The Radical New Reality of Systems Science

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Perceiving Phenomena as Corresponding Relationships through 'Network Vision' 

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The 'Field Perception' of 'Network Vision'

Network science is a fundamental aspect of overall systems science. It enables us to better perceive things and phenomena as expressions of how connected parts influence each other, thus 'correspond' to manifest as 'an entity.' It prioritizes concern with the overall relationships between parts or elements, rather than focusing on parts in isolation and considering them to collectively constitute 'the whole.'  Rather, it seeks to reveal the corresponding actions and interactions among parts as the actual manifestation of a system. That orientation involves a fundamental shift from a 'point focus' concerned with 'seeing parts' to a 'field perception' that is more concerned with what happens 'between' the parts.

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Point Focus: 'seeing things'                                      Field Perception: 'seeing relationships'

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This emphasis upon the 'connective in-between-ness' of phenomena can be understood in terms of how one literally perceives the 'image' of a painting or a landscape 'as a whole.'  One tends to perceive a 'continuity of diverse and contrasting yet corresponding elements.' There is no  particular 'sequence' or 'hierarchy,' no particular 'progression' or 'process' inherent in the image 'as a whole.' 'An image,' as a constellation of correspondent relationships, is a networked system of associations among parts, which is, thereby, 'more than the sum of its parts.' We 'experience' an image or landscape as a 'relational whole' rather than as separate parts of color and forms.

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​An image or a landscape is a relational constellation that becomes a 'synergistic whole'--

the 'properties' of which are not 'in the parts' but 'between' these:

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This 'way of perceiving' is obviously reflexive. But it has been greatly de-emphasized by how our reductively mechanistic modern mentality 'thinks about' or conceives 'how the world works.' We are conditioned to 'see' and think in terms of material things, and events arranged in sequences of causes and effects.  Indeed, that way of perceiving is fundamental to our reductive science. Yet, surprisingly, that same mentality has led to the emergence of systems science and its 'relationally holistic' insights arising from in part from network analysis.

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'Field Perception' and Insight into Complex Adaptive Systems

Most importantly, network science and this notion of 'network vision' facilitate understanding how particular types of interacting influences can enable the unpredictably emergent self-organization and self-direction of complex adaptive systems. Only by examining the totality of flows of influence among system parts, and the ways these simultaneously 'feed back' into each other to generate unpredictably emergent effects or traits, can we gain better understanding of how self-organization emerges and can result in the 'agency' of system self-direction.

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Perceiving System Networks as 'Relational Fields'

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Networks as the Constellation of Relational Fields

A network perspective focuses upon how parts of an entity or system are 'connected' by the effects or influence these exert upon each other. From this perspective, all phenomena are constituted by specific relationships between component parts -- by how identifiable parts or components 'fit together,' or the ways these 'relate to each other.' From atoms and molecules, weather events and buildings, to living cells to human societies, it is the relationships between parts that order their forms and generate their "properties" as distinct entities. In this view,  'a system' is constituted by a 'field' or 'constellation' of actions or interactions that 'connect' its parts. Thus, things or entities are not simply 'physical materials' or sequences of occurrences, but the 'wholeness' of the relationships between their components -- refered to here as a 'relational constellation.' That 'wholeness' is not a 'thing,' not a 'sum of the parts, not a 'sequence of  actions,' but a 'relational totality.'

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The Necessity of 'Seeing Networks' as Abstract Models

Given our tendency to 'see' through 'point focus' and reflexively identify entities as uniform or as 'so many parts,' and the fact that many network relationships are effectively 'invisible,' perceiving relational network constellations requires abstract modeling. To represent the relational constellations of systems, we must represent these in an abstract, schematic form that enables us to visualize how influence can move between parts. The most basic style of such schematic abstraction involves the terms "nodes, links, and hubs," along with terms for describing how influence moves across these. Of course, this is an extreme reduction of the actual complexity of real systems. But it allows for demonstrating some sense of 'network morphology,' or how connections between 'parts' or elements allows influence to move between these. So there are basic "topological" models for how network "connectivity" or how parts constellate. Then there are more image-based styles of representing connectivity and the flows of influence. The goal is to 'see' connections and interacting 'flows.'

