Model-based Management: A Cybernetic Concept · 1984), cybernetic concepts are also transferred to social systems. For that purpose, the application of perception and epistemology

■ Research Paper

Model-based Management: A CyberneticConcept

Markus Schwaninger*University of St Gallen, St Gallen, Switzerland

The purpose of this contribution is to elaborate an integrative framework for model-basedmanagement, drawing on the concepts of cybernetics. This conceptual frame shouldenhance managers’ understanding of structures that give rise to patterns of system behav-ior, helping them to design more effective policies and improve their practice in general.We flesh out the commonalities between technical, biological and social cybernetics. Ananalysis is undertaken to make the available concepts fertile for the social domain. Theseare then synthesized into an integrative framework for a model-based, cyberneticallygrounded management. Copyright © 2014 John Wiley & Sons, Ltd.

Keywords model-based management; cybernetics; organizations; integrative managementframework; transdisciplinarity

INTRODUCTION

Since its beginnings in the 1940s, cybernetics—thescience of ‘control and communication in the ani-mal and in the machine’ (Wiener, 1948)—hasturned towards multiple fields of application. Aclear distinction differentiates technical, biologicaland social cybernetics. In addition, cyberneticschools have evolved in medicine, psychology,pedagogy, anthropology and epistemology.

The conceptual differences between the respec-tive contexts of application are often ignored(Ackoff and Gharajedaghi, 1984). This leads toimproper transfers of concepts, models andmethodologies between the technical, biological

and social domains. A paradoxical situationresults, namely, that concepts and methods areoften unduly transferred while adequate and fer-tile transmissions are not carried out. Generally,at least the domains of technical, biological andsocial cybernetics have differentiated and segre-gated themselves in a way that communicationamong them is rare. There is a ‘red thread’ thatis often neglected—the generic concepts that areapplicable to any one of these domains. Webelieve that in a time of growing dynamic com-plexity in organizational settings, new ways ofmanagement are necessary. As demonstratedelsewhere, the effectiveness of managers andleaders will increasingly depend on the use offormal models. Therefore, a model-based man-agement has been advocated (Schwaninger,2010; Grösser et al., 2014). This idea is fundamen-tally linked to the concepts of cybernetics, which

*Correspondence to: Markus Schwaninger, Institute of Management,University of St Gallen, Dufourstrasse 40a, CH-9000 St Gallen,Switzerland.E-mail: [email protected]

Received 6 June 2013Accepted 17 April 2014Copyright © 2014 John Wiley & Sons, Ltd.

Systems Research and Behavioral ScienceSyst. Res. 32, 564–578 (2015)Published online 15 May 2014 in Wiley Online Library(wileyonlinelibrary.com) DOI: 10.1002/sres.2286

are in principle an invaluable pool of knowledge tobe tapped by the community of managers andleaders.The purpose of this contribution is to elaborate

an integrative framework for model-based man-agement,1 which makes the variety of cyberneticconcepts fruitful, helping managers to reflectupon and improve their practice. Frameworksof this kind are needed and can empower execu-tives, leaders, entrepreneurs, politicians and soon—in short, managers—to be more effective.This has been demonstrated recurrently in therealm of general management, which is of inter-est here (e.g., Ulrich and Krieg, 1972; Bleicher,1996; Ulrich, 2001; Rüegg-Stürm, 2005; Martin,2007). The novelty about our approach is two-fold. First, the framework to be elaborated hereis based on a survey of the principles of cybernet-ics from its beginnings to the present. Second,this framework is directed to the specific needsof a model-based management.We will explore the basic concepts available

from cybernetics, and how they emerged. Theunderstanding of these concepts—the heuristicpower of which is undisputed—should enhancetheir fertile use whenever applications tocomplex issues or problems are at stake.The aim of elaborating a cybernetic framework

for model-based management begs for a solidtheoretical–conceptual foundation. We havedecided to build the argument starting with theorigins of cybernetics. We then proceed to sketchout the evolution of cybernetics across thedifferent fields of application. On that basis, theconceptual building blocks for model-basedmanagement will be introduced, with a view totheir relevance and embodiment in the differentapplication fields. Consequently, these compo-nents will be synthesized into an integrativecybernetic framework for model-based manage-ment. Instead of delving into the details of exam-ples on how to use the framework, we restrictourselves to highlighting its function as adiagnostic device, and its enabling function forthe transdisciplinary inquiry necessary in a

context of complexity and change. Or, in brief, theframework provides a transdisciplinary ‘code’, bywhich management can be improved. The leveragehere is based on the enhancement of managers’understanding of the system they manage.

ORIGINS OF CYBERNETICS

To shape the future, we must first understand thepast. The word ‘cybernetics’ stems from kybernetiké,the ancient Greek expression for the art ofsteersmanship; kybernétes names the steersman.Already, Plato used cybernetic ideas when he char-acterized the statesman as the steersman of society.

