geyer the challenge

8/6/2019 GEYER the Challenge

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The challenge of sociocybernetics

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until now cybernetics has had relatively little influence on mainstreamsociology[2]. Since the 1960s several social scientists have neverthelessbeen inspired by cybernetics and have applied it to the social sciences:Deutsch[3] and Easton[4] in political science; Buckley[5,6], and laterBurns and Buckley[7], Burns et al.[8], Burns and Flam[9] andBaumgartner and Midttun[10] in sociology and economics.

Nevertheless, and in spit e of the above caveats , it seems wort hwhile toreconnoitre what recent developments in cybernetics could mean for sociology.In sociological theorizing, the focus has s lowly shifted, over the last fewdecades, from trying to explain the structure and stability of social systems to

analysing the processes that cause them to change and evolve towards greaterlevels of complexity, from trying to help maintain homeostasis in a top-downfashion to explaining morphogenesis as a result of interpenetrating bottom-upprocesses. Cybernetics has always concentrated on both: the results of input-output t ransformat ion processes may be explained by the structure of thesystem, while that s tr ucture can in tur n be conceived as the resultant of previous processes. Recent developments in cybernetics, however, haveconcentrated increasingly on the analysis of interacting processes, includingeven the observers of these processes, and thus the possibility of a potentiallyfertile theory transfer should certainly not be excluded.

Within cybernetics, it is customary to distinguish first- and second-ordercybernetics, and to prevent misunderstanding we will keep to this usage,although classical versus modern cybernetics or first-generation versus second-generation cybernetics might be a preferable terminology. The issue here is thatsecond-order cybernetics originated in reaction to what were seen as thedeficiencies of first-order cybernetics, and has the tendency – as often happens– to create its own niche by overst ressing t he differences with first -ordercybernetics. In order to clarify the differences between the two, we will do thesame, with the caveat that they are largely a matter of relative stress, and thatmuch of what is now known as second-order cybernetics was already adheredto by first-order cyberneticians.

Class ical (first-order) cyberneticsFirst-order cybernetics originated in the 1940s[11,12], and indeed tried to steerobserver-external systems. Although it had an interdisciplinary orientation, it

might be called an engineering approach, and focused on studying feedback loops and control systems, and on constr ucting intelligent machines[13].

Here are a few examples: Norbert Wiener[14], often considered the father of cybernetics, was engaged in automating the operation of anti-aircraft batteries,which led to the construction of ILLIAC, the world’s first computer. Shannonand Weaver[15], working at Bell laboratories in the late 1940s, were confrontedwith the problem of how to reduce noise in telephone lines, and developedinformation theory. MIT’s Minsky and Papert[16] and Minsky[17] constructedamong others M. Speculatrix, a small robot that could find its way out of a dark



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room, moving dexterously around objects towards the light; he initiated the nowflourishing field of artificial intelligence (AI).

With its stress on efforts to st eer especially technological devices, anddeveloping all kinds of control systems, it is not amazing that first-ordercybernetics was especially interested in negative feedback loops, rather thanpositive ones. When a negat ive feedback loop either occurs naturally, or isconstructed, the performance or output of a system is compared with a presetgoal, and corrective action is taken whenever there is a deviation from that goal.The thermostat of a central heating system may serve as an example: there is afeedback loop from the thermostat to the heater, whenever room temperaturerises above a certain ma ximum, or falls below a certain minimum. It is

noteworthy that even in this simple example, although clearly it is a controlsystem, there is no specific controlling agent; control is dispersed through thesystem, and any part of it could be said to control the rest of the system.

As a result of the above, first-order cybernetics – with its engineeringapproach and the corresponding stress on constructing control systems, andwith its predilection for negative rather than positive feedback phenomena –was interested primarily in homoeostasis or equilibrium-maintenance, or atleast in restoring a system’s equilibrium whenever it was disturbed by externalinfluences impinging on that system. As is still the case in much of science,environment mastery was an implicit goal, based on the Newtonian conceptionof an, in principle, orderly universe: admittedly complex, but knowable bymeans of a continuing and cumulative effort to discover its basic laws. Positive

feedback loops, which cause morphogenesis rather than homoeostasis, and arethe motor behind change, were paid much less attention. A simple example of apositive feedback loop is cumulative interest, or to put it more esoterically: “thedevil shits on the big heap”, recently formulated in economics[18] as the law of increasing returns.

Early efforts to apply this homeostasis-oriented type of cybernetics orsyst ems theory to the field of the social sciences, as for example those of Parsons et al .[19], met with the resistance of a social science community whichby then had turned overwhelmingly liberal, and considered the systems orcybernetic approach to be not only conservative, but also too simplistic,mechanistic and linear to be really applicable to the world of human interaction.

Leaving in the middle for the moment to what extent this left-wing or liberalcriticism is generally correct, one can certainly say that some applications of first-order cybernetics like system dynamics – a simulation proceduredeveloped originally by Forrester[20] and Meadows[21] to simulate thebehaviour of systems with several feedback loops – have made remarkableinroads in the genera l scientific community. One need only th ink of theenormous popularity of the Club of Rome repor t, even among laymen[22],where systems modelling was applied to an extremely complicated problemarea, with many interacting variables.

The liberal criticism is understandable – the stress of first-order cyberneticswas indeed largely, though not exclusively, on constructing mechanical control




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syst ems – but not quite correct. In the Club of Rome report, for example,positive feedbacks were certainly important, and the same goes for many othertechnological systems: one of the world’s p erhaps most impressivetechnological achievements, the atom bomb, would be unthinkable withoutpositive feedbacks. As Van der Zouwen[23] put it succinctly: without negativefeedback loops the organism cannot maintain itself in its environment, andwithout positive feedback loops it has no chance of survival as a species in viewof environmental changes to which it has to adapt by setting new goals.

The potential usefulness of the classical (first-order) cyberneticapproach for sociology

While the concepts and procedures of second-order cybernetics, to be discussedlater, have some specific advantages for sociology, dealing as they are with theinteractions of self-organizing, self-referential systems, and thus with thecontinuous emergence of new levels of complexity, the principles of first-ordercybernetics can certainly also be applied fruitfully to the field of the socialsciences.

System boundaries Cybernetics or systems theory – we use these terms interchangeably, as theypertain to virtually the same fields of enquiry – holds that one can carve outmentally and arbitrarily any part of the universe[24] and call it a system.However, once one has thus delineated the boundaries of the system, one should

keep to them, at least for a while. This follows from the so-called black boxapproach, named after the early metal boxes containing electrical wiring andcircuitry, which were invariably painted b lack. The black box approachpresupposes that the external observer can never really observe the systemfrom within, but can only determine what goes in (the input) and what comesout (the output). From the differences between the two, inferences can then bemade about the way the system works, depending of course on the mindset of the observer.

The way system boundaries are drawn is obviously observer dependent , time dependent and most importantly also problem dependent . In other words: twoobservers may be inclined to draw slightly different boundaries when talkingabout the same problem; and the same observer may draw the boundaries of asystem to be studied differently tomorrow than today. Finally, even when theboundaries are not drawn differently as a result of time or observer dependence,they may be drawn in a different way because a different problem needs to bestudied. It is necessary to be fully aware of this when one has to determine whatfalls inside and what falls outside one’s field of enquiry, and when one has toformulate a research design.

