CYBERNETICS AND SOCIAL SCIENCE:

THEORIES AND RESEARCH IN SOCIOCYBERNETICS

*


Felix Geyer
SISWO (Netherlands Universities' Institute for Co-ordination of
Research in Social Sciences), Amsterdam, the Netherlands

Johannes van der Zouwen
Department of Social Research Methodology, Vrije Universiteit,
Amsterdam, the Netherlands


This article was published in Kybernetes, 20(6):81-92, 1991. Copyright MCB University Press 1991


(*) We want to thank dr W.A. Verloren van Themaat for keeping up a regular supply of food for thought for this contribution.






1. Introduction

Apart from a few forerunners [1], one might say that general systems theory and cybernetics started in the 1930s [2] and underwent a period of relatively rapid development during [3] and shortly after [4] World War II. Already in this period, many of the scientists involved saw the potential relevance of their approach for both society and for the study of society, i.e., the social sciences. The title of Wiener's [5] book "The human use of human beings " is typical in this respect.

We will not deal here with those studies where attention was focused on the possible effects of cybernetics - or rather: applications of cybernetics - on society itself, interesting though they may be [6, 7, 8]. Such studies often had a futurological bent, and described the expected "cyberneticization" of society as a result of the "cybernetic revolution": the large-scale introduction of computers, information technology, and robotics.

Rather, we will focus on those researchers who tried to apply the concepts, insights and methods of cybernetics to the study of society and its parts: social systems and processes. Three quite different avenues of approach are at issue here:
1) the addition of social or sociological variables and components to models of control and information systems that used to be analyzed from a purely technological viewpoint;
2) the use of concepts and theories from cybernetics/GST to describe and explain the behaviour and structure of social systems;
3) the application of systems methodology (especially in the areas of model construction and computer simulation) to the investigation of social structures and processes [9, 10, 11, 12].

Obviously, all of the social sciences - sociology, psychology, political science, criminology, anthropology, etc. - have social systems as their main object of study. Therefore, it is hardly amazing that the established social sciences took quite a critical look at the suggestions for new theoretical approaches and new techniques emanating from the young and rather upstart discipline of cybernetics.

Nevertheless, a number of researchers, coming both from the social sciences and from cybernetics, became convinced that the systems approach could prove useful in the study of social systems, and possibly also in the solution of social problems. Some well-known researchers characterizing this trend in the late sixties and early seventies are: Buckley [13, 14], Berrien [15], Kuhn [16], Parra-Luna [17], Rastogi [18] and Sutherland [19]. They engaged in a type of theory construction that might be called social systems theory, or social cybernetics. Within this group of social cyberneticians the feeling gradually increased that a sensible application of cybernetics to social systems, in view of the specific nature of the latter, demanded some rather essential changes in cybernetics, and that the social sciences themselves could contribute to bringing about such changes. In order to typify this change of accent a new term was coined in 1978: sociocybernetics [20].


2. Sociocybernetics: its nature and the causes of its emergence

Now, what was this change in cybernetics itself that was stimulated by the increasing application of cybernetic concepts to social science problems? Mainly one from first-order to second-order cybernetics, at first called the "new cybernetics" [21]. Indeed, it can be argued that the emergence of this second-order cybernetics was largely due to the increasing focus, within general systems theory, on the social sciences - a field where the inapplicability of first-order cybernetics often, though certainly not always, became evident.

It should be realized in this context that many of the objections against the systems approach within the social science community [22] were actually objections against the feedback-based, homeostasis-oriented and sometimes indeed rather simplistic control paradigm of first-order cybernetics: technocratic bias, unwarranted reductionism, implicit conservatism, etc. Such a paradigm does not reckon with some typical characteristics of social systems to be discussed below, like their tendency for self-steering and self-reference.

This is certainly not to deny that there is a type of system within a society that can indeed be planned, governed and steered, but this is mainly because such systems have been designed to be of this type in the first place, i.e. to exemplify the concept of the control paradigm. This is the area of social cybernetics mentioned above, where the principles of first-order cybernetics can indeed be applied. After all, not all of modern society exemplifies a tendency for self-steering. Steering enterprises and organizations like hospitals and universities is a respectable and often well-remunerated activity, and in such situations the classical control paradigm can offer a meaningful analytical tool for understanding and possibly even improving the steering process.