Later on, various precursors of modern cyber-netics showed up, particularly in the 19th cen-tury. André-Marie Ampère (1843) developed theidea of a science, which he titled cybernétique.That science embodied, within an overall systemof connaissances humaines, the general knowledgeabout governance in political space. James ClerkMaxwell (1868) with his centrifugal governor laidthe cornerstone for control theory. Modern cyber-netics was founded by several actors:

• Rosenblueth et al. (1943), with an article about‘Behavior, Purpose and Teleology’, in whichthe concept of feedback was operationalized.

• The Macy Conferences (1946–1953), which as-sembled the leading members of the cyberneticresearch community, including McCulloch,Wiener and von Foerster, among others. Thesegatherings were dedicated to the topic ‘circularcausal, and feedback mechanisms in biologicaland social systems’.

• Norbert Wiener, with his opus ‘Cybernetics orControl and Communication in the Animaland the Machine’ (1948), which is consideredthe basic opus of cybernetics.

That last title conveys the insight that the pro-cesses of control and communication are equallypresent in the technical world, nature and thesocial domain, and structurally equivalent in aspecific sense. Already, in his quoted work of1948, Wiener includes society as a universe ofinformation processes in his analysis (pp. 155ff.).

Contemporaries and colleagues of Wiener de-velop cybernetic concepts and apply them in the

1 By ‘model-based management’, we understand a management of or-ganizations, that is, governance, control and leadership sustained byformal models (see also Section on An Integrative Framework forModel-based Management).

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Copyright © 2014 John Wiley & Sons, Ltd. Syst. Res. 32, 564–578 (2015)DOI: 10.1002/sres.2286

Model-based Management 565

physiological context (Ashby, 1952, 1956) and es-pecially in neurophysiology (McCulloch, 1965).In later works by Wiener (1954) and other au-thors (e.g. Beer, 1959; Deutsch, 1969; Luhmann,1984), cybernetic concepts are also transferred tosocial systems. For that purpose, the applicationof perception and epistemology is made (Powers,1973; von Foerster, 1984) to man–machine sys-tems, conversations and learning (Pask, 1975).

In the offing at that time, a distinction betweena first-order cybernetics and a second-order cybernet-ics emerges. Heinz von Foerster in that contextdraws a distinction between ‘cybernetics of ob-served systems’ and ‘cybernetics of observingsystems’ (von Foerster and Rebitzer, 1974). Inthe first case, concepts such as information, feed-back, adaptation, homeostasis and control orgovernance occupy the centre of attention. Inthe second case, the observer becomes part ofthe observed system; interest falls on phenomenasuch as self-organization, self-reference and theconstruction of realities.

The early cybernetic studies broach mainly twoaspects that are crucial for dealing with complex,dynamic systems: communication (via languageand information) and control or governance(with its components of regulation and steering).In addition, the cybernetic perspective makespossible the inquiry into invariant structures notonly in a descriptive sense but also in a prescrip-tive one, and in a way that transcends disciplines.Here, two insights of the early cyberneticianswere path breaking: first, that one and the samestructure can be ascertained in biological systems(living beings and organisms), in technical systems(power plants, energy networks, etc.) and in socialsystems (organizations and societies); second, thatthe knowledge of these structures can be used forthe design of technical, biological and social sys-tems. The term socio-technical system therefore be-came widely adopted in language use.

These new insights were congruent with anidea stemming from general systems theory: theidea that systems of any kind can be describedand explained with one and the same formalapparatus (von Bertalanffy, 1968; Rapoport,1986). In this way, structural invariances (‘isomor-phisms’), the same principles that rule differentkinds of systems, should be uncovered. For

example, a law of exponential growth can be lo-cated equally in colonies of bacteria and in humanpopulations, in the latter with behavior patternssuch as conflict and cooperation or the progressionin the number of scientific publications. A similarcase is the logistic growth curve (‘S-curve’), whichmaps the process and the limitation of growth. It isapplied in biology, ecology, economy andsociology, for example, to technological substitu-tion processes and population development.Cyberneticians were the ones who were able toexplain dynamic phenomena of that kind as aresult of feedback processes.2

Feedback is used in virtually all scientific fields,from physics, chemistry and ecology to the socialand economic sciences, as a principle for the expla-nation of system behavior and the design ofsystems. This is also the case in applied disciplinessuch as engineering and management.

Cybernetics takes insights and concepts fromindividual disciplines (e.g. Boltzmann’s entropyformula from statistical mechanics) and brings itinto a larger context. In other words, it opensthese insights or concepts by means of generali-zation into new fields of application. For exam-ple, in the information theory by Shannon andWeaver (1949: 51), an equation for the computa-tion of entropy that is practically identical withthe Boltzmann formula can be found.

Cybernetics has also made a topic accessible toscientific analysis, which, up to that point, hadbeen reserved to metaphysics—teleology, that is,the study of goal orientation. Feedback is a mech-anism that provides goal direction, as Rosenbluethet al. (1943) showed in conceptual terms.

TECHNICAL, BIOLOGICAL AND SOCIALCYBERNETICS

In this section, we sketch out how the three applica-tion domains of cybernetics—technical, biological

2 Feedback is that process in a system by which an outcome variable isredirected—normally via a control system (regulator and governor)—as an input, such that the object system’s behavior changes. Hence, thesystem changes itself. The complementary concept of feedforward de-notes an information process by which a disturbance is registered exante, before it impinges on the object system, and the respective infor-mation is directed to the control system, which thereupon can changethe system anticipatively (Figure 3).