For example, one can define an individual as a system confined by his or herbody. But when one is looking at that individual from a medical perspective, itmay be relevant to include the input: all the food, drugs and alcohol which havebeen ingested lately, not to mention the output. When looking at that individual



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as a scientist, it may be relevant to include at the very least his word processor,if not the entire support structure of his institute or depar tment. When lookingat his emotional problems, one may have to enlarge the system by including hisfamily members, as is done in systems-oriented family therapy. One of the firstto develop this now flourishing field was Watzlawick[25]. An extreme exampleof problem dependence occurred at a cybernetic conference, where the speakerproduced a saw, convinced everyone it was indeed a normal saw, and thenstar ted using it quite passably as a fiddle.

Even when a problem has been more or less specified – for example theadaptation of ethnic minorities – one still has to decide how wide the systemboundaries should be drawn in order to promise the most interesting research

results. One might focus on the interactions between individuals and theirenvironment if one wants to determine what Sennett and Cobb[26] called “thehidden injuries of class”, between families and their environment if one wantsto focus on defensive or adaptive joint strategies, or between the group as awhole and its environment if one wants to compare the adaptation problems of different ethnic groups.

Systems, subsystems and suprasystems Since one can draw arbitrarily the boundaries of a system when developing aresearch design, one can not only decide how one wants to define the systemunder consideration, but one is also free to decide how one wants to define thesubsystems – i.e. component parts that should especially be looked at – and the

suprasystem(s) it forms part of. In the above case, for example, and obviouslydepending on one’s research goals, one might select the ethnic family as thesystem under consideration, the family members as the subsystems, and theethnic minority group and the nation in which it lives as the suprasystems. Oneobviously needs to be extremely careful how one defines such a hierarchicallynested set of systems, as this will determine the kind of research results one willobtain.

Circular causality Many of us have still been educated to consider circular reasoning as beingwrong: a mistake known in logic as the “circulus vitiosus ”. Something cannotcause itself; but cybernetics says it indeed can. One of the importantcontributions of first-order cybernetics has been to increase the awareness of ubiquitous circular processes, in technology, in nature, and in society. Thecircular causal cycle may be short – like A causes B and B causes A – or it maybe long and cycle through the entire alphabet or more, in which case it will beharder to discover.

It may be interesting to speculate on what has caused the narrowing of thiscircular model of causal thinking dur ing the cumulative build up of theNewtonian-Laplacean world image, with its clockwork model of the universe,its mechanistic rather than organic bias, and its st ress on linear causal chainsunfolding through time, from past to future[27]. This resistance against circular




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causality may itself be part of a circular process where every success of thedeveloping natural sciences since the seventeenth century strengthened theconviction that hard work could lay bare the implicit rules, the hiddenclockwork of the universe, which in turn attracted new scientists to engage inthis prestigious area, whose research results t hen further st rengthen theNewtonian image of the world.

For whatever reason, much of empirical sociology still follows the linearmodel: admittedly, multivariate analysis may demonstrate that a phenomenonhas many different causes, and many different consequences, but this is still afar cry from concentrat ing on finding circular causal chains, whereby thephenomenon in question helps to create itself. It barely needs comment, of

course, that such a concentration on unravelling circular causal chains wouldenormously complicate empirical research. The idea of circular causality has acertain intuitive attr activeness, and a logical one as well, but to prove itsexistence empirically is quite another matter.

Positive and negative feedback loops Both positive and negative feedback loops are examples of circular causality.They can either occur spontaneously, in nature as well as in society, or they canbe engineered. As was mentioned before, negative feedback loops are of specialinterest to first-order cybernetics, since its pu rpose generally was to steersystems by keeping them on a certain course, rather than have them changedirection, i.e. to let them fluctuat e within a specified marg in around an

equilibrium. However, positive feedback loops were certainly alreadyrecognized in the engineering efforts of first-order cybernetics, but it was theoriginally biology-based second-order cybernetics tha t gave them specialattention. Logically so, as they cause change rather than stability.

As an aside, it should be noted here that Maruyama[28] had already spokenin 1963 of the “second cybernetics” (not s econd-order cybernetics), thusdesignating the cybernetics which concentrates on deviation-amplifyingmutual causal systems, where positive rather than negative feedback, andmorphogenesis rather than morphostasis are at issue.

Often, negat ive feedback loops will spontaneously emerge in humaninteraction when that interaction continues over a certain period of time. Afamous example is formed by the well-known prisoner’s dilemma which, whenplayed over several cycles, changes from a non-zero-sum game to a zero-sumone: at first, both prisoners tend to betray one another to maximize their ownprofit. Rapoport[29] discovered that both partners start empathizing with theother’s position after a while, and then both converge to what he calls a tit-for-tat strategy: an honest move will be rewarded by an honest counter-move, anda dishonest one will be punished by a dishonest counter-move.

Simulation While a technique rather than a concept, one can certainly say that simulation,originally a t echnique of first-order cybernetics, is used widely now also in



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second-order cybernetics to study phenomena of emergence, and has become amuch-used tool in the social sciences as well as in most other disciplines. Withthe increasing mass-scale availability of high-speed computing equipment, evenon PCs, it becomes possible to simulate realistically ever more complexproblems, with the possibility to incorporate an increasing number of interacting va riables in one’s models. The obvious advantage of suchsimulations is that one can investigate the effects of changing some of thevariables without actually changing them in reality, i.e. without engaging inpolicy action. Also, simulations with complex models allow one to discoverlatent consequences of certain intended actions, and to forecast the emergenceand the effects of counter-intuitive behaviour.

Modern (s econd-order) cyberne ticsSecond-order cybernetics originated some 30 years later than first-ordercybernetics, in the early 1970s. The term was coined by Heinz von Foerster[30]in a p aper for the 1970 meeting of the American Society for Cybernetics,entitled “Cybernetics of cybernetics”. He defined first-order cybernetics as thecybernetics of observed systems, and second-order cybernetics as thecybernetics of observing systems.

Indeed, one of the main differences with first-order cybernetics is that second-order cybernetics explicitly includes the observer(s) in the systems to be studied.Moreover, it generally deals with living systems , and not with developingcontrol systems for inanimate technological devices. These living systems

range from simple cells all the way up the evolutionary scale to human beings;while the observers themselves are obviously also human beings. Thus, incontrast to the engineering approach of first-order cybernetics, most of second-order cybernetics could be said to have a mainly biological approach, or at thevery leas t a b iological basis. As Umpleby[31] sta tes, this difference hasimportant consequences:

(1) Living systems, no matter how primitive, have a “will” of their own.They exhibit what Maturana and Varela[32,33] have termed autopoiesis or self-production: they not only reproduce, but also produce their own“spare parts” whenever necessary, generally utilizing elements fromtheir environment. Living systems thus are organizationally closed, butinformationally open.

(2) As a result, living systems are inherently more difficult to steer; theirinteractions with their environment are more difficult if not impossible toforecast more than a few moves ahead. Thus, second-order cyberneticshas become realistic about the possibilities for steering, and hasconcentrated more on understanding the evolution of biological andsocial complexity than on controlling it.

(3) Given this, it is understandable that second-order cybernetics is moreinterested in morphogenesis and positive feedback loops, than inhomeostasis and negative feedback loops.




