2015 yolles&fink cybernetic orders kybernetes part 1 k 12 2014 0302

A general theory of genericmodelling and paradigm shifts:

part 1 the fundamentalsMaurice Yolles

Business School, Liverpool John Moores University, Liverpool, UK, andGerhard Fink

IACCM, Vienna University of Economics and Business, Vienna, Austria

AbstractPurpose Anticipating behaviour and responding to the needs of complexity and the problematic issuesthat they can generate requires modelling to facilitate analysis and diagnosis. Using arguments ofanticipation as an imperative for inquiry, the purpose of this paper is to introduce generic modellingfor living systems theory, and assigns the number of generic constructs to orders of simplex modelling.An nth simplex order rests in an nth order simplex cybernetic space. A general modelling theory of higherorders of simplexity is given, where each higher order responds to every generic construct involved,the properties of which determining the rules of the complex system being that is represented.Higher orders of simplexity also explain greater degrees of complexity relatively simply, and give rise tothe development of new paradigms that are better able to explain perceived complex phenomena.Design/methodology/approach This is part 1 of three linked papers. Using principles that arisefrom Schwarzs living systems set within a framework provided by cultural agency theory, and witha rationale provided by Rosens and Dubois concepts of anticipation, the papers develops a generalmodelling theory of simplex orders. It shows that with the development of new higher orders,paradigm shifts can occur that become responsible for new ways of seeing and resolving stubbornproblematic issues. The paper is composed of two parts. Part 1 establishes the fundamentals fora theory of modelling associated with cybernetic orders. Using this, part 2 establishes the principles ofcybernetic orders using simplex modelling. This will include a general theory of generic modelling.Part 3 extends this, developing a fourth order simplex model, and exploring the potential for higherorders using recursive techniques through cultural agency theory.Findings Cultural agency theory can be used to generate higher simplex through principles ofrecursion, and hence to create a potential for the generation of families of new paradigms. The ideaof conceptual emergence is also tied to the rise of new paradigms.Research limitations/implications The use of higher order simplex models to represent complexsituations provides the ability to condense explanation concerning the development of particularsystem behaviours, and hence simplify the way in which the authors analyse, diagnose and anticipatebehaviour in complex situations. Illustration is also given showing how the theory can explain theemergence of new paradigms.Practical implications Cultural agency can be used to structure problem issues that mayotherwise be problematic, within both a top-down and bottom up approach. It may also be used toassist in establishing behavioural anticipation given an appropriate modelling approach. It may also beused to improve and compress explanation of complex situations.Originality/value A new theory of simplex orders arises from the new concept of genericmodelling, illustrating cybernetic order. This permits the possibility of improved analysis anddiagnosis of problematic situations belonging to complex situations through the use of higher ordersimplex models, and facilitates improvement in behavioural anticipation.Keywords Behaviour, Adaptation, Emergence, Complexity, Systems theory, CyberneticsPaper type Research paper Kybernetes

Vol. 44 No. 2, 2015pp. 283-298

Emerald Group Publishing Limited0368-492X

DOI 10.1108/K-11-2014-0255

The current issue and full text archive of this journal is available on Emerald Insight at:www.emeraldinsight.com/0368-492X.htm

The authors thank Jose Manuel Perez Rios for his constructive comments on an early version ofthis paper.

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Genericmodelling and

paradigmshifts: part 1

IntroductionEver since Beer (1980) and Luhmann (1995), it has been useful to see societies as complexliving systems. However, it is now almost a common adage that society (and hence thesocial situations encountered there-in) is becoming more complex. An explanation comesfrom Ionescu (1975) who tells us that societies have become centrifugal, creating morecomplexity and intensity, as they have become politically centripetal thereby facilitatingunrepresentative corporate decision making as they accumulate of political power.

Complexity and modellingSo what is a complex system? A dictionary definition is: the state or quality of beingintricate or complicated, but this does not really illuminate anything. A clearer definitioncomes from the systems community where complexity represents something that hasmany parts that interact with each other in multiple ways. Yolles (1999) has elaborated onthis by defining five dimensions of complexity: computational complexity, having a largenumber of interactive parts or variables; technical (or cybernetic) complexity, havinga tangle of control processes that are difficult to discern because they are numerous andhighly interactive, and thus when there is limited future predictability; organisationalcomplexity, having a tangle of rules that guide interactions between a set of identifiableparts of a situation, or specification of the attributes that it has; personal complexity, havingdifferent and conflicting subjective views of a situation; and emotional complexityhaving a tangle of emotional vectors are projected into a situation by its participantsseen as emotional involvement.

