Complex adaptive systems

CAS is a dynamic network of many agents (cells, species, individuals, firms, nations) acting in parallel, constantly acting and reacting to each other.

The control of a CAS tends to be highly dispersed and decentralized.

Coherent behavior in the system, it said to arise from competition and cooperation. The result of the system is shaped by decisions made every moment by many individual agents.

re-phrasing of Kevin Dooley's definition.

"CAS [complex adaptive systems] are systems that have a large numbers of components, often called agents, that interact and adapt or learn." - John H. Holland

Axelrod & Cohen[6] identify a series of key terms from a modeling perspective:

  • Strategy, a conditional action pattern that indicates what to do in which circumstances
  • Artifact, a material resource that has definite location and can respond to the action of agents
  • Agent, a collection of properties, strategies & capabilities for interacting with artifacts & other agents
  • Population, a collection of agents, or, in some situations, collections of strategies
  • System, a larger collection, including one or more populations of agents and possibly also artifacts.
  • Type, all the agents (or strategies) in a population that have some characteristic in common
  • Variety, the diversity of types within a population or system
  • Interaction pattern, the recurring regularities of contact among types within a system
  • Space (physical), location in geographical space & time of agents and artifacts
  • Space (conceptual), “location” in a set of categories structured so that “nearby” agents will tend to interact
  • Selection, processes that lead to an increase or decrease in the frequency of various types of agent or strategies
  • Success criteria or performance measures, a “score” used by an agent or designer in attributing credit in the selection of relatively successful (or unsuccessful) strategies or agents.

via Wikipedia

NEME derives from "Notice. Engage. Mull. Exchange." - essentially equivalent to "sensing", "thinking", "planning" and "doing."

Learning in a Complex Adaptive System occurs by putting ideas and beliefs that are generated by Mulling to tests in the physical realm of action, Engaging in space. Then Noticing Events in Time Replication is accelerated by then sharing results via NEME info tokens.

revised from EbDish

The Scientific World Is Round

Evolution of complexity
Living organisms are complex adaptive systems. Although complexity is hard to quantify in biology, evolution has produced some remarkably complex organisms. This observation has led to the common idea of evolution being progressive and leading towards what are viewed as "higher organisms".

If this were generally true, evolution would possess an active trend towards complexity. As shown below, in this type of process the value of the most common amount of complexity would increase over time. Indeed, some artificial life simulations have suggested that the generation of CAS is an inescapable feature of evolution.

Properties

Complex adaptive systems have many properties and the most important are,

  • Emergence: Rather than being planned or controlled the agents in the system interact in apparently random ways. From all these interactions patterns emerge which informs the behaviour of the agents within the system and the behaviour of the system itself. For example a termite hill is a wondrous piece of architecture with a maze of interconnecting passages, large caverns, ventilation tunnels and much more. Yet there is no grand plan, the hill just emerges as a result of the termites following a few simple local rules.

 

  • Co-evolution: All systems exist within their own environment and they are also part of that environment. Therefore, as their environment changes they need to change to ensure best fit. But because they are part of their environment, when they change, they change their environment, and as it has changed they need to change again, and so it goes on as a constant process. ( Perhaps it should have been Darwin's "Theory of Co-evolution". )

 

  • Sub optimal: A complex adaptive systems does not have to be perfect in order for it to thrive within its environment. It only has to be slightly better than its competitors and any energy used on being better than that is wasted energy. A complex adaptive systems once it has reached the state of being good enough will trade off increased efficiency every time in favour of greater effectiveness.

 

  • Requisite Variety: The greater the variety within the system the stronger it is. In fact ambiguity and paradox abound in complex adaptive systems which use contradictions to create new possibilities to co-evolve with their environment. Democracy is a good example in that its strength is derived from its tolerance and even insistence in a variety of political perspectives.

 

  • Connectivity: The ways in which the agents in a system connect and relate to one another is critical to the survival of the system, because it is from these connections that the patterns are formed and the feedback disseminated. The relationships between the agents are generally more important than the agents themselves.

 

  • Simple Rules: Complex adaptive systems are not complicated. The emerging patterns may have a rich variety, but like a kaleidoscope the rules governing the function of the system are quite simple. A classic example is that all the water systems in the world, all the streams, rivers, lakes, oceans, waterfalls etc with their infinite beauty, power and variety are governed by the simple principle that water finds its own level.