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(4) Although first-order cybernetics certainly included important biologists– like Von Bertalanffy[34], one of the founders of General System Theory– the impetus for second-order cybernetics came largely from biologyand neurophysiology, and in a later stage also from epistemology. This isnot to say that biology does not profitably use first-order cyberneticconcepts: homeostasis, for example, remains an important concept inbiology to explain different processes like hormonal balance,maintenance of temperature, etc. However, many biological phenomenathat have to do with growth, change and emergence demand anexplanation in terms of second-order cybernetics. Matu rana[35], forexample, considers knowledge to be a biological phenomenon. Attentionwas thus focused on the observer, and the biological basis of perceptionand knowledge acquisition processes. In epistemology, second-ordercyberneticians became interested in the nature of knowledge, language,cognition and communication.

(5) It was thus logical that the concept of self-reference was developed andstressed, especially biological and linguistic self-reference. A satisfyingtheory of biology should account for the existence of theories of biology;likewise, an adequate theory of cognition should give an understandingof understanding. The view of language changed from language as astring of symbols representing external “reality” to language as actionsfor co-ordinating actions. Umpleby[36] gives the example of

“performative utterances” like “I now pronounce you husband and wife”,where the social status of two people is transformed, while thistransformation is described at the same time.

(6) Summing up: in second-order cybernetics, the system – whether anindividual or a group – is defined as having the ability to reflect on itsown operations on the environment, and even on itself. These operat ionsgenerate variety in the environment, or in itself, which can reflexively berecognized as being due to sys temic variation, which makes themrecursive: observations can be observed, communications can becommunicated, etc.

Apart from von Foerster, several other authors haven given concise definitionsof the differences between first-order and second-order cybernetics[30]. These

differences refer to, respectively, the purpose of a model versus the purpose of the modeller (Pask), controlled systems versus autonomous systems (Varela),the interaction among variables in a system versus interaction between theobserver and the system observed (Umpleby), and theories of social systemsversus theories of the interaction between ideas and society (Umpleby). Thelatter difference seems to reflect the respective approaches of Parsons as a first-order systems theorist interested in system stability and system maintenance,and Luhmann as a second-order cybernetician interested more in change andmorphogenesis.



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Second-order cybernetics and the philosophy of science While second-order cybernetics agrees with many of the tenets of themainstream Newtonian philosophy of science – like the need to dist inguishscience from non-science, e.g. by means of Popper’s falsifiability criterion, theprinciple of verification through experimentation, the procedure of refutingconjectures by tr ial and error, etc. – it goes against it in some importantrespects[36]:

(1) It disagrees with the idea that observations are independent of thecharacteristics of the observer. Von Glasersfeld[37] developed thephilosophy of constructivism as an alternative to the realism of

mainstream philosophy of science, i.e. the idea that every individualconstructs his or her own reality to fit personal experience[38]. Theknowledge built up this way fits, but does not match the world of experience. It is considered an advantage of this approach that itsupposedly increases tolerance, by leading to what De Bono[39] prefersto call “proto-truth”, with all the relativistic implications this entails,rather than truth in any absolute sense. Perhaps the best illustration of such an inbuilt sense of relativity is presented in the Mel Brooks movieHistory of the World , where Moses comes down Mount Sinai carryingthree top-heavy tablets with the 15 Commandments, stumbles and dropsthe last tablet to smithereens, then relaxes, shrugs, and says: “Well, tenleft”.

(2) In classical philosophy of science, theories do not affect the phenomenathey describe; it would be preposterous to assume that the Second Law of Thermodynamics would speed up the decay of the universe. But insecond-order cybernetics, interaction between social theories and socialsystems is taken for granted[40]. Economic theories do change economicsystems, and often entire societies, as any East-European in the audiencecan testify. They are often developed precisely because the theorists dowant to change social systems.

(3) A core point of disag reement, however, at least with some extremeproponents of second-order cybernetics, is that they reject the necessity,claimed among others by Popper, of the unity of method. The methodsused for the physical sciences cannot be used for the biological and social

sciences, if only because they are self-organizing, self-referential andautopoietic. However, mainstream second-order cyberneticians have amore moderate viewpoint, i.e. that there is at least some unity of methodacross the sciences.

Clearly, second-order cybernetics d isag rees st rongly with the st ill neo-positivist, Newtonian mainstream philosophy of science in the above mentionedrespects[41], although this disagreement is certainly not the prerogative of second-order cybernetics: Norbert Wiener, for example, devoted a fascinatingchapter[42] to the difference between Newtonian and Bergsonian time. The




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changes suggested by second-order cybernetics amount to a scientificrevolution in Kuhnian terms; but, as Umpleby[43] suggests, the time has comeperhaps to return to a period of normal science. The way to do this is byst ressing the correspondence principle: i.e. the new theory, second-ordercybernetics, should reduce to the old theory, the mainstream philosophy of science, to which it corresponds for those cases in which the old theory isknown to hold. In other words, a new and previously neglected dimension isadded.

The usefulness of second-order cybernetic concepts for sociologyBefore dealing with some of the main concepts of second-order cybernetics –

self-organization, self-reference, self-steering, autocatalysis and autopoiesis – itis interesting to note that Norbert Wiener, the father of cybernetics, was quiteambivalent himself on the applicability of cybernetics to the social sciences andto society[1]. On the one hand, Wiener was thoroughly convinced that thebehaviour of humans, animals and machines could be explained by making useof the same cybernetic principles: communication, control of entropy throughlearning by means of feedback, etc. This is evident already from the titles of histwo best-known books: The Human Use of Human Beings [14] and Cybernetics: or Control and Communication in the Animal and the Machine [42]. On the otherhand, Wiener was quite pessimistic about the applicability of cybernetics tosocial systems, for at least two reasons:

(1) Social science data usually exemplify short stat istical time series,

affected by varying environmental conditions, while ideally one wouldneed long runs under invariant conditions.

(2) Wiener moreover considered the social sciences as the discipline wherethe coupling between observer and observed is hardest to minimize inboth directions: apart from the obvious disadvantages of observerdependence, the researcher also inevitably influences the subjects of hisresearch, and can sometimes even act as a catalyst in processes of sudden change: how many strikes have not broken out just after theresearchers measuring job satisfaction left the premises?

In a sense, one might say these two objections are interrelated: to the extent thatthe observer tends to influence his subjects more than in the natural sciences, hethereby contributes to a disruption of the constancy of the conditions neededfor longer statistical time series[42, pp. 24-5]. This is of course not to deny thatmany examples can be found of the inverse: content analysis or non-par ticipantobservation does not influence the subjects of the research, while bombardingprotons or vivisection clearly does.

The difference between first- and second-order cybernetics was describedbefore, but it should be clear already from the main concepts: while theconcepts of first-order cybernetics do not point specifically to either thesystem or its environment, the important concepts of second-order cyberneticsall start with “self”, if not in English, then in Greek (“autopoiesis”). While it may



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be interesting to speculate about the extent to which this “selfishness” is relatedto the increasingly rapid social change taking place since the late 1960s, thiswould fall outside the bounds of this article.