Sociocybernetics, a type of social cybernetics basing itself upon second-order rather than first-order cybernetics, indeed implies a few changes in perspective:

1) It stresses and gives an epistemological foundation for science as an observer-observed system; feedback and feedforward loops are not only constructed between the objects that are observed, but also between them and the observer - who in that sense is always part of the system under investigation, contrary to the assumptions of first-order cybernetics, where the researcher explicitly stands outside the system, looks at it "objectively" (or at best intersubjectively, together with a few other researchers) and often tries to control and steer it.

2) It thus emphasizes the subjective, time- and observer-dependent character of knowledge, which is not supposed to be "out there", waiting to be discovered, nor viewed as an environment-independent end result of the individual's inner cognitive processes; rather, it is seen as being constructed and continually reconstructed by the individual in open interaction with his environment.

3) It is actor-oriented, and assumes that different actors have developed their own goals in open interaction with their specific environment, and try to realize these in interaction with other actors; while such interaction implies a certain degree of "control" by the environment, the actor-oriented systems approach does not assume control in the strict sense that the individual's goals and actions can be steered from outside or from above in this respect [23].

4) Indeed, sociocybernetics shifts attention from systems that are being controlled to the self-steering capacities of the elements of social systems, whether they are individuals or institutions or groups of collaborating individuals. Consequently, new subjects for research emerge: the nature and genesis of the norms on which steering decisions are based, the information transformations, based on both observations and norms, that are necessary to arrive at steering decisions, the learning processes behind repeated decision making, etc.

5) Similarly, because of its clear recognition of the self-steering potential of a great many units in an increasingly complex world, sociocybernetics inevitably tends to concentrate on problems associated with change and growth, rather than with stability, as is the case with first-order cybernetics - where all forms of change are inherently viewed as disturbances or deviations that have to be corrected. For example, especially when several systems try to steer each other or an outside system, attention is focused on the nature of, and the possibilities for, communication or dialogue between these systems. Also, when the behaviour of a system has been explained in the classical way, through environmental influences and systemic structure, the problem is raised of the "why" of this structure itself, qua origin and development, and the "why" of its autonomy with respect to the environment. In systems terminology: the questions of morphogenesis and autopoiesis.

A few important trends in recent sociocybernetic research will have become implicitly clear from the above; for reasons of space, they can be only summarily illustrated in the following. The subjects of the social sciences - human individuals and groups - are different from those of the natural sciences in that they tend to be self-organizing, self-steering and self-referential. At the very least, they can be viewed as autopoietic in the biological sense; but on top of that, by referring to themselves, they develop their own goals in often unforeseen ways that cannot be steered from the outside, but depend on their planning activities, executed on the basis of their "mapping" of their environment and their own place in it.

Thus, sociocybernetics has concentrated on the themes summarily described in the following paragraphs:
- (section)3: what do social systems consist of, i.e., how should one define their components: simply as individuals, or as actors [23, 24], or as communications [25]?
- (section)4: how do they change: through planning, through spontaneous generation of differences, or through hierarchical control mechanisms?
- (section)5: what are the consequences of their self-referentiality: can they be steered, is forecasting possible?


3. The components of social systems:

Individuals seem to be the obvious candidates as components of social systems. Baumgartner [26], Burns [27] and others [28, 29], however, make a rather convincing case for actors; these can be either individual or collective actors (institutions, associations, etc.). Their actor-oriented systems approach enables them to bridge the "micro-macro" gap - the famous gap in social science thinking between the individual and society, between freedom and determinism, between bottom-up explanations of society that depart from the activities of individuals conceived as goal-seeking, self-regulating systems, and top-down explanations which see individuals as subservient to system-level criteria for system stability.

Luhmann [25] engaged in a "theory transfer" from biology by reconceptualizing the autopoiesis concept developed by Maturana and Varela [30] to make it applicable to the field of the social sciences. He defended the thesis that social systems do not consist of individuals (or roles, or even acts), as commonly conceptualized, but of communications. When one tries to generalize the usages of the concept of autopoiesis to make it also truly applicable to social systems, the biology-based theory of autopoiesis should therefore be expanded into a more general theory of self-referential autopoietic systems. It should be realized that social systems are based upon another type of autopoietic organization than living systems: namely on communication as mode of meaning-based reproduction.

While communications rather than actions are thus viewed as the elementary unit of social systems, the concept of action is admittedly necessary to ascribe certain communications to certain actors. The chain of communications can thus be viewed as a chain of actions - which enables social systems to communicate about their own communications and to choose their new communications, i.e. to be active in an autopoietic way. Such a general theory of autopoiesis has important consequences for the epistemology of the social sciences: it draws a clear distinction between autopoiesis and observation, but also acknowledges that observing systems are themselves autopoietic systems, subject to the same conditions of autopoietic self-reproduction as the systems they are studying.


