🦋Science: On Reinforcement Learning (Quotes)

"[reinforcement learning is a]  training paradigm where the neural network is presented with a sequence of input data, followed by a reinforcement signal." (Joseph P Bigus, "Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support", 1996)

"[reinforcement learning is a] learning mode in which adaptive changes of the parameters due to reward or punishment depend on the final outcome of a whole sequence of behavior. The results of learning are evaluated by some performance index." (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"[reinforcement learning is a] learning method which interprets feedback from an environment to learn optimal sets of condition/response relationships for problem solving within that environment" (Pi-Sheng Deng, "Genetic Algorithm Applications to Optimization Modeling", Encyclopedia of Artificial Intelligence, 2009)

"[reinforcement learning is a] sub-area of machine learning concerned with how an agent ought to take actions in an environment so as to maximize some notion of long-term reward. Reinforcement learning algorithms attempt to find a policy that maps states of the world to the actions the agent ought to take in those states. Differently from supervised learning, in this case there is no target value for each input pattern, only a reward based of how good or bad was the action taken by the agent in the existent environment." (Marley Vellasco et al, "Hierarchical Neuro-Fuzzy Systems" Part II, Encyclopedia of Artificial Intelligence, 2009)

"[reinforcement learning is a] a type of machine learning in which an agent learns, through its own experience, to navigate through an environment, choosing actions in order to maximize the sum of rewards." (Lisa Torrey & Jude Shavlik, "Transfer Learning",  2010)

"[reinforcement learning is a] a machine learning technique whereby actions are associated with credits or penalties, sometimes with delay, and whereby, after a series of learning episodes, the learning agent has developed a model of which action to choose in a particular environment, based on the expectation of accumulated rewards." (Apostolos Georgas, "Scientific Workflows for Game Analytics", Encyclopedia of Business Analytics and Optimization", 2014)

"[reinforcement learning is a]  type of machine learning in which the machine learns what to do by discovering through trial and error the way to maximize a reward." (Gloria Phillips-Wren, "Intelligent Systems to Support Human Decision Making", 2014)

"[reinforcement learning] stands, in the context of computational learning, for a family of algorithms aimed at approximating the best policy to play in a certain environment (without building an explicit model of it) by increasing the probability of playing actions that improve the rewards received by the agent." (Fernando S Oliveira, "Reinforcement Learning for Business Modeling", 2014)

"The knowledge is obtained using rewards and punishments which there is an agent (learner) that acts autonomously and receives a scalar reward signal that is used to evaluate the consequences of its actions." (Nuno Pombo et al, "Machine Learning Approaches to Automated Medical Decision Support Systems", 2015)

"It is also known as learning with a critic. The agent takes a sequence of actions and receives a reward/penalty only at the very end, with no feedback during the intermediate actions. Using this limited information, the agent should learn to generate the actions to maximize the reward in later trials. For example, in chess, we do a set of moves, and at the very end, we win or lose the game; so we need to figure out what the actions that led us to this result were and correspondingly credit them." (Ethem Alpaydın, "Machine learning : the new AI", 2016)

"[reinforcement learning is a] learning algorithm for a robot or a software agent to take actions in an environment so as to maximize the sum of rewards through trial and error." (Tomohiro Yamaguchi et al, "Analyzing the Goal-Finding Process of Human Learning With the Reflection Subtask", 2018)

"Training/learning method aiming to automatically determine the ideal behavior within a specific context based on rewarding desired behaviors and/or punishing undesired one." (Ioan-Sorin Comşa et al, "Guaranteeing User Rates With Reinforcement Learning in 5G Radio Access Networks", 2019)

"Brach of the Artificial Intelligence field devoted to obtaining optimal control sequences for agents only by interacting with a concrete dynamical system." (Juan Parras & Santiago Zazo, "The Threat of Intelligent Attackers Using Deep Learning: The Backoff Attack Case", 2020)

"Machine learning approaches often used in robotics. A reward is used to teach a system a desired behavior." (Jörg Frochte et al, "Concerning the Integration of Machine Learning Content in Mechatronics Curricula", 2020)

"This area of deep learning includes methods which iterates over various steps in a process to get the desired results. Steps that yield desirable outcomes are content and steps that yield undesired outcomes are reprimanded until the algorithm is able to learn the given optimal process. In unassuming terms, learning is finished on its own or effort on feedback or content-based learning." (Amit K Tyagi & Poonam Chahal, "Artificial Intelligence and Machine Learning Algorithms", 2020)