Self-organization Due to the increas ing complexity of today’s world, and the s eemingintractability of many of its problems, contemporary scientific interest in manydisciplines has centred on the emergent, self-organizing properties of certaincomplex aggregates. Especially developments in biology in this respect havestimulated second-order cybernetics, although first-order cybernetics wascertainly also aware of self-organizing systems, but did not pursue this line of

enquiry. Norbert Wiener, for example, mentions the synchronization of thebehaviour of fireflies[42], while Ashby also stressed self-organization in hisDesign for a Brain [13] and also in An Introduction to Cybernetics [44].

Recent developments in cognitive science demonstrate the emergence of self-organization (itself a matter of emergence) as a core concept. Cognitive sciencecan be viewed as the result of an interdisciplinary effort which includesneuroscience, AI, linguistics, philosophy and cognitive psychology. Though it isbarely three decades old, one can already distinguish two schools: cognitivismand connectionism.

Cognitivism and cognitive technology gave the impetus for the development of AI. Cognitivism conceived mind as a manipulation according to the rules of deductive logic, as localized and serial information processing of symbols

which were supposed to represent an external reality[45, pp. 4-5]. In this respect,it seems to be close to first-order cybernetics, although during the Macyconferences of the late 1940s[46] it was already hypothesized that brains haveno centr al logical processor, and no firm r ules or sp ecific spots to st oreinformation, but rather operate on the principle of distributed intelligence.

In spite of this, cognitivism was the mainstream paradigm in cognitivescience until well into the 1970s, when its drawbacks became more apparent[45,pp. 85-6]:

q At some stage, symbolic information processing encounters the so-called“Von Neumann bottleneck”: it is based on rules which are appliedsequentially, which obviously gives problems with large numbers of sequential operations, as in weather forecasting or image analysis.

q Moreover, symbolic informat ion processing is localized, rather thandistributed, and local malfunctioning of some of the symbols or rulestherefore tends to result in overall malfunctioning, without the resiliencetowards disturbances which distributed processing offers.

Connectionism , in clear contradistinction to cognitivism, incorporates many of the views of second-order cybernetics. It is explicitly bottom-up rather than top-down: it eschews abstract symbolic descriptions, but assumes simple, “stupid”,neuron-like components which develop cognitive capacities when appropriatelyconnected. In other words: the intelligence is in the structure, in the connections




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made – hence the name connectionism – and not in the components. Thus, itstresses emergence and self-organization.

Hebb’s rule, formulated in 1949, occupies a central place in connectionism. Itstates that learning is based on postulated changes in the brain as a result of correlated activity between neurons. If two neurons tend to be active together,their connection is strengthened; if not, then it is weakened. In this way history,which is always a history of transformations, is incorporated. Simulations of asimple network of neurons according to Hebb’s rule demonstrate that patternrecognition is possible, after a learning phase in which some of the connectionsare strengthened or weakened, when the number of patterns presented is nomore than about 15 per cent of the participating neurons[45, pp. 87-8].

The components of neural networks do not need an external processing unitwhich guides their operations; they work locally according to local rules, butsince they are part of a network, global co-operation emerges when the states of all participating neurons reach a mutually satisfactory state. This passagefrom local r ules to global coherence is the essence of self-organ ization –otherwise also denoted as emergent or global properties, network dynamics,non-linear networks, complex syst ems, etc. Once one cares to look for it,emergence abounds. In different domains, like vortices and lasers, chemicaloscillations, genetic networks, population genetics, immune networks, ecologyand geophysics, self-organizing networks give rise to new properties.

Varela[45] describes simulation experiments with extremely simple cellularautomata, whose components are arranged in a circle, can only have only two

states (0 or 1), and change their states according to simple rules (i.e. the states of their two neighbouring components). These experiments have demonstratedthat they can be divided into four classes or at tractors:

(1) in simple attractors all components end up either being all active (1) orbeing all inactive (0);

(2) in somewhat more complex cyclical at tractors spa tial periodicitiesemerge: some components remain active, while others do not;

(3) a third type of attractor, also cyclical, runs through at least two cyclesbefore returning to the same state;

(4) finally, for a few rules the resulting dynamics give rise to the chaoticattractors, studied especially in chaos theory, where one cannot detect

any regularities either in space or t ime, although such systems mayretur n unexpectedly to perfect order, as has been Prigogine’s point of departure in his theory of complexity.

When these simple cellular automata are coupled structurally to an externalworld, for example by dropping them in a simulated “primal soup” of 0s and 1s,with the rule that each component takes over the state of the environmentalelement it encounters, then nothing happens with the first and fourth class of attractors: they go back to their homogeneous state, respectively remain in theirchaotic state. The cyclical attractors, however, demonstrate an admittedly



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primitive kind of intelligent behaviour, for which they were definitely notprogrammed: for example, some types react only to double perturbations fromthe environment; others, the so-called odd-sequence recognizers, only to anuneven number of perturbations.

These experiments show that a simple system with a form of closure (thenetwork’s internal dynamical emergences), which is coupled structurally to anenvironment (replacement of each component by the state of the element itencounters) selects (or enacts ) a domain of dist inctions from a world of randomness which is relevant to its structure. On the basis of its autonomy, thesystem selects or enacts a domain of significance, which involves some minimalinterpretation. While admittedly a far cry from describing anything like humanintelligence, these experiments present a minimal example of how autonomoussystems can draw significance from a random background, which may not betotally unlike the way humans draw significance from an a fter all neutraluniverse: i.e. by being autonomous, or having operational closure, and by beingcoupled structurally.

The implications of these developments in cognitive science for sociology arepotentially interesting because of the possible analogies which may exist withthe many cases of self-organization in human societies:

q autonomous systems, though in this case simulated on a computerwhose coupling with the environment is specified by input-outputrelations, and thus by an outside source, give meaning to their

interactions on the basis of their own history, rather than on the basis of the intentions of the programmer – or should one say a manipulatingenvironment?

q neural networks, and possibly other networks like human networks aswell, produce emergent phenomena as a result of both simultaneousprocesses (the emergent pattern itself arises as a whole) and sequentialones (par ticipating components have to engage in back-and-forthactivity to produce the pattern[45, p. 98]).

Self-reference The phenomenon of self-reference is assumed to be typical of human beings,both on the individual and the group level, although recent work with apes

seems to open up the possibility that they too may have some degree of self-reference. Nevertheless, self-reference – at least in the sense used here – is not aconcept in first-order cybernetics, which – as Norbert Wiener stressed soexplicitly – concerns itself with the commonalities between man, animals andmachines, rather than with the differences between them. Three meanings of self-reference may be distinguished in this respect:

(1) the “neutral” meaning, which is used also and especially in first-ordercybernetics, and is also applicable to non-biological systems, where “self-referencing control” indicates that any changes in the state of a system




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are dependent on the state of that system at a previous moment, likebirth rate being dependent on population size;

(2) the “biological” meaning, where senses and a memory are the minimumrequirements, and where a self-referential system can be defined as asystem that contains information and knowledge about itself, that is, itsown state, structure and processes; like, for example, human beings[47];

(3) the “stronger” second-order cybernetics meaning used here, where thesystem – whether an individual or a social system – collects informationabout its own functioning, which in turn can influence that functioning;minimal requirements in this case are self-observation, self-reflection and

some degree of freedom of action.One of the main characteristics of social systems, distinguishing them frommany other systems, is indeed their potential for self-referentiality in the lattersense. This means not only that the knowledge accumulated by the systemabout itself in turn affects both the structure and the operation of that system,but it also implies that in self-referential systems like social systems, feedback loops exist between parts of external reality on the one hand, and models andtheories about these parts of reality on the other hand.

