Catalysing Network Consciousness in Leaderless Groups

This is our second paper (2012 Consciousness Reframed International Research Conference)
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unpacking metadesign 3D modeling

Authors: John Backwell + John Wood

Department of Design, Goldsmiths, University of London

Keywords: metadesign | holarchy | synergy | network consciousness


This paper refers to one of a number of metadesign methods that were developed to facilitate non-hierarchical teams. It describes how a matrix framework was used to help teams to create, maintain and develop their self-identity. The primary aim is to increase what the authors call 'network consciousness' (Backwell & Wood, 2009), in which consciousness is described as a ‘low-grade system for keeping records’ (Minsky, in Horgan, 1993). This concept may be controversial as it embodies a digital, therefore, coarse-grained methodology for mapping (shared) consciousness. Also, by depicting animate and inanimate entities as agencies that are dynamic and equal in status; and by emphasizing relations rather than players, we aim to develop an emancipatory approach that transcends the dualistic mindset. Using this digital approach, data about all relations and their interdependencies are recorded as a set of signature 'profiles'. These are then aggregated as a macroscopic snapshot of the whole system that enables the system to attract implicit consensus within a given team. Furthermore, it inherently considers impact upon that beyond the remit of the team and, thereby, ‘seeding’ new and coherent behaviour without the need for top-down management.


Network Consciousness

What we call ‘network consciousness’ was a useful, if crude, method that supported our AHRC & EPSRC-funded metadesign research that began in 2005. We define metadesign as a self-reflexive framework within which teams of designers, and other experts, can re-direct the context, purpose and role of their practice, in order to orchestrate more comprehensive and integrated outcomes. It would, for example, reconcile top-down and bottom-up initiatives to create ecological design solutions that might otherwise be overlooked by politicians and scientists. One reason why ‘design thinking’ would complement politics and science is that designers are trained to change behaviour in ways that are more imaginative, direct and remedial than that of politicians and scientists. Donella Meadows has shown that the methods used by governments, i.e. agreeing targets, fiscal measures and legislation, are the least effective (Meadows, 1999). Similarly, while open-minded evidence gathering and ‘objective’ truth claims are vital aspects of science, a great deal of time has been wasted on the scientific debate about whether human activities have caused climate change, rather than on the more designerly question of how to proceed in case there is climate change. Imaginative reform is urgently needed. While current species losses exceed all levels detected at any time in the last 63 million years, it is extremely unlikely that we can meet targets agreed at the 2010 Nagoya World Biodiversity Summit (Gross & Williams, 2010; Harrop, 2011). Even if we could, there are practical reasons why this would fail to achieve their intended aims as a recent scientific paper showed. The fact that between 86% and 91% of species are undiscovered or uncharted (c.f. Mora, Tittensor, Adl, Simpson & Worm, 2011) indicates that the biosphere is far too emergent, complex and dynamic to be managed using bureaucratic terms of reference to find expedient political deliverables.


Mapping Network Consciousness

In order to achieve a necessary paradigm change, we need a radical revision of the traditional professional roles and responsibilities that keep everyone within their own comfort zone. This would entail integrating managerial and epistemological issues by ‘re-languaging’ everything (c.f. Nieuwenhuijze & Wood, 2006) within a raising of ‘network consciousness’. By this we mean the state of reciprocal awareness among critical parts of a paradigmatic system (c.f. Wood & Backwell, 2009). ‘Network consciousness’ is a prerequisite to communication and ‘structural coupling’ (Maturana & Varela, 1980) and these are pre-requisites to the survival of any living system, whether it is a biological organism, society, or corporate brand. It is therefore surprising that, in the era of ‘open source’, ‘crowd-sourcing’ and ‘sharealike’ communities we know more about individual creativity than network consciousness. Our practical experiments combine intellectual theories with somatic practices, and therefore we describe it as a field of knowing (c.f. Koestler, 1967; Wood, 2010). However, we have chosen to model it using a simplified, atomistic model of consciousness. In this respect, the authors were inspired by Marvin Minsky’s controversial claim that certain computer programs are more conscious than individual humans (Minsky, in Horgan, 1993). While some may find it shocking to make a direct comparison between inanimate, digital machines and living human organisms, Minsky’s approach enables us to map heterogeneous entities within a common framework. We think it can help metadesigners to map relational aspects of the biosphere, rather than identifying it as a set of ‘resources’.


