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vii This book is about understanding. [JLJ - no, this book is about understanding... everything]
viii Systems science is ultimately about gaining more complete understanding... systems science can be considered the universal science. All sciences seek to gain and organize knowledge systematically.
xi something interesting has been developing in... all fields. People are beginning to recognize that the kinds of problems we seek to solve no longer involve single domains of knowledge and skills. Rather every field is experiencing the need to involve other fields and do integrative (what has been called cross-disciplinary) work.
xi It seems strange to say that there have not been any introductory textbooks in systems science... There are general books that are about systems science or systems thinking, but they do not attempt to systematically outline the subtopics and then provide an integrated perspective of the whole subject.
xii We think the subject of systems science will begin to take a front seat in education because the grasp of systemness is a powerful mental framework for thinking about literally everything in the world.
xii As you are about to find out, systems science is a huge subject.
xii We use Wikipedia links extensively throughout the book where we think the information is good
xv as the kinds of endeavors humans have been undertaking keep getting more and more complex, with components needed from multiple disciples, the need for a higher-level viewpoint and an ability to grapple with complex patterns has emerged as a new capability needed by society.
xv general systems science and systems thinking apply everywhere... The world needs many more systems scientists to help integrate the work of specialists.
p.6 All too often, disciplines are so specialized they do not know how to talk to one another... systems science... can serve as a critical disciplinary bridge for a society that increasingly recognizes the need for interdisciplinary understanding to grapple with daunting and complex systemic problems.
p.15 What patterns run through all this? Can we decipher principles of systemic dynamics that would shed light on the emergence of complex and seemingly volatile dynamics of living systems...?
p.30 Our ability to conceptualize a system is thought to be built right into the human brain. We automatically (subconsciously) categorize, note differences and similarities, find patterns, detect interconnections and patterns, and grasp changes over time (dynamics).
p.37 The natural world produces many phenomena with regular but always varied patterns such as we observe in the shapes of clouds and plants.
p.43 a complex, multi-faceted problem can be controlled only by means that have as much complexity (variety) as the problem being addressed. Sometimes referred to as "the first law of cybernetics," Ashby's variety law is the antithesis of the always attractive search for a "silver bullet" that will somehow make a complex problem go away.
p.44 Remington and Pollack, specialists in the management of complex projects, draw the corollary for real-life situations: the first order of business is to get the complex, interconnected dimensions of the problem in view.
p.59 We build models from common or repeated experiences, but often need to tweak them with education... The model is not in fact the reality, but a simplified version of reality that typically focuses on one or a few elements designed to address the question immediately at hand. Because of this selective simplification, no model can be pursued single-mindedly without producing unexpected and problematic "side effects."
p.63 understanding is never full, perfect, or complete, though continually open to revision... Our living world eludes complete understanding not only because of the limited and selective nature of models, but because, unlike some passive object, it becomes different as it is understood differently. Understanding guides the ways we act, and systems are reshaped in response, actively as well as passively.
p.71 Systems have behavior even when we can't see it directly.
p.73 "A system is a set of things - people, cells, molecules, or whatever - interconnected in such a way that they produce their own pattern of behavior over time..." Donella Meadows, 2008
p.79 Models are just abstract representations of systems that capture the essential features of that system. They can be as simple as diagrams or as abstract as mathematical functions.
p.84 Gregory Bateson... described information as "a difference that makes a difference" (Bateson 1972). Not every difference available is read as meaningful in a given interpretive scheme.
p.85 Knowledge is the base of patterned expectation against which incoming information can be interpreted.
p.86 We have a basic template in our brains for identifying patterns when we see them, even from the sketchiest exposure
p.119 Systems that persist in time and interact with their environments achieve this by virtue of maintaining an internal organization that is stable due to the strong coupling between components that make it up. There are other factors as well
p.119 Percepts are low-level patterns that are integrated in the sensory cortex of higher vertebrates
p.122 A pattern is any set of components that stand in an organized relationship with one another from one instance of a system to another. Patterns exist in both spatial and temporal domains.
p.128 A pattern can now be defined as the set of all relations that organize the set of features at any given level in a hierarchy of features, into a map. The map is a representation of the actual object in an abstract, but usable form.
p.129 Our conceptual grasp of the universe begins with our ability to create categories based on common features and differences between categories based on different features.
p.129 The process of decomposing an image into features, checking the specific features and their relations, and finding a mapping between the set of features and an identifiable object is called pattern recognition. It is what our brains do magnificently and what we are starting to teach computers how to do, even if primitively.