Concretely, whenever social scientists accumulate systematically newknowledge about the structure and functions of their society, or aboutsubgroups within t hat society, and when sub sequently they make th atknowledge known, through their publications or sometimes even through the

mass media – in principle also to those to whom that knowledge pertains – theconsequence often is that such knowledge will be invalidated, because theresearch subjects may react to this knowledge in such a way that the analysesor forecasts made by the social scientists are falsified. In this respect, socialsystems are different from many other systems, including (most?) biologicalones. There is a clearly two-sided relationship between self-knowledge of thesystem on the one hand, and the behaviour and structure of that system on theother hand.

Biological systems, like social systems, admittedly do show goal-orientedbehaviour of actors, self-organization, self-reproduction, adaptation andlearning. But it is only psychological and social systems which arrivesystematically , by means of experiment and reflection, at knowledge about theirown structure and operating procedures, with the obvious aim to improve these.This holds true on the micro-level of the individual, as in psychoanalysis orother self-referential activities, and on the macro-level of world society, as inplanning international trade or optimal distribution of available resources.

For social scientists, the consequences of self-referentiality are interesting notonly for gaining an insight in the functioning of social systems, but also for themethodology and epistemology used to study them. There is a paradox here:the accumulation of knowledge often leads to a utilization of that knowledgeboth by the social scientists and the objects of their research – which maychange the validity of that knowledge[48,49].



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The usual examples of self-referential behaviour in social science consist of self-fulfilling and self-defeating prophecies. Henshel[48], for example, hasstudied serial self-fulfilling prophecies, where the accuracy of earlierpredictions, themselves influenced by the self-fulfilling mechanism, impacts onthe accuracy of the subsequent predictions. In much of empirical social scienceresearch, however, self-referential behaviour does not loom large – which israther amazing in view of its supposedly being an essential characteristic of individual human functioning. However, if one does not see UFOs, this neithermeans they are not there, nor that one is blind. In this case the researchmethodology used may be an issue: survey research, where people are askedwhat they think or feel, offers little opportunity to bring out self-referential

behaviour, while depth interviews, which concentrate more on the “why” thanthe “what” of people’s opinions have a better chance to elicit self-referentialremarks in this respect.

Self-steering The futility of large-scale and detailed planning efforts has led to the increasingrealization that both individuals and organizations are to a large extent self-steering. After all, perfect planning would imply perfect knowledge of thefuture, which in turn would imply a totally deterministic universe in whichplanning would not make any difference. While recognizing the usefulness of efforts to st eer societies, a cost-benefit analysis , especially in t he case of intensive steering efforts, will often turn out to be negative: intensive steering

implies intensive social change, i.e. a long and uncertainty-increasing timeperiod over which such change takes place, and also an increased chance forchanging planning preferences and for conflicts between different emergingplanning paradigms during such a period. Nevertheless, given a few humancognitive predispositions, there unfortunately seems to exist a bias foroversteering rather than understeering[50].

An historical overview of planning efforts concludes that – in spite of intensified theorizing and energetic attempts to create a thoroughly plannedsociety during the last two centuries – the different answers given so farregarding the possibility of planning cancel each other out. There is even noconsensus about a formal definition, though usually planning is seen as morecomprehensive, detailed, direct, imperative or expedient when compared with

other steering activities which are not defined as planning. Increasedknowledge about human (i.e. self-referential) systems often does not help us toimprove our planning of such systems. Aulin[51,52] tried to answer two basicquestions in this respect:

(1) Should one opt for the “katascopic” or the “anascopic” view of society; inother words, should the behaviour of individuals and groups be plannedfrom the top down, in order for a society to survive in the long run, orshould the insight of actors at every level, including the bottom one, beincreased and therewith their competence to handle their environment




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more effectively and engage more successfully in goal-seekingbehaviour?

(2) What should be the role of science, especially the social sciences, in viewof the above choice: should it try mainly to deliver useful knowledge foran improved steering of the behaviour of social systems and individuals,or should it strive to improve the competence of actors at grass rootslevel, so that these actors can steer themselves and their ownenvironment with better results?

To answer these questions, Aulin followed a cybernetic line of reasoning whichargues for non-hierarchical forms of steering. Ashby’s Law of Requisite Variety

indeed implies a Law of Requisite Hierarchy in the case where only the survivalof the system is considered, i.e. if the regulatory ability of the regulators isassumed to remain constant. The need for hierarchy, however, decreases if thisregulatory ability itself improves – which is indeed the case in advancedindustrial societies, with their well-developed productive forces andcorrespondingly advanced distribution apparatus (the market mechanism).Since human societies are not simply self-regulating systems, but self-steeringsystems aiming at an enlargement of their domain of self-steering, there is apossibility nowadays, at least in sufficiently advanced industrial societies, for acoexistence of societal governability with ever less control, centra lizedplannning and concentration of power.

As the recent breakdown of the Soviet Union and its gigantic planning

apparatus 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 theintellectual processes dealing with information are self-steering – and not onlyself-regulating – and consequently cannot be steered from the outside bydefinition. In other words: there should be no excessive top-down planning, andscience should help individuals in their self-steering efforts, and certainlyshould not get involved in the maintenance of hierarchical power systems.

Of course, this is not to deny that there is a type of system within a societythat can indeed be planned, governed and steered, but this is mainly becausesuch systems have been designed to be of this type in the first place, i.e. toexemplify the concept of the control paradigm of first-order cybernetics –although even in first -order cybernetics control does not necessar ily implyhierarchy, as even the simple case of the thermostat mentioned beforedemonstra tes. After all, in most developed countr ies, and even in manyunderdeveloped ones, the ra ilways r un on t ime, in spit e of self-steeringemployees being involved. Most armies, though also replete with self-steeringindividuals, are still based on st rict hierarchical control and neverthelessfunction reasonably well, although it has to be admitted that modern,technologically sophisticated and information-driven armies[53] seem to thrivemore on bottom-up initiative, while armies that explicitly incorporate such self-steering principles and bottom-up initiative in their training – like the Israel



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Defence Forces – are among the most successful. Thus, while there certainlystill is a limited place for organizations that exemplify a more or lesshierarchical control paradigm, modern, complex multi-group society in itsentirety, conceptualized as a matrix in which such systems grow and thrive, cannever be of this type.

If one investigates a certain system with a research methodology based onthe control paradigm, the results are necessarily of a conservative nature;changes of the system as such are almost prevented by definition. According toDe Zeeuw[54] a different methodological paradigm is needed if one wants tosupport social change of a fundamental nature and wants to prevent “post-solution” problems; such a paradigm is based on a multiple-actor design, does

not strive towards isolation of the phenomena to be studied, and likewise doesnot demand a separation between a value-dependent and a value-independentpart of the research outcomes.

