashby, “as the study of systems that are open to energy but closed to information and control—systems that are ‘information-tight.’ ” 26

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Computer Model of Cognition

When the cyberneticists explored patterns of communication and control, the challenge to understand “the logic of the mind” and express it in mathematical language was always at the very center of their discussions. Thus for over a decade the key ideas of cybernetics were developed through a fascinating interplay among biol- ogy, mathematics, and engineering. Detailed studies of the human nervous system led to the model of the brain as a logical circuit with neurons as its basic elements. This view was crucial for the invention of digital computers, and that technological breakthrough in turn provided the conceptual basis for a new approach to the scientific study of mind. John von Neumann’s invention of the computer and his analogy between computer and brain functioning are so closely intertwined that it is difficult to know which came first.

The computer model of mental activity became the prevalent view of cognitive science and dominated all brain research for the next thirty years. The basic idea was that human intelligence resembles that of a computer to such an extent that cognition—the process of knowing—can be defined as information processing— in other words, as manipulation of symbols based on a set of rules. 27

The field of artificial intelligence developed as a direct consequence of this view, and soon the literature was full of outrageous claims about computer “intelligence.” Thus Herbert Simon and Allen Newell wrote as early as 1958:

There are now in the world machines that think, that learn and that create. Moreover, their ability to do these things is going to increase rapidly until—in the visible future—the range of problems they can handle will be coextensive with the range to which the human mind has been applied. 28

This prediction is as absurd today as it was thirty-eight years a g°> yet it is still widely believed. The enthusiasm among scientists and the general public for the computer as a metaphor for the

human brain has an interesting parallel in the enthusiasm of Descartes and his contemporaries for the clock as a metaphor for the body. 29 For Descartes the clock was a unique machine. It was the only machine that functioned autonomously, running by itself once it was wound up. This was the time of the French Baroque, when clock mechanisms were widely used to build artful “lifelike” machinery, which delighted people with the magic of their seemingly spontaneous movements. Like most of his contemporaries, Descartes was fascinated by these automata, and he found it natural to compare their functioning to that of living organisms:

We see clocks, artificial fountains, mills and other similar machines which, though merely man-made, have nonetheless the power to move by themselves in several different ways. ... I do not recognize any difference between the machines made by craftsmen and the various bodies that nature alone composes. 30

The clockworks of the seventeenth century were the first autonomous machines, and for three hundred years they were the only machines of their kind—until the invention of the computer. The computer is again a novel and unique machine. It not only moves autonomously once it is programmed and turned on, it does something completely new: it processes information. And since von Neumann and the early cyberneticists believed that the human brain, too, processes information, it was natural for them to use the computer as a metaphor for the brain and even for the mind, just as it had been for Descartes to use the clock as a metaphor for the body.

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 even questioned anymore.

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Computer scientists contributed significantly to the firm establishment of the information-processing dogma by using expressions such as “intelligence,” “memory,” and “language” to describe computers, which led most people—including the scientists themselves—to think that these terms refer to the well-known human phenomena. This, however, is a grave misunderstanding, which has helped to perpetuate, and even reinforce, the Cartesian image of human beings as machines.

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 readymade in the outside world, to be picked up by the cognitive system), but interacts with the environment by continually modulating its structure. 31 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. 32 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.

These considerations imply that certain tasks should never be left to computers, as Joseph Weizenbaum asserted emphatically in his classic book, Computer Power and Human Reason. These tasks include all those that require genuine human qualities such as wisdom, compassion, respect, understanding, or love. Decisions and communications that require those qualities will dehumanize our lives if they are made by computers. To quote Weizenbaum:

A line dividing human and machine intelligence must be drawn. If there is no such line, then advocates of computerized psychotherapy may be merely the heralds of an age in which man has finally been recognized as nothing but clockwork. . . . The very asking of the question, “What does a judge (or psychiatrist) know that we cannot tell a computer?” is a monstrous obscenity. 33

Impact on Society

Because of its link with mechanistic science and its strong connections to the military, cybernetics enjoyed a very high prestige among the scientific establishment right from the beginning. Over the years this prestige increased further as computers spread rapidly throughout all strata of industrial society, bringing about profound changes in every area of our lives. Norbert Wiener predicted those changes, which have often been compared to a second industrial revolution, during the early years of cybernetics. More than that, he clearly perceived the shadow side of the new technologies he had helped to create:

Those of us who have contributed to the new science of cybernetics .. . stand in a moral position which is, to say the least, not very comfortable. We have contributed to the initiation of a new science which . . . embraces technical developments with great possibilities for good and for evil. 34

Let us remember that the automatic machine ... is the precise economic equivalent of slave labor. Any labor which competes with slave labor must accept the economic conditions of slave labor. It is perfectly clear that this will produce an unemployment situation in comparison with which the present recession and even the depression of the thirties will seem a pleasant joke. 35

It is evident from these and other similar passages in Wiener’s writings that he showed much more wisdom and foresight in his assessment of the social impact of computers than his successors. Today, forty years later, computers and the many other “information technologies” developed in the meantime are rapidly becoming autonomous and totalitarian, redefining our basic concepts and eliminating alternative worldviews. As Neil Postman, Jerry Man- der, and other technology critics have shown, this is typical of the “megatechnologies” that have come to dominate industrial societies around the world. 36 Increasingly, all forms of culture are being subordinated to technology, and technological innovation, rather

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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. As Neil Postman put it succinctly, “When a computer is used for learning, the meaning of‘learning’ is changed.” 37 The use of computers in education is often praised as a revolution that will transform virtually every facet of the educational process. This view is promoted vigorously by the powerful computer industry, which encourages teachers to use computers as educational tools at all levels—even in kindergarten and preschool!—without ever mentioning the many harmful effects that may result from these irresponsible practices. 38

The use of computers in schools 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. As Theodore Roszak shows in detail in The Cult of Information, information does not create ideas; ideas create information. Ideas are integrating patterns that derive not from

information but from experience. 39

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, as C. A. Bowers has argued eloquently, language is metaphoric, conveying tacit understandings shared within a culture. 40 In this connection it is also important to note that the language used by computer scientists and engineers is full of metaphors derived from the military—“command,” “escape,” “fail-safe,” “pilot,” target, and so on—which introduce cultural biases, reinforce stereotypes, and inhibit certain groups, including most young, school-age girls, from fully participating in the learning experience. 1 A related issue of concern is the connection between com-

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puters and the violence and militaristic nature of most computer- based video games.

After dominating brain research and cognitive science for thirty years and creating a paradigm for technology that is still widespread today, the information-processing dogma was finally questioned seriously. 42 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.

PART THREE

The Pieces of the Puzzle