p.131 there are now numerous examples of pattern recognition processes operating in computers that emulate the processes that take place in living brains.
p.134 the human brain perceives objects based on their boundaries and their behaviors in interactions with their environments... The internal dynamics of systems consist of flows and forces that allow components of a system to interact with one another in a wide variety of ways.
p.134 Organization arises over time by the ongoing interactions of different components or subsystems, each with their unique boundary attributes. Complexity arises from the potential of a bounded system based on its components and the possible interactions between them.
p.134 The more potential complexity a system possesses, the more varied the possible outcomes of system evolution and dynamics become, and growth of complexity brings yet further potential.
p.138 different structures arise within networks to produce different functions.
p.162 The full significance of any component can be understood only in terms of the whole system to which it belongs, for the dynamic consequences of changes to a given component varies with its place in the systemic structure. And because of networked interdependencies, the consequences of a given change can be hard to anticipate.
p.171 "complex systems"... have properties we can generally agree upon:
- Complex systems often display behaviors that surprise us. We cannot easily predict what a complex active system will do next, even when the external conditions are seemingly the same.
- Complex systems require that a considerable amount of work be done in order to "understand" them.
- Complex systems cannot be easily described, and certainly not by simply listing their parts (obtained from the previous concern).
p.171 The more we understand a system... the less often it will surprise us... One possible measure of complexity might come from a ratio of our amount of surprise to our amount of understanding.
p.191 We now bring dynamics back into the picture... Components and subsystems don't generally just sit there in a static structure. They more often have behaviors.
p.212 A complex system surprises us often because by virtue of its complexity we fail to understand all of the parts, connections, and behaviors. [JLJ - yes, including all of the ways it can be articulated]
p.214 Learning is a form of adaptivity... that seems to involve some aspects of evolutionary process.
p.222 Adaptivity, in its basic form, involves change in the behavior of a system, but while using existing resources in response to environmental change.
When an environment changes in ways that have a bearing in the function of a given system, the system can persist and succeed only if it has a capacity to adapt to that change.
p.223 Adaptivity involves the use of information and internal control systems to manage the adapting process... the new conditions must somehow register on the system, and the system must have the inner capacity to respond to the change.
p.244 Resilience is the capacity for an adaptive system to rebound to normal function after a disturbance or, if need be, to adapt to a modified function should the disturbance prove to be long-lived.
p.245 The general resilience of a living organism depends on its ability to respond to changes in its environment. Specifically an organism must react to stressors and counteract their effects.
p.291 We frame our activity in terms of a richly textured fabric of expectations
p.292 Real systems are processes whose internal structures and responses to input messages reflect their expectations. Adaptive systems, sentient or not, will modify their behavior on the basis of information flows changing their expectations. A human being will react to surprise (information) by learning and changing behavior.
p.295 active systems can change their internal structure, and hence their functions or behaviors, as a result of receiving messages that are informational.
p.297 We might consider knowledge as the cumulative expectations with which a system moves into the future. In this sense we say that the system knows what it needs to know in order to exist comfortably in the flows of its current environment.
p.303-304 anticipation is a way of feeling one's way into the future, both alert for opportunities and perhaps even more alert for dangers. Anticipation of future events serves both purposes for complex adaptive systems. Anticipation allows such a system to alter its behavior so as to exploit opportunities or to avoid threats.
p.329 Heuristics, in general, are what we could describe as "rules of thumb" or rules that work most of the time but are not guaranteed in the same way that algorithms are guaranteed. Because they are not guaranteed, good heuristics involve coming up with alternatives when what usually works does not... heuristics are useful in the realm of inductive and abductive inference. Induction involves logic that builds generalizations from multiple specific instances that seem to point in a general direction... abduction... When we have constructed a causal chain based on inductive rules, we can also abduct, working backward, to infer a cause.
p.330-331 Instincts are heuristic approaches to survival. They are tested by natural selection and if found wanting will eventually go extinct. Nevertheless, while proving fit for the animal's survival, such instincts represent a "quick-and-dirty" way to solve problems for which no algorithmic solution might exist.
p.346 big brains like ours have evolved the ability to encode complex models of things in the world and how they work. In other words, we have the ability to build models of systems in our minds. This ability is crucial... in that models can be used to anticipate the future.