"Reinforcement learning is also a subset of AI algorithms which creates independent, self-learning systems through trial and error. Any positive action is assigned a reward and any negative action would result in a punishment. Reinforcement learning can be used in training autonomous vehicles where the goal would be obtaining the maximum rewards." (Vijayaraghavan Varadharajan & Akanksha Rajendra Singh, "Building Intelligent Cities: Concepts, Principles, and Technologies", 2021)

🦋Science: On Failure (Quotes)

"Every detection of what is false directs us towards what is true: every trial exhausts some tempting form of error. Not only so; but scarcely any attempt is entirely a failure; scarcely any theory, the result of steady thought, is altogether false; no tempting form of error is without some latent charm derived from truth." (William Whewell, "Lectures on the History of Moral Philosophy in England", 1852)

"Scarcely any attempt is entirely a failure; scarcely any theory, the result of steady thought, is altogether false; no tempting form of Error is without some latent charm derived from Truth." (William Whewell, "Lectures on the History of Moral Philosophy in England", 1852)

"We learn wisdom from failure much more than from success. We often discover what will do, by finding out what will not do; and probably he who never made a mistake never made a discovery." (Samuel Smiles, "Facilities and Difficulties", 1859)

"One word characterizes the most strenuous of the efforts for the advancement of science that I have made perseveringly during fifty-five years, and that word is failure." (William Thomson [Kelvin], Nature Vol. 54, 1896)

"This history constitutes a mirror of past and present conditions in mathematics which can be made to bear on the notational problems now confronting mathematics. The successes and failures of the past will contribute to a more speedy solution of notational problems of the present time." (Florian Cajori, "A History of Mathematical Notations", 1928)

"[…] the statistical prediction of the future from the past cannot be generally valid, because whatever is future to any given past, is in tum past for some future. That is, whoever continually revises his judgment of the probability of a statistical generalization by its successively observed verifications and failures, cannot fail to make more successful predictions than if he should disregard the past in his anticipation of the future. This might be called the ‘Principle of statistical accumulation’." (Clarence I Lewis, "Mind and the World-Order: Outline of a Theory of Knowledge", 1929)

"The failure of the social sciences to think through and to integrate their several responsibilities for the common problem of relating the analysis of parts to the analysis of the whole constitutes one of the major lags crippling their utility as human tools of knowledge." (Robert S Lynd, "Knowledge of What?", 1939)

"Science condemns itself to failure when, yielding to the infatuation of the serious, it aspires to attain being, to contain it, and to possess it; but it finds its truth if it considers itself as a free engagement of thought in the given, aiming, at each discovery, not at fusion with the thing, but at the possibility of new discoveries; what the mind then projects is the concrete accomplishment of its freedom." (Simone de Beauvoir, "The Ethics of Ambiguity", 1947)

"Common sense […] may be thought of as a series of concepts and conceptual schemes which have proved highly satisfactory for the practical uses of mankind. Some of those concepts and conceptual schemes were carried over into science with only a little pruning and whittling and for a long time proved useful. As the recent revolutions in physics indicate, however, many errors can be made by failure to examine carefully just how common sense ideas should be defined in terms of what the experimenter plans to do." (James B Conant, "Science and Common Sense", 1951)

"Scientific method is the way to truth, but it affords, even in principle, no unique definition of truth. Any so-called pragmatic definition of truth is doomed to failure equally." (Willard v O Quine, "Word and Object", 1960)

"Catastrophes are often stimulated by the failure to feel the emergence of a domain, and so what cannot be felt in the imagination is experienced as embodied sensation in the catastrophe. (William I Thompson, "Gaia, a Way of Knowing: Political Implications of the New Biology", 1987)

"What about confusing clutter? Information overload? Doesn't data have to be ‘boiled down’ and  ‘simplified’? These common questions miss the point, for the quantity of detail is an issue completely separate from the difficulty of reading. Clutter and confusion are failures of design, not attributes of information." (Edward R Tufte, "Envisioning Information", 1990)