 

  • Iteration: Small changes in the initial conditions of the system can have significant effects after they have passed through the emergence - feedback loop a few times (often referred to as the butterfly effect). A rolling snowball for example gains on each roll much more snow than it did on the previous roll and very soon a fist sized snowball becomes a giant one. 

 

  • Self Organising: There is no hierarchy of command and control in a complex adaptive system. There is no planning or managing, but there is a constant re-organising to find the best fit with the environment. A classic example is that if one were to take any western town and add up all the food in the shops and divide by the number of people in the town there will be near enough two weeks supply of food, but there is no food plan, food manager or any other formal controlling process. The system is continually self organising through the process of emergence and feedback.

 

  • Edge of Chaos: Complexity theory is not the same as chaos theory, which is derived from mathematics. But chaos does have a place in complexity theory in that systems exist on a spectrum ranging from equilibrium to chaos. A system in equilibrium does not have the internal dynamics to enable it to respond to its environment and will slowly (or quickly) die. A system in chaos ceases to function as a system. The most productive state to be in is at the edge of chaos where there is maximum variety and creativity, leading to new possibilities.

 

  • Nested Systems: Most systems are nested within other systems and many systems are systems of smaller systems. If we take the example in self organising above and consider a food shop. The shop is itself a system with its staff, customers, suppliers, and neighbours. It also belongs the food system of that town and the larger food system of that country. It belongs to the retail system locally and nationally and the economy system locally and nationally, and probably many more. Therefore it is part of many different systems most of which are themselves part of other systems.

 

More properties:

  • Adaptive elements - Stands to reason that a CAS will be adaptive. What it means though is that we can change. Change the circumstances, the influences on, and conditions of, a life and the healthy person will adapt. They will cope. They will have resilience.
  • Simple rules - Although the outcomes of change are complex they come about on the basis of simple rules. Each building block, or element, or agent can be quite simple.
  • Non-linearity - In linear systems a certain impact will always deliver the same effect. However, in nonlinear systems, there are always number of factors which continuously influence each other. Through feedback loops every element in the system dynamically influences all the other elements.

Non-linearity is responsible for the next two properties -

  • Emergent behaviour - Novelty. A CAS is naturally continuously creative developing new ways of responding and coping, producing new behaviours previously unseen
  • Not predictable in detail - Because of non-linearity and emergence it is not possible to predict detailed outcomes. This has been described as the “butterfly effect” – small changes in the starting conditions of a system lead to large, unpredictable differences in outcomes. This is very important when thinking about prognosis – it might be possible to predict how things, statistically, might turn out, but it is impossible to predict accurately for this one person how things are going to go.
  • Inherent order - Through these mechanisms a CAS has a self-maintaining capability. When you think about the complexity of a human being you might wonder what holds it all together? What controls all the various elements and produces the co-ordinated behaviours? Is there some life-force, or some organ, some conductor of the orchestra, that keeps it altogether? Well, no. A CAS has inherent order. It’s the actual complexity of the system which allows the system to self co-ordinate.
  • Context and embeddedness - “No man is an island”. Systems all exist within, and in interaction with, other systems. This means that to understand any particular organism, for example, to understand any individual human being, you need to situate them in their environment, see them in their contexts and connections.
  • Co-evolution - As an individual changes those changes impact on everything that individual is connected with, so as individuals grow, so do their environments.

In addition to these properties of a CAS there are other ones which are shared with all complex systems (whether adaptive or not)

  • Attractors - The most widely known type of attractor is a “point attractor”. In astronomy a “Black Hole” is a good example. In your bath tub, the drain is one! A “point attractor” pulls everything towards itself. There are two other important attractor types. “loop attractors” – these are attractors with two points of equal power both of which exert their influence on their surroundings resulting in two alternating states which the system flip flops between. The other kind is the “chaos attractor” which doesn’t look like an attractor at all because everything around it is chaotic.
  • Far from equilibrium points - A complex system is not static. It does not constantly maintain the status quo by keeping everything in fine balance. Complex systems move towards instability by moving to what are termed far from equilibrium points.
  • Bifurcators - At a far from equilibrium point the system acts as if it has a choice. It can go one way, or another. A bifurcator is like a crossroad.
  • Phase transitions - At a far from equilibrium point the system can suddenly change its whole state. An example of this is boiling water. As you apply more heat the water molecules become more agitated and at the “boiling point” the liquid water changes state and becomes steam – a gas.