Autocatalysis and cross-catalysis Laszlo[55,56] describes two varieties of (chemical) catalytic cycles: autocatalyticcycles, in which the product of a reaction catalyses its own synthesis, and cross-catalytic cycles, where two different products or groups of products catalyseeach other’s synthesis. An example of a model of a cross-cata lytic cycle,developed by Prigogine and colleagues (see Laszlo[55,56], is the Brusselator :

(1) A→ X

(2) B + X →

Y + D (3) 2X + Y → 3X

(4) X → E

With X and Y as intermediate molecules, there is an overall sequence in whichA and B become D and E . Step 3 can be seen as autocatalysis, while steps 2 and3 in combination describe cross-catalysis. Autocatalytic sets (A→ B → C →…→ Z → A) bootstrap their own evolution, provided the complexity of interactions is rich enough; the system then changes from a subcritical to asupercritical state, and autocatalysis follows. Kauffman[57,58] even uses theconcept to explain the origins of life from a “primal soup” of simple chemicalelements as an inevitable production of order, rather than as a unique andextremely unlikely historical accident. Simple chemical laws coupled with thepresence of a sufficient number of frequently interacting elements produce evermore complex elements, with new characteristics, that often turn out to be partof new catalytic processes at higher levels of molecular complexity – processeswhich in turn boost the emergence of still higher levels of complexity. Alongsimilar lines, Swenson[59] likewise maintains that – in spite of the Second Lawof Thermodynamics – “the world is in the order production business”.

The economist Arthur[60], collaborating closely with Kauffman at the SanteFe Inst itute, applied the concept of autocata lytic sets to the economy: theeconomy too bootstraps its own evolution, as it g rows more complex over time.




23

Beyond a certain critical threshold, phase transitions occur; stagnantdeveloping countries can enter the take-off stage when their economy hasdiversified sufficiently. Increased trade between two countries in a subcriticalstate can similarly produce a more complex and interwoven economy whichbecomes supercritical and explodes outward. Cata lytic effects might alsooperate in phase trans itions that are considered negative, where criticalthresholds of violence are passed as in Northern Ireland or Bosnia.

Autopoiesis Autopoiesis, or “self-production”, is a concept introduced in the 1970s by thebiologists Maturana[61,62] and Varela[63] with the aim to differentiate the

living from the non-living. An autopoietic system was defined as a network of interrelated component-producing processes such that the components ininteraction generate the same network that produced them.

Although Maturana and Varela considered the concept applicable only inbiology, and not in the social sciences, an interesting “theory tr ansfer” wasmade by Luhmann[64]. He defended the quite novel thesis here that, while socialsystems are self-organizing and self-reproducing systems, they do not consist of individuals or roles or even acts, as commonly conceptualized, but of communications . It should not be forgotten that the concept of autopoiesis wasdeveloped while studying living systems. When one tries to generalize theusages of this concept to make it also truly applicable to social systems, thebiology-based theory of autopoiesis should therefore be expanded into a more

general theory of self-referential autopoietic systems. It should be realized thatsocial and psychic systems are based on another type of autopoieticorganization than living systems: namely on communication andconsciousness, respectively, as modes of meaning-based reproduction.

While communications rather than actions are thus viewed as the elementaryunit of social systems, the concept of action admittedly remains necessary toascribe certain communications to certain actors. The chain of communicationscan thus be viewed as a chain of actions – which enables social systems tocommunicate about their own communications and to choose their newcommunications, i.e. to be active in an autopoietic way. Such a general theory of autopoiesis has important consequences for the epistemology of the socialsciences: it draws a clear distinction between autopoiesis and observation, butalso acknowledges that observing systems are themselves autopoietic systems,subject to the same conditions of autopoietic self-reproduction as the systemsthey are studying.

The theory of autopoiesis thus belongs to the class of global theories, i.e.theories that point to a collection of objects to which they themselves belong.Classical logic cannot really deal with this problem, and it will therefore be thetask of a new systems-oriented epistemology to develop and combine twofundamental distinctions: between autopoiesis and observation, and betweenexternal and internal (self-)observation. Classical epistemology searches for theconditions under which external observers arrive at the same results, and does



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not deal with self-observation. Consequently, societies cannot be viewed, in thisperspective, as either observing or observable. Within a society, all observationsare by definition self-observations.

One of the first effort s to apply the concepts of both aut opoiesis andautocatalysis to the social sciences was made by Gierer[65]. He demonstrated“empirically” – by computer simulation – that inequality can be explained asresulting from the cumulative interaction over time of the autocatalytic, self-enhancing effects of certain init ial advant ages (e.g. generalized wealth ,including education) with depletion of scarce resources. It then tur ns out thatstriking 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.

The sc ience of complexity: a convergence of paradigmsSince complex modern societies – as compared to simpler ones – are highlydynamic and interactive, and thus change at accelerated rates, they aregenerally in a far-from-equilibrium situation. According to Prigogine andStengers[66] – who distinguish systems in equilibrium, systems fluctuatingnear equilibrium through feedback, and systems far from equilibrium – non-linear relationships obtain in systems which are far from equilibrium, whererelatively small inputs can trigger massive consequences. At such“revolutionary moments” or bifurcation points, chance influences, but does nottake over from determinism and the direction of change is inherently impossible

to predict: a disintegration into chaos, or a “spontaneous” leap to a higher levelof order or organization – a so-called “dissipative structure”, because it requiresmore energy to sustain it, compared with the simpler structure it replaces.

In stressing this possibility for self-organization, for “order out of chaos”,Prigogine and Stengers[66] come close to the concept of autopoiesis. In modernsocieties, the mechanistic and deterministic Newtonian world view –emphasizing stability, order, uniformity, equilibrium, and linear relationships between or within closed systems – is being replaced by a new paradigm. Thisnew paradigm is more in line with today’s accelerated social change, andstresses disorder, instability, diversity, disequilibrium, non-linear relationships between open systems, morphogenesis and temporality . Prigogine and Stengerscall it the science of complexity [66, p. 209]. It is exemplified by, among othersPrigogine himself, Maturana[61,62], Varela[63], Laszlo[55,56], and “second-order cybernetics” in general: i.e. the (non-mechanistic) study of open systemsin interaction with their observers.

Social scientists, often still thinking in terms of linear causality, would bewell-advised to really study Prigogine’s theoretical approach and t ry out theexplanatory powers of his conceptual vocabulary – fluctuations, feedback amplification, disssipative structures, bifurcations, (ir)reversibility, auto-andcross-catalysis, self-organization, etc. – on the phenomena they study. Thisholds true as well for the concepts and methods of second-order cybernetics ingeneral, as discussed in the foregoing. It is already, however, quite difficult to




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apply first-order cybernetics – which also fully recognizes non-linearities – tosocial science data sets, and it may seem virtually impossible to do the samewith second-order cybernetics; we will come back later on the reasons why thisis the case. But indeed, second-order cybernetics is a paradigm that does more

justice to the constantly emerging novel complexities of ongoing humaninteraction, and does not postulate simplistic assumptions about the constancyof human behaviour.

The name one gives to this paradigm, or rather this convergence of paradigms over the last two decades, is a matter of secondary importance.What is reassuring in this novel and, therefore, risky field of research is thatthere seems to be indeed a convergence of paradigms: all the blind men seem to

have their hands on the same elephant. We have generally called this field heresecond-order cybernetics; but it might also be designated by other names likecognitive science, general systems theory, AI, artificial life (AL), or perhapsindeed most aptly the science of complexity. Its main points should be clear bynow:

q Complexity is in the software, not in the hardware; it is in the structurerather than in the elements making up the structure, in the way simplebuilding blocks are organized as a result of simple laws, and not in thebuilding blocks themselves.

q The emergence of complexity is a bottom-up process, without anycentral controller leading it, rather than a top-down one; it is a matter of

local units, acting according to local laws, producing new levels of complexity by interacting.The interesting new field of AL[67,68], demonstrates these points by

means of computer simulation. The flocking behaviour of birds, forexample, has been simulated with amazing accuracy by computer“boids” following three simple rules: maintain minimum distance fromother “boids”; match velocities with other “boids”; move towards thecentre of the mass of birds.