"When a system is predictable, it is already performing as consistently as possible. Looking for assignable causes is a waste of time and effort. Instead, you can meaningfully work on making improvements and modifications to the process. When a system is unpredictable, it will be futile to try and improve or modify the process. Instead you must seek to identify the assignable causes which affect the system. The failure to distinguish between these two different courses of action is a major source of confusion and wasted effort in business today." (Donald J Wheeler, "Understanding Variation: The Key to Managing Chaos" 2nd Ed., 2000)

"Good science is more than the mechanics of research and experimentation. Good science requires that scientists look inward-to contemplate the origin of their thoughts. The failures of science do not begin with flawed evidence or fumbled statistics; they begin with personal self-deception and an unjustified sense of knowing." (Robert A Burton, "On Being Certain: Believing You Are Right Even When You're Not", 2008)

"[…] in cybernetics, control is seen not as a function of one agent over something else, but as residing within circular causal networks, maintaining stabilities in a system. Circularities have no beginning, no end and no asymmetries. The control metaphor of communication, by contrast, punctuates this circularity unevenly. It privileges the conceptions and actions of a designated controller by distinguishing between messages sent in order to cause desired effects and feedback that informs the controller of successes or failures." (Klaus Krippendorff, "On Communicating: Otherness, Meaning, and Information", 2009)

"To get a true understanding of the work of mathematicians, and the need for proof, it is important for you to experiment with your own intuitions, to see where they lead, and then to experience the same failures and sense of accomplishment that mathematicians experienced when they obtained the correct results. Through this, it should become clear that, when doing any level of mathematics, the roads to correct solutions are rarely straight, can be quite different, and take patience and persistence to explore." (Alan Sultan & Alice F Artzt, "The Mathematics that every Secondary School Math Teacher Needs to Know", 2011)

"Failure is so much more interesting because you learn from it. That's what we should be teaching children at school, that being successful the first time, there's nothing in it. There's no interest, you learn nothing actually." (Sir James Dyson, The Observer, [interview] 2014)

"Although cascading failures may appear random and unpredictable, they follow reproducible laws that can be quantified and even predicted using the tools of network science. First, to avoid damaging cascades, we must understand the structure of the network on which the cascade propagates. Second, we must be able to model the dynamical processes taking place on these networks, like the flow of electricity. Finally, we need to uncover how the interplay between the network structure and dynamics affects the robustness of the whole system." (Albert-László Barabási, "Network Science", 2016)

🦋Science: On Creativity (Quotes)

"[…] science conceived as resting on mere sense-perception, with no other source of observation, is bankrupt, so far as concerns its claim to self-sufficiency. Science can find no individual enjoyment in nature: Science can find no aim in nature: Science can find no creativity in nature; it finds mere rules of succession. These negations are true of Natural Science. They are inherent in it methodology." (Alfred N Whitehead, "Modes of Thought", 1938)

"The act of discovery escapes logical analysis; there are no logical rules in terms of which a 'discovery machine' could be constructed that would take over the creative function of the genius. But it is not the logician’s task to account for scientific discoveries; all he can do is to analyze the relation between given facts and a theory presented to him with the claim that it explains these facts. In other words, logic is concerned with the context of justification." (Hans Reichenbach, "The Rise of Scientific Philosophy", 1951)

"The design process involves a series of operations. In map design, it is convenient to break this sequence into three stages. In the first stage, you draw heavily on imagination and creativity. You think of various graphic possibilities, consider alternative ways." (Arthur H Robinson, "Elements of Cartography", 1953)

"[…] the difference between creative thinking and unimaginative competent thinking lies in the injection of a some randomness. The randomness must be guided by intuition to be efficient." (John McCarthy et al, "A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence", 1955)

"At each level of complexity, entirely new properties appear. [And] at each stage, entirely new laws, concepts, and generalizations are necessary, requiring inspiration and creativity to just as great a degree as in the previous one." (Herb Anderson, 1972)

"Facts do not ‘speak for themselves’; they are read in the light of theory. Creative thought, in science as much as in the arts, is the motor of changing opinion. Science is a quintessentially human activity, not a mechanized, robot-like accumulation of objective information, leading by laws of logic to inescapable interpretation." (Stephen J Gould, "Ever Since Darwin", 1977)