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Basic topological models of network "connectivity":      Abstract social net graphic:           A more representational network illustration:

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The 'How' versus the 'Why' of Network Configuration and Operations

How networks form and function to produce particular effects and properties of their systems can be fairly well analyzed -- though not completely in complex adaptive systems. The latter type function purposefully to order and sustain their existence. This purposeful activity can only be understood in terms of 'why,' as in 'from what motivation or purpose.' We are familiar with the question 'why' when regarding human or even animal behaviors. But network science shows we must also regard complex adaptive systems that are not composed discreetly as biological bodies with brains in a similar manner. Large scale systems emerging from the interactions of individual persons, like societies and human institutions, or plant and animal species, like ecosystems, behave purposefully. Thus, understanding these requires both an analysis of 'how' and one of 'why.' That means applying a kind of 'psychological' perspective to these system networks.

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Network Science and the Conundrum of 'Technically Mysterious' Phenomena

Consequently, to understand how systems manifest, or 'how things happen,' we must investigate just how they result from some 'relational field' or 'constellation' of actions and interactions. That effort, however, turns out to be exceedingly challenging to our ordinary modes of perception. Indeed, it is profoundly confounding to our reflexively materialistic, mechanistic, predictably deterministic, modern worldview. It compels us to 'go beyond' thinking solely in terms of sequentially occurring, predetermining causes and effects. What is most confounding about network science is how it confronts us with factual evidence (derived from reductive quantification of the effects of self-organizing systems), that there is more to 'how things happen' than strictly deterministic causation can explain. Systems and network science take reductive analysis 'as far as it can go' in deconstructing the 'ways things happen' in complex adaptive systems. But what the resulting evidence indicates is not an inadequacy in our ability to measure and calculate, but rather, that there are dynamical relationships which cannot be fully measured and calculated. Thus, we must consider that there are indeed phenomena which are 'technically mysterious' from a mechanistically causal perspective. Through this science, we have 'reduced phenomena to the irreducible.'  How, then, are we to comprehend this conundrum? Both the insights and self-demonstrated limitations of network science indicate we require a further modality of representing this reality. And that, it turns out, involves metaphoric symbolism. But, to get to that topic, we must go a bit further into 'seeing networks.'

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What must We Perceive to 'See' Networks?

Revealing the 'Invisible World' of Systems as 'Relational Constellations'

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​​As a further overview, below are some aspects of network relationships that need to be acknowledged and tracked if we are to have a generally realistic perspective on 'what is actually happening,'

Network Relationships can be 'Visible or Invisible'

​There are many instances of connectivity between system parts, and of the flow of influence between them. A railroad network is overtly physical, as are the trains moving around it. Electrical circuits have visible wires, across which the flow of electrical current can easily be measured. The human body has physical networks of blood vessels and nerves with similarly observable flows of influence. Nonetheless, all aspects of these elements are not entirely visible to our optical observation when we use 'point focus' to see the wholeness of a system. When it comes to larger scale systems like ecologies and societies, the network connectivity and flows of influence become fantastically complex and obscured. It is the interactions of the flows of influence, and ways these effect the entire system in unpredictably emergent ways that is the most 'invisible.'

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Network Relationships can be Sequential, Recursive, or Fully Interactive

Influence flows across networks can occur in sequentially progressive ways, or 'loop back' to create circular influences upon each other. That recursive flow can be amplified by connectivity that allows influence to flow bi-directionally between parts simultaneously. That can result in concurrently interdependent interactivity, which promotes the emergence of unpredictable effects upon the entire system. These are simple as concepts, but the combination of these dynamics in actual systems results in astonishing complexity and infinitely unpredictable variations of phenomena. In effect, a system's 'history' continues to influence it as influence recirculates among parts over time. So there is often an ongoing 'evolution' relationships that become more mutually interactive.