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566 Markus Schwaninger

and social—have evolved and differentiated them-selves (Figure 1). The focus will be on the middlecolumn, named ‘cybernetic thread’. As cyberneticsalso has ramifications into the other fields of thesystems approach, the other two columns will beput into play, also.

Technical Cybernetics

Cybernetics in the first place became very prom-inent in the technological domain. Its principlesbecame the fertile soil for breeding technicalapplications. An outstanding development inthat domain is control theory (Figure 1, leftstrand). This discipline is practised today in al-most all areas of technology. It plays a crucial rolein most disciplines of engineering. Its domains ofapplication encompass not only simple machin-ery (e.g. thermostats and mechanical governors)but also sophisticated systems of propulsion

technology, safety engineering (e.g. antilock brak-ing systems (ABS) and power plant control), energyand transportation technology (e.g.wind turbines,trains and signal technology), aircraft and spacetechnology, computer and communication technol-ogy and robotics. In the automation of production,control and information processes, a huge increaseinmodel building, simulation and algorithmizationhas occurred (von Känel et al., 1990).

Also inspired by technical cybernetics, severalpragmatic (inter)disciplines have emerged, suchas micro-system technique, man–machine sys-tems and systems engineering. These disciplineshave in common a commitment to the holisticdesign of complex systems, following an interdis-ciplinary approach. The field of man–machinesystems, for example, rests on ergonomy,cognitive science, software and control theory(Johannsen, 1993). Bionics, which is also funda-mentally influenced by cybernetics, is bound tolearn from nature for the formation of technical

Figure 1 Overview of the systems approaches. An earlier version of this diagram was published in Schwaninger (2009b)




systems, and recently also for the organization ofsocial systems (Figure 1, middle). Disciplinessuch as the last mentioned are rooted in technicalcybernetics, but beyond the technological con-text, they also include humans in their designapproaches.

Biological Cybernetics

After technology, cybernetic thinking has alsospread throughout biology, physiology and ecology.Prominent applications are in neurophysiology(McCulloch, 1965; Figure 1, middle) and medicine(Brown, 1985; Tretter, 2005). The biocyberneticianFrédéric Vester, originally a physician, succeededin applying cybernetics to physiological processes(Vester, 1976). Later on, he openednewpaths to cop-ingwith ecological issues concernedwith cyberneticmethods, fuelled by his transdisciplinary approachto systems analysis (Vester, 1999). Finally, Maturana(a physician) and Varela (a biologist) have to bementioned, who carried out cybernetic researchin the area of biology (e.g.Maturana and Varela,1973) and accomplished studies in epistemology(Maturana and Varela, 1987; Varela et al., 1991).

The design of social systems, including the socio-technical systems, can learn from the functioning ofhigher-order ecosystems. Their functioning caninspire planners, politicians andmanagers in bring-ing about a livable world.

Social Cybernetics

The application of cybernetics to social systemsalso developed after the cybernetics of technolog-ical systems. A foundation was laid by StaffordBeer with his pioneering work ‘Cybernetics andManagement’ (1959). Beer (1979, 1981, 1985) isthe father of management cybernetics. Heexpanded his ideas concerning the applicationof cybernetic principles to the management of or-ganizations and framed his viable system model,which specifies the necessary and sufficient pre-conditions for the viability of any organization.Beyond that, Beer (1994) came up with the TeamSyntegrity protocol—a model for the organiza-tion of social processes that enhances the

effectiveness of collaboration in large groups(‘infosets’). In the wake of Beer’s work, manyapplication-oriented and methodologicalcontributions to organizational cybernetics haveaccrued, which often refer to the viable systemmodel (e.g.Gomez, 1978; Espejo and Harnden,1989; Clemson, 1991; Espejo et al., 1996;Hoverstadt, 2008; Malik, 2008; Türke, 2008;Schwaninger, 2009a; Espejo and Reyes, 2011; PérezRíos, 2012) (Figure 1, middle).

Gordon Pask with his conversation theory(1976) and his work on learning (1975) madeimportant contributions to the cybernetics ofhuman, social and socio-technical systems.Gregory Bateson (1973, 1980) in his oeuvreconnected studies that are epistemological in na-ture with other inquiries into human and socialsystems (Figure 1, right strand).

A milestone on the way from first-order cyber-netics to second-order cybernetics is the first opusabout principles of self-organization (von Foersterand Zopf, 1962). Studies published therein (in par-ticular Ashby, 1962) opened a new perspective: thepostulate was—similar to the new physics(Heisenberg, 1959: 93)—to include the observer inthe cybernetic system. The protagonist of thisconceptual innovation was Heinz von Foerster,the director of the Biological Computer Laboratoryat the University of Illinois. Von Foersterformulated the lead difference already mentionedbetween ‘observed systems’ (cybernetics I) and‘observing systems’ (cybernetics II; von Foersterand Rebitzer, 1974), which would be seminal forthe investigation of social systems.

