✨Performance Management: Skills (Just the Quotes)

"By far the most valuable possession is skill. Both war and the chances of fortune destroy other things, but skill is preserved." Hipparchus, Commentaries, 2nd century BC)

"Let a man practice the profession which he best knows." (Cicero, "Tusculanarum Disputationum", cca. 45 BC)

"Numeracy has two facets - reading and writing, or extracting numerical information and presenting it. The skills of data presentation may at first seem ad hoc and judgmental, a matter of style rather than of technology, but certain aspects can be formalized into explicit rules, the equivalent of elementary syntax." (Andrew Ehrenberg, "Rudiments of Numeracy", Journal of Royal Statistical Society, 1977)

"Five coordinating mechanisms seem to explain the fundamental ways in which organizations coordinate their work: mutual adjustment, direct supervision, standardization of work processes, standardization of work outputs, and standardization of worker skills." (Henry Mintzberg, "The Structuring of Organizations", 1979)

"Training is the teaching of specific skills. It should result in the employee having the ability to do something he or she could not do before." (Mary A Allison & Eric Anderson, "Managing Up, Managing Down", 1984)

"The skills that make technical professionals competent in their specialties are not necessarily the same ones that make them successful within their organizations." (Bernard Rosenbaum, "Training", 1986)

"[…] data analysis in the context of basic mathematical concepts and skills. The ability to use and interpret simple graphical and numerical descriptions of data is the foundation of numeracy […] Meaningful data aid in replacing an emphasis on calculation by the exercise of judgement and a stress on interpreting and communicating results." (David S Moore, "Statistics for All: Why, What and How?", 1990)

"[By understanding] I mean simply a sufficient grasp of concepts, principles, or skills so that one can bring them to bear on new problems and situations, deciding in which ways one’s present competencies can suffice and in which ways one may require new skills or knowledge." (Howard Gardner, "The Unschooled Mind", 1991)

"Education is not the piling on of learning, information, data, facts, skills, or abilities - that's training or instruction - but is rather making visible what is hidden as a seed." (Thomas W Moore, "The Education of the Heart", 1996)

"Even when you have skilled, motivated, hard-working people, the wrong team structure can undercut their efforts instead of catapulting them to success. A poor team structure can increase development time, reduce quality, damage morale, increase turnover, and ultimately lead to project cancellation." (Steve McConnell, "Rapid Development", 1996)

"Success or failure of a project depends upon the ability of key personnel to have sufficient data for decision-making. Project management is often considered to be both an art and a science. It is an art because of the strong need for interpersonal skills, and the project planning and control forms attempt to convert part of the 'art' into a science." (Harold Kerzner, "Strategic Planning for Project Management using a Project Management Maturity Model", 2001)

"Even with simple and usable models, most organizations will need to upgrade their analytical skills and literacy. Managers must come to view analytics as central to solving problems and identifying opportunities - to make it part of the fabric of daily operations." (Dominic Barton & David Court, "Making Advanced Analytics Work for You", 2012)

"The biggest thing to know is that data visualization is hard. Really difficult to pull off well. It requires harmonization of several skills sets and ways of thinking: conceptual, analytic, statistical, graphic design, programmatic, interface-design, story-telling, journalism - plus a bit of ‘gut feel.’ The end result is often simple and beautiful, but the process itself is usually challenging and messy." (David McCandless, 2013)

"Finding the right answer is important, of course. But more important is developing the ability to see that problems have multiple solutions, that getting from X to Y demands basic skills and mental agility, imagination, persistence, patience." (Mary H Futrell)

"Productivity is the name of the game, and gains in productivity will only come when better understanding and better relationships exist between management and the work force. [...] Managers have traditionally developed the skills in finance, planning, marketing and production techniques. Too often the relationships with their people have been assigned a secondary role. This is too important a subject not to receive first-line attention." (William Hewlett, "The Human Side of Management", [speech])

"Solving problems is a practical skill like, let us say, swimming. We acquire any practical skill by imitation and practice." (George Polya)

🕸Systems Engineering: Principles (Just the Quotes)

"[...] there is a universal principle, operating in every department of nature and at every stage of evolution, which is conservative, creative and constructive. [...] I have at last fixed upon the word synergy, as the term best adapted to express its twofold character of ‘energy’ and ‘mutuality’ or the systematic and organic ‘working together’ of the antithetical forces of nature. [...] Synergy is a synthesis of work, or synthetic work, and this is what is everywhere taking place. It may be said to begin with the primary atomic collision in which mass, motion, time, and space are involved, and to find its simplest expression in the formula for force, which implies a plurality of elements, and signifies an interaction of these elements." (Lester F Ward, "Pure Sociology", 1903)

"The true nature of the universal principle of synergy pervading all nature and creating all the different kinds of structure that we observe to exist, must now be made clearer. Primarily and essentially it is a process of equilibration, i.e., the several forces are first brought into a state of partial equilibrium. It begins in collision, conflict, antagonism, and opposition, and then we have the milder phases of antithesis, competition, and interaction, passing next into a modus vivendi, or compromise, and ending in collaboration and cooperation. […] The entire drift is toward economy, conservatism, and the prevention of waste." (James Q Dealey & Lester F Ward, "A Text-book of Sociology", 1905)

"[...] the concept of 'feedback', so simple and natural in certain elementary cases, becomes artificial and of little use when the interconnexions between the parts become more complex. When there are only two parts joined so that each affects the other, the properties of the feedback give important and useful information about the properties of the whole. But when the parts rise to even as few as four, if every one affects the other three, then twenty circuits can be traced through them; and knowing the properties of all the twenty circuits does not give complete information about the system. Such complex systems cannot be treated as an interlaced set of more or less independent feedback circuits, but only as a whole. For understanding the general principles of dynamic systems, therefore, the concept of feedback is inadequate in itself. What is important is that complex systems, richly cross-connected internally, have complex behaviours, and that these behaviours can be goal-seeking in complex patterns." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"The homeostatic principle does not apply literally to the functioning of all complex living systems, in that in counteracting entropy they move toward growth and expansion." (Daniel Katz, "The Social Psychology of Organizations", 1966)

"Traditional organizational theories have tended to view the human organization as a closed system. This tendency has led to a disregard of differing organizational environments and the nature of organizational dependency on environment. It has led also to an over-concentration on principles of internal organizational functioning, with consequent failure to develop and understand the processes of feedback which are essential to survival." (Daniel Katz, "The Social Psychology of Organizations", 1966)

"Cybernetics, based upon the principle of feedback or circular causal trains providing mechanisms for goal-seeking and self-controlling behavior." (Ludwig von Bertalanffy, "General System Theory", 1968)

"Perhaps the most important single characteristic of modern organizational cybernetics is this: That in addition to concern with the deleterious impacts of rigidly-imposed notions of what constitutes the application of good 'principles of organization and management' the organization is viewed as a subsystem of a larger system(s), and as comprised itself of functionally interdependent subsystems." (Richard F Ericson, "Organizational cybernetics and human values", 1969)  

"Open systems, in contrast to closed systems, exhibit a principle of equifinality, that is, a tendency to achieve a final state independent of initial conditions. In other words, open systems tend to 'resist' perturbations that take them away from some steady state. They can exhibit homeostasis." (Anatol Rapaport, "The Uses of Mathematical Isomorphism in General System Theory", 1972)

"[Hierarchy is] the principle according to which entities meaningfully treated as wholes are built up of smaller entities which are themselves wholes […] and so on. In hierarchy, emergent properties denote the levels." (Peter Checkland, "Systems Thinking, Systems Practice", 1981)

"Effect spreads its 'tentacles' not only forwards" (as a new cause giving rise to a new effect) but also backwards, to the cause which gave rise to it, thus modifying, exhausting or intensifying its force. This interaction of cause and effect is known as the principle of feedback. It operates everywhere, particularly in all self-organising systems where perception, storing, processing and use of information take place, as for example, in the organism, in a cybernetic device, and in society. The stability, control and progress of a system are inconceivable without feedback." (Alexander Spirkin, "Dialectical Materialism", 1983)

"A cardinal principle in systems theory is that all parties that have a stake in a system should be represented in its management." (Malcolm Knowles, "The Adult Learner: A Neglected Species", 1984)

"Systems thinking is a discipline for seeing wholes. It is a framework for seeing interrelationships rather than things, for seeing patterns of change rather than static 'snapshots'. It is a set of general principles- distilled over the course of the twentieth century, spanning fields as diverse as the physical and social sciences, engineering, and management. [...] During the last thirty years, these tools have been applied to understand a wide range of corporate, urban, regional, economic, political, ecological, and even psychological systems. And systems thinking is a sensibility for the subtle interconnectedness that gives living systems their unique character." (Peter Senge, "The Fifth Discipline", 1990)

"Systems thinking is a framework for seeing interrelationships rather than things, for seeing patterns rather than static snapshots. It is a set of general principles spanning fields as diverse as physical and social sciences, engineering and management." (Peter Senge, "The Fifth Discipline", 1990)

"Evolution is a technological, mathematical, informational, and biological process rolled into one. It could almost be said to be a law of physics, a principle that reigns over all created multitudes, whether they have genes or not." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"System engineering is the art and science of creating effective systems, using whole system, whole life principles." (Derek Hitchins, 1995)

"The basic principle of an autocatalytic network is that even though nothing can make itself, everything in the pot has at least one reaction that makes it, involving only other things in the pot. It's a symbiotic system in which everything cooperates to make the metabolism work - the whole is greater than the sum of the parts." (J Doyne Farmer, "The Second Law of Organization" [in The Third Culture: Beyond the Scientific Revolution], 1995)

