The Radical New Reality of Systems Science

The science of matter and energy, or "physics," has given us profound understanding of the material world. It reveals the constraints imposed upon what matter and energy can 'do' in our particular universe. These constraints are known as the "laws of physics" because they limit how matter forms and changes. This knowledge provides us with astonishingly accurate predictive abilities. We can manipulate chemical molecules and send rockets to the moon. Such abilities derive from the reductively quantitative and mathematical modes of analysis underlying what is termed "scientific method." By measuring the state of matter and energy in one moment, we can discern what changes are possible in future moments because these are 'pre-determined.' All events are predictable, changes are proportional to pre-existing factors, and 'every action has an equal and opposite reaction.' The 'out put' of an event is determined by the 'in put.' All phenomena in this material world are determined by predictable 'forces.'  Thus, 'how things are ordered,' or dis-ordered, results from these calculable factors. If we have enough information about the states of matter and energy in a given situation, then we can potentially calculate and predict 'what happens next.' All change is 'caused' by pre-existing factors. The existing 'order' of factors relative to each other in one moment will determine what the ordering, or dis-ordering, of conditions will be in a subsequent moment.

 

In this view of 'how things happen,' causation is strictly deterministic -- nothing happens that does not result from preexisting physical factors. We can call this a 'mechanistic worldview' because it represents all events in terms of 'parts' that 'act upon each other' in predictable ways. It has been our modern, science-based version of 'how the world works.' One does not have to be well versed in the science to understand its concepts. You swing a bat, hit a ball, and the subsequent trajectory of the ball will be determined by the physical factors of mass, force, and direction involved. If you seek to change the trajectory of the ball, you simply manipulate these variable factors. This is extremely practical knowledge and has come to underlie our common modern worldview. It provides us with the capacity, through technological devices based upon it, to manipulate and control a vast array of phenomena. Thus, it has seemed accurate to assume it 'explains everything.'

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However, in recent decades, the same reductive scientific method of quantification and calculation of material factors which generate physical science has been increasingly applied to the dynamical behaviors of interactive systems. Here, scientific analysis of physical states is used to investigate how multiple parts and factors interact as a system of relationships, how those relationships change, and what effects or 'properties' these manifest.  So, this is not the science of the material world per se, but the use of that knowledge to study how matter and energy become 'arranged' or organized by interacting relationships to form the phenomena of systems, whether geological, biological, or social.

 

That area of study became known as "systems science." Beginning at least in the 1950s, with study of weather systems, then ecological and biological ones, as well as societies and economies, reductive scientific research began to reveal evidence that did not fit well with the expectation of deterministic predictability. From this work emerged the ideas of "chaos theory," in which it became evident that increasingly organized forms of activity can arise from more disordered actions, such as when a tropical storm becomes a hurricane. In effect, that involves disorder enabling increased ordering. That work led to "complexity theory" and "complex systems science," notably developed at the Santa Fe Institute in New Mexico. 

 

In this work, the term chaos no longer refers simply to a condition of randomness, or the absence of discernible ordering, but to turbulent activity within which more ordered forms spontaneously appear then dissipate again -- like the momentary patterns of whirls in the flow of a river. The terms complex and complexity similarly came to be used to indicate something different from complicated. Complexity theory uses these terms to describe dynamical activity in which multiple factors interact and influence each other -- becoming interdependent in ways that are extremely difficult, perhaps impossible, to fully measure, calculate, and predict. So, chaotic conditions involve spontaneous order formation that exhibits the interdependent interactivity of complex dynamics, which give rise to the spontaneous order formation. A 'complex system' is sometimes distinguished from a chaotic one because the former tends to be able to sustain the interdependent interactions from which emerge its self-ordering.

 

Both chaotic and complex systems manifest unpredictable increases in ordered forms. That is, increased organization of forms and events appear in ways that can not be fully accounted for by deterministic causation, in reference to pre-existing factors alone. These increases of and order seem to be 'coming out of nowhere,' from a deterministic perspective. Further, that ordering can result in 'system behaviors' that both sustain and adapt the transient forms arising within chaotic turbulence. Subsequently, these traits were termed "emergent properties," because they 'emerged' from the interdependent interactions of system dynamics in ways that were not explicitly determined by pre-existing physical factors. By quantitatively measuring changes in system forms, functions, and effects, it became evident that the 'out puts' of these systems were not fully consistent with the 'in puts.' Something exceedingly strange, from the perspective of physics' deterministic causation, was occurring.

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Here, we enter the realm of "complex adaptive systems science." This aspect of systems science focuses upon systems with complex dynamics that manifest capacity to adapt to changes in their environments by adjusting, or re-self-organizing, their internal forms and functions -- in ways that can sustain their own existence. They 'adapt' in the sense that they re-self-configure in ways promote their 'survival.' This is most obvious in biological systems, But, such adaptive system behaviors are also found even in systems that are not constituted as animals with central nervous systems and brains. Collective systems like ecologies and societies are shown to respond adaptively in response to both internal and external perturbations.