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Sequential influence:         Recursive feedback:         Fully interactive feedback:      'Evolving' increase in complexity of feedback loops:

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Network Relationships can have Predictably Deterministic or Unpredictably Emergent effects
More sequential influences in network relationships are relatively easy to detect and understand (or 'see') in terms of cause and effect. Recursive and fully interactive "feedback" flows produce influence that then influences the forms and further flows of influence between parts -- leading to emergent properties. Here, you can think of stock markets, in which thousands of traders are continually acting and reacting to each other's choices, all of which corresponds within the overall system to constellate its self-organizing relational totality. The synergistically emergent effects of interdependently interactive influence feedback in a network is modeled as a "hidden layer" of transformative effects. "Neural network" modeling represents the interdependenies of influence interactivity associated with emergent effects as a "hidden layer" of network relationships, which are inherently inaccessible to sequentially causal differentiation:​

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​Neural network model showing an inaccessible "hidden layer"

of interactions between measurable "inputs" and "outputs":

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Networks Relationships are 'Constellated' by "Nodes, Links, and Hubs" with "Biased" Flows of Influence

Modeling system networks involves the terms like "nodes, links, and hubs." Nodes roughly indicate distinct 'parts' of a system and links are the connections between these (a node can also be termed a "vertex," and a link a "spoke" or "edge"). Some nodes are multiply connected to many other nodes, or are connection points between 'clusters' of connected nodes. These can be identified as hubs. Hubs tend to be locations in a network where feedback flows of influence are concentrated or relayed to other parts of a network. However, what these aspects of modeling do not reveal is just how influence moves back and forth among nodes. Complex networks can regulate how and when influence flows among nodes. Thus, there are 'biases' in a complex network that 'shuttle' inter-node influence in response to changing conditions within a system or its external environment. In this way, complex networks can reconfigure how influence flows between and effects parts, resulting in changing system activities, by selectively adjusting the 'bias' of feedback flows. That is what makes complex adaptive systems adaptive. So, to 'see' networks we must somehow perceive how flows of influence between parts, or nodes, change and under what conditions -- or, how and 'why,' or for what purpose, the flows are prioritized or 'biased.'

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Networks 'Constellate' Internal Constraints that Maintain, Regulate, and Direct Systems by Selective 'Bias'

Flows of feedback influence across a network configure and maintain a system. In material objects like rocks and hammers, physical forces correspond in particular relationships to 'constrain' the underlying matter in ways that 'constellate' the object and its properties. Interdependently interactive feedback flows in complex systems configure emergent patterns of constraint on a system's operations by inducing 'biases' that redirect influence flows to alter effects upon the system. These result in sustained self-organization and purposeful self-direction, which give a system its characteristic properties. These network generated constraints can either amplify or dampen (suppress) particular aspects of a system's activity. Both amplifying and dampening of feedback flows become networked into a kind of 'cooperative opposition' from which emerge particular system traits. System self-ordering, self-regulation in response to disruptions, and adaptive self-reconfiguration, all derive from network induced constraints and a capacity to selectively configure various combinations of these. Thus, we can think of networks as patterns of constraint on the potential forms and activities of parts.

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Interactive feedback flows of influence across network:           Formation of internal patterns of constraint that regulate system activity:

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A System's Network Constellation can be Static, Dynamic, or Inconsistently Variable yet Self-Similar over Time

In some systems, the relational network is relatively fixed or static, as in the structural relationships between the parts of a building. In these systems, the influence of parts on each other is largely determined by the physical configuration of network connectivity, In complex adaptive systems, the dynamically changing flows of influence can change dramatically and even reconfigure the structural arrangement of network connectivity. Most confoundingly, this This variable, thus inconsistent, form of relational constellation associates with self-directing systems. This aspect is what can make a complex system fundamentally, quantitatively, 'more than the sum of its parts.'

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Static network connectivity/influence among parts:                Schematic of influence correspondence in ​network of a house:​

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​Dynamically interactive network relationships and feedback influence among parts:      

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​Simultaneously interacting relationships in each moment:           Evolving network relational constellation over time:

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Relational Constellations of Adaptively Self-Organizing Systems are Inconsistent yet Self-Similar Over Time

Networks become 'dynamic' as influencing flows of feedback concurrently and recursively circulate among systems parts in varying ways. Such activity can sustain relative continuity in response to disruptions so the system remains identifiably "self-similar" over time. But this variability can also be improvizationally amplified to reconfigure the system. This self-regulated variability is fundamental to the emergence of adaptive system self-direction. Thus, complex adaptive system networks can reconfigure their relational constellation relative to changing internal or external environmental conditions by selectively altering the ways influence flows are 'biased.' A body's immune system can be activated to produce significant alterations in the overall system in response to infection. Animals switch from waking to sleeping states. Caterpillars induce radical transformation of their existing relational constellations to become butterflies. These re-configurations have been described in reference to "dynamical attractors." In this concept, an existing network configuration is conceived as if it is being 'pulled or pushed' into a particular pattern. Thus, changes in a system's form or functions can be understood as if its network is 'responding to' different "attractors."