 Painful Multi-Symptom Disorders: A Systems Perspective

C. Richard Chapman.

An insect hive exemplifies a CAS, as does the immune system. When elements of a system are of interest, for example worker ants within an ant colony or antigen-presenting cells within the immune system, then modelers may designate the CAS as individual based or agent based. CASs manifest ever-changing, self-organizing behavior in response to a variable environment, and they move toward, but never sustain, equilibrium. In a classic paper, Prigogne and Stengers (1984) termed this behavior “order through fluctuations.”

1.1.2 Features of Complex Systems

Any complex system, including a CAS, has several fundamental distinguishing features. A CAS has additional properties because it continuously adapts to an environment. We first introduce these features here and subsequently explore their utility for describing and investigating the physiological and psychological impact of nociception.

1.1.3 Lack of Central Control

Complex systems differ from simple systems in that they lack central control. A control hierarchy with a leader at the top simply does not exist. Rather, the power spreads over a decentralized structure and multiple agents combine to generate the actual system behavior. A building heating system is a noncomplex, closed system in which a single component, a thermostat, controls system behavior. In this case, the whole can never be more complex than the sum of its parts. When control emerges from the collective in a way that exceeds the sum of the contribution of the individual agents, as it does in an insect swarm, then true complexity exists and the collective behaves in a manner more complex than the individual agent within it could ever achieve. In a complex system, control is an emergent property. That is, control appears spontaneously and is unpredictable solely on the basis of information about the individual components.

1.1.4 Emergence

Complexity researchers regard emergent phenomena as normal properties of dynamic, self-organizing systems. In principle,emergence is the process of deriving some new and coherent structures, patterns, and properties in a complex system. For example, an insect colony exhibits purposeful and intelligent adaptive behavior that makes possible foraging for food, defense, and reproduction. This property, which we may loosely term intelligence, is unpredictable from what we know about the individual insect and appears spontaneously. Emergence is readily apparent in the behaviors of an insect swarm, a flock of migrating birds, a human crowd, and indeed in human culture. As a general principle, complexity theorists hold that emergent phenomena occur due to patterning of interactions (nonlinear and distributed) between the elements of the system over time. One might describe acute-phase tissue inflammation as an emergent property of the immune system. Complex behaviors emerge as a result of often nonlinear, spatiotemporal interactions among a large number of component systems at different levels of system organization.

1.1.5 States and State Transitions

Complex systems of all types are dynamic and function in states; that is, relatively stable modes of operation. Complexity theorists refer to a collection of system properties as a state and the set of all possible states of a system is its state space. Basically, the total number of properties transmitted by a system, and detected by an observer, defines the complexity of that system. For the sake of illustration, consider familiar objects rather than complex systems. For a coin toss, there are only two states, namely heads and tails, but for a computer screen with a resolution of 800 × 600 pixels and 256 colors, the number of states is 256 to the power of 480,000. Of course, in a CAS, some states are much more likely to occur than others, and experience shapes the probability of transitions to certain states.

Complex systems sometimes undergo abrupt and unpredictable shifts in states. State transitions, often called phase transitions, are everywhere in nature. These are abrupt, nonlinear changes in a system. Water can change from solid to liquid and then to vapor with increasing temperature. The human brain can shift from waking consciousness to slow-wave sleep, and from that to paradoxical sleep, as a function of circadian rhythm. During combat, a soldier may become totally insensitive to injury, a state transition that fosters survival.

1.1.6 Attractors

Although complex systems are dynamic and self-organizing, when perturbed they go into disorder and then settle back into relatively stable states with relatively simple behavioral patterns. The transition from disorder to order reduces complexity and defines the new state space. A common metaphor describes a ball falling onto a three-dimensional landscape surface with peaks and depressions. The ball will roll away from the peaks and eventually settle into a depression. The depression, or basin, represents a subset of a system’s state space that the system can enter but not leave, unless boundary conditions or perturbations bring about reorganization. This is an attractor.

Systems are inherently dynamic, and so the interaction of the ball with the landscape may change over time, as the system’s environment varies. In this respect, a state space has a trajectory over time and may change as its environment changes. For example, one might characterize an ion channel as a two-state, or on-off, system and the probability of the on state will vary across time as a function of change in the system’s environment. Naturally, a CAS has a history, and the experience of prior states may influence the probability of occurrence of future states.