"Contrary to what happens at equilibrium, or near equilibrium, systems far from equilibrium do not conform to any minimum principle that is valid for functions of free energy or entropy production." (Ilya Prigogine, "The End of Certainty: Time, Chaos, and the New Laws of Nature", 1996) 

"These, then, are some of the basic principles of ecology - interdependence, recycling, partnership, flexibility, diversity, and, as a consequence of all those, sustainability... the survival of humanity will depend on our ecological literacy, on our ability to understand these principles of ecology and live accordingly.(Fritjof Capra, "The Web of Life", 1996)

"[…] swarm intelligence is becoming a valuable tool for optimizing the operations of various businesses. Whether similar gains will be made in helping companies better organize themselves and develop more effective strategies remains to be seen. At the very least, though, the field provides a fresh new framework for solving such problems, and it questions the wisdom of certain assumptions regarding the need for employee supervision through command-and-control management. In the future, some companies could build their entire businesses from the ground up using the principles of swarm intelligence, integrating the approach throughout their operations, organization, and strategy. The result: the ultimate self-organizing enterprise that could adapt quickly - and instinctively - to fast-changing markets." (Eric Bonabeau & Christopher Meyer, "Swarm Intelligence: A Whole New Way to Think About Business", Harvard Business Review, 2001)

"In complexity thinking the darkness principle is covered by the concept of incompressibility [...] The concept of incompressibility suggests that the best representation of a complex system is the system itself and that any representation other than the system itself will necessarily misrepresent certain aspects of the original system." (Kurt Richardson, "Systems theory and complexity: Part 1", Emergence: Complexity & Organization Vol.6" (3), 2004)

"The model theory postulates that mental models are parsimonious. They represent what is possible, but not what is impossible, according to assertions. This principle of parsimony minimizes the load on working memory, and so it applies unless something exceptional occurs to overrule it." (Philip N Johnson-Laird, Mental Models, Sentential Reasoning, and Illusory Inferences, [in "Mental Models and the Mind"], 2006)

"This reduction principle - the reduction of the behavior of a complex system to the behavior of its parts - is valid only if the level of complexity of the system is rather low." (Andrzej P Wierzbicki & Yoshiteru Nakamori, "Creative Space: Models of Creative Processes for the Knowledge Civilization Age", Studies in Computational Intelligence Vol.10, 2006)

"Principle of Equifinality: If a steady state is reached in an open system, it is independent of the initial conditions, and determined only by the system parameters, i.e. rates of reaction and transport." (Kevin Adams & Charles Keating, "Systems of systems engineering", 2012)

"In an information economy, entrepreneurs master the science of information in order to overcome the laws of the purely physical sciences. They can succeed because of the surprising power of the laws of information, which are conducive to human creativity. The central concept of information theory is a measure of freedom of choice. The principle of matter, on the other hand, is not liberty but limitation - it has weight and occupies space." (George Gilder, "Knowledge and Power: The Information Theory of Capitalism and How it is Revolutionizing our World", 2013)

"A key discovery of network science is that the architecture of networks emerging in various domains of science, nature, and technology are similar to each other, a consequence of being governed by the same organizing principles. Consequently we can use a common set of mathematical tools to explore these systems. " (Albert-László Barabási, "Network Science", 2016)

"The exploding interest in network science during the first decade of the 21st century is rooted in the discovery that despite the obvious diversity of complex systems, the structure and the evolution of the networks behind each system is driven by a common set of fundamental laws and principles. Therefore, notwithstanding the amazing differences in form, size, nature, age, and scope of real networks, most networks are driven by common organizing principles. Once we disregard the nature of the components and the precise nature of the interactions between them, the obtained networks are more similar than different from each other." (Albert-László Barabási, "Network Science", 2016)

🕸Systems Engineering: Communication Theory (Just the Quotes)

"We have decided to call the entire field of control and communication theory, whether in the machine or in the animal, by the name Cybernetics, which we form from the Greek [...] for steersman. In choosing this term, we wish to recognize that the first significant paper on feedback mechanisms is an article on governors, which was published by Clerk Maxwell in 1868, and that governor is derived from a Latin corruption [...] We also wish to refer to the fact that the steering engines of a ship are indeed one of the earliest and best-developed forms of feedback mechanisms." (Norbert Wiener, "Cybernetics", 1948)

"Incomplete knowledge of the future, and also of the past of the transmitter from which the future might be constructed, is at the very basis of the concept of information. On the other hand, complete ignorance also precludes communication; a common language is required, that is to say an agreement between the transmitter and the receiver regarding the elements used in the communication process [... The information of a message can] be defined as the 'minimum number of binary decisions which enable the receiver to construct the message, on the basis of the data already available to him.' These data comprise both the convention regarding the symbols and the language used, and the knowledge available at the moment when the message started." (Dennis Gabor, "Optical transmission" [in: "Information Theory: Papers Read at a Symposium on Information Theory"], 1952) 

"The theory of communication is partly concerned with the measurement of information content of signals, as their essential property in the establishment of communication links. But the information content of signals is not to be regarded as a commodity; it is more a property or potential of the signals, and as a concept it is closely related to the idea of selection, or discrimination. This mathematical theory first arose in telegraphy and telephony, being developed for the purpose of measuring the information content of telecommunication signals. It concerned only the signals themselves as transmitted along wires, or broadcast through the aether, and is quite abstracted from all questions of 'meaning'. Nor does it concern the importance, the value, or truth to any particular person. As a theory, it lies at the syntactic level of sign theory and is abstracted from the semantic and pragmatic levels. We shall argue [...] that, though the theory does not directly involve biological elements, it is nevertheless quite basic to the study of human communication - basic but insufficient." (Colin Cherry, "On Human Communication", 1957)

"A more viable model, one much more faithful to the kind of system that society is more and more recognized to be, is in process of developing out of, or is in keeping with, the modern systems perspective (which we use loosely here to refer to general systems research, cybernetics, information and communication theory, and related fields). Society, or the sociocultural system, is not, then, principally an equilibrium system or a homeostatic system, but what we shall simply refer to as a complex adaptive system." (Walter F Buckley, "Society as a complex adaptive system", 1968)

"The general notion in communication theory is that of information. In many cases, the flow of information corresponds to a flow of energy, e. g. if light waves emitted by some objects reach the eye or a photoelectric cell, elicit some reaction of the organism or some machinery, and thus convey information." (Ludwig von Bertalanffy, "General System Theory", 1968) 

"The 'flow of information' through human communication channels is enormous. So far no theory exists, to our knowledge, which attributes any sort of unambiguous measure to this 'flow'." (Anatol Rapoport, "Modern Systems Research for the Behavioral Scientist", 1969)

"The term hypothesis testing arises because the choice as to which process is observed is based on hypothesized models. Thus hypothesis testing could also be called model testing. Hypothesis testing is sometimes called decision theory. The detection theory of communication theory is a special case." (Fred C Scweppe, "Uncertain dynamic systems", 1973)

"Communication theory deals with certain important but abstract aspects of communication. Communication theory proceeds from clear and definite assumptions to theorems concerning information sources and communication channels. In this it is essentially mathematical, and in order to understand it we must understand the idea of a theorem as a statement which must be proved, that is, which must be shown to be the necessary consequence of a set of initial assumptions. This is an idea which is the very heart of mathematics as mathematicians understand it." (John R Pierce, "An Introduction to Information Theory: Symbols, Signals & Noise" 2nd Ed., 1980)

"Communication theory tells us how many bits of information can be sent per second over perfect and imperfect communication channels in terms of rather abstract descriptions of the properties of these channels. Communication theory tells us how to measure the rate at which a message source, such as a speaker or a writer, generates information. Communication theory tells us how to represent, or encode, messages from a particular message source efficiently for transmission over a particular sort of channel, such as an electrical circuit, and it tells us when we can avoid errors in transmission." (John R Pierce, "An Introduction to Information Theory: Symbols, Signals & Noise" 2nd Ed., 1980)

"In communication theory we consider a message source, such as a writer or a speaker, which may produce on a given occasion any one of many possible messages. The amount of information conveyed by the message increases as the amount of uncertainty as to what message actually will be produced becomes greater. A message which is one out of ten possible messages conveys a smaller amount of information than a message which is one out of a million possible messages. The entropy of communication theory is a measure of this uncertainty and the uncertainty, or entropy, is taken as the measure of the amount of information conveyed by a message from a source. The more we know about what message the source will produce, the less uncertainty, the less the entropy, and the less the information." (John R Pierce, "An Introduction to Information Theory: Symbols, Signals & Noise" 2nd Ed., 1980)

"The amount of information conveyed by the message increases as the amount of uncertainty as to what message actually will be produced becomes greater. A message which is one out of ten possible messages conveys a smaller amount of information than a message which is one out of a million possible messages. The entropy of communication theory is a measure of this uncertainty and the uncertainty, or entropy, is taken as the measure of the amount of information conveyed by a message from a source. The more we know about what message the source will produce, the less uncertainty, the less the entropy, and the less the information." (John R Pierce, "An Introduction to Information Theory: Symbols, Signals and Noise", 1980)

"Thus, information is sometimes associated with the idea of knowledge through its popular use rather than with uncertainty and the resolution of uncertainty, as it is in communication theory." (John R Pierce, "An Introduction to Information Theory: Symbols, Signals & Noise" 2nd Ed., 1980)