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The Logic of Complexity's Unpredictable Self-Organization

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A New Science or a New View of Reality?​

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Does that mean we now have a 'new science?' Well, it is the same method. But it is producing results our current science-based cultural worldview had assumed were not possible. What we confront here is not a 'change in science' but a profound shift in the scientific basis for how we form our cultural perspectives on 'how the world actually works.'

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In summary, the same scientific methods of reductive analysis that affirm the deterministic 'laws of physics' also reveal the unpredictable phenomena of spontaneously emergent ordering, self-sustaining system self-organization, and adaptive system 'self-direction.' For many, even in science, these unpredictably emergent properties of chaotic and complex systems are profoundly disturbing to their familiar mechanistic worldview. If the deterministic 'laws of physics' are actual, then reality 'just cannot be' that way. People are confounded by the notion that 'events can happen that are not directly caused by preceding events.' The further implication that 'systems without brains' can purposefully adapt their 'behaviors' to promote their continued existence  -- as if these had 'agency' -- is even more preposterous. Nonetheless, the science is rigorous and the evidence extensive. We are confronted by a paradoxical contrast between predictably deterministic causation and unpredictably emergent self-organization.

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The math, theory, and terminology facilitating complex systems science is daunting to any average person. But there are basic concepts that can be understood in more ordinary terms. These are presented here in reference to fpir fundamental topics:

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Systems:

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​A system is a set of identifiable relationships between multiple parts that establishes a 'boundary' or distinguishing itself from an external environment. The notion of 'a system' is often understood as a structure of relationships between elements that are arranged in a hierarchical manner, the whole of which is directed or controlled 'from the top down' or 'the center out.'​ However, in systems science, the range of how systems are conceived is vastly more complex. Indeed, relatively few actual natural or human systems operate as fixed, hierarchical structures.

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In this science, a system can be as simple as a basic electrical circuit or as complex as an ecology or society. As such, 'the system' is intrinsically 'something more than' its parts taken separately. As a whole, it manifests properties that its parts alone do not. Wiring, switches, and batteries can constitute a simple system that produces light on demand when they influence or effect each other. The 'out put' of this type of system, the light, does not manifest without the collective actions of the parts. But it can be predicted from the material properties of its parts, when placed in a given set of relationships. The light is the 'out put' of the 'sum of its parts,' or its 'in puts.' This is characteristic of mechanistic systems. The changes and effects of the system's operation is proportional to its parts in a given relationship.

 

A society, as a set of relationships, becomes 'more than' a group of individual people. But a society is a "complex adaptive system" (CAS). This type of system is distinguished as one in which the relationships between the parts can influence each other in ways that change the forms and functions of the system unpredictably. Such systems are dynamic in the sense that influence not only 'moves' between the parts, but can change in ways that unpredictably alter the entire configuration of the relationships of the parts, thus the 'behavior' or 'out put' of the system. That capacity to unpredictably rearrange internal relationships among parts, thus the overall performance of the system, makes CAS  'more than the sum of their parts.' That is, the operations or properties of a CAS , (its 'out puts') are often not predictable in advance, from the known properties of their parts. 

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What makes these types of systems peculiar, from the perspective of physic's deterministic causation, is not only their unpredictable emergent increases in order, or their ability to sustain and adapt it, but the types of effects or properties that self-ordering can produce. These systems can result in purposeful 'behaviors.' intelligence, and autonomous agency, or 'free will.' From the perspective of deterministic causation, none of these 'out puts' of these systems 'should' exist.

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Feedback:

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In a simple electrical circuit, current flows in a linear direction between the parts, by way of connections, create the predictably collective effect of generating light. In a complex adaptive system, the connecting relationships between parts tend to be multiple, so that the effects of parts on each other move across the system simultaneously in multiple directions. In this way, parts influence each other recursively in 'feedback loops.' Actions move through the system and return to influence the part which originated the action. People in a social group are constantly responding to how others respond to their own actions, altering their subsequent behaviors in response to the 'feedback' they are receiving. This constitutes interactive interdependency that arises from concurrent flows of recursive feedback. These feedback loops can interact in ways that become literally impossible to fully track and identify.

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It might sound mundane, but it is from these often impenetrable tangles of feedback, whose interactions modify each other to produce interactive interdependency in the relationships between system parts, that the capacity of complex system to self-regulate and re-configure their operations in adaptive ways arises. It is what makes them unpredictably self-directing. It also makes them paradoxically robust yet also prone to disruptions that can result in their abrupt collapse.