The most comprehensive project for thegrounding of a system theory or cyberneticsof social systems was undertaken by the sociol-ogist Niklas Luhmann. In his opus ‘SocialSystems’ (1995/1984), a detailed blueprint fora theory system was presented, which wouldbe conferred to the various functional subsys-tems of a society (e.g. Luhmann, 1990, 1994).In line with the programme of cybernetics II,autopoiesis, self-organization and self-referencewere among the topics recurrently addressedby Luhmann. Luhmann’s pupils have pursuedhis argument and have translated it mainlyinto the organizational context (e.g.Willke,1996; Baecker, 2003).




Finally, under the term ‘soft systems’, diversemethodologies have come about, which weredeveloped for applications in the realm of organi-zations (Figure 1, right strand).The three domains of reality just discussed

exhibit very different features. Even so, cyberneticsis applicable to all of them, owing to its abstractnature and focus on the invariances inherent inall kinds of systems. We will now try to elicit theconcepts by which these invariances are captured.

CYBERNETIC CONCEPTS—BUILDING BLOCKSFOR A FRAMEWORK OF MODEL-BASEDMANAGEMENT

If it is so that the science of cybernetics is applied toall three phenomenal domains, two questionsspring up: which cybernetic concepts are usedthe same way in all three object domains? Andthen, are any concepts specific to single domainsand therefore cannot be used in transcending thedomains?In science and in practice, differences between

the three domains are often negated. As an exam-ple, men and machines are often equated, that is,they are understood as interchangeable systemcomponents that react deterministically to exter-nal impulses. Consequently, this position isprominently represented in the dawning modernera by LaMettrie, the French exponent of a mate-rialistic, mechanistic world view and author ofthe opus ‘L’Homme Machine’ (1748). This stanceis also present today among certain decision the-orists, for example in their idea that organisms,organizations and adaptive machines are notonly similar but also functionally equivalent(Crowther-Heyck, 2005). In economics, the mech-anistic metaphor has had a paradigmatic signifi-cance from the era of mercantilism (17thcentury) until the neoclassical era (Ötsch, 1993).In our day, the inclination to design and steer or-ganizations as if they were trivial machines is lessa problem of theory than of the practical world.3

However, these forms of reductionism have been

refuted. For both the human and organizationalrealms, the non-trivial agent who can react toimpulses in unforeseeable ways is a more appro-priate concept (after von Foerster, 1984). RussellAckoff and Jamshid Gharajedaghi (1984) haveargued cogently for a distinction between thedomains mentioned earlier.4 They differentiatethem as follows:

• The mechanistic model: It implies that a sys-tem can be understood completely, if one un-derstands the relationships between its parts.These are considered as sufficient for the ex-planation of the connection between causeand effect. The system behaves deterministi-cally. Applied to organizations, the mechanis-tic model conforms to the image of ahierarchically structured system governed cen-trally by a totally autonomous authority.

• The organismic model: It is based on the ideaof a system that depends on its environment.In order to survive, it must adapt and learn.Survival is the highest goal, for the attainmentof which growth is essential. Shrinkage is asynonym for degradation and decay. An or-ganismically conceived organization is struc-tured hierarchically, but as thinking andsensing are separated from each other, gover-nance is not completely centralized. Someparts show certain measures of self-control,but they cannot control the functions theymust perform.

• The model of social systems: In contrast to anorganism that can change its structure only toa limited extent, but nevertheless survives, asocial system exercises nearly full control overits structure. The effective management of a so-cial system does not require the control of themutually independent parts, but rather of theinteractions among these parts and the interac-tions between the system and its environment.Analysis alone is insufficient for the study ofsuch a system; it must be complemented bysynthetic thinking. As the parts of an

3 The ubiquity of bureaucratic controls and hurdles (Kanter, 1983: 56f.)and the re-engineering movement (Hammer and Champy, 1993) areprominent manifestations of mechanistic design.

4 Formal analytical methods enable one to describe systems of verydifferent types—nonliving systems, organisms and organizations—by one and the same set of formulas (Rapoport, 1968). Even so, we stillface the aforementioned differences between the types, independent ofour belief, whether or not they can be eliminated in principle andtherefore will be outdated sooner or later.




organization have their own goals and values,the deterministic model, according to whichthe causal relationships are exhaustivelyknown, is useless in this case.

This brief account of the distinction between thecharacteristic models of different domains leads tothe conclusion that distinct ideas of governancehave to be employed. Neither the mechanisticnor the organismic model is sufficient for dealingwith social systems. Oft-heard ideas such as ‘theorganization must function like a machine’ or‘the company is like an animal’ are inopportune.