1.1.7 Nesting

A complex system always has the feature of nesting; that is, subsystems nest within it, and it nests within a higher-level complex system. Each system level can have its own state transitions, and these transitions occur within a higher-level system. Therefore, the first challenge we face in engaging the idea of multi-symptom disorder is deciding upon a level of inquiry. That is, we must single out one level of a hierarchically organized complex system as the System of Interest and define the levels above it as its environment. We could choose the sensory end organ, the dorsal horn of the spinal cord, the brain, the family, or the American culture. Because multi-symptom disorders happen to individual people, we normally select the individual as our System of Interest. Social systems such as the family comprise our system’s environment, and various psychological and physiological subsystems nest within the individual.

Figure 1.1 broadly illustrates the principle of hierarchal system nesting, within which the investigator defines the System of Interest, depicted as the Wider System of Interest in Figure 1.1. For our purposes, the Wider System of Interest is the individual, or person. The Environment immediately surrounding the person is his or her social network: family, friends, work environment, and perhaps involved health care professionals. Figure 1.1 designates this as the Wider Environment; that is, the surrounding social community, its economy, its culture, and all of the influences, opportunities, support, and hassles that this can exert upon the individual and his/her social network. Many stressors reside in the Wider Environment. The Narrow System of Interest, nested within the Wider System of Interest, refers to the physical and psychological health of the person. It is this to which health care providers normally attend. Of course, the Narrow System of Interest contains multiple physiological subsystems that are the concern of medicine and the targets of medical diagnosis and evaluation.

FIGURE 1.1. Hierarchal system nesting.

FIGURE 1.1

Hierarchal system nesting.

Causal influences are bidirectional and extend across system levels. For example, the interactions of the person, or Wider System of Interest, with the family, or Environment, if negative, can create stress at the psychological and physical level with negative effects on health. Conversely, improvements in health, or Narrow System of Interest, can positively influence the interactions of the person with his or her environment. The concept of dysregulation, which we discuss below, applies at all levels of the system. Dysregulation within the Wider Environment, or society, can evoke consequent dysregulation within the Environment, or family, and this subsequently can dysregulate the person and compromise health itself.

1.2 FEATURES OF COMPLEX ADAPTIVE SYSTEMS

In addition to the characteristics of all complex systems, CASs express the following features.

1.2.1 Adaptation and Agency

Adaptation is the continual adjustment of an agent to its changing environment. An agent is a living entity, a self-organizing system, and an individual entity operating purposefully within its environment in the service of adaptation. The concept of agent equates with the individual when the focus of study is on the interaction of the individual with the environment, especially the social environment. Grimm and colleagues advocate the concept of agent-based complex system, sometimes termed individual-based complex system, which directly identifies the individual in the world as an agent (Grimm et al. 2005).

The complexity investigator imputes agency to aspects of nested subsystems whenever an element exhibits some degree of autonomy. For example, the migratory cells of the immune system serve as agents for the detection of toxins, invading microorganisms and tumor development, all of which are invisible to the nervous and endocrine systems. Moreover, dendritic cells serve as professional antigen-presenting agents. They appear in peripheral organs such as skin where they encounter and capture antigens. They then migrate to the T cell areas of lymphoid tissues and present the processed antigens in order to elicit antigen-specific T cell responses.

Whatever the level of inquiry, agents are semi-autonomous units that evolve over time and help to maximize adaptation. They scan their environment and develop schemata, which are perceptual and/or motor patterns comprising rules for interpretation and action. Therefore, multiple agent-based subsystems exist in principle, nested within and working in service of the CAS of interest.

1.2.2 Equilibrium and Homeostasis

Complex systems are open, dissipative and operate far from equilibrium, but they tend to move toward equilibrium after disturbance and disorder. Physiologically, the bottom line for equilibrium is homeostasis. Although many writers equate homeostasis with adaptive adjustment, McEwen points out that homeostasis strictly applies to a limited set of systems concerned with maintaining the essentials of the internal milieu (McEwen 2000). The maintenance of homeostasis is the control of internal processes truly necessary for life such as thermoregulation, blood gases, acid base, fluid levels, metabolite levels, and blood pressure. McEwen’s distinction is critical because homeostasis has no adaptive features.