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             Self-maintaining network activity:                 Self-transforming network activity:        Changing network form as "attractor states":​

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Relational Constellations of Complex System Networks as 'The Forests of Trees'​

The above references indicate the challenge of perceiving how interconnected, interacting 'parts' can be the basis for the unpredictable emergence of a larger system network with unpredictable properties, such as self-organizing self-direction. The old saying, 'You can't see the forest for the trees' represents this paradox of separate entities giving rise to an additional system that is 'more than' the properties of the constituting entities assessed individually. To 'see the forest' we must 'see' it as a interdependent relational field of correspondences,  whose overall effects are synergistic, rather than as parts acting upon each other in predictably sequential ways. ​

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​The parts / trees:                                                                          The emergent relational whole / forest:

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Relational Networks can be Semiotic 'Meaning-Making' Systems

​​The interactively interdependent dynamics of complex adaptive systems can 'process data' about both their internal and external environmental conditions in ways that generate 'meaningful information.' Similarly, they can manifest 'signals' that communicate specific meaning to other networks. When under attack by insects, plants can emit chemicals that are intepreted by other plants, enabling their internal networks to initiate the production of compounds to ward off the insects even before these arrive. This activity is termed semiosis, in reference to how language 'signifies meaning' in mental systems. A semiotic system is a 'meaning making' relational network. It requires some form of 'memory storage' within a system that provides references for interpreting data relative to historical responses of a system to data about its operations and environment. This interpretive operation is fundamental to a system's capacity to respond selectively to external conditions. Human language is perhaps the most elaborate and abstract version of such an interpretive network. But similar semiotic activity can be identified in all animals and has even been posed as the most fundamental attribute of living systems. That notion is termed "biosemiosis."

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Complex adaptive system networks can interpret raw data semiotically,

relative to 'past experience,' to facilitate meaningful actions:​

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System Networks as Internal Relational Constellations that Manifest 'In Relationships with' External Relational Fields

As discreet entities, things or systems manifest as 'internal' fields of correspondences between parts that organize them as a relational constellation. It is the 'patterning' of the correspondences among their component parts that gives them their specific 'appearance,' traits, or properties. The actions and interactions among parts 'configure' the identifiable form. boundaries, and properties of a system. However, all systems manifest 'in relation to' external factors and environments, both 'from' and through interactions with other discreet systems. Thus, to fully understand the way a system manifests involves perceiving its 'extended relational field,' as a network in relationship to other networks. ​Complex adaptive systems, like animals and animal species, can only manifest 'within' extended or external relational networks

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​'A wolf' as internal network:            'Wolf' as extended species network:           'Wolf' as extended ecological network:

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​Extended Relational Fields as Originating, Identifying, and 'Purpose-Revealing' System Correspondences

This notion of extended relational fields is important to understanding how any given 'thing' actually manifests and what consequences its manifestation might potentially have. 'Seeing' the wholeness of a thing, event, or system requires perceiving its network as an expression of, or relationally entwined with, external relational fields. An ordinary pencil is a simple object. It is constituted by wood, metal, rubber, graphite, and glue. The ways these parts 'fit together,' or correspond, to produce the properties of a pencil as a marking and erasing instrument, are easily described in terms of physical materials and deterministic causation. However, both the origins of that pencil and just what those properties might prove to promote 'in the world' are entangled in a vastly extended external relational field of correspondences. The materials used in an ordinary pencil often originate from forests, mines, factories, and markets that are scattered around the planet. So both the materials and the production of it derive from numerous complex adaptive systems, natural and human, that are effectively intrinsic to its seemingly simple physical properties.

 

Indeed, 'a pencil' only manifests because it has been 'conceived' within the semiotic relational constellations of human mental systems -- or consciousness. Without that relationship, it is not even a 'pencil,' but only physical materials.  ​Thus, these extended network correspondences not only reveal the origin of 'a pencil, but are also crucial to its 'identity' as an object used for drawing or writing. In the absence of this extended relational field, there is no human system to create it 'for the purpose of' those activities. Similarly, a wolf, or wolves for that matter, have no identity without the extended network of an ecosystem with which their properties are interdependent: wolves must have an ecosystem and that ecosystem requires the wolves to generate its own specific relational constellation. Without the wolves, it becomes a different relational constellation.