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Feedback as a general concept includes some important distinctions. Firstly, there is the notion of "amplifying feedback" versus "dampening feedback" (also termed "positive" versus "negative"). Here, it can be observed that changes in a system's form or operation result from feedback between parts that either amplifies a particular tendency, or from feedback that discourages, blocks, or dampens one.  In this way, interaction between system parts promotes or restricts some aspect of potential system action. How a system regulates these conflicting impulses of amplification and dampening underlies the emergence of its self-regulation.

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A further distinction is that between 'feed back' and 'feed forward' influences. In feedback, the actions of one system part effect others that then 'feed back into' its own activity. Subsequent alterations of that part's activity, influenced by the feedback, are sometimes considered as 'feed forward' influences. This distinction adds some nuance to how we imagine the impenetrably complex flows of recursively interactivity that leads to interdependency among parts of a complex system.

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Networks:

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Systems and the flows of feedback between their parts can be modeled as abstract networks. The field of network science has been greatly facilitated by computational research in computer development. It models how the parts of a system are connected and how flows of influence between the system parts flow and feedback throughout a system. An electrical circuit is a network of connections between parts. These are referred to as "nodes" and "links." If the nodes, or parts, are not appropriately linked, then the circuit will fail -- the lights will not turn on.  The parts of a system function collectively in relation to how these are linked, thus how influence moves and feeds back across it. This is called "network connectivity." The connectivity of mechanical systems requires links that direct influence between nodes in a relatively fixed, linear manner, to generate a predictable result, as in an electrical motor or engine.  In contrast, the dynamically changeable, unpredictable operations of complex adaptive systems require a more non-linear set of links that enable multiple flows of influence forming recursive feedback loops.

 

The pattern of links between network parts alone tell us how a network is structured. The directions of flow of influence and feedback across those links constitutes the relationships between them that result in a system's operations or behavior. The interactive interdependency of CAS network relationships is what gives them capacity to alter the flows of influence and feedback among their parts. Thus connectivity involves both a structure of links and relationships that enable regulation of how influence flows across those.

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          A network connectivity structure:                           A potential version of that network's connectivity relationships

 

 

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Connectivity imposes constraints upon a system's network operations that result in the particular forms of its unpredictably emergent functions or 'behaviors.' What is most profound to comprehend is that CAS can alter their own connectivity in ways that can prove adaptive in promoting the continued operation of the system. How feedback flows, when it is suppressed or amplified, is orchestrated by continual adjustments in a network's connectivity relationships.

 

So: system networks can be as a simple as a basic electrical circuit, with a directionally sequential flow of influence, or as complexly interactive as a social group, with simultaneous, mutually modifying flows of influence that cannot be specifically sequenced to demonstrate deterministic causation of the collective result in the system's overall operations..

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​The concurrently interactive flow of influence and feedback across a CAS network necessarily results in some significant conflict and disorder. Unlike mechanical systems, which function according to a consistent flow, CAS function from underlying network instability. Thus, when it comes to complex adaptive systems, their networks are exceedingly difficult to model because their constantly variable flows of feedback, along with a capacity for adaptive re-configuration, make them dynamically inconsistent -- network connectivity is continually modulating itself.

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So CAS network feedback flows and connectivity tend to be in constant flux and never exactly repeat. Again, this is due to the ongoing emergence of their self-ordering from underlying disorder,and their constant adaptive activities. Yet each system does manifest overall similarities that characterize it as a type of system, whether as a member of an animal species or a type of corporation.

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​Self-Organization:

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​Now we come to the peculiar consequences of the above traits of complex adaptive systems: the self-organization of disordered systems. This means that a set of parts or factors can be observed to become more organized, relative to each other, as a result of their interactions, and subsequent feedback between them. 

 

The capacity of complex adaptive systems to adapt their forms and functions, by re-configuring flows of feedback among their parts, arises from self-organizing interacting relationships between those parts. System self-organization from less ordered states ranges from the reflexive arrangement of system parts in static material objects like crystals and snowflakes, to the adaptive fluidity of systems like living organisms. Self-ordering can manifest in chaotic systems such as a weather, where interactions among parts (water and air) are 'driven' by factors of heat, to become the more organized system of a hurricane. But it is also the basis of how complex systems manifest consistent self-regulation, or, conversely, self-transformation. Self-ordering can either maintain relative consistency from underlying disorder, as in your body temperature, or completely reconfigure a system -- as when caterpillars become butterflies. It even generates the purposeful self-direction we term agency. Thus we can conceive of successive 'levels' of self-ordering, each emerging from a lower level to 'build upon' that existing basis of interactive interdependency in a system's network connectivity. Now, bear in mind that each level emerges unpredictably from underlying disorder to become a more ordered system state, the partly ordered, yet still significantly inconsistent or disordered aspects of which, enable the further emergence of an even more ordered state. Self-organization 'builds upon itself' but always requires some underlying instability for its further emergence. That is why one can speak of 'order from disorder.' And, indeed, that the more complex the organization, the more it derives from underlying layers of self-ordering disorder. Too much consistency can obstruct this emergence.