Seeking better self-management

Another advantage of mapping network consciousness is that we can use it to analyse the way we organise ourselves. For examples, although humans have an ancient familiarity with top-down forms of management, the authors have found that fixed hierarchies are suboptimal in terms of their adaptability. There are several reasons for this. For one thing, the language/s by which a given problem is addressed tend to be chosen, or modified, by those at the top of the hierarchy, rather than by those who are closer to the immediate task in hand. An opposite version of hierarchy is what Arthur Koestler called ‘holarchy’, an organization in which the whole is governed by its parts. Functionally speaking, this means that each player, or agent, within a given ‘whole’ (or ‘holon’) must feel accountable, and act responsively and appropriately, in helping to maintain the status of the whole system. In reflecting upon an idealized model of 100% network consciousness we will therefore discuss the cultural and epistemological effects that stem from a customary focus on the individual, rather than the group. Since the Enlightenment, researchers have spent far more time thinking about individual experience, individual creativity and individual emotions than they have in coming to terms with the essentially collaborative nature of all human endeavours. How might we redress the balance, say, by re-imagining ‘consciousness’ as a phenomenon that extends beyond the boundaries of individual, social or, even, animate organisms?


An Ecological and Evolutionary Context

In developing their Gaia hypothesis (1966) Lynn Margulis (c.f. Margulis, 1988) and James Lovelock (c.f. Lovelock, 1988) found that the distinction between living and inanimate entities to be unhelpful. They showed how a coalescence of complex physical, chemical, biological, ecological, phenomenal, cognitive and metacognitive elements enables the biosphere to maintain homeostasis. Rupert Sheldrake’s (1981) work confirms that evolution not only operates at biological levels but, also, at physical and chemical ones. Similarly, Vladimir Vernadsky’s 1926 term ‘noosphere’ (c.f. Vernadsky, 1988) depicts the biosphere as a geological entity shaped by life (i.e. including collective human cognition). The popular idea of an emerging global consciousness (e.g. Pierre Teilhard de Chardin, 1959) serves to hasten its own emergence by attracting interest and investment in digital networks, etc. Philosophically speaking, it also needed its own framework of thought, e.g. Nicholas of Cusa’s (1401-1464) theory of the universe as an infinite, de-centred or omni-centric whole. Cusa believed that, while each viewpoint carries some truth, it is only true when seen in relation to other parts in the whole. Ultimately, this insight resists full description using symbolic codes of communication. However, twentieth century science has made it easier to map the consciousness of networks, with developments in connectionism, chaos theory, emergence, swarm intelligence, and network theory.


Learning from ecosystems

One benefit of mapping network consciousness is its application to (design) management. Although humans have an ancient familiarity with top-down forms of management, the authors believe that fixed hierarchies are suboptimal in terms of their adaptability. There are several reasons for this. For one thing, the language/s by which a given problem is addressed tend to be chosen, or modified, by those at the top of the hierarchy, rather than by those closer to a given task in hand. An obverse of hierarchy is what Arthur Koestler called ‘holarchy’, in which each part regulates its actions to maintain the unity of the whole. Functionally speaking, this requires each player, or agent, within a given ‘whole’ (or ‘holon’) to feel accountable, and to act accordingly. Since the Enlightenment, researchers have spent far more time thinking about individual experience, individual creativity and individual emotions than they have in coming to terms with the essentially collaborative nature of all human endeavours. In terms of increasing biodiversity, we will need to focus on whole systems and emergent outcomes, rather than focus on leaders, ideologies and ‘truths’.


Designing for synergy

Our methodology applies some systemic mapping methods first used in medicine (Kvitash & Gorodetsky, 2003). Our basic building block for these maps uses the synergistic outcome gained by combining different ‘agents’, which may be animate or inanimate, virtual or actual. Agents may therefore be individuals, factors, material resources or problems. By choosing and combining, say, two existing agents we may expect to find three, where the third represents the relationship between the two. Metadesigners would seek to set up relationships in such a way that the relations are synergistic (c.f. Corning, 1983). Obviously, the more variables we have, the more combinations (and possible synergies) we get. However, this process cannot be scaled up too far without limiting the efficacy of the process. Mathematically speaking, the 20 in the diagram below would produce up to 190 relationships. Even with fewer links (as in this diagram), manageability reduces as complexity increases. This problem is compounded if we also combine the outcomes (synergies) with other agents to create second, third, or subsequent orders of synergy.

Fig. 1 - A mapping of selected agents - the lines depict their interrelations

It is therefore sensible to design for maximum benefits from minimum resources. A simple analysis of three-agent and four-agent systems (see figure 2) shows that, for example, where 3 players (nodes) may be combined to produce the same number of possible synergies (i.e. 3 lines), 4 players may combine to produce 6 synergies (i.e. twice this number). This is explained by a mathematical law of topology (c.f. Euler, 1751) that can be regarded as a law of universal abundance (Fuller, 1969) and harnessed in scholarship (Wood, 2000) and manifold forms of entrepreneurship (Wood, in Chapman, 2007; Wood, 2010).