Three interdependent systems control the process of homeostasis: neural, immune, and endocrine (Goetzl and Sreedharan 1992). From the CAS perspective, specific processes must exist to protect and preserve homeostasis. Generic threats to homeostasis include environmental extremes, excessive physical exertion, depletion of essential resources, abnormal feedback processes, aging, and disease. Perturbations from the environment can threaten homeostatic regulation at any time.

1.2.3 Allostasis and Stress

Allostasis is an adaptive process in the service of homeostasis; it dynamically adapts multiple internal systems to changes in the environment and coordinates their responses (McEwen 2000Korte et al. 2005). Changes in the external or internal environment trigger physiological coping mechanisms. These mechanisms insure that the processes sustaining homeostasis stay within normal range. The allostatic process, which involves substantial autonomic activity, depends upon the coordinating effects of agent messenger substances that also serve as mediators and determinants of neural regulatory processes, particularly hormones, neurotransmitters, peptides, endocannabinoids, and cytokines. I describe this more fully below.

Stress is the resource-intensive process of mounting adaptive coping responses to challenges that occur in the external or internal environment. A stressor is any event that elicits a stress response. It may be a physical or social event, an invading microorganism, or a signal of tissue trauma. Selye (1936) first described this response as a syndrome produced by “diverse nocuous agents.” He eventually characterized the stress response as having three stages: alarm reaction, resistance, and if the stressor does not relent, exhaustion. The normal stress responses of everyday life consist of the alarm reaction, resistance, and recovery. Stressors have as their primary features intensity, duration, and frequency. The impact of a stressor is the magnitude of the response it elicits. This impact involves cognitive mediation because it is a function of both the predictability and the controllability of the stressor.

Allostasis is the essence of the stress response because it mobilizes internal resources to meet the challenge that a stressor represents. Stressors may be multimodal and complex or unimodal and simple. When a stressor persists for a long period of time, or when repeated stressors occur in rapid succession, allostasis may burn resources faster than the body can replenish them. The cost to the body of allostatic adjustment, whether in response to extreme acute challenges or to lesser challenges over an extended period of time, is called allostatic load.

1.2.4 Feedback

In open systems, self-regulation and self-organization depend upon feedback, which determines stability. That is, information about the output of a system passes back to the input and thereby dynamically controls the level of the output. Figure 1.2 illustrates two fundamental feedback principles: negative and positive feedback. These are essential constructs in all areas of the biological, behavioral, and social sciences as well as in engineering and complexity science (Jones 1973Thomas and D’Ari 1990Northrop 2000Flood and Carson 1993).

FIGURE 1.2. Negative and positive feedback.

FIGURE 1.2

Negative and positive feedback.

Negative feedback generally involves a circuit and a controller with a set point, and it works toward establishing equilibrium. Figure 1.3illustrates an adaptive negative feedback system that depends upon systemic circulation. Negative feedback regulation occurs throughout physiology and is a fundamental principle of endocrinology. Negative feedback acts to insure system stability and to maintain homeostasis. The difference between normal set point and current condition gauges allostatic load. Negative feedback continually moves a system away from imbalance and disorder toward balance and order. In principle, biological systems are always nested, and the set point for a negative feedback loop is generally under the control of a larger system within which it is embedded. Disturbance of a set point compromises negative feedback and is a potential cause of dysregulation.

FIGURE 1.3. Negative feedback with a controller and set point.

FIGURE 1.3

Negative feedback with a controller and set point.

Positive feedback loops also occur such that, when a variable changes, the system responds by changing that variable even more in the same direction, generating escalation and rapid acceleration (Ferrell 2002). This is a process that abandons stability for instability. From an adaptation point of view, positive feedback loop capability is essential for meeting acute threat with defensive arousal or reproductive opportunity with sexual arousal. Positive feedback loops make state change possible.

A simple example of positive feedback is autocrine signaling. A cell may produce a substance, such as an activated microglial secreting a pro-inflammatory cytokine. The presence of the secreted cytokine in the cell’s environment stimulates the cell to produce still more of the cytokine. Such autocrine signaling, through a combination of strong nonlinearity and positive feedback, promotes cellular instability and allows transient inputs to shift the cellular system between two steady states, that is, bistability (Shvartsman et al. 2002). Brandman and colleagues pointed out that positive feedback allows systems to convert graded inputs to decisive all-or-none outputs (Brandman et al. 2005).