p.346 Storytelling is really what the brain does. We experience the world as a sequence of events that flow, generally, from one to the next over time. When we try to communicate with others and with ourselves, we actually construct stories.
p.357 Systems that can employ computation based on information input are capable of adaptivity in ways not achievable by other kinds of system.
p.357 the actual effective results of information flows and computational processes... The benefit of these sub-processes is, first, to obtain stability in a changing world; second, to provide a mechanism for resilience in that world when things change a great deal; and third, to provide a way to learn so as to be preadapted to future changes.
p.361 The systems that are most adaptive actually anticipate changes based on more extensive models of how the environment works in general, a deeper grasp of causal relations that will allow them to preemptively modify their responses to avoid the higher costs of repairing damaged subsystems that can occur as a result of the changes. We will see that anticipatory systems are, in fact, the most adaptive of all CASs and, in general, the most successful in sustaining themselves over long time spans.
p.372 The problem for most systems is maintaining a stable state in the face of environmental disturbances. [JLJ - or in warfare, to acquire and maintain a net-excess of coercive/motive force (in both the short- and long-term), which by threatening a variety of multiple objectives, allows the system to move indirectly towards desired goals, perhaps even preventing other agents from doing the same. My point is that when playing a social game, you want to do more than just maintain an equilibrium with your opponent.]
p.378-379 The objective of the control is to minimize the deviations from the ideal... In many real systems, parameter signals are corrupted by noise [JLJ - not usually the situation faced by a machine playing a game. In real-world control situations, the world is known in such detail as to make control the main problem (as in driving a car). In playing a game of strategy, we must construct scenarios to determine how the interlocking forces on the gameboard are likely to resolve, by asking ourselves "How might I proceed?", and producing "musings" which are then used to construct the scenarios. We aim for a stance where the adaptive capacity to mobilize coercion is estimated and sustainability developed.]
p.390 Humans... move into the future with anticipation. This is an important advantage when it comes to control processes: if one can somehow calculate and begin a response to a change before it occurs, the problem of lag time is greatly reduced.
p.394 living systems are the epitome of adaptive systems.
p.399 Adapting responses based on the running history of stimulus experiences is called demand-driven plasticity.
p.400 An anticipatory controller is one that can use a forward signal (e.g., feed-forward from the inputs) that gives a warning that deviations in the primary output are about to be felt. The anticipator can then use this warning to initiate responses even before the trouble begins and, in essence, nip the problem in the bud.
p.401 An associative anticipatory controller is one that can exploit implied causal relations (time-lagged correlated events) to respond to stimuli from non-impacting "cue" events rather than waiting for the impactful stimulus event. The system uses a new sensory system to detect changes in the associated environmental parameter (a cue at time t1) to trigger... the responder... The response... starts before the stimulus event
p.401 How do the differences registered by the senses become "differences that make a difference," that is, information that guides activity? The data gathered by sensory input has to be processed interpretively in order to become meaningful information
p.402 an occurrence of an event in AF [the associated environmental parameter] somehow causes as event in MF [the meaningful environmental parameter].
p.402 The eventAF is non-meaningful in the sense that it does not have any impact on the critical parameter in the responding system, except through the meaningful environment parameter. But if it occurs sufficiently before the eventMF, and reliably so, then it can act as a cue event. The associator must recognize this cue and trigger the response... One can see in this ability to form time-lagged linkages an essential foundation for the emergence of imagination. The stimulus of the cue fires the neuron-embedded association with the effect, which becomes present in anticipation.
p.412 In order for a coordinator in any kind of complex system to successfully perform its duties, it needs a model of the entire set of sub-processes over which it has control... [footnote]Some authors have adopted the term "second-order cybernetics" to acknowledge the role of model building and using in making complex decisions for control purposes. See Heylighen and Joslyn (2001, p.3).
p.414 In most processes, there is a need to match the timing of the availability of resources with the timing of the need for use of that resource. A buffer is used to hold the resource when it is obtained.
p.421 Many systems cannot control their environments, but they can change their own situation relative to the environment, thereby coordinating themselves with that environment.