"Cybernetics is concerned with scientific investigation of systemic processes of a highly varied nature, including such phenomena as regulation, information processing, information storage, adaptation, self-organization, self-reproduction, and strategic behavior. Within the general cybernetic approach, the following theoretical fields have developed: systems theory (system), communication theory, game theory, and decision theory." (Fritz B Simon et al, "Language of Family Therapy: A Systemic Vocabulary and Source Book", 1985)

"General evolution theory, based on the integration of the relevant tenets of general system theory, cybernetics, information and communication theory, chaos theory, dynamical systems theory, and nonequilibrium thermodynamics, can convey a sound understanding of the laws and dynamics that govern the evolution of complex systems in the various realms of investigation [...]. The basic notions of this new discipline can be developed to give an adequate account of the dynamical evolution of human societies as well. Such an account could furnish the basis of a system of knowledge better able to orient human beings and societies in their rapidly changing milieu." (Ervin László et al, "The Evolution of Cognitive Maps: New Paradigms for the Twenty-first Century", 1993)

"Communication theory is enormously rich in the range of ideas that fall within its nominal scope, and new theoretical work on communication has recently been flourishing. Nevertheless, despite the ancient roots and growing profusion of theories about communication, I argue that communication theory as an identifiable field of study does not yet exist." (Robert T Craig "Communication Theory as a Field", 1999)

"The very essence of mass communication theory is a simple but all-embracing expression of technological determinism, since the essential features depend on what certain technologies have made possible, certain technologies have made possible, especially the following: communication at a distance, the multiplication and simultaneous distribution of diverse ‘messages’, the enormous capacity and speed of carriers, and the limitations on response. There is no escaping the implication that public communication as practised in modern societies is profoundly shaped by these general features." (Denis McQuail, "McQuail's Reader in Mass Communication Theory", 2002)

"Without an understanding of causality there can be no theory of communication. What passes as information theory today is not communication at all, but merely transportation." (Marshall McLuhan, "The Book of Probes : Marshall McLuhan", 2011) 

"Cybernetics is an interdisciplinary science. It originated ‘at the junction’ of mathematics, logic, semiotics, physiology, biology and sociology. Among its inherent features, we mention analysis and revelation of general principles and approaches in scientific cognition. Control theory, communication theory, operations research and others represent most weighty theories within cybernetics 1.0." (Dmitry A Novikov, "Cybernetics 2.0", 2016)

✨Performance Management: Efficiency (Just the Quotes)

"Simplicity is the soul of efficiency." (Austin Freeman, "The Eye of Osiris", 1911)

"Efficiency and economy imply employment of the right instrument and material as well as their right use in the right manner." (Louis D Brandeis, "St. Louis & Ohio Railway v. U.S.", 1928)

"If we view organizations as adaptive, problem-solving structures, then inferences about effectiveness have to be made, not from static measures of output, but on the basis of the processes through which the organization approaches problems. In other words, no single measurement of organizational efficiency or satisfaction - no single time-slice of organizational performance can provide valid indicators of organizational health." (Warren G Bennis, "General Systems Yearbook", 1962)

"But waste is often hard to find. The costs of not-doing tend to be hidden in the figures. […] Waste runs high in any business. Man, after all, is not very efficient. Special efforts to find waste are therefore always necessary." (Peter F Drucker, "Managing for Results: Economic Tasks and Risk-taking Decisions", 1964)

"The myth of efficiency lies in the assumption that the most efficient manager is ipso facto the most effective; actually the most efficient manager working on the wrong task will not be effective." (R Alec Mackenzie, "The Time Trap", 1972)

"Effectiveness is the foundation of success - efficiency is a minimum condition for survival after success has been achieved. Efficiency is concerned with doing things right. Effectiveness is doing the right things." (Peter Drucker, "Management: Tasks, Responsibilities, Challenges", 1973)

"It is more important for the manager to get his information quickly and efficiently than to get it formally." (Henry Mintzberg, "The Nature of Managerial Work", 1973)

"Overemphasis of efficiency leads to an unfortunate circularity in design: for reasons of efficiency early programming languages reflected the characteristics of the early computers, and each generation of computers reflects the needs of the programming languages of the preceding generation." (Kenneth E Iverson, "Notation as a Tool of Thought", 1979)

"The practice of first developing a clear and precise definition of a process without regard for efficiency, and then using it as a guide and a test in exploring equivalent processes possessing other characteristics, such as greater efficiency, is very common in mathematics. It is a very fruitful practice which should not be blighted by premature emphasis on efficiency in computer execution." (Kenneth E Iverson, "Notation as a Tool of Thought", 1979)

"There is an especially efficient way to get information, much neglected by most managers. That is to visit a particular place in the company and observe what's going on there." (Andrew S Grove, "High Output Management", 1983)

"Most managers are rewarded if their unit operates efficiently and effectively. A highly creative unit, in contrast, might appear ineffective and uneven, and rather crazy to an outside or inside observer." (William G Dyer, "Strategies for Managing Change", 1984)

"The chain of command is an inefficient communication system. Although my staff and I had our goals, tasks, and priorities well defined, large parts of the organization didn't know what was going on. Frequent, thorough, open communication to every employee is essential to get the word out and keep walls from building within the company. And while face-to-face communication is more effective than impersonal messages, it's a good idea to vary the medium and the message so that no one (including top management) relies too much on ''traditional" channels of communication." (William H Peace, Harvard Business Review, 1986)

"The premise here is that the hierarchy lines on the chart are also the only communication conduit. Information can flow only along the lines. [...] The hierarchy lines are paths of authority. When communication happens only over the hierarchy lines, that's a priori evidence that the managers are trying to hold on to all control. This is not only inefficient but an insult to the people underneath." (Tom DeMarco, "Slack: Getting Past Burnout, Busywork, and the Myth of Total Efficiency", 2001)

"Management can be defined as the attainment of organizational goals in an effective and efficient manner through planning, organizing, staffing, directing, and controlling organizational resources." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

"An organization’s culture is the underlying set of key values, beliefs, understandings, and norms shared by employees. These underlying values and norms may pertain to ethical behavior, commitment to employees, efficiency, or customer service, and they provide the glue to hold organization members together. An organization’s culture is unwritten but can be observed in its stories, slogans, ceremonies, dress, and office layout." (Richard L Daft, "Organization Theory and Design", 3rd Ed., 2010)

"Efficiency refers to the amount of resources used to achieve the organization’s goals. It is based on the quantity of raw materials, money, and employees necessary to produce a given level of output. Effectiveness is a broader term, meaning the degree to which an organization achieves its goals." (Richard L Daft, "Organization Theory and Design", 3rd Ed., 2010)

"I think there is a profound and enduring beauty in simplicity; in clarity, in efficiency. True simplicity is derived from so much more than just the absence of clutter and ornamentation. It's about bringing order to complexity." (Jonathan Ive, 2013)

"Simplicity in a system tends to increase that system’s efficiency. Because less can go wrong with fewer parts, less will. Complexity in a system tends to increase that system’s inefficiency; the greater the number of variables, the greater the probability of those variables clashing, and in turn, the greater the potential for conflict and disarray. Because more can go wrong, more will. That is why centralized systems are inclined to break down quickly and become enmeshed in greater unintended consequences." (Lawrence K Samuels,"Defense of Chaos: The Chaology of Politics, Economics and Human Action", 2013)

🕸Systems Engineering: Goals (Just the Quotes)

"Linking the basic parts are communication, balance or system parts maintained in harmonious relationship with each other and decision making. The system theory include both man-machine and interpersonal relationships. Goals, man, machine, method, and process are woven together into a dynamic unity which reacts." (George R Terry, "Principles of Management", 1960)

"[System dynamics] is an approach that should help in important top-management problems [...] The solutions to small problems yield small rewards. Very often the most important problems are but little more difficult to handle than the unimportant. Many [people] predetermine mediocre results by setting initial goals too low. The attitude must be one of enterprise design. The expectation should be for major improvement [...] The attitude that the goal is to explain behavior; which is fairly common in academic circles, is not sufficient. The goal should be to find management policies and organizational structures that lead to greater success." (Jay W Forrester, "Industrial Dynamics", 1961)

"Most of our beliefs about complex organizations follow from one or the other of two distinct strategies. The closed-system strategy seeks certainty by incorporating only those variables positively associated with goal achievement and subjecting them to a monolithic control network. The open-system strategy shifts attention from goal achievement to survival and incorporates uncertainty by recognizing organizational interdependence with environment. A newer tradition enables us to conceive of the organization as an open system, indeterminate and faced with uncertainty, but subject to criteria of rationality and hence needing certainty." (James D Thompson, "Organizations in Action", 1967)

"Cybernetics, based upon the principle of feedback or circular causal trains providing mechanisms for goal-seeking and self-controlling behavior." (Ludwig von Bertalanffy, "General System Theory", 1968)

"General systems theory is the scientific exploration of 'wholes' and 'wholeness' which, not so long ago, were considered metaphysical notions transcending the boundaries of science. Hierarchic structure, stability, teleology, differentiation, approach to and maintenance of steady states, goal-directedness - these are a few of such general system properties." (Ervin László, "Introduction to Systems Philosophy", 1972)

"At the very least (there is certainly more), cybernetics implies a new philosophy about (1) what we can know, (2) about what it means for something to exist, and (3) about how to get things done. Cybernetics implies that knowledge is to be built up through effective goal-seeking processes, and perhaps not necessarily in uncovering timeless, absolute, attributes of things, irrespective of our purposes and needs." (Jeff Dooley, "Thoughts on the Question: What is Cybernetics", 1995)