The complexity of a social situation is dependent upon the paradigm from which itis seen. Paradigms have three attributes (Morgan, 1980) that allow this to happen:

(1) Constructs, which give a complete view of reality or way of seeing.

(2) Social organisation, which creates new schools of thought; these are accompaniedby the development of particular language, providing a guide to paradigmdistinction.

(3) Concrete tools and texts are used for processes of scientific puzzle solving.

The constructs of (1) are usually symbolically woven into a figurative pattern that isa meaningful (semantic) theory. Without (2) there is no paradigm, and new paradigmsarise when at least (1) and (2), or (2) and (3), are satisfied. Complexity in the cyberneticsparadigm highlights the concept, for instance, of relativity, where different individualsand groups play different games, have different goals, and live in different conceptualworlds (Geyer and van der Zouwen, 1991). This relativity construct has the collectivesupport of the cybernetics community (the social organisation). It has developed toolsand texts to enable individuals or groups to be related in their ways of thinking, andcompared in their key beliefs, assumptions or values (Umpleby, 1997).

To better understand complex social situations, they need to be modelled. We takea (formal) model to be a representation of a semantic theory with a formal system.This is different from a schema, which has no underlying theory and no formal system,but is simply a set of ideas that may eventually develop into a theory. Formality occursthrough language that enables a set of explicit statements (propositions and theircorollaries) to be made that enable everything that might be expressed about the beliefsand other attributes within a given paradigm to be expressed in a self-consistent way.A formal system uses language that includes terms able to create meaningfulexplanations, and formulates a set of propositions as a relationship between a set of

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predefined symbolic elements. As a result of these meanings, semantic theoryemerges. The propositions define a logic that establishes a framework of thoughtand conceptualisation, and propositions that do not require proof are called axioms.The relationships may be implicit or explicit, and defined by logical operators thatthrough the terms and symbols, creates theory. When theory is manifested asform/structure with imputed rules, a model arises. The theory may be used to resolvereal or hypothetical posed problematic issues called situations, by using analysis anddiagnosis. Analysis uses the relationships in the model to distinguish between a set ofinteractive parts of the situation, thus enabling easier study. Diagnosis examinesthe analysed situations using the theory, in order to identify situational explanations.In systems science, this is done while keeping the whole situation as the frame ofreference. Analysis and diagnosis are typically more difficult when situations are complex.

For Zeigler (1984) there is a relationship between model complexity and complexreality. This relationship may be proportional for good models that can representincreasing complexity, and the closer the model is to a perceived complex reality, themore complex is the model. Ashby (1968) recognises that a good model is one thatgenerates a satisfactory way of viewing a situation, by being able to respond to thevariety that might occur in it through the generation of its own (requisite) variety.Weinberg (1975, p. 140) tells us that a good model should have three pragmatic goals:

(1) Completeness broad enough to encompass all phenomena of interest in orderto reduce surprise.

(2) Independence decomposing a set of inquiries into non-interacting qualities inorder to reduce metal effort.

(3) Minimalness to integrate the states of situations that are unnecessarilydiscriminated in order to make inquiry easier.

Another attribute of a good model is validity, i.e. does the model have a basis that makesit well grounded in logic or truth? Thus, if a statement is valid and all of its premises aretrue, then its conclusion is also true. This is a pragmatic definition, but there aretechnical attributes of validity that contradict the conclusion. Gdels (1931) explorationof validity resulted in his Incompleteness Theorem. This says that in a semantic theoryone cannot be sure that valid statements preserve truth an outcome that is apparentlysensitive to certain conditions (Murzi and Shapiro, 2014). Murzi and Shapiro note thatany attempt to prove a positive relationship between validity and truth leads toparadox, unless reference to the system is made externally.

In an attempt to resolve the paradox practically for organisational situations, Beer(1979, p. 311) adopted Whitehead and Russells (1910) concept of the metasystem.He recognised that every social system (with its own particular paradigm andlanguage) has associated with it a metasystem (with its own particular paradigmand language) that observes, controls and communicates with the system. It shouldhere be realised that paradigms create language through the terms that they adopt andthat are used in the development of theory and its models. Any arguments relatingto issues in paradigmatic language are therefore reflected in the models that a paradigmproduces to represent itself.