AL is the opposite of conventional biology: it tries to understand lifeby means of synthesis rather than analysis. It assumes, as stated above,that life is not a property of matter, but of organization of matter. Livingsystems are viewed as machines, with one difference from othermachines: that they a re constructed from the bottom up. Complexbehaviour does not need to have complex roots. On the contrary, top-down systems are forever running into combinations of events they donot know how to handle. Lindenmayer and Rozenberg[69], Prusinkiewiczand Haras[70] and Prusinkiewicz and Lindenmayer[71] simulated leavesof totally different plants by changing only slightly the bottom-up rulesfor their construction. There is no ghost in the machine: a population of simple elements following equally simple rules of interaction can behavein always surprising ways. The AL people are convinced that life is notlike a computation, but that it is a computation.



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q What can easily be demonstrated in computer simulations of neuralnetworks goes for human networks as well: the more densely they areinterconnected, the less likely they are to cycle through a limited numberof states, or to ever repeat the same state. The more interdependencegrows, the less likely it becomes that history will ever repeat itself, andcan therefore be more or less forecasted on t he bas is of previousexperience.

Growing interdependence implies increasing communication. AsLeydesdorff[72] has stated, “Communication systems change bycommunicating information to related communication systems; co-variation among systems, if repeated over time, can lead to co-evolution

(rather than evolution per se ). Conditions for stabilization of higher-ordersystems are specifiable: segmentation, stratification, differentiation,reflection, and self-organization can be dist inguished in terms of developmental stages of increasingly complex networks”.

Depart ing from Luhmann’s[65] conception of society as consisting of communications, rather t han actions of participating actors, andcommenting on Giddens’s structuration theory, Leydesdorff[73] arguescogently that the mutually conditioning relationship between structureand action can best be studied empirically by using the model of paralleldistribu ted p rocessing as employed in a rt ificial intelligence. “Thenetwork networks, and the actor acts”, i.e. the network performs its ownself-referential loops, independent of the specific actors involved.

q It should be clear by now that we have not been talking merely aboutcomplex systems in isolation – which probably do not even exist – butabout complex adaptive systems, interacting with an environment. Theyare everywhere one cares to look: brains, immune systems, ecologies,cells, developing embryos, but also sociocultural systems like politicalparties, economic systems, and even scientific communities.Holland[74,75], who was one of the first to simulate neuronal networks in1951, mentions the following characteristics[76]:

– They have many agents acting in parallel, and their control is highlydispersed, with any coherent behaviour resulting from competitionand co-operation among the agents themselves.

– They have many levels of organization, with agents at one levelserving as building blocks for the agents at the next higher level.

– These building blocks are rearranged constantly as a result of whatone might call either learning, experience, evolution or adaptation.

– They all anticipate the future to some degree, making “predictions”on the basis of “mental” models of their environment that act likecomputer subroutines under certain triggering conditions and thenexecute certain behaviours – no matter how simple, as in the case of bacteria.




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– They all have many niches they can exploit, whereby filling oneniche often opens up new ones that can be filled; complex adaptivesystems, in other words, always create new opportunities.

– As a consequence, it is meaningless to talk about complex adaptivesystems being in equilibrium: they can never get there, but arealways in transition. If they would get there, they would be dead.

– Likewise, the agents in complex adaptive systems cannot optimizetheir fitness, utility, etc.: the space of possibilities is simply too vastin an environment which is also complex and rapidly changing; theycan at best improve on some dimensions, but never optimize.

Second-order cybernetics: a bridge too far?If one accepts these criteria as being valid for complex adaptive systems, andrealizes that the social sciences indeed mainly study those systems – self-organizing, self-referential, autopoietic and thus with their own strategies andexpectations, with intertwining processes of emergence and adaptation – thenone is confronted with one of the core problems of sociology, economics andother social sciences: how to make a science out of studying a bunch of imperfectly smart agents exploring their way into an essentially infinite spaceof possibilities which they – let alone the social scientists researching them –are not even fully aware of.

There is indeed quite a methodological problem here. It is a lready very

difficult to apply the principles and methods (e.g. feedbacks and non-linearities)of first-order cybernetics to empir ical social research, much more so than tosociological theory, and nearly impossible to incorporate a second-ordercybernetics approach in one’s research design. Indeed, as far as empiricalresearch is concerned, second-order cybernetics may be a bridge too far, giventhe research methodology and the mathematics presently available.

Applying the principles of first-order cybernetics in empirical researchalready poses heavy demands on the data sets and the methods of analysis:every feedback (X t → Y → X t +1), every interaction between variables (Z → (X → Y )), and every non-linear equation (Y = cX 2 + bX + a ), let alone non-lineardifferential equation (Y' = cY 2 + bY + a ), demands extr a parameters to beestimated, and quickly exhausts the information embedded in the data set.Admitting on top of that the second-order notions that the research subjects canchange by investigating them, let a lone being aware of the fact that thesesubjects may reorganize themselves on the basis of knowledge acquired bythem during the research, exceeds the powers of analysis and imagination of even the most sophisticated methodologists: it equals the effort to solve anequation with at least three unknowns.

In the case of second-order cybernetics these problems indeed multiply: howdoes one obtain reliable data within such a framework, where nothing isconstant and everything is on the move, let alone base policy-relevant decisionson such data? How can one still forecast developments when at best



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retrospective analysis of how a new level of complexity has emerged seemspossible? Certainly, these are problems that are far from solved, and a lot of work lies ahead before hypotheses derivable from second-order cybernetics willbe fully testable. Nevertheless, the opportunities offered by this paradigm topresent a truly realistic analysis of the complex adaptive behaviour of interacting groups of agents seems to good to pass up.

But the inherent problem remains: the more realistic – and therefore lessparsimonious – a theory, the more complex it becomes, and the more difficult totest the hypotheses and subhypotheses derived from it which are used incollecting and interpreting the data. If one accepts that social systems have ahigh degree of complexity, cybernetic theories become more relevant and

fitting, but less testable as they grow more complex themselves, as is the casewith second-order cybernetics as compared to first-order cybernetics. There iscertainly a challenge here, for theorists and methodologists alike.

For the time being, sociology should perhaps model itself more onmeteorology than on the nat ural sciences, and force itself to give up theambit ion to make accurate medium- and long-term predictions, except indelimited areas of research where complexity is still manageable or can be moreor less contained. Ex post facto explanation of how things have come to be asthey are is already difficult enough for social scientists nowadays. The best theymay do at the turn of the millennium is to get a grip on the underlying laws of change, perhaps by a theory transfer from those subfields within biology wheresecond-order cybernetics was developed, and consequently to develop further

the theories, the non-linear mathematics and the simulation techniques requiredto investigate the growth of complexity of human society.This might ultimately result in adequate and empirically falsifiable models of

self-referential, self-steering and self-organizing actors on individual and supra-individual levels, interacting with each other in ever more intricate networks todevelop new and unforeseen higher levels of complexity, with new actorsengaging in new activities, speeding up the growth of complexity even more.The best one can do as sociologists under these circumstances seems to acceptthat there is not any one desirable and sustainable state for society – only near-continuous transition, often coupled with the impossibility to forecast even thenear future – and that consequently one can engage at best in some degree of damage control, by pointing out the probability of future catastrophes to thosewho might be able to help avert them.