"Cybernetics is a science of purposeful behavior. It helps us explain behavior as the continuous action of someone (or thing) in the process, as we see it, of maintaining certain conditions near a goal state, or purpose." (Jeff Dooley, "Thoughts on the Question: What is Cybernetics", 1995)

"System engineering is a robust approach to the design, creation, and operation of systems. In simple terms, the approach consists of identification and quantification of system goals, creation of alternative system design concepts, performance of design trades, selection and implementation of the best design, verification that the design is properly built and integrated, and post-implementation assessment of how well the system meets (or met) the goals." (NASA, "NASA Systems Engineering Handbook", 1995) 

"Complex systems operate under conditions far from equilibrium. Complex systems need a constant flow of energy to change, evolve and survive as complex entities. Equilibrium, symmetry and complete stability mean death. Just as the flow, of energy is necessary to fight entropy and maintain the complex structure of the system, society can only survive as a process. It is defined not by its origins or its goals, but by what it is doing." (Paul Cilliers,"Complexity and Postmodernism: Understanding Complex Systems", 1998)

"Just as dynamics arise from feedback, so too all learning depends on feedback. We make decisions that alter the real world; we gather information feedback about the real world, and using the new information we revise our understanding of the world and the decisions we make to bring our perception of the state of the system closer to our goals." (John D Sterman, "Business dynamics: Systems thinking and modeling for a complex world", 2000)

"The manager [...] is understood as one who observes the causal structure of an organization in order to be able to control it [...] This is taken to mean that the manager can choose the goals of the organization and design the systems or actions to realize those goals [...]. The possibility of so choosing goals and strategies relies on the predictability provided by the efficient and formative causal structure of the organization, as does the possibility of managers staying 'in control' of their organization's development. According to this perspective, organizations become what they are because of the choices made by their managers." (Ralph D Stacey et al, "Complexity and Management: Fad or Radical Challenge to Systems Thinking?", 2000)

"The science of cybernetics is not about thermostats or machines; that characterization is a caricature. Cybernetics is about purposiveness, goals, information flows, decision-making control processes and feedback (properly defined) at all levels of living systems." (Peter Corning, "Synergy, Cybernetics, and the Evolution of Politics", 2005) 

"The single most important property of a cybernetic system is that it is controlled by the relationship between endogenous goals and the external environment. [...] In a complex system, overarching goals may be maintained (or attained) by means of an array of hierarchically organized subgoals that may be pursued contemporaneously, cyclically, or seriatim." (Peter Corning, "Synergy, Cybernetics, and the Evolution of Politics", 2005) 

🕸Systems Engineering: Feedback (Definitions)

"Feedback is the control of a system by reinserting into the system the results of its performance. If these results are merely used as numerical data for criticism of the system and its regulation, we have the simple feedback of the control engineer. If, however, the information which proceeds backwards from the performance is able to change the general method and pattern of the performance, we have a process which may  very well be called learning." (Norbert Wiener, "The Human Use of Human Beings: Cybernetics and Society", 1954)

"In general, this term is used to describe systems or inputs where the current output or state can modify the effect of input. A positive feedback acts as an amplifier or magnifier on the output (e.g., the rich get richer and the poor get poorer). A negative feedback acts to diminish large inputs and magnify small inputs. This becomes important in keeping a system in control or 'on target'." (William J Raynor Jr., "The International Dictionary of Artificial Intelligence", 1999)

"set of signals connected from the output terminals to some input terminals." (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"The process in which part of the output of a system is returned to its input in order to regulate its further output. Often this is done intentionally, in order to control the dynamic behavior of the system." (Moti Frank, "Active Learning and Its Implementation for Teaching", 2008)

"The return of a portion of the output of a process or system to the input, especially when used to maintain performance or to control a system or process." (Dino Ruta, "Organizational Implications of Managing the HRIS Employee Experience", 2009)

"Connections that travel backward in a neural network from higher to lower layers creating a loop in the network that allows signals to circulate within it." (Terrence J Sejnowski, "The Deep Learning Revolution", 2018)

"when the effect of a causal impact comes back to influence the original cause of that effect." (David N Ford, "A system dynamics glossary", System Dynamics Review Vol. 35 (4), 2019)

"it is the process that allows to have information of one variable on another or others, during the simulation and not at the end, this allows to adjust on the model to modify, if it were the case, possible decisions that do not affect the desired result by the organization." (Ernesto A Lagarda-Leyva & Ernesto A Vega-Telles, "Application of System Dynamics in a Gasoline Service Station: Decision Making Using Graphical Interface", 2020)

🕸Systems Engineering: Environment (Just the Quotes)

"The change from one stable equilibrium to the other may take place as the result of the isolation of a small unrepresentative group of the population, a temporary change in the environment which alters the relative viability of different types, or in several other ways." (John B S Haldane, "The Causes of Evolution", 1932)

"An isolated system or a system in a uniform environment (which for the present consideration we do best to include as a part of the system we contemplate) increases its entropy and more or less rapidly approaches the inert state of maximum entropy. We now recognize this fundamental law of physics to be just the natural tendency of things to approach the chaotic state (the same tendency that the books of a library or the piles of papers and manuscripts on a writing desk display) unless we obviate it. (The analogue of irregular heat motion, in this case, is our handling those objects now and again without troubling to put them back in their proper places.) (Erwin Schrödinger, "What is Life?", 1944)

"Clearly, if the state of the system is coupled to parameters of an environment and the state of the environment is made to modify parameters of the system, a learning process will occur. Such an arrangement will be called a Finite Learning Machine, since it has a definite capacity. It is, of course, an active learning mechanism which trades with its surroundings. Indeed it is the limit case of a self-organizing system which will appear in the network if the currency supply is generalized." (Gordon Pask, "The Natural History of Networks", 1960)

"Every isolated determinate dynamic system, obeying unchanging laws, will ultimately develop some sort of organisms that are adapted to their environments." (W Ross Ashby, "Principles of the self-organizing system", 1962)

"[...] in a state of dynamic equilibrium with their environments. If they do not maintain this equilibrium they die; if they do maintain it they show a degree of spontaneity, variability, and purposiveness of response unknown in the non-living world. This is what is meant by ‘adaptation to environment’ […] [Its] essential feature […] is stability - that is, the ability to withstand disturbances." (Kenneth Craik, 'Living organisms', "The Nature of Psychology", 1966)

"System theory is basically concerned with problems of relationships, of structure, and of interdependence rather than with the constant attributes of objects. In general approach it resembles field theory except that its dynamics deal with temporal as well as spatial patterns. Older formulations of system constructs dealt with the closed systems of the physical sciences, in which relatively self-contained structures could be treated successfully as if they were independent of external forces. But living systems, whether biological organisms or social organizations, are acutely dependent on their external environment and so must be conceived of as open systems." (Daniel Katz, "The Social Psychology of Organizations", 1966)

"Conventional physics deals only with closed systems, i.e. systems which are considered to be isolated from their environment. [...] However, we find systems which by their very nature and definition are not closed systems. Every living organism is essentially an open system. It maintains itself in a continuous inflow and outflow, a building up and breaking down of components, never being, so long as it is alive, in a state of chemical and thermodynamic equilibrium but maintained in a so-called steady state which is distinct from the latter." (Ludwig von Bertalanffy, "General System Theory", 1968)

"System' is the concept that refers both to a complex of interdependencies between parts, components, and processes, that involves discernible regularities of relationships, and to a similar type of interdependency between such a complex and its surrounding environment." (Talcott Parsons, "Systems Analysis: Social Systems", 1968)

"To adapt to a changing environment, the system needs a variety of stable states that is large enough to react to all perturbations but not so large as to make its evolution uncontrollably chaotic. The most adequate states are selected according to their fitness, either directly by the environment, or by subsystems that have adapted to the environment at an earlier stage. Formally, the basic mechanism underlying self-organization is the (often noise-driven) variation which explores different regions in the system’s state space until it enters an attractor. This precludes further variation outside the attractor, and thus restricts the freedom of the system’s components to behave independently. This is equivalent to the increase of coherence, or decrease of statistical entropy, that defines self-organization." (Francis Heylighen, "The Science Of Self-Organization And Adaptivity", 1970)

"The main object of cybernetics is to supply adaptive, hierarchical models, involving feedback and the like, to all aspects of our environment. Often such modelling implies simulation of a system where the simulation should achieve the object of copying both the method of achievement and the end result. Synthesis, as opposed to simulation, is concerned with achieving only the end result and is less concerned (or completely unconcerned) with the method by which the end result is achieved. In the case of behaviour, psychology is concerned with simulation, while cybernetics, although also interested in simulation, is primarily concerned with synthesis." (Frank H George, "Soviet Cybernetics, the militairy and Professor Lerner", New Scientist, 1973)

"For any system the environment is always more complex than the system itself. No system can maintain itself by means of a point-for-point correlation with its environment, i.e., can summon enough 'requisite variety' to match its environment. So each one has to reduce environmental complexity - primarily by restricting the environment itself and perceiving it in a categorically preformed way. On the other hand, the difference of system and environment is a prerequisite for the reduction of complexity because reduction can be performed only within the system, both for the system itself and its environment." (Thomas Luckmann & Niklas Luhmann, "The Differentiation of Society", 1977)