Now systems operate through paradigms, and like paradigms the views of thosewho populate them are bounded. They see themselves and their models from a withinperspective due to their membership of the system, and have no access to a broaderframe of reference. This is reflected in their use of language. The language of the

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system is a bounded construct because of its paradigmatic nature. Here then, propositionsabout the language itself cannot be expressed in the language itself. Consequently,another language that offers another frame of reference is required that is over andbeyond the language being used at the time. The demand then, is that a new frameof reference is required that can encompasses the systemic frame of reference. This newframe of reference belongs to the systems metasystem. Due to the close connectionbetween language and modelling, system models need to be viewed from higher orderframes of reference for validation. Practically, Beer adopted a modelling approach inwhich the system and metasystem is considered pairwise in a dyadic interaction, thisthroughout the whole system hierarchy. Interestingly, if one were to extend the modellingapproach beyond the pairwise interactive dyad to include higher order metasystems, thento preserve Beers resolution to the paradox, the metasystem-system relationship shoulditself be seen as a coupled formal system with a meta-metasystem. An iterative argumentfor this leads to the accumulation of higher order metasystems. This is an imperative forthe general theory of cybernetic orders that we shall pursue in this paper.

Returning to complexity, Ashby (1956) argued that in complex situations systemsare more usefully explored through their overall patterns of behaviour. Such patternsmay change indeterminably. A modelling approach that can both deal with increasingcomplexity and enable patterns of behaviour to be anticipated would help resolveconnected problems. This is one of the interests of this paper.

In dealing with increasing social system complexity there is a need to model situationsas simply and effectively as possible. According to Cohen and Stewart (1995, p. 232),complex situations become simpler with emergence that can collapse chaos and bringorder to a system that seems to be in random fluctuation. This is a fundamentalproposition of systems theory, and a property of the whole (rather than its containedparts). Cohen and Stewart (1995, pp. 411-419) also refer to simplex situations that havebeen exposed to some form of emergence. Modelling complexity provides improvedmodelling through simplexity, which Cohen and Stewart note may be recognised to haveoccurred when a set of rules can be identified that can explain a situation through largescale simplicities that have developed. Effectively, simplexity refers to the dialecticsbetween simplicity and complexity.

We distinguish simplex models from others by introducing the ideas of modellingsubstructure and superstructure. Simplex models have a fundamental substructure ontowhich superstructure is erected. It is the superstructure that is responsible for generatingmodel complexity and epistemic content. We now proposed that there are orders of simplexmodel. Like squeezing a lemon to get its juice, higher simplex orders conceptuallycompress complex situations more densely while extracting more meaning from them.This occurs through the use of higher order conceptual constructs that improve thecomprehension, diagnosis and resolution of stubborn problematic issues. For Maturanaand Varela (1979), higher order explanations are justified through meaningful theoryextensions which contribute to the understanding of complex situations by expandingcontexts, thereby reducing the assumption of all things being equal. It can also improvecontext dependent anticipation. An example of a simplex variable is autopoiesis (Maturana,1970a, b; Mingers, 1995), providing a new way of seeing social systems (Beer, 1980).

Living systems and anticipationIn developing the theoretical discourse of this paper, we use Schwarzs (1994) theory ofliving systems (also see: Yolles, 2006). This sits on the foundational work of Miller(1978) which reduces the complexity of the structure and organisation of living

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systems. Miller offered seven ontologically distinct levels of living systems thatrange in complexity from the lower levels of cell, organ, and organism, to higher levelsof group, organisations, societies, and supranational systems. In exploring this heprovides a common framework for analysing the nature, condition, structure and processof systems at various levels of complexity. This ability to compress complexity throughthe development of new terminology was important to living systems theory, andprovided a theoretical basis that has since been used in social cybernetic paradigms.

Social living social systems have pathologies that affect their viability and theircapacity to anticipate their future behaviour. Anticipation is important to livingsystems since it enables them to adapt to future conditions (Collier, 2006). Thus forinstance pathologies have been responsible for the development of the 2007/2008western economic crisis which shook western socio-economic viability, where analysishas revealed conflicts of interest in regulatory bodies, inadequate control processes(e.g. the failure of regulators, the credit rating agencies, and the market itself), no control offinancial excesses, and ultimately the use of the wrong models to guide controlprocesses (Levin and Coburn, 2011). This especially includes a lack of understandingof the complex dynamics of microscopic processes from which macroscopic processesarise [] (Yolles and Fink, 2013, p. 4). So, anticipating the future is pathologydependent, but it also requires a good model of a situation that is structuredetermined (Schwarz, 2001; Yolles and Dubois, 2001). Embedding anticipation intoa good model has consequences for Weinbergs view of completeness, allowing one torequire a strong structure that allows useful anticipation under complexity. So how issuch a structure created?

Rosen (1985) adopted the term anticipatory (living) system to indicate that ananticipatory model enables what we shall call dynamic projections for potentialbehaviour. For Dubois (2000) this constitutes model-based weak anticipation, ratherthan system-based strong anticipation. Weak anticipation occurs through somecognitive/mental model which may influence the systems structure. Stronganticipation occurs through the structure itself which ultimately influences itspatterns of behaviour and its viability. In Table I we indicate distinctions betweenweak and strong anticipation (Yolles, 2006).