In this way positive feedback can move a CAS toward an adaptive state transition that by definition has an all-or-none quality (Brandman et al. 2005). Setting up stable transition is essential for rapid adaptation. In the case of the dorsal horn, this could be a biphasic state transition, as described below. Inhibitory systems may also have positive feedback components that can wind up inhibitory processes and eventually shut down an excitatory state. Such processes may play a role in sleep, fatigue, and other conditions of hypo-arousal or impaired cognition. In the CAS framework, positive feedback loops and bistable states are the products of evolution and are essential for adaptation and survival.

Positive feedback loops do not normally operate independently within a CAS. Because every system is embedded within a larger system, a positive feedback loop is typically under the control of an overarching negative feedback system that limits overshoot and can eventually terminate the positive feedback loop. Positive feedback can persist or terminate through state shift transition or in response to an overarching system that acts on the basic mechanism from which the positive feedback system arises or by initiating an opponent process. For example, to prevent overshoot in a positive feedback excitatory process, the superordinate system may initiate a competing inhibitory process. Overarching systems typically control the on-off state of a positive feedback loop.

Feedback loops appear to exist reciprocally across nervous, endocrine, and immune subsystems and thereby contribute to overall system regulation. For example, such processes clearly play key roles in the interdependence of endocrine and immune systems (Besedovsky and del Rey 2000Rivest 2001). Glucocorticoid products of the hypothalamo-pituitary-adrenocortical (HPA) axis modulate the basal operations of cytokine-producing immune cells. Cytokines, in turn, influence the activity of the HPA axis. Thus, the products of one system provide messenger substances that serve a feedback function for another system.

Feedback loops are essential agents in system regulation. Negative feedback tends to sustain stability in an adaptive system despite changes in the external or internal environment, thereby minimizing allostatic load and protecting homeostasis. Positive feedback increases possibilities for change in system behavior and provides pathways to establish new set points for its negative feedback processes. More importantly, positive feedback is a mechanism for inducing rapid, adaptive state transitions that are necessary for emergency reactions in a threatening environment.

Negative and positive feedback can go awry within the nervous, endocrine, and immune systems. The result is a disease process. Negative feedback may fail when an endogenous messenger substance providing the feedback disappears, occurs in excess, or becomes confounded by exogenous products such as medications or substances of abuse that resemble the messenger substance in chemical structure. In some cases, negative feedback fails when an extraneous influence alters the set point. For example, chronic opioid pharmacotherapy in a male pain patient confuses the hypothalamo-pituitary-gonadal axis and results in hypogonadism (Daniell 2002Bliesener et al. 2005Daniell, Lentz, and Mazer 2006).

Positive feedback processes can also malfunction. When a positive feedback loop does not fulfill its natural purpose, it can generate an extreme shift in adaptive state. In some cases, this violates homeostasis and results in death. Although positive sensory input does not directly cause death, it can contribute to life-threatening conditions such as cardiovascular shock. Persistent, stressor-related positive feedback probably contributes to migraine headache, allodynia, severe idiopathic abdominal pain, noncardiac chest pain, and a variety of multi-symptom disorders.

1.2.5 Agent Connectivity

By definition, connections and interactions exist among the components, or agents, of a CAS, and these linkages define self-organization and behavior. The connectivity of a system is the nature and extent of such connections and interactions. It is from these connections that patterns form and feedback occurs. The relationships between the components, or agents, within a system are generally more important than the agents themselves.

Following a stressful event, connectivity insures an extensive, systemic response. It encompasses all forms of physiological information exchange: neural, blood-borne, extracellular, and immune. Neurotransmitters, peptides, hormones, endocannabinoids, and cytokines are among the tools of connectivity. Neural, endocrine, and immune systems are able to mount a concerted, fully coordinated response because these messenger substances constantly exchange information and provide feedback. Connectivity makes possible many negative and positive feedback functions.

Posted on Thursday, August 16, 2012 at 10:34:00 PM in Nemetics
Dan RD

By Dan RD

Yo! I'm mulling the future of everything.  My interests are so diverse I've decided to collapse them into the study of Nemetics -- an ongoing collaborative inquiry.

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