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Fig. 2 - triangle & tetrahedron depicting ‘agents’ (in green) and their relations (red)

In reality, the small numbers exemplified here are far lower that what we would get if we were also to combine relations with the original agents, and/or with each other to produce meta-relationships. This may be applied at 2nd, 3rd or subsequent orders of combination. Unfortunately, four is an optimal number in cognitive terms because the human mind finds it hard to grasp more than four interdependent variables at the same time (Fuller, 1949; Cowan, 2001).

Although network consciousness emerges quickly and easily in a small, well-chosen, well-trained team, its strength may make it solipsistic - i.e. when the team becomes too inwardly focused to notice external events. For this reason we have adopted a team format of 4+1, in which the role of the fifth member rotates through the team, each acting as a temporary observer of the external context, then as rapporteur. Figure 3 illustrates a ‘focal’ group (with red text) with other levels represented providing depth, understanding and purpose to the work of the entities within this group.

Figure 3 - Focus group with replicated subgroups (fractal)

Here {A.B.C.D.E.} represents a broad initial view of a metadesign group of five agents, each with a complementary role. Each agent is assumed to have ‘needs’ (that facilitate their role function) and ‘assets’ (that service other needs within the team). The illustration shows how the fifth member agents (C and E) may link with other groups {CA.CB.CC.CD.CE.} and {EA.EB.EC.ED.EE.}. This permits a more detailed profile to implemented {CCA.CCB.CCC.CCD.CCE.}. N.B. |A.B.C.D.E.| can be viewed later as a composite agent within a larger system. As these views are part of a scalar continuum this analysis is fractal, even though groups and subgroups may be fluid, or may appear to consist of dissimilar entities. By choosing appropriate schema it would be possible to create a nested model (see Figure 4, below).

Fig 4 - Focus group with flexible sub-groups

Our research has uncovered many implications and effects at all levels. The tool seeks to highlight strengths, dominancy, weaknesses, redundancy etc. within the group by maintaining the work pattern of four engaged in core work plus one.

Agents may be combined, non-hierarchically, with any number of entities shown in the other groups. This enables the network/s to match needs with resources. In practical terms, actual liaisons are far more complex, often taking place tacitly, below the threshold of human consciousness. Some find the process unsettling, as it requires human agents to relinquish their egoism and to trust in the whole team process. The group dynamics change constantly (see figure 5).


Fig 5 - Group of 5 operating as ‘4 plus 1’ (tetrahedral group with ‘temporary external agent’)

The external agent’s temporary task normally has 4 phases of activity:

1. INITIAL DEPARTURE - Self-observation

As self-imposed isolation by the External Agent begins, s/he will be required to experience the process individuation from the team. This may enable her/him to notice possible differences/conflicts with the collective view.

2. TEAM OBSERVATION - Comparison with group consensus

Once outside the group the External Agent develops an external perspective. This may be used to re-contextualise the team’s work. It may include observations of the team and, or, of external conditions, such as threats or opportunities.

3. OUTSIDE OBSERVATION - Comparison with external conditions

Having reflected upon his/her own state of being, and conditions within his/her the team, the External Agent observes relevant external conditions. This may be to see whether the original task has changed, or to note possible ways to work with other teams.

4. RETURN - feedback and steer

A return to the team allows feedback that may provide a ‘reality check’ for the team. The next External Agent to take over may be decided by consensus, according to the prevailing circumstances (See figure 6, below).

external agent life cycle.jpg

The elements for need and asset that pertain to group entities is summarised in Table 1, below.

Entity Matrix

r1:x% of need required to be met:xxxx% of overall assets that
:xto operate in groupare being made available
r2Indicates the impactBalance of assets (the ‘currency’
:xof needs met:xis likely to be time)

r1 – Initial conditions prior to any group ‘transactions’
r2 – Balance or present status following ‘transactions’

Table 1 - Entity profile showing operational need and available assets (in matrix form)

When each entity is so represented a group profile can be similarly constructed as the matrix sum of the entities.
If we consider the relationship A~B, a benefit/loss matrix is generated that impacts upon both entities and shown as a balance in r2 providing a resultant profile following a transaction. This in itself provides for an A~B emergent resource. If we map the group transactions in discreet moments in time the net needs and assets would highlight the intra-group processes. This is particularly pertinent for the 4+1 model. We can map the transactional impact of the tetrahedronal subgroup with its six resultant synergetic outcomes. The subgroup profile can be determined as stated previously and then considered in transaction with the ‘Rotational Interface’ entity. This powerfully provides the engagement of a fifth group member without excess complexity but with stage-wise development consciousness throughout the whole process.


The authors are indebted to Mrs. Ann Schlachter, of the Metadesigners Open Network, for her generous advice and kind assistance.


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Network consciousness notes (in progress)

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