As will be shown in due course, it is possible to represent both weak and stronganticipation together using a simplex model. This is part of a general systems modelwhich consists of a set of components (like subsystems and processes) to representa complex whole, working together for some purpose(s). Some of these componentswill be generic: conceptual constructs that provide perspective, and represent anentire systemic function (i.e. actions as sets of behavioural processes, or activities,

Anticipationtype Explanation

Weak Based on a model of the system and produces theoretical prediction based onstructural properties. As illustration, strategic management involves weakanticipation because it is model-based and involves an interpretation of theenvironment that occurs from an examination of behavioural perturbations

Strong Connected to operative management, and influenced by strategic managementOperative management involves strong anticipation that conditions(facilitates and constrains) the way that the organisation responds toenvironmental perturbations of its behaviour

Table I.Distinction between

weak and stronganticipation inself-organisingsocial systems

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or purposes). The systems model will ultimately have a generic structure composed ofgeneric constructs and non-generic positioned residues. A simplex model forms itsgeneric substructure, while other components form its superstructure. One of thepurposes of this paper is to present a family of simplex models which reflect differentorders of complexity, characterising properties of the cybernetic space to which theybelong. The superstructure added to any simplex model is determined by particularcontextual modelling interests, and adds epistemic content.

Simplex modelling and orders of cyberneticsAn order of simplex model sits in a cybernetic space of the same order. The cyberneticspace maintains generic rules that facilitate model building, and in the case of simplexmodelling, through the use of generic constructs. These have a capacity in a givenmodel to compress complexity by more effectively explaining attributes of complexsituations. There is a relationship between cybernetic order and generic constructorder, which this paper will explore in terms of simplex modelling.

Low orders of simplex modelling will be seen to be related to Piagets (1950/1972)concepts of learning set within a living system context. One aspect of this forVon Glasersfeld (1983) is instrumental learning, which may be explained as follows.A system is instrumental when it strategically manifests its goals or aims operatively.It learns instrumentally when strategic models that facilitate behaviour are modifiedby experience, where antecedents and consequences are related. This is distinct fromcognitive learning where knowledge is developed through experience and accumulatedor adjusted.

Learning requires anticipation (Von Glasersfeld, 2003, p. 7). This is elaborated on byDubois (1998), who links anticipation with control in modelling and simulation.He identifies incursive control which results in system stabilisation. He also identifieshyperincursive control, which is concerned with managing multiple possible scenariooutcomes. Boxer and Cohen (2000) explain that Dubois work in anticipative systems canbe formalised as second-order metatheories (formal descriptions of systemic genericstructures). This lies, they say citing Dubois (1995), at the basis of a third order cybernetics.

Structure of the paperThis paper has three parts. In it we take a generic modelling perspective to describesimplex orders of modelling substructure that characterise the cybernetic spacein which it sits. Overall we shall explain that: first order cybernetics is concerned withinter/action and self-organisation. Second order cybernetics is concerned with instrumentalgeneric inter/action in a social living system, permitting self-production, adaptation, andself-organisation. Third order cybernetics is concerned with cognitive generic inter/actionin a social living system, additionally permitting self-creation and novel adaptation.These and higher order cybernetic spaces will in due course be explored.

Using a modelling process that originates with Yolles (2006) and sits on the dynamiccomplex systems platform of Schwarz (1994), we shall:

(1) consider the nature of generic structuring;

(2) recognise that generic construct properties are responsible for what Cohen andStewart refers to as the rules of simplexity; and

(3) define orders of simplexity where higher orders are reflective of higherdegrees of complexity.

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We shall also propose a general theory of potential paradigmatic evolution throughincreasing orders of simplexity. This adopts principles of cultural agency theory(Yolles, 2006; Yolles and Fink, 2011), where agency has the properties of purpose, teleology(autonomy, coherence, and identity), self-organisation, self-reference, self-reflection,self-regulation, self-organisation, cultural self-reference, and adaptation.

This structure is represented across the three parts of this paper. In this part 1 weconsider the need to anticipate behaviour for purposes of social system viability.We then develop a simplex order modelling approach, where each higher order hasgreater conceptual compression capacity to increasingly simplify complex situations,improve the capacity for anticipation, and improve capacity to analyse and diagnose.

In part 2 of this paper we shall provide examples of first to third order simplexmodelling. We shall also propose a general theory of orders of simplex modelling.We use the principles of both recursion and incursion, where recursion is associatedwith formal modelling and incursion with informal modelling and qualitative inference.It is through recursion that orders of simplex modelling is defined, and throughincursion that qualitative (informal) inferences occur that can contribute to modellingconditions and determine modelling parameters.