This does not imply that all cybernetic con-cepts would be applicable to one single domainonly. Rather, most of them are relevant to all threeof them, albeit with different nuances. We willproceed to a survey of the concepts of cyberneticsas they emerged in the evolution of the field. Webegin with those concepts that are ubiquitousacross the whole range of domains of application:

• Feedback and feedforward: These phenom-ena are found in all three domains. One differ-ence is that social systems show a probabilisticcausality, whereas mechanical systems aremostly deterministic.5

• Information: The concept of information ishighly relevant independent of the domain. Thecybernetic definition of information as ‘that whichCHANGES us’ (Beer, 1979: 283) implies thatinformation and mere data are different things.Information is the component of a message thatis new to the addressee. Hence, it alwaysemerges in a receptor that notes ‘a differencewhich makes a difference’ (Bateson, 1973: 286).

• Control, governance: These abstractions referto the backward-oriented process of regulationand its forward-oriented counterpart—steering. Both phenomena inhere in all kindsof dynamic systems.

• Learning: This refers to the adoption of newbehaviors into the repertory of an organism,concretely as the acquisition of knowledge,competencies and skills. First-order learningoccurs by regulation in a process of error

elimination, whereas second-order learningarises by changing the goals or the mentalmodels. Finally, learning to learn better has beentermed ‘meta-learning’ or ‘deutero-learning’.Besides the existence of learning organismsand organizations, learningmachines have beenestablished since Turing (1950: 454ff.).

• Self-organization: A behavior that emanatesfrom the elements and structures of an evolvingsystem. It gives rise to forms that are neitherexternally devised nor imported. In line withthe principle of redundancy, self-organizing sys-tems show no separation of organizing, design-ing or controlling parts. Self-organization wasidentified first in chemistry and biology. In thetechnological domain, self-organization occursin multiagent systems. Finally, social systemsare self-organizing, as can be shown, for exam-ple, in politics and in socio-technical networks.

• Evolution: Evolutionary theory deals with thechange of the inheritable attributes of apopulation from generation to generation. Muta-tions increase the variety of types (➔ Variation).Whenever the individuals of a population differin regard to one or more attributes, a selectionmechanism provokes certain individuals toprocreate successfully with a higher probability(➔ Selection). Survival then is a consequence offitness. In this process, variation and selection en-hance the adaptation of a population in its envi-ronment. The concept of evolution stems frombiology (Darwin, 1859), but it also pertains to so-cial (e.g.Nelson and Winter, 1974) and technicalsystems (e.g.Rechenberg, 1973).

Certain concepts are connected mainly withthe organismic–biological domain, withoutexcluding technical and social systems applica-tions completely, as follows, for example:

• Adaptation: In organisms, adjusting to exter-nal influences is an ‘automatism’. It manifestsitself as changes in structure and behaviorfollowing external stimuli. In social systems,adaptation gains an eminently creativecomponent—reaching out to co-produce theenvironment.

• Homeostasis: This refers to themaintenance of astate of equilibrium in an open dynamic systemby means of an internal process of regulation.

5 In causal relationships, determinism refers to the view that cause andeffect—due to preconditions—are unequivocally predetermined andpredictable. In the probabilistic case, the incidence of a result can onlybe indicated with a certain probability.




This principle also functions in social systems,but it does so through the participation of agentswith their own goals and values.

• Emergence: This refers to the spontaneous ap-pearance of new system properties, owing tothe interplay of the system components. Inprinciple, foreseeing the new features of thewhole as a function of the properties of theparts is impossible. Emergence surfaces alsoin organizations; there, unexpected develop-ments can often be anticipated.

• Autopoiesis: The process by which an organ-ism (re)produces itself and maintains its exis-tence was ascertained by biologists. It is alsorecognized as a phenomenon that arises in or-ganizations (e.g. by Luhmann), in the sensethat these reproduce themselves by means ofoperational closure.6

Two concepts inherent in human and socialsystems are as follows:

• Reflexion: This is the highest form of self-reference, that is, of the relationship of a systemwith itself. Basal self-reference and reflexivityare those forms of self-reference that can beascertained in organisms (Schwaninger andGroesser, 2012). Reflexion, that is, the thinkingof a system about itself and its environment, isa function to be found in organizations, forexample, in strategy processes: the organizationand its interaction with the environment arecogitated.

• Ethos: Stemming from the Greek term forcharacter, custom, morals and manners, ethosdenotes the moral sense of a person or a socialsystem. For organizations, it is the totality ofconvictions, namely values and norms, thatconstitutes and stabilizes the unity and iden-tity of the system.

The conclusion from this survey is that ulti-mately almost all concepts of cybernetics are insome way applicable to systems in all three do-mains. In other words, most of these concepts are

valid across the whole range of domains of appli-cation. Even the concepts that are primarilyconnected to one domain only may lend them-selves to being anchored in other domains as well.For example, emergence or even reflexion, twoconcepts that stem from biology, can also be linkedto and implemented in a machine. Think of anabstract multiagent system, which can produceunexpected, ‘emergent’ features, or a computersystem with an inbuilt higher reasoning function.

Nevertheless, a fatal pitfall occurs in applyinga concept irrespective of the nature of a domain.An ominous example of inappropriate transferof concepts is the mechanistic approach to thecontrol of social systems. Although machinesare typically controlled by goals defined centrallyand outside the system, social systems aregoverned by values and goals that emanate fromboth the system as a whole and the componentsof that system (see the models of Ackoff andGharajedaghi introduced earlier).

