The Radical New Reality of Systems Science
Our Next
World View
New Network Vision (part 1)
'Seeing the Forests of Trees'
A Network Perspective Reveals the Relational Wholeness of Phenomena
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The concept of networks concerns how elements are connected and related to compose some 'entity'
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As a network, no thing or event is simply 'one thing' but an interconnected system of corresponding relationships
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It is these relationships between parts that order a system and give rise to its properties 'as a whole'
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A network perspective sees phenomena as 'relational fields' that necessarily derive from other, external networks
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To understand 'how things happen,' thus 'how the world actually works,' we must analyze these relationships
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Network science provides new perspectives on how to differentiate types of relationships and their effects
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Some types produce deterministically causal effects, while others result in unpredictably emergent ones.
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Some are directly measurable but others can only be inferred by their measurable effects -- thus are 'invisible'
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Learning to make these distinctions reveals how limited our understanding of complex systems tends to be
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It reveals the 'invisible emergence' of system functions, purpose, adaptation, and meaning making
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That enables us to perceive when systems are emergently self-ordering and self-directing, or have 'agency'
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Only through a network perspective can we begin to appreciate how complex adaptive systems 'make the world'
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We can characterize this 'network vision' of invisible fields of system interactions as 'seeing the forests of trees'
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Such 'seeing' requires not only science, but also metaphoric symbolism to reveal network dynamics and agency
'Trees versus Forests'
'Network Vision' as Perceiving the Invisible Relationships that Order and Animate the World
Networks as 'Relational Fields' of 'Constellated' Influence and Feedback
The concept of networks can be engaged to represent any set of associations between discreet entities, or 'parts,' that constitute some additional 'entity' or 'whole.' The basic idea is that any perceived entity or phenomenon is the expression of some network of relationships between parts, which, when viewed as a 'constellation,' can be regarded as a network of interacting influences, or 'originating factors,' that reveal the composition of 'a system.' A network 'works,' or creates particular effects, as a constellation of relationships. Here, the 'system' that is the phenomena is not the parts, nor the separate actions or influences of each part upon others, but the totality of those influences and the resulting overall interdependency of their interactions. That view can reveal not only sequences of actions or influence, but feedback loops which characterize the properties of the overall 'relational field.' But, how does one 'see' a network as the constellation of relational field?
The Inclusive 'Field Focus' of 'Network Vision'
To better perceive networks as constellated relational fields, one must overtly emphasize 'seeing' and 'thinking' in terms of 'the inbetween' of system parts. This can be approached as a difference between 'point focus' and 'field focus.' An effort must be made to notice not primarily parts and sequences but what 'happens among elements' and concurrently across a network. 'Seeing' networks as relational fields involves a shift in both visual and conceptual focus from 'things' to the relational fields between these:.
More familiar 'point focus' and the network perspective of 'field focus'
Network Structure and Types of Relational Fields
Systems science provides references for differentiating how networks are constellated as relational fields, how system parts are connected, thus how influence can move among parts in ways that generate the characteristic properties of a system. So, networks are analyzed in terms of "nodes" (parts), "links" (connections), and directional flows of feedback between nodes, across links. To gain insight into how a system 'does what it does,' even 'why,' requires understanding of network structure plus how feedback influence flows among parts.
Types of network structure connectivity: Examples of more and less hierarchical connectivity thus potential feedback flows:
Differentiating Relational Fields as Static or Dynamic, Causally or Emergently Influencing
The relational fields of networks can be configured in relatively static patterns of influence between parts, or in active, dynamically variable patterns of feedback interaction. Physical objects, like rocks and buildings, manifest as more static constellations of influence among nodes or parts, and thus tend to be more understandable as relationships of predictably deterministic causation. Complex adaptive systems are characterized by dynamically variable patterns of interacting feedback influence, which are more likely to result in unpredictably emergent self-ordering and agentic system properties. Gaining understanding of 'how a system happens' requires differentiating these factors.
Relatively static network structure of a building: The dynamically variable complex adaptive system of a forest ecology:
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Influence among network parts can flow in one direction, loop back, or flow in all directions at once:
Relational Fields in Complex Systems as 'The Forests of Trees'
The main notion here is that if we 'look' for parts and causal sequences, we will 'see' the separate entities or events of 'trees.' If we seek to perceive complex systems as 'wholes,' we must 'look' for interdependent feedback interactions that reveal emergent ordering and agentic properties -- thus the 'forests of the trees' as a relationally constellated dynamical network.
To track the relational field of a forest ecology one must first identify the parts (as in 'trees'),
then track the interactions between these, so as to 'see' the network of whole (as in 'the forest'):
Relational Fields are both Internal and External to Systems
Understanding an entity or phenomena in terms of a relational field constellation can be applied in both an internal and external sense. The relatively discreet whole of an individual wolf manifests as the network dynamics of its biological and psychological complex adaptive system components. But that bounded constellation of relationships also manifests within and as part of extended relational fields -- in relationship to other wolves and the local ecosystem within which an individual exits. Examining such extended relational networks provides information essential to comprehending the dynamics and properties of the internal ones.
An individual wolf manifests as an 'internal' relational field, which then exits by interactions with an 'external' field:
Relational Fields can become Semiotic or 'Meaning Making' System Networks
The networks of complex adaptive systems, most obviously animals and agent based super organism systems, manifest the emergent properties of interpreting data from their environment in ways that provide the system with meaningful information about how to respond adaptively. This semiotic or meaning-making activity is a profoundly significant aspect of network relationships.
Complex adaptive system networks can generate relational fields of information by 'making meaning' from data:
Network Vision beyond Science as Archetypal Analysis and Metaphoric Symbolism
This concept of 'seeing networks' as constellated relational fields can enhance awareness of how systems manifest through their network dynamics. But precise analysis of complex adaptive system networks is often not feasible. Getting a sense of how these systems 'do what they do' can be further enhanced in a similar but non-scientific manner of representation. An 'archetypal perspective' can track both the character and origins of phenomena through a relational networks of 'likeness' and derivational association. In addition, metaphoric symbolism can amplify our understanding of the interdependent dynamics, emergent ordering, and agentic properties of how complex phenomena manifest. Such symbolism is found in the imagery of many traditional cultures as well as in modern artistic expression.
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