In part 3 of the paper we shall create a fourth order simplex model. We shall thenargue, with illustration, that the implicit emergence of meaningful higher orders ofsimplex modelling have been responsible for paradigm shifts, each radically alteringthe way in which complexity is viewed. We can offer an illustration of this. It is possibleto refer to the influence of communicative action (borrowing the term from Habermas,1987) where purposeful action is predicated on social communication and agreement.Thus, social influence may be modelled as a first order cybernetic process involvingcommunicative action, when it is therefore seen as an environmental attribute.However, modelling social influence as a higher order attribute in a fourth ordercybernetic process gives it fundamental importance to the nature of self, as an elementof an autonomous system. The consequential dynamics of the whole assembly therebychanges. Changing the way in which complexity is viewed, using high order invariantconstructs to process information, can thus result in radical resolution of previouslystubborn problematic issues. We shall finally examine cultural agency theory in termsof simplex order generation.

Generic modellingGeneric modelling can be represented through orders of simplexity, each order sittingin its own cybernetic space of generic rules. While references to first and secondcybernetic orders are relatively common (e.g. Glanville, 2004), and sometimes to thirdcybernetics (Taschdjian, 1978; Boxer and Kenny, 1990; Boxer and Cohen, 2000;Pockock, 1999; Yolles and Dubois, 2001), there is no general theory of higher cyberneticorders. Here, our interest will be to create a general theory of simplex order that isreflective of cybernetic orders.

A simplex model is a modelling substructure that sits together with a superstructureof systemic constructs that involves epistemic content. The substructure is composedof generic constructs, of which there are two classes:

(1) Invariant generic constructs are ontic and represent a dynamic networks of(real) processes that manifest orders of agency attributes across related statesystems that occupy some part of a defined supersystem. The networks areinvariant in their epistemic (processing) nature, but the order that they take

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refers to rank in a hierarchy the meaning which can change with context.Operating in the supersystem, the construct may be explained as a semanticmanifold that acts as a channel between at least two ontologically coupledindependent domains with systemic states. While the invariant generic constructmanifests epistemic content dynamically between the domains, its nature is notsubject to epistemic variation. Examples are autopoiesis (Maturana and Varela,1979; Mingers, 1995) and autogenesis (Csnyi and Kampis, 1985), which areconstructs that can each, respectively, be seen as a network of first and secondorder processes that manifest meaning across the independent domains.While contextual frames of reference may change for invariant generic constructs,the nature of their relative manifesting functions do not.

(2) Variant generic constructs are an interconnected ontological assembly of statesystems in which meaning can vary as its epistemic properties change withcontext, thus making them semantically susceptible to recursive processes.In other words, variant generic constructs have the capacity to change becausethey are state systems with context sensitive epistemic content.

We can now offer a proposition: a simplex order is a system substructure defined by thegiven number of invariant generic constructs, with variant generic construct that act asthe invariant construct complement. One could not exist meaningfully without the other.There are always the same numbers of invariant generic constructs (if one includesfeedback as a collective entity) as there are variant generic constructs.

We have referred to recursion above. This constitutes a modelling procedure thatcan be repeated indefinitely. Glanville (2002, p. 25) defines it as a backward movementor return: e.g. a process by which the response to a statement raises that statementagain. It relates to all things that are applied to themselves, including the cyberneticsof cybernetics. It has also been defined by Yolles (1999) as the application of a wholeconcept or set of actions that occur at one systemic level of consideration to a lowerlogical systemic level of systemic consideration. It may also be argued in the followingway. If action as a functional operator is applied to some object/subject at one focus ina system hierarchy, then applying the same action to an object/subject at a lowerfocus constitutes recursion. However, any epistemic content that is part of those actionslikely changes with context during the transformation from one focus to the other.So, recursion is facilitated through the capacity of variant context-sensitive genericconstructs to change.

Beer (1959) adopts a proposition of viability important to recursion in viable systems:that every viable system contains and is contained in a viable system. Thus, consider anautonomous organisation with departments and divisions. This would enable Beer tosay that each department is autonomous and interactive in its division, as is each divisionin the organisation. It should be noted that the use of the word autonomy here may bereplaced by more or less autonomous since the nature of autonomy is a subjectivelyassessed relative concept (Beer, 1979, p. 119; Schwarz, 2001). To illustrate recursionunder this proposition, Beer (1979) models an organisation using his Viable SystemModel.Thus, to investigate a change in the department of an organisation such that itsviability is preserved, he might define two levels of recursion within the organisationssystem hierarchy. This enables the autonomous organisation to be evaluated forviability through its autonomous division, this through its autonomous departmentof interest. The division is thus seen as an autonomous subset of an autonomousorganisation, as is the department of the division.