The question of inappropriate concept transferhinges less on the concepts themselves than ontheir interpretation and application. For our pur-pose, the interest lies in the particular nature ofsocial systems.

To take an example, the control function in asocial system is by nature fundamentally differ-ent and more complex than in technological andorganismic systems. It is polycentric andmultimedial and embraces multiple autonomous,conscious and reflexive agents. In addition, theorganization as a whole is (self-)conscious.

Despite all their differences, the applications inall domains can learn from one another. For exam-ple, this was shown by Beer (1979, 1981, 1985),whose viable system model is inspired by humanneurophysiology. Conversely, technologists alsolearn from the functioning of natural organisms,as in bionics, the application of biological princi-ples to design in engineering (Blüchel and Malik,2006; Nachtigall and Wisser, 2013).

AN INTEGRATIVE FRAMEWORK FORMODEL-BASED MANAGEMENT

By model-based management, we apprehend an or-ganization’s management (governance, control

6 Operational closure is about the circularity of operations producingoperations and must not be confounded with closeness, which impliesisolation. An organism keeps all, or a certain part, of the relations be-tween its elements invariant, despite perturbations, which stem fromthe organism’s internal dynamics as well as from its relationships withother systems (Maturana et al., 1987: 180).




and leadership) sustained by formal models. Thehigh and growing importance of such models forthe viability of organizations has beenexpounded elsewhere (Schwaninger, 2010). Ascybernetics deals with governance or control ingeneral, it makes sense to support managementwith cybernetic models, that is, abstract represen-tations of concrete systems. Models are one, if notthe, crucial factor for the results that can beattained with the management process (theoremby Conant and Ashby, 1981). If that is so, thenthe quality of the models used is extremely impor-tant. The truth and quality of the models usedhinges primarily on the correct match of the modelwith the concrete (‘real’) system in focus. Thesethoughts can be summarized as follows: ‘Showme your model and I will tell you what you canreach’. This section embodies an effort to make ex-plicit the role of models in management, and theirrelationships with the objects of management,namely organizations, and the environment.

Given the diversity of the real systems and inview of the many conceptual components, whichwe have tried to make comprehensible, an inte-grative effort is called for at this point. It shouldmake this variety productive by synthesizingthe component concepts in such a way that ahigher meaning emerges. To this end, we havedeveloped an integrative framework for model-based management grounded in cybernetics.Frameworks are models with a broad scope,and usually of a qualitative type. They have beendefined as ‘broad schemes which support the ori-entation within a wide field. A framework offersdimensions and categories, by which a roughoverview and a first location, and possibly alsoa structuring of an issue or problem, can be un-dertaken’ (Schwaninger, 2010: 1422).

The frame presented here visualizes the con-cepts introduced in the last section, in their relativepositions and with their interrelationships.

The framework embodies a synthesis of thesecybernetic concepts, all of which are at the serviceof the viability and development of an organiza-tion. The integration of these components opensout into a generic meta-model, which shouldand can be an enabler for effective management.The rationale that guided the design of thismeta-model is expressed in a ‘language of

integration’—the cybernetic notation used in thefollowing diagrams.

As shown in Figure 2, the building blocks ofthe framework are as follows: (i) the concretesystem to be managed—the organization; (ii)the environment/milieu of that system; and (iii)the model system used by the management.

We now elaborate these components and their in-terrelationships in a more detailed way (Figure 3).

In an organization (‘the system’), technologi-cal, biological and social components are inte-grated on a higher level. That system adapts toits environment, which it can also influence. Thisis in principle a homeostatic way of functioning.In addition, the system is subject to change pro-cesses, which are essentially self-organizing, andit learns. The changes are part of a comprehen-sive evolutionary mode, in which new systemproperties can emerge. Although the organiza-tion is open in the sense that it can import people,information, energy and matter, it can also beconsidered operationally closed in that it obeysan internal circular logic by which it produces it-self (‘autopoiesis’).

The model system is coupled to the real systemand its environment through multiple informa-tion and communication processes. Informationis present almost everywhere in the diagram:feedback is an information process, as arefeedforward, decision, adaption of the model, re-flexion, etc. The concept of control is present inthe notion of control model. We wish to remindthe reader that control or governance, as already

Figure 2 Building blocks of the framework for model-basedmanagement




mentioned, is the umbrella term for steering (viafeedforward) and regulation (via feedback), be-cause often this clear distinction is not made.We understand control in an encompassing sensehere: it involves more than a mechanistic correc-tion of deviations leading to first-order learning.It also entails creative processes, by which newpaths can be discovered and new optionsinvented. This often leads to the revision of goalsand mental models (second-order learning).On the basis of the information provided to the

control unit via feedback and feedforward, thatunit influences and changes the real system bymeans of decisions and actions.7 In a dynamic,high-complexity context, it is essential that de-lays in this process be minimized. The postulateof ‘real-time control’ (Beer, 1979; Hetzler, 2010)is in line with this requirement. It is not enoughto gather the current system state. Rather, the

possible effects on future reality of decisions aswell as disturbances and potential emergent phe-nomena must already be captured. In this con-nection, simulation is of utmost importance,namely for exploring action spaces and the impli-cations of alternative options, strategies andstructures in particular.