 

Thus, a chaotically milling mass of people can manifest moments of more ordered relationships that then become the basis for the emergence of a more unified and focused 'protest march' with a collective purpose, which then can become the basis of for the emergence of a political action campaign -- a complex adaptive system that arises without any planning in advance.  This is not to say there is no 'planning' involved. Some people might plan to be in the street to protest, but these might have different motives and hold conflicting political ideologies. Some might be out for a stroll and get caught up in the crowd. Other factors like the weather, or spread of conflicting rumors might play a part in how the crowd interactivity increases its interdependency of influence and feedback until it suddenly manifests a further level of self-ordering to become a more overt protest. That increase in coherent interaction could then prompt suggestions of focused political action, the particular form or ordering of which would emerge from other contrasts within the system of the individuals involved. Consequently, what first emerged from a loosely associated crowd has, unpredictably and thus inexplicably from a strictly causal perspective, become a purposefully self-ordering and self-directing complex adaptive system that manifests agency in accomplishing a future purpose -- the continued adaptive behavior of which still relies upon diversity and disorder within the system network. When a system, such as a body or a political one, becomes too rigidly consistent, it loses its emergent adaptivity, becomes 'brittle,' and is more likely to collapse.

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This emergence of purposeful system behaviors from underlying disorder and lower levels of emergent ordering is indicated by terms like "autopoiesis" and "autogenic," meaning 'self-creating.' It is perhaps most tangibly obvious in the development of biological organisms, where genetic 'information' provides references for an organism's growth, but it is the actual, always partly disordered, interactivity of an organism's system network connectivity relationships that actually 'makes the living creature' -- emergently, unpredictably, in every moment.  Determistic mechanical systems cannot do this.

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As self-organization is an ongoing activity emerging from elements of underlying instability, it is expressed as the constantly fluctuating patterns of a system's network connectivity and feedback flows. Thus self-organization is not an exact reiteration of a 'program' but a constant 'tuning' of the system to maintain relative self-similarity over time and in response to disruptions.That is a crucial aspect to grasp: the self-ordering of complex adaptive systems is necessarily erratic thus its consistency is all the more mysterious. Any given CAS will self-organize in self-similar ways that characterize it as a type of system, whether as a human or as a political party. But the inherent variability of self-organization and its response to its external environments also promotes individualized versions of system types. Animals, social groups, and economies self-organize in generally distinctive was but also in individually characteristic ways. The resulting diversity between and within types of systems is part of the underlying contrast or 'disorder' that provides a basis for the emergence of larger scale complex systems, such ecologies or the "super organisms" of societies.

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This inherent inconsistency, thus unpredictability, in CAS self-organization is what enables their adaptive behaviors in response to disruptions. Both their ability to self-order with relatively self-similar consistency and to re-self-order for the sake of adaptive change  arise from inherent internal instability. This is most obvious in animals that can suddenly alter behaviors to facilitate attaining food or evading danger. But the same variability of underlying feedback network dynamics manifests in collective systems like flocks of birds or human corporations.  CAS can only 'do what they do,' act selectively to promote their continued existence, because their network connectivity and thus behaviors are not fully constrained by pre-existing conditions. Their ability to assemble and re-assemble their networks from multiple potential configurations provides the options for adaptive behavior.

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Lastly, it is essential to comprehend that emegent system self-organization, by definition, cannot derive directly from centralized or hierarchical 'command and control mechanisms.' The self-ordering of disordered systems emerges from unstable interactivity and interdependence among its parts. These increased elements of ordering involve localized as well as overall hierarchies of sequences and priorities, which can be reinforced by feedback from more chaotic underlying activity continually. So, hierarchical patterns of system operation are often expressions of emerging self-organization rather than a fixed command and control mechanism.

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Thus, emergent system self-ordering and self-directing is not a phenomena that can be directly controlled in an overall manner through calculated manipulation. It can be influenced by its environment, even disrupted to the point of system collapse. But there is no way to 'get your hands' directly upon its dynamics to predictably manipulate all its effects. Consequently, the science shows how many of the systems that we tend to assume operate as 'top down command and control' hierarchies, actually manifest primarily due to their self-ordering properties. That includes governments, institutions, and corporations. And this makes them ultimately 'beyond our direct control.' As participating parts, we individuals facilitate and influence them. But each is, to a significant degree, a kind of 'autonomous entity' that creates its self and its own characteristic behaviors. That is a further instance of how deluded we are about reality.

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Character-istic System Behavior:

The constant emergence of self-ordering promotes uniqueness in both each moment and in the overall patterning of systems.

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System Learning & Memory:

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