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Philosophical positioning in cyberneticsThere are a number of philosophical positions that may be relevant to discussionsabout cybernetic orders. These include positivism, post-positivism, radicalconstructivism and constructivism. In positivism the real world can be objectivelyknown, activities in it can be explained and predicted, and regularities are investigatedin terms of causal relationships between constituent elements. In post-positivism(Fischer, 1998), cause and effect are explored from the perspective of inquirers who maysee an objective reality differently due to their distinct cultural experiences anddifferent worldviews. Myers (1999) contends that most post-positivists areconstructivists who believe that we each construct our view of the world throughour perceptions of it. Perception and observation are deemed to be fallible, and so ourconstructions of it are imperfect. Objectivity is also a social rather than individualcharacteristic involving critique across a subject area.

Constructivism originates with Piaget (1950/1972) who sought a general pattern ofcognitive development in children. As a result he created a Genetic Epistemologyinvolving knowledge acquisition (Lesh and Doerr, 2003). Taber (2006, p. 131) notesPiaget and Inhelders (1973) demonstration that children who have not undertakenformal instruction might still construct their own ideas about their world experiences,and create their own meanings for words while developing language. Constructivismtherefore establishes a capacity for the self-construction/self-creation of knowledge(Boot and Hodgson, 1987) not embraced by post-positivism. While constructivism ispopular today, Taber (2006) notes that the literature is not coherent about its nature,with its conceptualisations becoming rarefied away from its original context andmeaning. In the same vein, Osborne (1996) notes that constructivism suffers froma flawed instrumental epistemology, and has confused the manner in which newknowledge is made with the manner in which old knowledge is learned. Notions of truth,he declares, have also been replaced by the concept of viability, leading to a failure todistinguish how one idea might be more viable than another denying objectivity andrationality. Constructivism, he tells us, also fails to recognise its own limitations.

Close to both post-positivism and constructivism, Von Glasersfeld used what he calledradical constructivism to explain some principles of cybernetics. Unlike in post-positivism,knowers are agnostic about their being an objective world, and relativism and the role ofknowledge in adaptability are central tenets. Von Glasersfelds (1995, p. 51) view of radicalconstructivism may be explained through two propositions:

P1. Dynamic Knowledge Adaptation: knowledge is not passively received, but isa process of dynamic adaptation towards viable interpretations of experience.

P2. Relativism: the function of cognition is adaptive, and serves the subjects organisationof the experiential world, not the discovery of an objective ontological reality.

In P1 the knower: is agnostic towards an objective reality (since we have no way ofknowing what that reality might be), and unlike constructivism, does not necessarilyconstruct knowledge of a real world (Dougiamas, 1998; Tural, 2006; Tuncer, 2009).However, the phrase not necessarily is not explained, leaving a semantic gap. To gainclarity here, we note Von Glasersfeld (1995, p. 121) statement:

[] this first person is assumed to be a constructor of knowledge. Thus the question ariseswhether the active agent, the subject that is supposed to reside in this first person, canspontaneously construct knowledge of him- or herself. It has often been said that it cannot,and that self-knowledge arises only from interaction with other persons.

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This interaction constitutes a social process. Seeing cognitive learning this way arisesfrom the work of Vygotsky (1962), who stresses the role of social interaction in thedevelopment of cognition and the construction of meaning. Von Glasersfeld (1995, p. 141)used the social aspects of Vygotskys theories on learning with selections of Piagets.One can accommodate the distinctions between Vygotskys and Piagets paradigms byrecognising the apparent conflict of whether knowledge creation is an individual orsocial construct. This conflict can be subdued by recognising that knowledge creationarises instrumentally in the first instance, from a strategic thought processes that hasbeen exposed to an operative environment. It occurs with conceptual emergence thatresults from a cognitive dissonance arising from a conflict between information aboutwhat known and what is perceived. This emergent virtual knowledge, as we shall call it,is coded information that can instrumentally contribute to behaviour. Virtual knowledgeis idiosyncratic and precurrent to cultural sediments of knowledge. In Piagets model,processes of knowledge self-creation occur as sediments of idiosyncratic knowledge,but there is no direct developmental normative process for this. An indirect processoccurs through operative interactions that can make knowledge sediments normativethrough experience. In Vygotskys model, social validation of virtual knowledgedefines knowledge, filters idiosyncrasy and generates normativity. The distinctionbetween Piagets and Vygotskys approaches therefore seems to lie in whetherknowledge creation is deemed to be the result of an idiosyncratic or a normativeprocesses of sedimentation. In Piagets model, knowledge self-creation is possible, butin Vygotskys it is not an immediate facility since knowledge is social rather thanindividual. Idiosyncratic virtual knowledge may contribute to adaptation, but it will notnecessarily improve viability. Social filtering of virtual knowledge creates normativeknowledge, which can then contribute to both social adaptation and viability. In suchsituations, adaptation may be seen socially as novel, implying that the recognition ofnovelty is ultimately a social phenomenon.

