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Knowledge Cybernetics

Knowledge Cybernetics as part of Post-Normal Science

Maurice Yolles , prof.m.yolles@gmail.com

Cybernetics Society Conference, King College, July 2008

Abstract

Knowledge cybernetics is part of complex systems, and a post-normal science approach principally concerned with the development of agents like autonomous social collectives that survive throughknowledge and knowledge processes. Deriving from epistemological antecedents created by Stafford Beer and explored through notions of ontology by Eric Schwarz, a new form of knowledge management arises that is connected with the notions of Marshall and her new radical classifications for knowledge. These ideas can be closely associated with concepts of lifeworld and the ideas of communicative action by Habermas, andleads to a useful knowledge cybernetic framework. This has the capacity to relate to and develop a variety of what might be thought of as otherwise disparate theories that can ultimately be expressed in terms of knowledge.

1. Introduction

Systems theory has been developed to allow us to model what we see about us so that we can increase our understanding of the problem-solving and decision-making processes that allow us to create improvement. It is not important whether the systems are regarded as a metaphor or as real, since they provide us with templates of ideal relationships and modes of being that can be applied to the complex human activity situations that we see around us. Where complex situations are represented as systems that over time represent characteristics of durability, notions of viable systems using cybernetic principles have developed. These enable us to explain how and why such durability continues, and gives us a better understanding about the nature of the complexity. There are very few theoretical formulations for autonomous viable systems, the most well known being that of managerial cybernetics as developed by Stafford Beer (1959, 1985). However, a different approach was developed by Eric Schwarz (1994, 1997), who recognises that viable systems can pass through processes of emergence and evolution of towards complexity and autonomy. This occurs through the development of patterns: patterns of self-organisation that accommodate phenomenal change through morphogenesis and new forms of complexity; patterns for long term evolution towards autonomy; and patterns that lead to systems functioning viably through their capacity to create variety. One of the problems with Schwarzs theory is that it is does not engage with theory that relate to human activity systems, for instance from social or psychological sciences. While it provides templates for creating structures and mechanisms of viability, it has no human related content. Knowledge cybernetics is a development of Schwarzs approach to modelling viable systems, drawing on a variety of other works to fill this epistemological gap.

Knowledge Cybernetics began as a theoretical development in post-normal science, and that was inspired by the conceptual construction of Schwarz (1994, 1997). Since its formal inception in 2006, it has had a number of empirical developments. These include, for instance: the Guo (2006) study of Organisational Patterning that empirically explores the pathology and coherence of a number of corporations in China; deriving from a study by Yolles (2007) that explores cultural mapping, Jirapornkul (2009) has empirically examined Thai corporate cultural coherence; Fink (2008) is also examining culture shock and stretch in multicultural environments; finally, Chaiporn Achakul is currently empirically exploring the relationship between knowledge profiling (Yolles, 2006) and motivation.

Knowledge cybernetics is a paradigm of complex systems. Complexity has been explored, for instance, by Nicolis and Prigogine (1989) and Cohen and Stewart (1994). It is also implicit to the theory of autonomous viable systems as explored by Beer (1959, 1985) and by Schwarz (1997). Just as the system is normally seen as a metaphor, knowledge cybernetics is metaphorical in that it:

explores knowledge formation and its relationship to information;

provides a critical view of individual and social knowledge, and their processes of communication and associated meanings,

seeks to create an understanding of the relationship between people and their social communities for the improvement of social collective viability, and an appreciation of the role of knowledge in this.

In a coherent autonomous human activity system knowledge occurs in structured patterns. This provides the structure that enables the system to recognise its existence, maintain itself, and change, and its manifestations constitute systemic content. While the notion of system (attributed to Bertalanffy, 1951 through his notion of the general system) is used to explain behavioural phenomena, its cybernetic exploration derives from the work of Rosenblueth, Wiener and Bigelow (1943) who were interested in its teleogical properties that relate to its identity, degree of autonomy and coherence.

Autonomous system theory was a particular interest of Beer (1979). He recognised the practical utility of the idea of the metasystem explored by Whitehead and Russell (1910) in their logical study of formal systems, and used it as way of exploring the viability of complex social systems (figure 1) through processes of self-regulation, self-organisation and control. A consequence has been the emergence of a new paradigm with its own new frame of reference that transforms the way in which organisations can be examined. It takes us away from the simple input-output model of a system, in which the system components behave such a way that they transform the inputs into the outputs, to a model that explains how such behaviour is controlled.

Beers paradigm effectively has two dimensions: one was ontological and the other epistemological (figure 2), though his explicit interest only ever lay in the latter. While epistemological approaches enable the nature of knowledge to be explored, ontological approaches define types of being in a way that enable complex cybernetic relationships to be expressed simply. This simplicity occurs because ontology (Poli, 2001 and 2005) can be represented as geometry. To explain this, consider that a function of ontology is to define a frame of reference that topologically distinguishes between arbitrarily defined distinct modes of being through the creation of a referencing system. Within a social context, this system then provides fo$$$r the creation of a social geometry through which component properties and relationships can be expressed and analytically explored. In Beers work, the ontology was implicit (Yolles, 2004) in that it analytically distinguishes between two types of behaviour, metasystemic that is connected with worldview and knowledge, and systemic that is to do with phenomenal energetic behaviour. The exploration of epistemological elements, however, was explicit, and resulted in Beers Viable System Model (VSM) that created an epistemological approach capable of analysing and diagnosing complex problem situations. An outline of VSM is provided in figure 3, though the more usual VSM map is provided in Figure 4 (from Yolles, 1999).

System

Political

Management

(

Eysenk, 1957

)

Autogenesis: principles

of

governance

Autopoiesis:

n

etwork of processes

to

manifest system of political

thought

as structure and

behaviour

Virtual/ideate

system

Political

Mindedne

ss

(

Eysenk, 1957

)

Social

Environment

Structural coupling

with the

environment

Impulses for behavioural

/structural

adjustment

Metasystem

Political

Centripetality

(Ionescu

, 1975

)

Figure 1: Nature of Complex Viable Systems

Knowledge Cybernetics

An analytical approach of form that

separates out complex

situations into distinct

entities that have independent

modes of being

An analytical approach that

explores the

nature and limits of

the knowledge content

of a given entity

Ontological

inquiry

Epistemology

inquiry

A geometry of Being that

analytically separates

a whole into independent but

related entities, enabling

their logical relationships to

be relatively simply explored

and

establishes

Explorations of

pathology and viability

and

enables

Figure 2: Distinguishing between Ontology and Epistemology

Coordination

S2

Environment

Integration/

control

S3

Audit

S3*

Management

Operations

System S1

Futures/

development

S4

Policy

S5

Metasystem

Link to operations only to

collect deficit of S2

Figure 3: Ontological Differentiations in the Viable System Modelbased on work of Stafford Beer

S5

self

-

reference policy

S4

closure

futures/ with respect to

development