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Relational Networks in Complex Adaptive Systems Manifest 'Adaptive Behavior' Relative to External Contexts

Complex systems are often discussed in terms of "behavior" in systems science. This term refers to how system networks self-organize in response to changes occurring either internally or externally in their environments. Complex adaptive system networks can interpret those changes in ways that generate adaptive behavior, meaning network re-configurations that 'serve the purpose' of promoting or sustaining the system's existence despite disruptions. An animal learning new hunting techniques is an obvious example. But adaptive behavior is also by

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Relational Networks manifest 'Memory' and 'Anticipation'

Memory and anticipation are familiar to humans and obvious in most animals. But network science assists in demonstrating how past formations of a relational network can persist in present ones, thereby providing references for interpreting data about present conditions and thus adaptively responding to potential future ones. In complex adaptive systems, recursive flows of feedback across a network form patterns that can persist in the network even as it re-configures itself into adaptive variations. Genetic information and written language are examples of this 'storage' of past patterning that can be selectively accessed by an actively self-ordering network, which is crucial to its adaptive self-direction. But, this 'persistence of the past' appears more subtle than the physical examples of genetic encoding and human writing. Human societies can be observed to 'revert' to past relational formations in ways that indicate the residual persistence of these in their network relationships -- as in shifts from authoritarian or fascistic patterns to more democratic ones, then back again.

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Relational Networks can 'Self-Orchestrate' without Central Control, through Synergistic Synchronization

A fundamental insight from systems science and network study is that networks can not only self-organize systems but selectively direct and re-direct their operations, by improvizationally 'orchestrating' various flows of influence and feedback across the network. This 'behavior' can both regulate and adaptively reconfigure a system by creating differing sequences and hierarchies of operations. What is most astounding about such purposeful 'self-orchestration' is that can be shown to manifest without any 'central controller' but rather in a synergistic manner from a seemingly chaotic 'everything happening all at once.' The ongoing formation and reformation of feedback flows that result in system changes, such as choosing between "fight or flight" behavior, emerges from disorderly or contradictory network activity in what is termed "synchonization." Suddenly the feedback flows synchronize into a dominant formation that provides direction for the system. These traits provide a primary distinction between predictably mechanistic systems and complex adaptive ones. It is also a basic factor in how the latter cannot be directly controlled -- because there is not persistent causal hierarchy of operations to predictably manipulate.

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The Meta-System Relational Constellations of Networked Networks

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​Perceiving Relational Network Constellations as Perspective, Context, and Scale Dependent

​As indicated by the notion of regarding any system as constituted by both an 'internal' relational constellation and extended 'external'  network, how we perceive a given system depends upon what perspective we use to 'see' it. From a mechanistic perspective, a system 'appears' to exist in isolation as a set of parts and factors that 'act upon' each other in a predictably deterministic manner. From a network perspective, it manifests as a 'relational whole.' But to understand as much as possible, we then have to 'see' that relational whole 'in relation to' its origins and context. Depending upon that larger scale context, a given system can be 'seen' differently. Essentially, expanding the scale for examining 'a system' beyond its immediate boundaries provides greater information about 'how it manifests' as a relational phenomena in a given context. Expanding our view to consider relational context also demonstrates how discreet systems manifest within the extended fields of particular contexts, of overlapping interdependencies. Those connections and flows of influence can be more direct or more 'round about.' Yet even the more obscure, indirect ones can prove fundamental to the manifestation and behaviors of particular discreet systems.

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The Networking of System Networks into Larger Scale Collective Networks

A larger scale, trans-system relational field perspective is essential for perceiving how collective or 'meta-system' systems emerge from densely interconnected sub-systems -- such as bee hives and human societies. Larger scale systems emerge from the networking or "nesting" of relatively discreet sub-systems into such meta-system networks -- which then manifest their own self-directing behaviors that are not directly determined by those of their constituting sub-systems. The behavior of individual persons can be primarily self-asserting and competitive in ways that create turbulence, instability, and conflict between them. Yet the behavior of the meta-systems of the societies and economies that emerge from the turbulently networked "nesting" of those individuals can 'act' in ways that conflict with the self-asserting of those individuals. The correspondent relational field of the meta-system of a society does not operate 'in the interest' of individuals, but 'for the purpose' of its own self-promotion, even 'at the expense' of those individuals.