Thus, it is not only knowledge, but also virtual knowledge that influences behaviour.When new socially filtered knowledge is embraced, the knower has identified with it, andthis requires self-reference. This circuital explanation to knowledge creation couplesVygotsky and Piaget by explaining the creation of knowledge in a more indirect way.In radical constructivism knowledge self-creation should therefore be seen as a socialfiltering process that is dependent on instrumental virtual knowledge. Without otherexplanations, social filtering may be inferred to be a first order cybernetic processes ofsystemic interaction through processes of communication (for instance as discussed byVon Glasersfeld, 1995, 2003). However, as we shall see in due course, there is potentiallymore to this as one moves to fourth order cybernetic modelling that ensures that onerecognises the social role in creating self-identity.

The distinction between Von Glasersfelds and Piagets conceptual interests is thatthe former was more concerned with adaptation, and the latter learning. In radicalconstructivism, knowledge self-creation is not strictly speaking a requirement, thoughvirtual knowledge creation would appear to be. This is because the self-creationprocess is not a requirement for adaptation, even though knowledge self-creation is inprinciple admitted if socially filtered. This is in contrast to constructivism, whereknowledge construction/creation is an important component of learning and thecreation of novelty. It requires knowing about the contingencies that constrain andchannel knowledge interconnections as this occurs.

In P2, relativism is an epistemic position that clarifies the scope and limitationsof knowledge, and this can be explored in terms of observers and the observed.

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For Glanville (2002) there is a need to include the observer in the creation of anunderstanding of the dynamics of a set of interactive objects, and the circularrelationship between the observed and observer. The observer also has a perspectivethat biases what is observed. Objectivity is therefore ameliorated. Glanville (2004,p. 1384) assigns this to second order cybernetics, and tells us that the distinctionbetween the first- and second-order cybernetics depends [] on a change in attitudeto the observer who, in second-order cybernetics, is understood to be both within thesystem being described and affected by it. Second order cybernetics is thus seen asdealing with observing systems and their subjectivity. System subjectivity, for Bruiger(1998), represents in the system itself statements about itself. These subjectivities mayresult in different representation of the objective real world, or a recognition that the realworld is constructed by observers.

Glanville (2004, p. 1380) asserts that the radical constructivism of Von Glasersfeld(1987) questions what there is to observe, and if it has not been observed by a givenobserver situation undecidability results. Undecidability is discussed by Boxer andCohen (2000, p. 21) in terms of adaptability, where an agent is capable of successfullyadapting to its environment by tracing out trajectories through a space of agenttheories via successive elaborations of its articulations. A theory of an agent maytherefore be formulated (by an observer of it) as a formal representation of itsarticulations that constitute a formal theory. A formal theory would have to includeformal models of the (observed) models of the world of the agent and of theirtrajectories, and should be able to account for three kinds of error in these models.In reference to Boxer and Cohen (2000), these errors include:

Correspondence, in which the agents models fail to anticipate its experience. Coherence, where an anticipated elaboration of the agents articulations renders

the theory itself internally inconsistent, resulting in the need by the observedagent of a change in its theory.

Undecidability, where an agent faces multiple possible elaboration of itsarticulations, each of which induces mutually inconsistent closures within whichto anticipate. The nature of closure, according to Van de Vijver (1999), is thatit provides stability, giving protecting from colonising stimuli, leaving opennessto new potentially meaningful stimuli, and providing a context from which tointerpret surroundings.

We have noted that the distinction between radical constructivism and constructivismis that the former does not explicitly require self-reference, while the latter does. This isbecause constructivism relates to the maintenance of identity (Boudourides, 2003), andhence external reference is also an attribute (Luhmann and Fuchs, 1994). If self-reference isnot an explicit part of the radical constructivist modelling process, then self-reference andexternal reference cannot be formally distinguished. However, a model may have inferredself-reference attributes when it may become associated with informal model qualities.This distinction between the formal and informal only becomes interesting when radicalconstructivism is deemed to involve (socially filtered) knowledge creation.