p.421 In many cases... feed-forward information allows a system to react more quickly to real-time events taking place... Messages from senses are interpreted with cause-and-effect anticipation... making foraging, hunting, hiding, and all sorts of purposive or actively adaptive environmental interaction possible... Controllers using feed-forward information can minimize disruptions
p.425 an electronics firm that does not constantly reinvent itself will soon be toast. Such systems are faced with both rapidly and, at another scale, more slowly changing relations with their environments. It is no longer possible to solely rely on tactical models and communications because these cannot readily adapt to changes that would ultimately alter the entity's access to resources and avoidance of dangers while maintaining its systemic integrity... these more complex systems... have many more degrees of freedom with respect to how they maintain their integrity... they have many more ways to interact with their environments and fulfill their needs... Such systems move beyond short-term tactics to become strategic
p.425 It is something of a truism that complex systems exist in complex environments. It is also a truism that for a complex system to continue to exist and function in a complex environment it had better be able to do more than just react to short-term fluctuations in that environment.
p.428 The trick of anticipatory adaptation can be further extended to provide an advantage to a CAS having the capability of building complex models not just of its tactical interactions but the interactions of those other associated elements in the environment... This is the strategic adaptation problem.
p.428 This strategic challenge varies with the complexity of the sources and sinks by which a system is sustained.
p.429 meaning is tied to the needs of a given organism
p.429 Tactics and strategy characterize systems that have the ability to in some way anticipate the future. We have seen that feed-forward information is critical for anticipation. Very complex dynamic systems... need models of the environment that will allow them to compute likelihoods of changes based on current information... Such systems need to develop longer-term scenarios in order to plan actions the logistical and tactical coordinators to take over various time scales... anticipation seems to demand that they need to think about their future and how to succeed. [JLJ - just like the military "wargaming" its way into the future, we see here that "thinking" is the apparent universal problem solver for complex systems. That answer works fine here, but now you need to define thinking.]
p.430 strategic thinking... is coupled with the ability to learn, remember, and especially to apply that in picturing at least a near future possible scenario in their minds. The essence of strategy is being able to control their behavior based on that picture. In the short term, this is tactics, but as the anticipated span and relational complexity of the situation ratchets up, we have the beginnings of strategic thinking.
p.435 Complex adaptive systems exist in complex environments... in order for CASs to be sustainable... they must have the resilience to adapt to many kinds of unforeseen changes in their environments.
p.441 The human brain is capable of dealing with an incredible amount of novelty in terms of what is happening in a person's environment... It can be argued that the human brain has become capable of modeling any kind of environmental situation that might come into existence. Indeed, we are often characterized as informavores or seekers of information (meaning novelty) because we seem to actively seek out what is new.
p.448 Heuristic components of control systems (computation) work most of the time on average. They only fail occasionally so that decisions based on heuristics are acceptable... Since most domains are not so solvable, living systems have little choice but to rely on heuristics.
p.461 Complex adaptive systems (CAS)... exhibit the dynamics of probing for an ever-changing adaptive fit.
p.468 In the most general sense, adaptation means changing a form or function in response to some force from the environment acting on the system.
p.470 A system of interest (SOI) interacts with its environment through its boundary
p.473 there isn't necessarily an a priori "right" solution to be gotten in evolution, just degrees of better fitness. And whatever fitness may be attained is never final, for the environment can always be counted on to change, thus changing the fitness relations... natural selection is an effective procedure for chasing better fitness endlessly!
p.474 Natural selection can only work on what is there to start with and its potentials to mutate in various ways. What remains after selection is only the best of the possible choices.
p.475 evolution does a reasonably good job of working out a complex systemic organization of mutual but continually changing fit.
p.491 Evolution is a stochastic search approach for solutions to fitness problems. [JLJ - biological evolution is a process that produces as output (after the passage of time) a descendant organism more fit for its environment. Variation and environmental selection are the mechanisms which accomplishes this. I don't see where "searching" comes into play - I see parallel "trials" and winners and losers.]
p.493 We are now ready to consider auto-organization... It turns out that this process is exactly a matter of chance generation of variations followed by selection of the most fit.
p.493 Recall that in systems of any complexity, it takes work, i.e., energy, both to create and to maintain structure... without maintenance, things fall apart.
p.498 Resilience and predictability are important building blocks of organization.
p.504 Emergence has to do with something new occurring [JLJ - emergence has to do with an unexpected/unanticipated interaction producing consequences which were not directly seen by the model being used to understand the situation]
p.505 Systems are constituted by relationships, so it should be no surprise that altering relationships changes the properties or that creating a more complex network of relationships may result in the emergence of a new sort of system with new properties.
p.519 A tool is any artifact that allows a human being to apply leverage to a work process so as to accomplish that process more quickly or more finely.