Managers and leaders always take their decisionsbased on their models, the mental model being atleast as important as the formal model. Modelsare always biased, incomplete and prone to error.Physiological and psychological perception filters,whose primary task is reducing complexity viaselection, entail such undesired side effects.Management models serve the purpose of compen-sating these counterproductive consequences of thefilters, in such away that decision-makersmay takeinto consideration even those aspects that wouldhave been ignored. These models should also fosteranalysis, synthesis and the recombination of ideas,information and knowledge, which is vital to thecreation of the new.

Model-based management is aimed at improv-ing the whole decision and implementation processby making available high-quality models. The

7 The control unit’s influence on the environment is indirect, as organiza-tion and environment are structurally coupled. Also, feedback andfeedforward from the environment do not impinge directly on the organi-zation, but only indirectly via the model. That is probably what Luhmannmeans when he claims that a system cannot be changed from outside, butonly ‘irritated’. More exactly, such irritations are internal to the system, em-anating from environmental impacts (Luhmann, 1997:118).

Figure 3 Integrative cybernetic framework for model-based management




connections between decisions and their effects onthe real system/environment as well as the conse-quent repercussions on the mental and formalmodels of the manager are critical levers for theupgrading of those models.

The most important component of a model-basedmanagement is the meta-level of self-reflexionand ethos. The former denotes the activity ofself-reference, including self-observation andwhat we call self-framing/reframing—on thepart of decision-makers: Is the model adequate?Does it answer the decisive questions? Can welearn from it? Which perceptual distortions needcorrection? Does the system change, and in whatdirection? Do the rules of the game shift? Doesthe model need modifications? These are strate-gic and developmental considerations inhabitinga long-term horizon. Finally, ethos brings in theultimate, normative component of values andnorms that should govern the organization as awhole: Are the governing values and rules stilladequate, in light of new imperatives? Do thesupreme goals need adjustment? Is a reframingor reconfiguration of the system required?Formally, the ethos is often cast in vessels suchas organizational identity, vision and mission.Independent of formal aspects, the ethos can fur-nish the crucial components that complete an ad-equate model (as postulated earlier), in that theybalance and eventually integrate the following:(i) the internal and external views and (ii) theshort-term and the long-term perspectives. Thehorizon here is very long term if not timeless.

The issues addressed here, namely processes ofself-reflexion and the formation of an ethos, call fora holistic, that is, unfragmented approach, despitethe division of labour in an organization. Transdis-ciplinary collaboration is needed. Cyberneticswiththe integrative framework for model-based man-agement outlined earlier is an enabling basis forsuch an approach.

MAKING USE OF THE FRAMEWORK

The integrative framework for model-basedmanagement supports diagnosis in the firstplace. Such a diagnostic function can discover ifan organization or other social system is

managed on the basis of a good model. More ex-actly, the diagnosis can discover whether themanagement of the organization is sufficient toensure its viability and development.

The framework furnishes the checkpoints bywhich the components of the management thatwork can be distinguished from those that do not.

This step can be made tangible by looking intothe deficits or failures that can occur. We intention-ally choose the failures: these are at the core of or-ganizational pathologies, because they tend toreveal more insights than the successes do (Malik,1982; Vedder, 1992). The pertinent diagnosticpoints will relate to the following categories8 :

(1) Structural deficits:

1.1 Dysfunctionalities of organizationalstructure, such as lack of autonomy inbusiness units, too small or too large re-dundancy/slack, lack of flexibility

1.2 Lack of or pathological self-organization

(2) Model and control deficits:

2.1 Erroneous mental models2.2 Missing or insufficient or wrong formal

models

(3) Perceptual deficits:

3.1 Insufficient feedback causing uninformedmanagement

3.2 Insufficient feedforward causing lack offorward orientation

(4) Behavioral deficits:

4.1 Lack of adaptation, misalignment withthe environment

4.2 Insufficient change, usually due to bar-riers to change and defensive routines

(5) Competence and creativity deficits:

5.1 Flaws in or erosion of core competencies5.2 Inaptness of reframing and recombination

of ideas, knowledge and information

There is a clear limitation to this use of theframework. It provides paragons for the differentmanagement functions, but it does not interpret

8 A more detailed analysis of organizational pathologies in a cyber-netic framework has been presented by Pérez Ríos (2012).




them in terms of concrete soft or hard variables. Inprinciple, the user of the framework must decidehow to evaluate each aspect of the organizationin terms of both soft and hard aspects. To give anexample, if a manager is unskilled in handlingthe interrelationships with her employees, this isa communication deficit in the widest sense. Itcould be merely qualitative (e.g. a negative atti-tude towards people), but it would also have avalue (high–low) in terms of information flow, thatis, in the feedback and feedforward dimensions.