"Science is not a heartless pursuit of objective information. It is a creative human activity, its geniuses acting more as artists than information processors. Changes in theory are not simply the derivative results of the new discoveries but the work of creative imagination influenced by contemporary social and political forces." (Stephen J Gould, "Ever Since Darwin: Reflections in Natural History", 1977)

"Science, since people must do it, is a socially embedded activity. It progresses by hunch, vision, and intuition. Much of its change through time does not record a closer approach to absolute truth, but the alteration of cultural contexts that influence it so strongly. Facts are not pure and unsullied bits of information; culture also influences what we see and how we see it. Theories, moreover, are not inexorable inductions from facts. The most creative theories are often imaginative visions imposed upon facts; the source of imagination is also strongly cultural." (Stephen J Gould, "The Mismeasure of Man", 1980)

"If intelligence is a capacity that is gradually acquired as a result of development and learning, then a machine that can learn from experience would have, at least in theory, the capacity to carry out intelligent behavior. [...] Humans have created machines that imitate us - that provide mirrors to see ourselves and measure our strength, our intellect, and even our creativity." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"Some methods, such as those governing the design of experiments or the statistical treatment of data, can be written down and studied. But many methods are learned only through personal experience and interactions with other scientists. Some are even harder to describe or teach. Many of the intangible influences on scientific discovery - curiosity, intuition, creativity - largely defy rational analysis, yet they are often the tools that scientists bring to their work." (Committee on the Conduct of Science, "On Being a Scientist", 1989)

"All of engineering involves some creativity to cover the parts not known, and almost all of science includes some practical engineering to translate the abstractions into practice." (Richard W Hamming, "The Art of Probability for Scientists and Engineers", 1991)

"Good engineering is not a matter of creativity or centering or grounding or inspiration or lateral thinking, as useful as those might be, but of decoding the clever, even witty, messages the solution space carves on the corpses of the ideas in which you believed with all your heart, and then building the road to the next message." (Fred Hapgood, "Up the infinite Corridor: MIT and the Technical Imagination", 1993)

"Indeed, knowledge that one will be judged on some criterion of ‘creativeness’ or ‘originality’ tends to narrow the scope of what one can produce (leading to products that are then judged as relatively conventional); in contrast, the absence of an evaluations seems to liberate creativity." (Howard Gardner, "Creating Minds", 1993)

"[…] creativity is the ability to see the obvious over the long term, and not to be restrained by short-term conventional wisdom." (Arthur J Birch, "To See the Obvious", 1995)

"The pursuit of science is more than the pursuit of understanding. It is driven by the creative urge, the urge to construct a vision, a map, a picture of the world that gives the world a little more beauty and coherence than it had before." (John A Wheeler, "Geons, Black Holes, and Quantum Foam: A Life in Physics", 1998)

"Simple observation generally gets us nowhere. It is the creative imagination that increases our understanding by finding connections between apparently unrelated phenomena, and forming logical, consistent theories to explain them. And if a theory turns out to be wrong, as many do, all is not lost. The struggle to create an imaginative, correct picture of reality frequently tells us where to go next, even when science has temporarily followed the wrong path." (Richard Morris, "The Universe, the Eleventh Dimension, and Everything: What We Know and How We Know It", 1999)

"Science, and physics in particular, has developed out of the Newtonian paradigm of mechanics. In this world view, every phenomenon we observe can be reduced to a collection of atoms or particles, whose movement is governed by the deterministic laws of nature. Everything that exists now has already existed in some different arrangement in the past, and will continue to exist so in the future. In such a philosophy, there seems to be no place for novelty or creativity." (Francis Heylighen, "The science of self-organization and adaptivity", 2001)

"This spontaneous emergence of order at critical points of instability is one of the most important concepts of the new understanding of life. It is technically known as self-organization and is often referred to simply as ‘emergence’. It has been recognized as the dynamic origin of development, learning and evolution. In other words, creativity - the generation of new forms - is a key property of all living systems. And since emergence is an integral part of the dynamics of open systems, we reach the important conclusion that open systems develop and evolve. Life constantly reaches out into novelty." (Fritjof  Capra, "The Hidden Connections", 2002)

"Evolution moves towards greater complexity, greater elegance, greater knowledge, greater intelligence, greater beauty, greater creativity, and greater levels of subtle attributes such as love. […] Of course, even the accelerating growth of evolution never achieves an infinite level, but as it explodes exponentially it certainly moves rapidly in that direction." (Ray Kurzweil, "The Singularity is Near", 2005)