"General systems theory and cybernetics supplanted the classical conceptual model of a whole made out of parts and relations between parts with a model emphasizing the difference between systems and environments. This new paradigm made it possible to relate both the structures (including forms of differentiation) and processes of systems to the environment." (Thomas Luckmann & Niklas Luhmann, "The Differentiation of Society", 1977)

"There is a strong current in contemporary culture advocating ‘holistic’ views as some sort of cure-all […] Reductionism implies attention to a lower level while holistic implies attention to higher level. These are intertwined in any satisfactory description: and each entails some loss relative to our cognitive preferences, as well as some gain [...] there is no whole system without an interconnection of its parts and there is no whole system without an environment." (Francisco Varela, "On being autonomous: The lessons of natural history for systems theory", 1977)

"Every system of whatever size must maintain its own structure and must deal with a dynamic environment, i.e., the system must strike a proper balance between stability and change. The cybernetic mechanisms for stability (i.e., homeostasis, negative feedback, autopoiesis, equifinality) and change (i.e., positive feedback, algedonodes, self-organization) are found in all viable systems." (Barry Clemson, "Cybernetics: A New Management Tool", 1984)

"Any system that insulates itself from diversity in the environment tends to atrophy and lose its complexity and distinctive nature." (Gareth Morgan, "Images of Organization", 1986)

"All systems evolve, although the rates of evolution may vary over time both between and within systems. The rate of evolution is a function of both the inherent stability of the system and changing environmental circumstances. But no system can be stabilized forever. For the universe as a whole, an isolated system, time’s arrow points toward greater and greater breakdown, leading to complete molecular chaos, maximum entropy, and heat death. For open systems, including the living systems that are of major interest to us and that interchange matter and energy with their external environments, time’s arrow points to evolution toward greater and greater complexity. Thus, the universe consists of islands of increasing order in a sea of decreasing order. Open systems evolve and maintain structure by exporting entropy to their external environments." (L Douglas Kiel, "Chaos Theory in the Social Sciences: Foundations and Applications", 1996)

"In a closed system, the change in entropy must always be 'positive', meaning toward death. However, in open biological or social systems, entropy can be arrested and may even be transformed into negative entropy - a process of more complete organization and enhanced ability to transform resources. Why? Because the system imports energy and resources from its environment, leading to renewal. This is why education and learning are so important, as they provide new and stimulating input (termed neg-entropy) that can transform each of us." (Stephen G Haines, "The Managers Pocket Guide to Systems Thinking & Learning", 1998)

"Neural networks conserve the complexity of the systems they model because they have complex structures themselves. Neural networks encode information about their environment in a distributed form. […] Neural networks have the capacity to self-organise their internal structure." (Paul Cilliers, "Complexity and Postmodernism: Understanding Complex Systems", 1998)

"All systems have a tendency toward maximum entropy, disorder, and death. Importing resources from the environment is key to long-term viability; closed systems move toward this disorganization faster than open systems." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"Systems thinking practices the exact opposite of this analytic approach. Systems thinking studies the organization as a whole in its interaction with its environment. Then, it works backwards to understand how each part of that whole works in relation to, and support of, the entire system’s objectives. Only then can the core strategies be formulated." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"Systems, and organizations as systems, can only be understood holistically. Try to understand the system and its environment first. Organizations are open systems and, as such, are viable only in interaction with and adaptation to the changing environment." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"Entropy [...] is the amount of disorder or randomness present in any system. All non-living systems tend toward disorder; left alone they will eventually lose all motion and degenerate into an inert mass. When this permanent stage is reached and no events occur, maximum entropy is attained. A living system can, for a finite time, avert this unalterable process by importing energy from its environment. It is then said to create negentropy, something which is characteristic of all kinds of life." (Lars Skyttner, "General Systems Theory: Ideas and Applications", 2001)

"The phenomenon of emergence takes place at critical points of instability that arise from fluctuations in the environment, amplified by feedback loops." (Fritjof Capra, "The Hidden Connections: A Science for Sustainable Living", 2002)

"Feedback and its big brother, control theory, are such important concepts that it is odd that they usually find no formal place in the education of physicists. On the practical side, experimentalists often need to use feedback. Almost any experiment is subject to the vagaries of environmental perturbations. Usually, one wants to vary a parameter of interest while holding all others constant. How to do this properly is the subject of control theory. More fundamentally, feedback is one of the great ideas developed (mostly) in the last century, with particularly deep consequences for biological systems, and all physicists should have some understanding of such a basic concept." (John Bechhoefer, "Feedback for physicists: A tutorial essay on control", Reviews of Modern Physics Vol. 77, 2005)

"The single most important property of a cybernetic system is that it is controlled by the relationship between endogenous goals and the external environment. [...] In a complex system, overarching goals may be maintained (or attained) by means of an array of hierarchically organized subgoals that may be pursued contemporaneously, cyclically, or seriatim." (Peter Corning, "Synergy, Cybernetics, and the Evolution of Politics", 2005)

"When defining living systems, the term dynamic equilibrium is essential. It does not imply something which is steady or stable. On the contrary, it is a floating state characterized by invisible movements and preparedness for change. To be in dynamic equilibrium is adapting adjustment to balance. Homeostasis stands for the sum of all control functions creating the state of dynamic equilibrium in a healthy organism. It is the ability of the body to maintain a narrow range of internal conditions in spite of environmental changes." (Lars Skyttner, "General Systems Theory: Problems, Perspective, Practice", 2005)

"Systematic usage of the methods of modern control theory to study physical systems is a key feature of a new research area in physics that may be called cybernetical physics. The subject of cybernetical physics is focused on studying physical systems by means of feedback interactions with the environment. Its methodology heavily relies on the design methods developed in cybernetics. However, the approach of cybernetical physics differs from the conventional use of feedback in control applications (e.g., robotics, mechatronics) aimed mainly at driving a system to a prespecified position or a given trajectory." (Alexander L Fradkov, "Cybernetical Physics: From Control of Chaos to Quantum Control", 2007)

"In physical, exponentially growing systems, there must be at least one reinforcing loop driving growth and at least one balancing feedback loop constraining growth, because no system can grow forever in a finite environment." (Donella H Meadows, "Thinking in Systems: A Primer", 2008)

"In that sense, a self-organizing system is intrinsically adaptive: it maintains its basic organization in spite of continuing changes in its environment. As noted, perturbations may even make the system more robust, by helping it to discover a more stable organization." (Francis Heylighen, "Complexity and Self-Organization", 2008)

"If universality is one of the observed characteristics of complex dynamical systems in many fields of study, a second characteristic that flows from the study of these systems is that of emergence. As self-organizing systems go about their daily business, they are constantly exchanging matter and energy with their environment, and this allows them to remain in a state that is far from equilibrium. That allows spontaneous behavior to give rise to new patterns." (Terry Cooke-Davies et al, "Exploring the Complexity of Projects", 2009)

"To remedy chaotic situations requires a chaotic approach, one that is non-linear, constantly morphing, and continually sharpening its competitive edge with recurring feedback loops that build upon past experiences and lessons learned. Improvement cannot be sustained without reflection. Chaos arises from myriad sources that stem from two origins: internal chaos rising within you, and external chaos being imposed upon you by the environment. The result of this push/pull effect is the disequilibrium [...]." (Jeff Boss, "Navigating Chaos: How to Find Certainty in Uncertain Situations", 2015)

More quotes on "Environment" at the-web-of-knowledge.blogspot.com.

🕸Systems Engineering: Feedback Loop (Definition)

"A feedback loop is a closed path of causal relations among variables. Feedback loops usually represent the process of monitoring the state of the system, the effects of decisions in the system state, and future decisions." (Luis F Luna-Reyes, "System Dynamics to Understand Public Information Technology", 2008)

"A circular chain of interactions, such that each element in the loop influences its own future level of activation. Feedback loops are also known as circuits." (Elizabeth Santiago-Cortés, "Discrete Networks as a Suitable Approach for the Analysis of Genetic Regulation", 2009)

"A feedback loop is a cycle in a directed graph whose edges can represent either positive or negative inputs." (Maria C A Leite & Yunjiao Wang, "Multistability, oscillations and bifurcations in feedback loops", Mathematical Biosciences and Engineering Vol 7 (1), 2010)

"A linked system of statements in a map in which the arrows show a path of links that feed back to the starting point. A feedback loop exists when the statements around the loop are all variables - that is, they can vary over time, typically increasing or decreasing, or getting better or worse. Feedback loops can be stable or generative (vicious or virtuous)." (Fran Ackermann et al, "Visual Strategy: Strategy Mapping for Public and Nonprofit Organizations", 2014)

[control *:] "A conceptual construct of control theory in which a comparison between a goal state and the measured current state drives a decision-making process for an action to bring the system closer to the goal state. The feedback loop increases the effectiveness of the defensive actions." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

"A feedback loop is a sequence of variables and causal links that creates a closed ring of causal influences." (David N Ford, "A system dynamics glossary", System Dynamics Review Vol. 35 (4), 2019)

[balancing *:] "a feedback loop in which the resultant effect of the causal links over time limits or constrains the movement of variables. Balancing loops seek equilibrium, trying to bring stocks to a desired state and keep them there. Also called a negative, compensating, goal-seeking or controlling feedback loop." (David N Ford, "A system dynamics glossary", System Dynamics Review Vol. 35 (4), 2019)

[reinforcing *:] "a feedback loop in which the sum effect of the causal links tends to strengthen (reinforce) the movement of variable values in a given direction due to positive feedback." (David N Ford, "A system dynamics glossary", System Dynamics Review Vol. 35 (4), 2019)