4. Social systems and change

Three important topics deserve attention here:
- is planning of large-scale social systems realistically possible?
- is equality among the composing units of social systems possible?
- is hierarchical control a prerequisite for governing large-scale social systems?

It seems impossible to evade the sociocybernetic paradoxes inherent in the observation, control and evolution of self-steering systems - especially the paradox important to policy-makers worldwide: how can one steer systems that are basically self-referential as well as self-steering? Many empirical studies are rather pessimistic about the possibilities of planning and steering a number of specific social systems, and even in a purely theoretical sense there is a planning paradox [31]. Perfect planning would imply perfect knowledge of the future, which in turn would imply a totally deterministic universe in which planning would not make any difference. While recognizing the usefulness of efforts to steer certain parts of societies, a cost-benefit analysis, especially in the case of intensive steering efforts of large-scale societal units, will often turn out to be negative; and there unfortunately seems to exist a bias for oversteering rather than understeering.

Unplanned change is quite another matter, however; it is not only quite possible, but even highly probable. In this respect, another interesting theory transfer, from virology this time, was made by Gierer [32]. He demonstrated, by performing a computer simulation, that inequality often results from the cumulative interaction over time of the self-enhancing effects of certain initial advantages (e.g. generalized wealth, including education) with depletion of scarce resources. It then turns out that striking inequalities can be generated from nearly equal initial distributions, where slight initial advantages tend to be self-perpetuating within the boundary conditions of depleting resources; it is here that the concept of autopoiesis, developed in the mid-seventies in cellular biology by Maturana and Varela [30], finds one of its first applications in social science.

As mentioned before, an important issue in social science has always been whether one should opt for the "katascopic" or the "anascopic" view of society; in other words, should the behavior of individuals and groups be planned from the top down, in order for a society to survive in the long run, or should the insight of actors at every level, including the bottom one, be increased and therewith their competence to handle their environment more effectively and engage more succesfully in goal-seeking behaviour? A logical question then becomes: what should be the role of the social sciences in view of the above choice? Should it try mainly to deliver useful knowledge for an improved steering of the behaviour of social systems and individuals, or should it strive to improve the competence of actors at grass roots level, so that these actors can steer themselves and their own environment with better results?

Aulin [33] clearly favours the latter possibility: he followed a cybernetic line of reasoning that argues for non-hierarchical forms of steering. Ashby's Law of Requisite Variety [34] indeed implies a "Law of Requisite Hierarchy" in the case where only the survival of the system is considered, i.e. if the regulatory ability of the regulators is assumed to remain constant. However, the need for hierarchy decreases if this regulatory ability itself improves - which is indeed the case in advanced industrial societies, with their well-developed productive forces and correspondingly advanced distribution apparatus (the market mechanism). Since human societies are not simply self-regulating systems, but self-steering systems aiming at an enlargement of their domain of self-steering, there is a possibility nowadays, at least in sufficiently advanced industrial societies, for a coexistence of societal governability with ever less control, centralized planning and concentration of power.

As the recent history of the Soviet Union demonstrates, this is not only a possibility, but even a necessity: when moving from a work-dominated society to an information-dominated one, less centralized planning is a prerequisite for the very simple reason that the intellectual processes dealing with information are self-steering - and not only self-regulating - and consequently cannot be steered from the outside by definition.


5. Social systems: self-observation and self-reference

In the above, Luhmann's [25] insight was mentioned that ultimately all observations within a society are self-observations. And indeed, one of the main characteristics of social systems, distinguishing them from many other systems, is their potential for self-referentiality. This means that the knowledge accumulated by the system itself about itself, in turn affects the structure and operation of that system, and therewith in turn produces new knowledge. Complete self-knowledge, whether for an individual or an entire society, is of course theoretically impossible: the new knowledge gained interacts with the knowledge already there and changes it. This is the case because, in self-referential systems like social systems, feedback loops exist between parts of reality on the one hand, and models and theories about these parts of reality on the other hand.

Concretely, whenever social scientists systematically accumulate new knowledge about the structure and functions of their society, or about subgroups within that society, and when they subsequently make that knowledge known, through their publications or sometimes even through the mass media - in principle also to those to whom that knowledge pertains - the consequence often is that such knowledge will be invalidated, because the research subjects may react to this knowledge in such a way that the analyses or forecasts made by the social scientists are falsified.