Glanville is interested in self-reference as part of a whole assembly, but it has notbeen said whether this might just be an implied attribute. Where it is not a formalattribute of a model taking an explicit part of the modelling process, models are notconstructivist. Where radical constructivism is deemed to only embrace instrumentallearning, the radical constructivist label may be seen as a misnomer, making it now

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perhaps even closer to post-positivism. In the same way that Spering (2001) seesthat communication events are an implied part of a cultural framework, we argue thatself-reference can be an informally inferred attribute associated with a living systemthat conditions modelling. The distinction between formal and informal inclusionis important since it determines whether knowledge self-creation is an explicit part ofthe modelling process, or just implied. We shall accept here that radical constructivisminformally embraces knowledge creation, arising as a consequence of first ordercybernetic social communication that occurs internally to a plural social system.

Von Glaserfsfeld provides no explanation of how the process of social filteringcreates normative knowledge in radical constructivism. To understand this, we need toreturn to the principles of learning as represented by Piaget (1950/1972), in manyrespects similar to the simpler approach by Agryris (1976) who proposed the idea ofsingle and double loop learning. Agryris identification of two loop learning levelsplaces a cybernetic stress towards learning, different from Von Glasersfelds interest inadaptation. Learning may be seen as a system control process that conditions thesystem for improved adaptation and viability. This leads us to consider three questionswhich this research may be able to respond to:

While Agtryris has talked of two levels of learning loop, how many levels oflearning loops actually exist, and if so what do higher order loops mean forcybernetic modelling?

What is the role of the learning loop, in particular as a control and discoveryprocess for cybernetic modelling, and how does it enhance our understanding ofreality?

How can one select starting points for multiple learning loops?

ConclusionThis paper begins by discussing social complexity and the need to model it simply.It argues that complexity is dependent on the paradigm that views it, leading to anidentification of the characteristics of complexity. The nature of the paradigm was alsoconsidered, and that it can be associated with the idea of conceptual emergence, whennew paradigms may rise. This should, it was argued, enables us to formulate a generaltheory of generic modelling, set within the framework of orders of simplex modelling,which has its seat in the work of Dubois (1998) on incursive and hypericursiveanticipatory systems.

How models respond to complexity was discussed, arguing that a good modelis more capable of representing a complex situation. The nature of a good model isdiscussed, and one of its attributes, validity is shown to be a problematic conceptbecause of Gdels Incompleteness Theory. This results in paradox when exploringvalidity unless the situation identified in a system is associated with a metasystem thatobserves, controls and communicates with it.

So, modelling complex systems ideally requires a distinction between the systemand the controlling metasystem. But this is not enough to model social complexity.Rather, there is a need to simplify complexity. To do this leads to a discussion ofsimplexity, the nature of which is to represent a dialectic between simplicity andcomplexity. This idea, taken from Cohen and Stewart and linked with cyberneticorders, leads to the concept of orders of simplexity that emerge from complexity.The requirement, then, was to determine what the structure of orders of a simplex

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model might formulation of simplex orders might be. This is resolved by looking at theneeds of anticipation as discussed by Rosen, and then by Dubois, and recognising thatanticipating behaviour in living systems is structure dependent. The way in whichthis recognition contributes to the development of the theory in this paper is to takeit that orders of cybernetics provide a propositional environment for the creation ofanticipatory models that is, models of a system the structures of which broadlydetermine their patterns of behaviour. Until now the discussion of cybernetic orders inthe literature has been an arbitrary process.

Linking simplex modelling to cybernetic order results in the idea of simplex order.A simplex order constitutes the substructure of a model that resides in an order ofcybernetics space with cybernetic rules that are determined by that space. The simplexsubstructure is determined from the paradigm within which it is composed, typicallyusing axiomatic propositions that do need to be shown to be true. Any additionalmodelling attributes constitute its superstructure that creates epistemic content for thesimplex substructure, though these typically might require some form of validation.Building on from this, simplex models were described as generic models, and the natureof generic variables was explained.

Finally, the philosophical underpinnings associated with cybernetic/simplex orderswas considered, distinguishing between positivism, post-positivism, radicalconstructivism and constructivism. It was explained that first order cybernetics ispositivism, second order cybernetics is radical constructivist, but with the caveat thatlearning is instrumental. Third order cybernetics is seen as constructivist.

In the next part 2 of this paper we will set up three cybernetic orders of simplexmodel, and then formulate a general theory of simplex orders. Following this in part 3of the paper we shall illustrate a fourth order cybernetics, and discuss how recursivemodelling can generate higher orders.

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Corresponding authorDr Maurice Yolles can be contacted at: [email protected]

For instructions on how to order reprints of this article, please visit our website:www.emeraldgrouppublishing.com/licensing/reprints.htmOr contact us for further details: [email protected]

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