"An ecology provides the special formations needed by organizations. Ecologies are: loose, free, dynamic, adaptable, messy, and chaotic. Innovation does not arise through hierarchies. As a function of creativity, innovation requires trust, openness, and a spirit of experimentation - where random ideas and thoughts can collide for re-creation." (George Siemens, "Knowing Knowledge", 2006)

"Learning emerges from discovery, not directives; reflection, not rules; possibilities, not prescriptions; diversity, not dogma; creativity and curiosity, not conformity and certainty; and meaning, not mandates." (Stephanie P Marshall, "The Power to Transform: Leadership That Brings Learning and Schooling to Life", 2006)

"Systemic problems trace back in the end to worldviews. But worldviews themselves are in flux and flow. Our most creative opportunity of all may be to reshape those worldviews themselves. New ideas can change everything." (Anthony Weston, "How to Re-Imagine the World", 2007)

"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)

"Imagination, as well as reason, is necessary to perfection in the philosophical mind. A rapidity of combination, a power of perceiving analogies, and of comparing them by facts, is the creative source of discovery." (Sir Humphry Davy)

❄️🧬Systems Thinking: On Control (Quotes)

"As soon as we are convinced that all technical and non-technical feedback systems are closely related, these relationships must not be distinguished by their specific designs in anatomy or technology; on the contrary their only common characterisation is the analogy of signal flows and the dynamics of control." (Hermann Schmidt, "Regelungstechnik - die technische Aufgabe und ihre wissenschaftliche, sozialpolitische und kulturpolitische Auswirkung", Verein Deutscher Ingenieure, Zeitschrift Vol. 85 (4), 1941)

"Besides electrical engineering theory of the transmission of messages, there is a larger field [cybernetics] which includes not only the study of language but the study of messages as a means of controlling machinery and society, the development of computing machines and other such automata, certain reflections upon psychology and the nervous system, and a tentative new theory of scientific method." (Norbert Wiener, "Cybernetics", 1948)

"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)

"It is my thesis that the physical functioning of the living individual and the operation of some of the newer communication machines are precisely parallel in their analogous attempts to control entropy through feedback. Both of them have sensory receptors as one stage of their cycle of operation: that is, in both of them there exists a special apparatus for collecting information from the outer world at low energy levels, and for making it available in the operation of the individual or of the machine. In both cases these external messages are not taken neat, but through the internal transforming powers of the apparatus, whether it be alive or dead. The information is then turned into a new form available for the further stages of performance. In both the animal and the machine this performance is made to be effective on the outer world. In both of them, their performed action on the outer world, and not merely their intended action, is reported back to the central regulatory apparatus." (Norbert Wiener, "The Human Use of Human Beings", 1950)

"The striking parallel between the economic models that are currently under discussion and some engineering systems suggests the hope that in some way the rapid progress in the development of the theory and practice of automatic control in the world of engineering may contribute to the solution of the economic problems." (Arnold Tustin, "The Mechanism of Economic Systems", 1953) 

"Feedback is a method of controlling a system by reinserting into it the results of its past performance. If these results are merely used as numerical data for the criticism of the system and its regulation, we have the simple feedback of the control engineers. If, however, the information which proceeds backward from the performance is able to change the general method and pattern of performance, we have a process which may be called learning." (Norbert Wiener, 1954)

"The purpose of ‘Engineering Cybernetics’ is then to study those parts of the broad science of cybernetics which have direct engineering applications in designing controlled or guided systems. It certainly includes such topics usually treated in books on servomechanisms. But a wider range of topics is only one difference between engineering cybernetics and servomechanisms engineering. A deeper - and thus more important - difference lies in the fact that engineering cybernetics is an engineering science, while servomechanisms engineering is an engineering practice." (Qian Xuesen, "Engineering Cybernetics", 1954)