"Is a closed chain pattern of cause and effect reaction connections from a stock, activated by decisions, rules, physical laws, or actions." (Tatiana C Valencia & Stephanie J Valencia, "Cultivating Flow and Happiness in Children", 2020)

"Feedback loop is defined as a system used to control the level of a variable in which there is an identifiable receptor (sensor), control center (integrator or comparator), effectors, and methods of communication." (Lumen Learning, Anatomy and Physiology I [course])

🕸Systems Engineering: Optimization (Just the Quotes)

"The Systems Engineering method recognizes each system is an integrated whole even though composed of devices, specialized structures and sub-functions. It is further recognized that any system has a number of objectives and that the balance between them may differ widely from system to system. The methods seek to optimize the overall system function according to the weighted objectives and to achieve maximum capability of its parts." (Jack A Morton, "Integrating of Systems Engineering with Component Development", Electrical Manufacturing, 1959)

"The process of formulating and structuring a system are important and creative, since they provide and organize the information, which each system. 'establishes the number of objectives and the balance between them which will be optimized'. Furthermore, they help identify and define the system parts. Furthermore, they help identify and define the system parts which make up its 'diverse, specialized structures and subfunctions'." (Harold Chestnut, "Systems Engineering Tools", 1965)

"The Systems engineering method recognizes each system is an integrated whole even though composed of diverse, specialized structures and sub-functions. It further recognizes that any system has a number of objectives and that the balance between them may differ widely from system to system. The methods seek to optimize the overall system functions according to the weighted objectives and to achieve maximum compatibility of its parts." (Harold Chestnut, "Systems Engineering Tools", 1965)

"Game theory is a collection of mathematical models designed to study situations involving conflict and/or cooperation. It allows for a multiplicity of decision makers who may have different preferences and objectives. Such models involve a variety of different solution concepts concerned with strategic optimization, stability, bargaining, compromise, equity and coalition formation." (Notices of the American Mathematical Society Vol. 26 (1), 1979) 

"Because the individual parts of a complex adaptive system are continually revising their ('conditioned') rules for interaction, each part is embedded in perpetually novel surroundings (the changing behavior of the other parts). As a result, the aggregate behavior of the system is usually far from optimal, if indeed optimality can even be defined for the system as a whole. For this reason, standard theories in physics, economics, and elsewhere, are of little help because they concentrate on optimal end-points, whereas complex adaptive systems 'never get there'. They continue to evolve, and they steadily exhibit new forms of emergent behavior." (John H Holland, "Complex Adaptive Systems", Daedalus Vol. 121 (1), 1992) 

"Mathematical programming (or optimization theory) is that branch of mathematics dealing with techniques for maximizing or minimizing an objective function subject to linear, nonlinear, and integer constraints on the variables."  (George B Dantzig & Mukund N Thapa, "Linear Programming" Vol I, 1997)

"The whole idea of a system is to optimize - not maximize - the fit of its elements in order to maximize the whole. If we merely maximize the elements of systems, we end up suboptimizing the whole [...]" (Stephen G Haines, "The Managers Pocket Guide to Systems Thinking & Learning", 1998)

"Optimization by individual agents, often used to derive competitive equilibria, are unnecessary for an actual economy to approximately attain such equilibria. From the failure of humans to optimize in complex tasks, one need not conclude that the equilibria derived from the competitive model are descriptively irrelevant. We show that even in complex economic systems, such equilibria can be attained under a range of surprisingly weak assumptions about agent behavior." (Antoni Bosch-Domènech & Shyam Sunder, "Tracking the Invisible Hand", 2000)

"The players in a game are said to be in strategic equilibrium (or simply equilibrium) when their play is mutually optimal: when the actions and plans of each player are rational in the given strategic environment - i. e., when each knows the actions and plans of the others." (Robert Aumann, "War and Peace", 2005)

"Swarm intelligence can be effective when applied to highly complicated problems with many nonlinear factors, although it is often less effective than the genetic algorithm approach [...]. Swarm intelligence is related to swarm optimization […]. As with swarm intelligence, there is some evidence that at least some of the time swarm optimization can produce solutions that are more robust than genetic algorithms. Robustness here is defined as a solution’s resistance to performance degradation when the underlying variables are changed. (Michael J North & Charles M Macal, Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation, 2007)

"Optimization is more than finding the best simulation results. It is itself a complex and evolving field that, subject to certain information constraints, allows data scientists, statisticians, engineers, and traders alike to perform reality checks on modeling results." (Chris Conlan, "Automated Trading with R: Quantitative Research and Platform Development", 2016)

"It is the field of artificial intelligence in which the population is in the form of agents which search in a parallel fashion with multiple initialization points. The swarm intelligence-based algorithms mimic the physical and natural processes for mathematical modeling of the optimization algorithm. They have the properties of information interchange and non-centralized control structure." (Sajad A Rather & P Shanthi Bala, "Analysis of Gravitation-Based Optimization Algorithms for Clustering and Classification", 2020)

More quotes on "Optimization" at the-web-of-knowledge.blogspot.com.

🏗️Software Engineering: Coupling (Just the Quote)

"Make the coupling between modules visible." (Brian W Kernighan & Phillip J Plauger, "The Elements of Programming Style", 1974)

"If all of the elements in a large system are loosely coupled to one another, then any one element can adjust to and modify a local a local unique contingency without affecting the whole system. These local adaptations can be swift, relatively economical, and substantial." (Karl E Weick, "Educational organizations as loosely coupled systems", 1976)

"Watch for coupling that's too tight. 'Coupling' refers to how tight the connection is between two classes. In general, the looser the connection, the better. Several general guidelines flow from this concept: Minimize accessibility of classes and members. Avoid friend classes, because they're tightly coupled. Make data private rather than protected in a base class to make derived classes less tightly coupled to the base class. Avoid exposing member data in a class's public interface. Be wary of semantic violations of encapsulation. Observe the 'Law of Demeter' [...]. Coupling goes hand in glove with abstraction and encapsulation. Tight coupling occurs when an abstraction is leaky, or when encapsulation is broken." (Steve C McConnell," Code Complete: A Practical Handbook of Software Construction", 1993)

"One problem area for refactoring is databases. Most business applications are tightly coupled to the database schema that supports them. That's one reason that the database is difficult to change. Another reason is data migration. Even if you have carefully layered your system to minimize the dependencies between the database schema and the object model, changing the database schema forces you to migrate the data, which can be a long and fraught task." (Martin Fowler et al, "Refactoring: Improving the Design of Existing Code", 2002)

"Coupling describes how tightly a class or routine is related to other classes or routines. The goal is to create classes and routines with small, direct, visible, and flexible relations to other classes and routines, which is known as "loose coupling." The concept of coupling applies equally to classes and routines […] Good coupling between modules is loose enough that one module can easily be used by other modules." (Steve C McConnell, "Code Complete: A Practical Handbook of Software Construction" 2nd Ed., 2004)

"Design is the bridging activity between gathering and implementation of software requirements that satisfies the required needs. […] The fundamental goal of design is to reduce the number of dependencies between modules, thus reducing the complexity of the system. This is also known as coupling; lesser the coupling the better is the design. On the other hand, higher the binding between elements within a module (known as cohesion) the better is the design." (Vasudeva Varma, "Software Architecture: A Case Based Approach", 2009)

"What can you do to actually make your code tell the truth as clearly as possible? Strive for good names. Structure your code with respect to cohesive functionality, which also eases naming. Decouple your code to achieve orthogonality. Write automated tests explaining the intended behavior and check the interfaces. Refactor mercilessly when you learn how to code a simpler, better solution. Make your code as simple as possible to read and understand." (Peter Sommerlad, [in Kevlin Henney’s "97 Things Every Programmer Should Know", 2010])

"The primary intent behind the principle of encapsulation is to separate the interface and the implementation, which enables the two to change nearly independently. This separation of concerns allows the implementation details to be hidden from the clients who must depend only on the interface of the abstraction. If an abstraction exposes implementation details to the clients, it leads to undesirable coupling between the abstraction and its clients, which will impact the clients whenever the abstraction needs to change its implementation details. Providing more access than required can expose implementation details to the clients, thereby, violating the 'principle of hiding'." (Girish Suryanarayana et al, "Refactoring for Software Design Smells: Managing Technical Debt", 2015)

"Architecture is the tension between coupling and cohesion." (Neal Ford)

🕸Systems Engineering: Behavior (Just the Quotes)

"[Disorganized complexity] is a problem in which the number of variables is very large, and one in which each of the many variables has a behavior which is individually erratic, or perhaps totally unknown. However, in spite of this helter-skelter, or unknown, behavior of all the individual variables, the system as a whole possesses certain orderly and analyzable average properties. [...] [Organized complexity is] not problems of disorganized complexity, to which statistical methods hold the key. They are all problems which involve dealing simultaneously with a sizable number of factors which are interrelated into an organic whole. They are all, in the language here proposed, problems of organized complexity." (Warren Weaver, "Science and Complexity", American Scientist Vol. 36, 1948)

"The first attempts to consider the behavior of so-called 'random neural nets' in a systematic way have led to a series of problems concerned with relations between the 'structure' and the 'function' of such nets. The 'structure' of a random net is not a clearly defined topological manifold such as could be used to describe a circuit with explicitly given connections. In a random neural net, one does not speak of 'this' neuron synapsing on 'that' one, but rather in terms of tendencies and probabilities associated with points or regions in the net." (Anatol Rapoport, "Cycle distributions in random nets", The Bulletin of Mathematical Biophysics 10(3), 1948)