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​Systems networked by 'overlapping' relationships:                       Abstract representations of interpersonal social networks

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​Larger scale system networks emerge from collective interactions among discreet sub-systems:

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​The "Agent-Based" Meta-System Networks of "Super Organisms"​

​Individual systems such as animals and persons are considered "agents" in systems science. This term indicates the advanced capacity of their internal relational networks for generating complex perception and meaning-making interpretation of their environments, resulting in more extreme levels of selective system self-direction, or adaptive agency. "Agents" demonstrate sophisticated capacity for 'semiotic signalling' to each other, resulting in complex inter-system communication and collective cooperation. When these "agents" become closely interrelated in larger scaled system networks the result can be what are termed "super organisms." These meta-networks are thus called "agent-based systems." These include ant colonies, bee hives, and the human systems of institutions, corporations, states, and economies. From this communication-based networking of multiple agents emerges a system that 'behaves like an individual organism,' yet does not have the central nervous system, brain, or even discrete body of a biological life form -- thus termed a "super organism."

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​The agent-based networking of a bee hive:                                       The agent-based networking of a financial market:      ​​​​

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Most significantly, these "super organisms" effectively engage the interdependent interactions of their agent's capacity for adaptive behaviors to emergently self-organize and self-direct as an additional system network with its own self-direction and purposeful self-assertion. Thus, the flows of interacting influence among their agents influence the larger relational field of the meta-system but the agents cannot directly control the "super organism." It manifests its own purposeful autonomy. This insight is crucial to understanding how humans create social, financial, and political systems that then 'act for the purpose of their own self-assertion' and not necessarily in ways that humans intended or expect. The agency of agents becomes engaged in the "super organism's" self-assertion in ways that can obstruct that of the agents' self-assertion.

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The "super organism" networks emerging from agent interactions can act autonomously for their own self-assertion:

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Meta-System Interdependency Creates Relational Networks of Mutually Regulating, Reciprocal Constraints on Sub-Systems:

Just as internal system networks order and direct the system by imposing constraings of flows of influence, so to interactions of meta-systems. By continually adapting to each other, while asserting their internal self-organization and self-assertion, interacting system networks impose constraints upon each other. This activity is represented by the concept of "co-evolution," in which each species evolves through relational responses with other species. That results in the emergence of the meta-systems of ecologies. The emergence of that meta-system network constitutes self-organizing 'feedback' among the plants and animals. In this way the self-asseriong of individual species is both restrained and enabled, resulting in mutual benefits that make it possible for each individual network to persist. Whether plant and animal species in an ecosystem or humans in social groups, interactions order both sub- and meta-system regimens by imposing limits on each other's activity. Thus, competition between systems, as in species, can actually be 'seen' as promoting 'co-operation' in a shared ecosystem network. This co-operative effect has been termed "mutlalism." Yet it is an ongoing emergence of relational fields that necessarily arises from diversity and turbulent instabilities at all levels of network formation. The regulating constraints are not 'phsyical laws' but relational feedback network patterns. The same relational dynamics extend to the scale of the biosphere and its relationship to the non-biological earth systems.  Biological life influences the planet in ways that can promote or degrade the networked conditions for the evolution of biological life.

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​Individual systems become linked in relational networks that constrain their interactions to constitute 'The Forest'​​​

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​From the predictable constraints of physical matter emerge the unpredictable ones of ecosystem networks,

then of the biosphere, which in turn influences other earth systems -- 'it's all interdependently related':​​​

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From ecosystems to climate, societies, economies, marriages, and human minds, we exist in and are the creations of mutually modifying, feedback-driven, self-creating, self-ordering, and self-directing relational networks. The world is an on-going, disproportionally emergent, often purposeful, networked becoming, whose order and disorder are mutually important to its continually synergistic self-ocreation. This mutually generating, emergent interplay cannot be understood solely in terms of predictable physical materials and forces. It must be conceived in terms of recursively interacting feedback networks from which emerge system properties that are not to be 'found' in their material components.

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Climate systems, ecologies, personal relationships, and societies are all complex systems

whose relational networks 'make their selves,' out of their own disorder,

and whose interdependent interactions collectively 'make the world":

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