In this respect, social systems are different from many other systems, including biological ones. There is a clearly two-sided relationship between knowledge about the system on the one hand, and the behaviour and structure of that system on the other hand. Biological systems, like social systems, admittedly do show goal-oriented behaviour of actors, self-organization, self-reproduction, adaptation and learning. But it is only social systems that arrive systematically, by means of experiment and reflection, at knowledge about their own structure and operating procedures, with the obvious aim to improve these. Their environment mapping includes an image of themselves, as embedded in and interacting with their environment, and they can perform "simulations" on this self-image.

The accent of sociocybernetic theorizing now seems to have shifted from the paradoxes inherent in efforts at steering self-steering systems to the consequences of self-referentiality, in the sense of self-observation, both for the functioning of social systems and for the methodology and epistemology used to study them. We do have a paradox here too: the accumulation of knowledge often leads to a utilization of that knowledge - both by the social scientists and the objects of their research - which may change the validity of that knowledge.

For reasons of space, we can only discuss one example: Henshel [35], who analyzes what he terms credibility and confidence loops in social prediction. He extends the well-known notions of self-fulfilling [36] and self-defeating prophecies to serial self-fulfilling prophecies, where the accuracy of the earlier predictions, themselves influenced by the self-fulfilling mechanism, impacts upon the accuracy of the subsequent predictions. He distinguishes credibility loops and confidence loops.

In credibility loops, source credibility, i.e. the credibility of the forecaster, becomes significant, because it is the same forecaster who is issuing repeated predictions. There is a deviation-amplifying positive feedback loop here between: 1) a self-fulfilling mechanism, 2) the accuracy of the prediction, and 3) the credibility of the forecaster. Examples given by Henshel include the effects of pre-election polling on election results, of stock market predictions on share prices, of intelligence testing on school success, etc.

Confidence loops are similar to credibility loops, but differ in what is held constant, or uniform, across the repeated prediction iterations. In this case it is not the person of the predictor which must remain the same, in order for the associated credibility to rise or fall, but continuity across predictive iterations in the prediction itself which is at issue. It must exhibit constancy in either rank-order or direction on successive pronouncements. Such uniformity in the direction of the prediction, together with the postulated self-fulfilling mechanism, produces increased accuracy, which in turn produces increased confidence in the prediction as iterations of the loop unfold. Henshel's examples here include inflationary spirals, validation of criminality theories, attribution theory, etc.

Of course, feedback loops involving a self-defeating mechanism lower rather than increase predictive accuracy over several iterations. When inserting a self-defeating dynamic in the system, an oscillating system is created in which the time paths of the key variables now oscillate instead of assuming a monotonic form. The so-called cobweb cycle is a good example here.

We have chosen to mention Henshel's research in some detail because it highlights fascinating differences between the natural and the social world, and between the natural and the social sciences:
1) It demonstrates the existence of two "nested" differences between the natural world and the social world: self-fulfilling or self-defeating prophecies exist only within the social world, while moreover these self-fulfilling or -defeating tendencies are magnified by the feedback loops in which they are embedded, and impact directly upon the accuracy of the predictions made.
2) It also demonstrates differences between prediction in the natural vs. the social sciences: The existence of credibility loops and confidence loops suggests that, on certain occasions at least, the social sciences can pull themselves up by their own bootstraps, in terms of improving their predictive accuracy, a suggestion also explicitly made by Rapoport [37]. Such a "bootstrap" enhancement of accuracy is not possible for prediction in the natural sciences. The social sciences appear to be aided especially with respect to the accuracy of directional and ordinal predictions, in ways which are impossible for natural phenomena. If a social scientist issues a directional or ordinal prediction, he may be aided by self-fulfilling dynamics. On the other hand, if the same social scientist issues a quantified prediction, he may be damaged in ways which do not apply to the natural science world. That is, for quantified prediction his accuracy may be damaged by the same self-fulfilling dynamics.

Self-defeating tendencies necessarily reduce rather than increase the accuracy of directional and ordinal predictions, and again have equivocal but usually damaging effects on quantified accuracy. Considering both tendencies, self-fulfilling and self-defeating, we find that the weaker forms of prediction (directional and ordinal) are sometimes aided, sometimes damaged. Quantified predictions, long taken as the hallmark of mature science, are ordinarily damaged. In terms of obtaining precision and high accuracy in quantified forecasts, the social sciences are therefore uniquely disadvantaged as a result of the existence of self-fulfilling and self-defeating tendencies in the social world as opposed to the natural world.