"Every organism represents a system, by which term we mean a complex of elements in mutual interaction. From this obvious statement the limitations of the analytical and summative conceptions must follow. First, it is impossible to resolve the phenomena of life completely into elementary units; for each individual part and each individual event depends not only on conditions within itself, but also to a greater or lesser extent on the conditions within the whole, or within superordinate units of which it is a part. Hence the behavior of an isolated part is, in general, different from its behavior within the context of the whole. [...] Secondly, the actual whole shows properties that are absent from its isolated parts." (Ludwig von Bertalanffy, "Problems of Life", 1952)

"In our definition of system we noted that all systems have interrelationships between objects and between their attributes. If every part of the system is so related to every other part that any change in one aspect results in dynamic changes in all other parts of the total system, the system is said to behave as a whole or coherently. At the other extreme is a set of parts that are completely unrelated: that is, a change in each part depends only on that part alone. The variation in the set is the physical sum of the variations of the parts. Such behavior is called independent or physical summativity." (Arthur D Hall & Robert E Fagen, "Definition of System", General Systems Vol. 1, 1956)

"Systems engineering is the name given to engineering activity which considers the overall behavior of a system, or more generally which considers all factors bearing on a problem, and the systems approach to control engineering problems is correspondingly that approach which examines the total dynamic behavior of an integrated system. It is concerned more with quality of performance than with sizes, capacities, or efficiencies, although in the most general sense systems engineering is concerned with overall, comprehensive appraisal." (Ernest F Johnson, "Automatic process control", 1958)

"To say a system is 'self-organizing' leaves open two quite different meanings. There is a first meaning that is simple and unobjectionable. This refers to the system that starts with its parts separate (so that the behavior of each is independent of the others' states) and whose parts then act so that they change towards forming connections of some type. Such a system is 'self-organizing' in the sense that it changes from 'parts separated' to 'parts joined'. […] In general such systems can be more simply characterized as 'self-connecting', for the change from independence between the parts to conditionality can always be seen as some form of 'connection', even if it is as purely functional […]" (W Ross Ashby, "Principles of the self-organizing system", 1962)

"Synergy is the only word in our language that means behavior of whole systems unpredicted by the separately observed behaviors of any of the system's separate parts or any subassembly of the system's parts." (R Buckminster Fuller, "Operating Manual for Spaceship Earth", 1963)

"[…] cybernetics studies the flow of information round a system, and the way in which this information is used by the system as a means of controlling itself: it does this for animate and inanimate systems indifferently. For cybernetics is an interdisciplinary science, owing as much to biology as to physics, as much to the study of the brain as to the study of computers, and owing also a great deal to the formal languages of science for providing tools with which the behaviour of all these systems can be objectively described." (A Stafford Beer, 1966)

"We've seen that even in the simplest situations nonlinearities can interfere with a linear approach to aggregates. That point holds in general: nonlinear interactions almost always make the behavior of the aggregate more complicated than would be predicted by summing or averaging." (Lewis Mumford, "The Myth of the Machine" Vol 1, 1967)

"In complex systems cause and effect are often not closely related in either time or space. The structure of a complex system is not a simple feedback loop where one system state dominates the behavior. The complex system has a multiplicity of interacting feedback loops. Its internal rates of flow are controlled by nonlinear relationships. The complex system is of high order, meaning that there are many system states (or levels). It usually contains positive-feedback loops describing growth processes as well as negative, goal-seeking loops. In the complex system the cause of a difficulty may lie far back in time from the symptoms, or in a completely different and remote part of the system. In fact, causes are usually found, not in prior events, but in the structure and policies of the system." (Jay W Forrester, "Urban dynamics", 1969)

"To adapt to a changing environment, the system needs a variety of stable states that is large enough to react to all perturbations but not so large as to make its evolution uncontrollably chaotic. The most adequate states are selected according to their fitness, either directly by the environment, or by subsystems that have adapted to the environment at an earlier stage. Formally, the basic mechanism underlying self-organization is the (often noise-driven) variation which explores different regions in the system’s state space until it enters an attractor. This precludes further variation outside the attractor, and thus restricts the freedom of the system’s components to behave independently. This is equivalent to the increase of coherence, or decrease of statistical entropy, that defines self-organization." (Francis Heylighen, "The Science Of Self-Organization And Adaptivity", 1970)

"As the complexity of a system increases, our ability to make precise and yet significant statements about its behavior diminishes until a threshold is reached beyond which precision and significance (or relevance) become almost mutually exclusive characteristics." (Lotfi A Zadeh, 1973)

"When a mess, which is a system of problems, is taken apart, it loses its essential properties and so does each of its parts. The behavior of a mess depends more on how the treatment of its parts interact than how they act independently of each other. A partial solution to a whole system of problems is better than whole solutions of each of its parts taken separately." (Russell L Ackoff, "The future of operational research is past", The Journal of the Operational Research Society Vol. 30 (2), 1979)

"Given an approximate knowledge of a system's initial conditions and an understanding of natural law, one can calculate the approximate behavior of the system. This assumption lay at the philosophical heart of science." (James Gleick, Chaos: Making a New Science, 1987)

"Linear relationships are easy to think about: the more the merrier. Linear equations are solvable, which makes them suitable for textbooks. Linear systems have an important modular virtue: you can take them apart and put them together again - the pieces add up. Nonlinear systems generally cannot be solved and cannot be added together. [...] Nonlinearity means that the act of playing the game has a way of changing the rules. [...] That twisted changeability makes nonlinearity hard to calculate, but it also creates rich kinds of behavior that never occur in linear systems." (James Gleick, "Chaos: Making a New Science", 1987)

"Systems thinking is a special form of holistic thinking - dealing with wholes rather than parts. One way of thinking about this is in terms of a hierarchy of levels of biological organization and of the different 'emergent' properties that are evident in say, the whole plant (e.g. wilting) that are not evident at the level of the cell (loss of turgor). It is also possible to bring different perspectives to bear on these different levels of organization. Holistic thinking starts by looking at the nature and behaviour of the whole system that those participating have agreed to be worthy of study. This involves: (i) taking multiple partial views of 'reality' […] (ii) placing conceptual boundaries around the whole, or system of interest and (iii) devising ways of representing systems of interest." (C J Pearson and R L Ison, "Agronomy of Grassland Systems", 1987) 

"The term chaos is used in a specific sense where it is an inherently random pattern of behaviour generated by fixed inputs into deterministic (that is fixed) rules (relationships). The rules take the form of non-linear feedback loops. Although the specific path followed by the behaviour so generated is random and hence unpredictable in the long-term, it always has an underlying pattern to it, a 'hidden' pattern, a global pattern or rhythm. That pattern is self-similarity, that is a constant degree of variation, consistent variability, regular irregularity, or more precisely, a constant fractal dimension. Chaos is therefore order (a pattern) within disorder (random behaviour)." (Ralph D Stacey, "The Chaos Frontier: Creative Strategic Control for Business", 1991)

"Unfortunately, recognizing a system as chaotic will not tell us all that we might like to know. It will not provide us with a means of predicting the future course of the system. It will tell us that there is a limit to how far ahead we can predict, but it may not tell us what this limit is. Perhaps the best advice that chaos 'theory' can give us is not to jump at conclusions; unexpected occurrences may constitute perfectly normal behavior." (Edward N Lorenz, "Chaos, spontaneous climatic variations and detection of the greenhouse effect", 1991)

"Because the individual parts of a complex adaptive system are continually revising their ('conditioned') rules for interaction, each part is embedded in perpetually novel surroundings (the changing behavior of the other parts). As a result, the aggregate behavior of the system is usually far from optimal, if indeed optimality can even be defined for the system as a whole. For this reason, standard theories in physics, economics, and elsewhere, are of little help because they concentrate on optimal end-points, whereas complex adaptive systems 'never get there'. They continue to evolve, and they steadily exhibit new forms of emergent behavior." (John H Holland, "Complex Adaptive Systems", Daedalus Vol. 121 (1), 1992)

"Fundamental to catastrophe theory is the idea of a bifurcation. A bifurcation is an event that occurs in the evolution of a dynamic system in which the characteristic behavior of the system is transformed. This occurs when an attractor in the system changes in response to change in the value of a parameter. A catastrophe is one type of bifurcation. The broader framework within which catastrophes are located is called dynamical bifurcation theory." (Courtney Brown, "Chaos and Catastrophe Theories", 1995)

"When the behavior of the system depends on the behavior of the parts, the complexity of the whole must involve a description of the parts, thus it is large. The smaller the parts that must be described to describe the behavior of the whole, the larger the complexity of the entire system. […] A complex system is a system formed out of many components whose behavior is emergent, that is, the behavior of the system cannot be simply inferred from the behavior of its components." (Yaneer Bar-Yamm, "Dynamics of Complexity", 1997)

"Modelling techniques on powerful computers allow us to simulate the behaviour of complex systems without having to understand them.  We can do with technology what we cannot do with science.  […] The rise of powerful technology is not an unconditional blessing.  We have  to deal with what we do not understand, and that demands new  ways of thinking." (Paul Cilliers,"Complexity and Postmodernism: Understanding Complex Systems", 1998)