Unfortunately, interdisciplinary and international-comparative researchcentres to study complexity-related problems, though sorely needed, barelyexist as yet, although the systemic rather than piecemeal approach they couldprovide is required by the sheer complexity and interdependence of present-daysocietal problems. This is partly due to a lack of political commitment to financethem on the part of politicians who have to be re-elected every four years, butpartly it is also caused by the fact that most researchers are not yet educated towork in truly interdisciplinary teams that presuppose an open mind, and atleast a reasonable knowledge of the other disciplines involved.




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Ashby’s Law of Requisite Variety states that only variety within a system canforce down the variety due to the system’s environment – at least if the systemis fully to make sense of the latter and to be able to steer it. Both the

international political system and the international scientific system still have along way to go in this respect, and are barely organized yet as reasonablyintegrated systems that are aware of their policy options. Grass roots pressurefrom below, such as is now visible regarding environmental pollution problems

in most of the Western “television-democracies”, is probably required to forcepoliticians and scientists alike to finally get their act together and to star ttackling other complexity-related problems as well.

Notes and references

1. Geyer, F. and Van der Zouwen, J., “Norbert Wiener and the social sciences”,Kybernetes , Vol.23 No. 7, 1994, pp. 46-61.

2. Geyer, F. and van der Zouwen, J., “Cybernetics and social science: theories and research in

sociocybernetics”, Kybernetes , Vol. 20 No. 6, 1991, pp. 81-92.

3. Deutsch, K.W., The Nerves of Government: Models of Polit ical Communication and Control , The Free Press of Glencoe, New York, NY, 1963.

4. Easton, D., A Framework for Political Analysis , Prentice-Hall, Englewood Cliffs, NJ, 1965.

5. Buckley, W., Sociology and Modern Systems Theory , Prentice-Hall, Englewood Cliffs, NJ,

1967.

6. Buckley, W. (Ed.), Modern Systems Research for the Behavioral Scientist: A Sourcebook ,Aldine, Chicago, IL, 1968.

7. Burns, T.R. and Buckley, W., Power and Control: Social Structures and their

Transformation , Sage, London, 1976.

8. Burns, T.R., Baumgartner, T. and DeVillé, P.,Man, Decisions, Society: The Theory of Actor System Dynamics for Social Scientists , Gordon and Breach, New York, NY, 1985.

9. Burns, T.R. and Flam, H., The Shaping of Social Organization – Social Rule System Theory with Applications , Sage, London, 1987.

10. Baumgartner, T. and Midttun, A.,The Polit ics of Energy Forecasting: A Comparative Study of Energy Forecasting in Western Europe and North America , Clarendon, Oxford, 1987.

11. Rosenblueth, A., Wiener, N. and Bigelow, J., “Behavior, purpose, and teleology”, Philosophy of Science , Vol. 10, 1943, pp. 18-24, reprinted in Buckley, W. (Ed.), Modern Systems Theory for the Behavioral Scientist , Aldine, New York, NY, 1968.

12. McCullough, W. and Pitt s, W., “A logical calculus of the ideas immanent in nervous

activity”, Bulletin of Mathematical Biophysics , Vol. 5, 1943, pp. 115-33.

13. Ashby, W.R.,Design for a Brain – The Origin of Adaptive Behavior , Wiley, New York and

Chapman and Hall, London, 1952.

14. Wiener, N., The Human Use of Human Beings – Cybernetics and Society , Doubleday,

Garden NY, City, 1956, Houghton Mifflin, Boston, MA, 1950-1954; 2nd edition, Da Capo,

New York, NY, 1988.

15. Shannon, C.E. and Weaver, W., The Mathematical Theory of Communication , 5th ed.,University of Illinois Press, Chicago, IL, 1963.

16. Minsky, M. and Papert, S., Perceptrons: An Introduction to Computational Geometr y (expanded ed.), MIT Press, Cambridge, MA, 1988.

17. Minsky, M.,The Society of Mind , 2nd ed., Simon & Schuster (Touchstone), New York, NY,1988.



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18. Arthur, W.B., “Positive feedbacks in the economy”, Scientific American , February 1990,pp. 92-9.

19. Parsons, T., Bales, R.F. and Shils, E.A., Working Papers in the Theory of Action , The FreePress, Glencoe, IL, 1953.

20. Forrester, J.W.,World Dynamics , 2nd ed., Wright-Allen Press, Cambridge, MA, 1973.

21. Meadows, D.H., The Limits to Growth: A Report to the Club of Rome’s Project on the Predicament of Mankind , Universe Books, New York, NY, 1972.

22. The Dutch translation sold no less than a quarter of a million copies.

23. Van der Zouwen, J., in comments on this article.

24. Except for the universe itself, as Luhmann has noted, since it presumably has noboundaries.

25. Watzlawick, P., Beavin, J. and Jackson, D., The Pragmatics of Human Communication ,

Norton, New York, NY, 1967.26. Sennett, R. and Cobb, J., The Hidden Injur ies of Class , Cambridge University Press,

Cambridge, 1977.

27. To prevent misunderstanding, the following should be noted:

q In circular causality, the causal feedback may be either linear or non-linear; someauthors conceive of linear thinking as merely implying that the later elements of thecausal chain are not the cause of previous elements; in other words, no causal loop ispostulated. In chaos theory, however, non-linearity means that the feedback in thedifferential equation is non-linear.

q Newton’s differential equa tions are not all necessarily linear, as in the case of themovement of three masses; these analytically unsolvable equations led Poincaré to hischaos theory avant la lettre .

28. Maruyama, M., “The second cybernetics: deviation-amplifying mutual causal processes”,

in Buckley, W. (Ed.), Modern Systems Research for the Behavioral Scientist: A Sourcebook ,Aldine, Chicago, IL, 1968, pp. 304-16.

29. Rapoport, A. and Chammah, A.M., Prisoner’s Dilemma: A Study in Conflict and Cooperation , University of Michigan Press, Ann Arbor, MI, 1965.

30. Von Foerster, H., “Cybernetics of cybernetics”, paper delivered at 1970 annual meeting of the American Society for Cybernetics, 1970.

31. Umpleby, S.A., “The cybernetics of conceptual systems”, paper prepared for the Instituteof Advanced Studies, Vienna, 8 March 1993.

32. Maturana, H. and Varela, F., Autopoiesis and Cognition: T he Realization of the Living ,Reidel, Dordrecht/Boston, 1980.

33. Maturana, H. and Varela, F., The Tree of Knowledge: T he Biological Roots of Human Understanding , New Science Library, Boston, MA, 1988.

34. Von Bertalanffy, L., General System Theory: Foundations, Development, Applications ,Braziller, New York, NY, 1975.

35. Maturana , H., “Neurophysiology of cognition”, in Garvin, P. (Ed.), Cognition: A Multiple View , Spartan Books, New York, NY, 1970, pp. 3-24.

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