6. The impact of social cybernetics on the social sciences:

We have described some of the main research trends in the interface between cybernetics/GST and the social sciences, broadly conceived. Those studies dealing with the influence of cybernetics/GST on society itself (the "cybernetization" of society), rather than on the social sciences which study society in all its aspects, have been explicitly excluded. Furthermore, we have mentioned only in passing those studies which apply insights from especially first-order cybernetics to those parts of society than can, at least in principle, be steered: the field of social cybernetics. And we have concentrated on those studies that deal with aspects or elements of society where steering is difficult, if not impossible - because they are self-steering, self-organizing and self-referential, and consequently hard to analyze, let alone to forecast.

Specifically, we have looked at social cybernetics and sociocybernetics as part of the work being done in cybernetics in general, and we can certainly conclude that social cybernetics has developed into an important and fully accepted area of cybernetics as a whole. However, the same cannot be maintained if we look at the influence of sociocybernetics and social cybernetics on social science theorizing and research.

In order to empirically check our negative stereotype in this respect, and provide a sound basis for this article, a search was performed, for each of the periods 1970-1974, 1975-1979, 1980-1984, 1985-1989 and 1990-1991 in SocialSciSearch and Sociological Abstracts, for each of the following terms: autopoiesis, boundary (of system), complexity, entropy, feedback, hierarchy, homeostasis, morphogenesis, and system. The general idea was to determine the degree of penetration of systems thinking in the social sciences, and more specifically to analyze the rise and fall of certain terms. The results, however, were rather disappointing; for reasons of space, we have omitted the two tables we originally intended to include, but what they boil down to is simply:
1) that the frequencies with which the above terms occur are generally only a small or even very small fraction of one percent of all abstracts covered, with the exception of "system" itself, and "variety", both probably often used in a non-GST sense; and
2) that, in spite of the low frequencies, systems terms do appear in a certain order: first, in the early seventies, feedback and homeostasis, next, during the period 1975-1985 morphogenesis, and finally, since 1980 and especially 1985 autopoiesis.

Perhaps even more significant, the most important authors in social cybernetics are barely quoted in the mainstream social science literature, with the exception of a few intellectual giants like Niklas Luhmann and Herbert Simon. A recent review of sociological accomplishments by Bryant and Becker - see especially Peter Abell's review article therein [38] - does not explicitly mention any contribution of social cybernetics to the social sciences. There is, however, at least an indirect influence in the area of methodology, where various modelling and simulation methods now employed in the social sciences were originally developed in systems science.

Generally, articles on social cybernetics, including sociocybernetics, tend to appear in systems journals rather than social science journals. Recognition for social cybernetics with the cybernetic/general systems community thus certainly does not imply recognition in the social science world. One can only speculate why this might be the case; perhaps because social cybernetics so far has produced a lot of intellectually stimulating and innovative theory and also a respectable body of sound methodology, but very little empirical theory-testing research. This type of reserach is admittedly difficult to design, especially in sociocybernetics where one has to deal with self-referential phenomena.

The above trends are illustrated in a 300-item bibliography of relevant systems literature [39], compiled as a basis for this contribution; it has grown too lengthy for reproduction here, but is available from the authors. This bibliography includes references to social cybernetics, and also to previous efforts at describing the relationship between cybernetics and the social sciences, notably by Buckley, Cavallo and Bråten [11, 13, 14, 40].


7. Conclusion

Concluding, it can be said:
1. that empirical social science research utilizes increasingly, and with good results, methods taken from systems methodology;
2. that social science theory, on the other hand, still barely uses the conceptual scheme of GST/cybernetics;
3. that the mostly theoretical studies stimulated by the sociocybernetic approach are generally fertile and thought-provoking;
4. that consequently the social sciences would be well advised to drop their negative stereotype against a previous and admittedly more primitive form of systems approach that did not do justice to the inherent complexity of social systems, and perhaps did have a somewhat conservative bias;
5. that social cyberneticians - and especially sociocyberneticians - on the other hand spent too little energy so far on the empirical testing of sociocybernetic theories, although such verifiability may be an inherent problem, since much of sociocybernetic theory is not formulated in an explicitly falsifiable form.

It is only when sociocybernetic theory will have been proven usable, applicable and empirically tested that cybernetics can bring more to the social sciences than just buzz words. And this indeed could be an agenda for the nineties!



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