"It is, however, fair to say that very few applications of swarm intelligence have been developed. One of the main reasons for this relative lack of success resides in the fact that swarm-intelligent systems are hard to 'program', because the paths to problem solving are not predefined but emergent in these systems and result from interactions among individuals and between individuals and their environment as much as from the behaviors of the individuals themselves. Therefore, using a swarm-intelligent system to solve a problem requires a thorough knowledge not only of what individual behaviors must be implemented but also of what interactions are needed to produce such or such global behavior." (Eric Bonabeau et al, "Swarm Intelligence: From Natural to Artificial Systems", 1999)

"With the growing interest in complex adaptive systems, artificial life, swarms and simulated societies, the concept of “collective intelligence” is coming more and more to the fore. The basic idea is that a group of individuals (e. g. people, insects, robots, or software agents) can be smart in a way that none of its members is. Complex, apparently intelligent behavior may emerge from the synergy created by simple interactions between individuals that follow simple rules." (Francis Heylighen, "Collective Intelligence and its Implementation on the Web", 1999)

"Chaos theory reconciles our intuitive sense of free will with the deterministic laws of nature. However, it has an even deeper philosophical ramification. Not only do we have freedom to control our actions, but also the sensitivity to initial conditions implies that even our smallest act can drastically alter the course of history, for better or for worse. Like the butterfly flapping its wings, the results of our behavior are amplified with each day that passes, eventually producing a completely different world than would have existed in our absence!" (Julien C Sprott, "Strange Attractors: Creating Patterns in Chaos", 2000)

"The tipping point is that magic moment when an idea, trend, or social behavior crosses a threshold, tips, and spreads like wildfire." (Malcolm T Gladwell, "The Tipping Point: How Little Things Can Make a Big Difference", 2000)

"In principle, a self-organising system cannot be constructed, since its organisation and behaviour cannot be prescribed and created by an external source. It emerges autonomously in certain conditions (which cannot be prescribed either). The task of the researcher is to investigate in what kind of systems and under what kind of conditions self-organisation emerges." (Rein Vihalemm, "Chemistry as an Interesting Subject for the Philosophy of Science", 2001)

"A self-organizing system not only regulates or adapts its behavior, it creates its own organization. In that respect it differs fundamentally from our present systems, which are created by their designer. We define organization as structure with function. Structure means that the components of a system are arranged in a particular order. It requires both connections, that integrate the parts into a whole, and separations that differentiate subsystems, so as to avoid interference. Function means that this structure fulfils a purpose." (Francis Heylighen & Carlos Gershenson, "The Meaning of Self-organization in Computing", IEEE Intelligent Systems, 2003)

"Emergence is not really mysterious, although it may be complex. Emergence is brought about by the interactions between the parts of a system. The galloping horse illusion depends upon the persistence of the human retina/brain combination, for instance. Elemental gases bond in combination by sharing outer electrons, thereby altering the appearance and behavior of the combination. In every case of emergence, the source is interaction between the parts - sometimes, as with the brain, very many parts - so that the phenomenon defies simple explanation." (Derek Hitchins, "Advanced Systems Thinking, Engineering and Management", 2003)

"The existence of equilibria or steady periodic solutions is not sufficient to determine if a system will actually behave that way. The stability of these solutions must also be checked. As parameters are changed, a stable motion can become unstable and new solutions may appear. The study of the changes in the dynamic behavior of systems as parameters are varied is the subject of bifurcation theory. Values of the parameters at which the qualitative or topological nature of the motion changes are known as critical or bifurcation values." (Francis C Moona, "Nonlinear Dynamics", 2003)

"This reduction principle - the reduction of the behavior of a complex system to the behavior of its parts - is valid only if the level of complexity of the system is rather low." (Andrzej P Wierzbicki & Yoshiteru Nakamori, "Creative Space: Models of Creative Processes for the Knowledge Civilization Age", Studies in Computational Intelligence Vol.10, 2006)

"How is it that an ant colony can organize itself to carry out the complex tasks of food gathering and nest building and at the same time exhibit an enormous degree of resilience if disrupted and forced to adapt to changing situations? Natural systems are able not only to survive, but also to adapt and become better suited to their environment, in effect optimizing their behavior over time. They seemingly exhibit collective intelligence, or swarm intelligence as it is called, even without the existence of or the direction provided by a central authority." (Michael J North & Charles M Macal, "Managing Business Complexity: Discovering Strategic Solutions with Agent-Based Modeling and Simulation", 2007)

"In ecology, we are often interested in exploring the behavior of whole systems of species or ecosystem composed of individual components which interact through biological processes. We are interested not simply in the dynamics of each species or component in isolation, but the dynamics of each species or component in the context of all the others and how those coupled dynamics account for properties of the system as a whole, such as its persistence. This is what people seem to mean when they say that ecology is ‘holistic’, an otherwise rather vague term." (John Pastor, "Mathematical Ecology of Populations and Ecosystems", 2008)

"You can’t navigate well in an interconnected, feedback-dominated world unless you take your eyes off short-term events and look for long term behavior and structure; unless you are aware of false boundaries and bounded rationality; unless you take into account limiting factors, nonlinearities and delays." (Donella H Meadow, "Thinking in Systems: A Primer", 2008)

"Complexity theory can be defined broadly as the study of how order, structure, pattern, and novelty arise from extremely complicated, apparently chaotic systems and conversely, how complex behavior and structure emerges from simple underlying rules. As such, it includes those other areas of study that are collectively known as chaos theory, and nonlinear dynamical theory." (Terry Cooke-Davies et al, "Exploring the Complexity of Projects", 2009)

"[...] a high degree of unpredictability is associated with erratic trajectories. This not only because they look random but mostly because infinitesimally small uncertainties on the initial state of the system grow very quickly - actually exponentially fast. In real world, this error amplification translates into our inability to predict the system behavior from the unavoidable imperfect knowledge of its initial state." (Massimo Cencini," Chaos: From Simple Models to Complex Systems", 2010)

"System dynamics is an approach to understanding the behaviour of over time. It deals with internal feedback loops and time delays that affect the behaviour of the entire system. It also helps the decision maker untangle the complexity of the connections between various policy variables by providing a new language and set of tools to describe. Then it does this by modeling the cause and effect relationships among these variables." (Raed M Al-Qirem & Saad G Yaseen, "Modelling a Small Firm in Jordan Using System Dynamics", 2010)

"Complex systems defy intuitive solutions. Even a third-order, linear differential equation is unsolvable by inspection. Yet, important situations in management, economics, medicine, and social behavior usually lose reality if simplified to less than fifth-order nonlinear dynamic systems. Attempts to deal with nonlinear dynamic systems using ordinary processes of description and debate lead to internal inconsistencies. Underlying assumptions may have been left unclear and contradictory, and mental models are often logically incomplete. Resulting behavior is likely to be contrary to that implied by the assumptions being made about' underlying system structure and governing policies." (Jay W Forrester, "Modeling for What Purpose?", The Systems Thinker Vol. 24 (2), 2013)

"Each systems archetype embodies a particular theory about dynamic behavior that can serve as a starting point for selecting and formulating raw data into a coherent set of interrelationships. Once those relationships are made explicit and precise, the "theory" of the archetype can then further guide us in our data-gathering process to test the causal relationships through direct observation, data analysis, or group deliberation." (Daniel H Kim, "Systems Archetypes as Dynamic Theories", The Systems Thinker Vol. 24 (1), 2013)

"Swarm intelligence (SI) is a branch of computational intelligence that discusses the collective behavior emerging within self-organizing societies of agents. SI was inspired by the observation of the collective behavior in societies in nature such as the movement of birds and fish. The collective behavior of such ecosystems, and their artificial counterpart of SI, is not encoded within the set of rules that determines the movement of each isolated agent, but it emerges through the interaction of multiple agents." (Maximos A Kaliakatsos-Papakostas et al, "Intelligent Music Composition", 2013)

"The problem of complexity is at the heart of mankind’s inability to predict future events with any accuracy. Complexity science has demonstrated that the more factors found within a complex system, the more chances of unpredictable behavior. And without predictability, any meaningful control is nearly impossible. Obviously, this means that you cannot control what you cannot predict. The ability ever to predict long-term events is a pipedream. Mankind has little to do with changing climate; complexity does." (Lawrence K Samuels, "The Real Science Behind Changing Climate", LewRockwell.com, August 1, 2014)

"Complex systems are networks made of a number of components that interact with each other, typically in a nonlinear fashion. Complex systems may arise and evolve through self-organization, such that they are neither completely regular nor completely random, permitting the development of emergent behavior at macroscopic scales." (Hiroki Sayama, "Introduction to the Modeling and Analysis of Complex Systems", 2015)

"System dynamics [...] uses models and computer simulations to understand behavior of an entire system, and has been applied to the behavior of large and complex national issues. It portrays the relationships in systems as feedback loops, lags, and other descriptors to explain dynamics, that is, how a system behaves over time. Its quantitative methodology relies on what are called 'stock-and-flow diagrams' that reflect how levels of specific elements accumulate over time and the rate at which they change. Qualitative systems thinking constructs evolved from this quantitative discipline." (Karen L Higgins, "Economic Growth and Sustainability: Systems Thinking for a Complex World", 2015)

"A complex system means a system whose perceived complicated behaviors can be attributed to one or more of the following characteristics: large number of element, large number of relationships among elements, non-linear and discontinuous relationship, and uncertain characteristics of elements." (Chunfang Zhou, "Fostering Creative Problem Solvers in Higher Education: A Response to Complexity of Societies", Handbook of Research on Creative Problem-Solving Skill Development in Higher Education, 2017)

More quotes on "Behavior" at the-web-of-knowledge.blogspot.com.