THE FRAMEWORKASATRANSDISCIPLINARYENABLER

This study has shown that almost all the conceptsof cybernetics have their importance in each oneof the domains considered (technical, biologicaland social). The proposition formulated at the out-set, that cybernetic concepts are often conferredunduly between domains of application, holds inthe following sense: One and the same conceptcan be applied in different domains, but then it isoften interpreted in mistaken ways. In regard tosocial systems, this is particularly the case withthe control function, which in social systems is ofanother nature andmore complex than in the tech-nological and organismic domains.Social systems are scientific objects of a specific

type: if the distinctions made in this text hold, thena specific language is needed to talk about organi-zations and their management and to study them.Management needs knowledge coming frommany disciplines (Ulrich, 2001). Directing an orga-nization is not only an economic problem but alsoa sociological, communicative and ecologicalissue. Leading in an organization also has psycho-logical, technological and informational aspects.Therefore, an interdisciplinary view is fre-

quently postulated: as the problems at handovertask the individual, the cooperation of expertsfrom different disciplines is called for. Hence, theinterdisciplinary approach is supposed to solvethe complexity problem. How does interdisciplin-arity function? It rests upon the communicationbetween exponents ofmultiple fields. For that pur-pose, one person per set of twomust knowor learnthe language of the other person in that set, in

order to overcome the language problem at the in-terface (Espejo et al., 1996). Such a strategy canworkwell only in a context of low variety, for example iftwo agents cooperate with each other. If several ormany exponents of different disciplines have tocommunicate in a team, the group must agree ona common code. The United Nations and many in-ternational schools have settled on English as theirstandard of communication.

Here, we are referring to the question ofselecting a language that solves the problem ofcommunication in multidisciplinary settings.The issue here is making the necessary varietyof perspectives productive, which is necessaryin dealing with complex systems. Whenanalysing a societal problem, for example, ofpublic health, it is not enough to conduct eco-nomic, epidemiological and sociological analysesseparately from each other. Rather an integrationof perspectives is necessary. The interdisciplinarypath as defined earlier then is not functional. It isnecessary to pursue a transdisciplinary approach.In such a case, all members of the responsiblegroup use one and the same language.9 Besidesthe ethnic languages, the kind of language we areaddressing here is a code that facilitates the cap-ture of subject matters of different scientific andprofessional fields.

In principle, the formal sciences, mathematics inparticular, make available such a ‘code’. Mathemat-ics is highly precise, but not especially directed todealing with complex dynamic systems. Cybernet-ics offers a formal apparatus that was developedespecially for coping with complexity. In the senseof General System Theory (von Bertalanffy, 1968),cybernetics is suitable for representing dynamicsystems of widely differing contents and whateverdegree of resolution or size. Cybernetics is generallybetter suited for that purpose than mathematics,even if the latter is more precise.

Transdisciplinary collaboration is the operationof a multidisciplinary group on the basis of ashared formal apparatus or language. Hence,the integrative cybernetic framework for model-based management offered in the precedingsection is such a vehicle—an enabler for

9 Professional translators may be necessary to uphold the communica-tion system.




transdisciplinary inquiry, discourse and collabora-tion, bywhich an organization can copewith com-plexity more effectively. Cybernetic theories andconcepts are predisposed to support such commu-nication and cooperation. Therefore, cybernetics isa strong integrative factor for leadership and man-agement. This holds at least for all those cases inwhich organizations are at stake—systems thathave to cope with complexity and change, strivefor sustaining viability and evolve over time.

OUTLOOK

The cybernetically grounded integrative frame-work for model-based management developedin this paper is highly abstract. That is a strongpoint: it embodies a synthesis of concepts as theymight be of interest to academics. As far as prac-titioners are concerned, this frame can help thembetter ‘understand the world’ or, more down-to-earth, make sense of what they are experiencingand doing in their management and leadershipwork—an important prerequisite for improvingmanagerial skills.

The role of cybernetics in the context of model-based management is in making transparent thestructure andbehavior of complex dynamic systems,for a better understanding of the following: (i) whatis going on in the system and (ii) what is to be doneto direct the behavior and evolution of the system ona desirable course. It need not be emphasized thatthe framework has to be complemented by modelsand methods to become operational, for example,structural models and methodologies for modellingand dynamic simulation.

In this contribution, a pertinent framework hasbeen developed, on the basis of the conceptualbuilding blocks bred within the history of cyber-netics. The presented structure is embedded intoan evolutionary process and therefore still devel-opable. But even now, it already has value as aheuristic device for gaining knowledge andimproving management.

From the integrative cybernetic frameworkpresented here, one can gather an overarchingcue: ultimately, the interaction of environment,control and real systems is the driving force forprogress. Model-based management is, by and

large, a way of learning with models. In this sense,model-based management has an exceedinglyhigh potential for the improvement of manage-ment and organization. It will be worthwhilepursuing this path of research further, in orderto actualize that potential.

ACKNOWLEDGEMENTS

The author would like to thank Professor StefanGroesser and two anonymous reviewers for theirvaluable comments that have been of great helpin finishing this paper.

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Model-based Management: A Cybernetic Concept · 1984), cybernetic concepts are also transferred to social systems. For that purpose, the application of perception and epistemology