p.527 "Nothing in biology makes sense except in the light of evolution" Theodosius Dobzhansky, 1973 essay [JLJ - Dobzhansky lifted this from Pierre Teilhard de Chardin (1881-1955): "Evolution is the light which illuminates all facts, a trajectory which all lines of thought must follow - this is what evolution is."]
p.538 Richard Dawkins (1987) coined the phrase "Blind Watchmaker" to highlight the notion that nature produces systems that could pass as designed by a designer, but, in fact, are the result of blind processes. This is closely related to auto-organization.
p.568 species do not just evolve, they co-evolve with other equally evolving, adaptive organisms. Coevolution is already implicit in the notion of selection for environmental fit, for a critical element of adaptation to environment is fit with the other organisms and physical features that together constitute... the ecosystem.
p.574 As Norgaard puts it: "Everything is interlocked, yet everything is changing in accord with the interlockedness" (Norgaard 1994, p. 26).
p.582 in the short term, everything is gridlocked. In the longer term, everything changes.
p.584-585 The phenomenon of evolution is possibly one of the more important aspects of systems science...Evolution is an ongoing process that generally involves systems becoming more complex over time.
p.591 An important aspect to grasp is that there is probably no such thing as complete or perfect understanding. However, that doesn't mean we can't have practical understanding of very complex systems.
p.594 Systems analysis is often practically applied to organizational systems with the intent of engineering and constructing a "better" system... The purpose of systems analysis is gaining understanding.
p.621 An adaptive system is one that has the capacity already built in to adapt to a change. Adaptation is just the temporary shift in resource allocation internally in order to meet a change in demands on the system's existing response processes.
p.625 A model is an abstract representation of the real system.
p.629 the system knowledge base actually contains all of the knowledge needed to construct the model.
p.643 SA [Systems Analysis] is applicable to everything that is a system, and since, as we have argued, everything is a system in one sense or another, everything can be analyzed in this systematic way. That doesn't mean that we always have all the tools we need to perform adequate analysis.
p.645 Students should learn that all decisions are made on the basis of models. Most models are in our heads. Mental models are not true and accurate images of our surroundings, but are only sets of assumptions and observations gained from experience. Jay Forrester, 1994
p.645-646 Systems thinking is about understanding how the world, or at least some portion of it, works. The way we do this is to build models of the system based on how we think it works and what we already know. the model is then used to answer questions that we do not understand.
p.650 All models are incomplete reductions of the real system so can never really represent any kind of ultimate knowledge. Understanding is not some kind of absolute property. It is a question of relative understanding... The question that systems thinkers have to answer is: "How deeply do we need to understand a system?"... It depends on what kinds of problems we are trying to solve with respect to the system... Systems need to be understood because doing so offers some kind of advantage to the understander... there is always a point where the information returns on analysis effort diminish and deeper understanding brings no further benefit.
p.650-651 As we ourselves are components in a dynamic relational process, the process itself continually shifts and adjusts in response to the way we understand and act within it.
p.651 one of the primary technical issues that need to be resolved early in the modeling process is what questions you are trying to answer and peg the level of resolution of the model to those.
p.652 There is a price for too little and a price for too much [JLJ - model] resolution.
p.653 Models of complex processes are notoriously complex themselves.
p.659 the predictions coming from models can only be as good as the quality of the models themselves.
p.659 Scenario testing can be particularly useful if the modeler is seeking some kind of control over the future of the system.
p.659 The sciences are more interested in understanding systems than using predictions or scenarios for purposes of exploitation.
p.694 models are dynamic representations of not only the things but also the relations between things and how those change over time (dynamics).
p.696 Modeling is essential to getting a completion of understanding.
p.701 One of man's cognitive competencies is the ability to recognize the capacity for an object (or concept) to be used in a way, not originally part of its "design," to achieve an objective. William Gibson (1904-1979), the American psychologist called this capacity "affordance." It turns out to be a critical element in the process of invention... Every human, to one extent or another, is capable of using what is at hand to accomplish a goal whether what is at hand was intended for that purpose or not.
p.703 Sometimes affordance is required to see a new way to use an old thing in the process.
p.703 Engineers often do explorations of their own when they have no guidance from science.
p.706 Most of our modern problems... are of this complex nature.
p.729 Tools are instruments that extend and amplify the capacity of the human body to do work... Engineering is actually the art of creating tools or making tools better.
p.731 More and more of the "problems" that our civilization face are inherently complex and, hence, systemic. Their solution, if they can be solved, will involve systems engineering.
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