"Cybernetics might, in fact, be defined as the study of systems that are open to energy but closed to information and control-systems that are 'information-tight'." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"Systems engineering embraces every scientific and technical concept known, including economics, management, operations, maintenance, etc. It is the job of integrating an entire problem or problem to arrive at one overall answer, and the breaking down of this answer into defined units which are selected to function compatibly to achieve the specified objectives. [...] Instrument and control engineering is but one aspect of systems engineering - a vitally important and highly publicized aspect, because the ability to create automatic controls within overall systems has made it possible to achieve objectives never before attainable, While automatic controls are vital to systems which are to be controlled, every aspect of a system is essential. Systems engineering is unbiased, it demands only what is logically required. Control engineers have been the leaders in pulling together a systems approach in the various technologies." (Instrumentation Technology, 1957) 

"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)

"A real system is subject to perturbations and it is never possible to control its initial state exactly. This raises the question of stability: under a slight perturbation will the system remain near the equilibrium state or not?" Joseph P LaSalle & Solomon Lefschetz, "Stability by Liapunov's Direct Method with Applications", 1961) 

"[…] 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)

"According to the science of cybernetics, which deals with the topic of control in every kind of system (mechanical, electronic, biological, human, economic, and so on), there is a natural law that governs the capacity of a control system to work. It says that the control must be capable of generating as much 'variety' as the situation to be controlled. (Anthony S Beer, "Management Science", 1968)

"In the language of cybernetics, maintaining reactions can be outlined as follows: the sensing material receives information about the external environment in the form of coded signals. This information is reprocessed and sent in the form of new signals through defined channels, or networks. This new information brings about an internal reorganization of the system which contributes to the preservation of its integrity. The mechanism which reprocesses the information is called the control system. It consists of a vast number of input and output elements, connected by channels through which the signals are transmitted. The information can be stored in a recall or memory system, which may consist of separate elements, each of which can be in one of several stable states. The particular state of the element varies, under the influence of the input signals. When a number of such elements are in certain specified states, information is, in effect, recorded in the form of a text of finite length, using an alphabet with a finite number of characters. These processes underlie contemporary electronic computing machines and are, in a number of respects, strongly analogous to biological memory systems." (Carl Sagan, "Intelligent Life in the Universe", 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)

"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 Wright Forrester, "Urban dynamics", 1969)

"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 non‐linear 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." (Jay F Forrester, "Urban Dynamics", 1969)

"In self-organizing systems, on the other hand, ‘control’ of the organization is typically distributed over the whole of the system. All parts contribute evenly to the resulting arrangement." (Francis Heylighen, "The Science Of Self-Organization And Adaptivity", 1970)

"In no system which shows mental characteristics can any part have unilateral control over the whole. In other words, the mental characteristics of the system are imminent, not in some part, but in the system as a whole." (Gregory Bateson, "Steps to an Ecology of Mind", 1972)

"The essence of cybernetic organizations is that they are self-controlling, self-maintaining, self-realizing. Indeed, cybernetics has been characterized as the “science of effective organization,” in just these terms. But the word “cybernetics” conjures, in the minds of an apparently great number of people, visions of computerized information networks, closed loop systems, and robotized man-surrogates, such as ‘artorgas’ and ‘cyborgs’." (Richard F Ericson, "Visions of Cybernetic Organizations", 1972)

"A person is changed by the contingencies of reinforcement under which he behaves; he does not store the contingencies. In particular, he does not store copies of the stimuli which have played a part in the contingencies. There are no 'iconic representations' in his mind; there are no 'data structures stored in his memory'; he has no 'cognitive map' of the world in which he has lived. He has simply been changed in such a way that stimuli now control particular kinds of perceptual behavior." (Burrhus F Skinner, "About behaviorism", 1974)

"The subject of study in systems theory is not a 'physical object', a chemical or social phenomenon, for example, but a 'system': a formal relationship between observed features or attributes. For conceptual reasons, the language used in describing the behavior of systems is that of information processing and goal seeking (decision making control)." (Mihajlo D Mesarovic & Y Takahara, "Foundations for the mathematical theory of general systems", 1975)

"The treatment of the economy as a single system, to be controlled toward a consistent goal, allowed the efficient systematization of enormous information material, its deep analysis for valid decision-making. It is interesting that many inferences remain valid even in cases when this consistent goal could not be formulated, either for the reason that it was not quite clear or for the reason that it was made up of multiple goals, each of which to be taken into account." (Leonid V Kantorovich, "Mathematics in Economics: Achievements, Difficulties, Perspectives", [Nobel lecture] 1975)

"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)

"The autonomy of living systems is characterized by closed, recursive organization. [...] A system's highest order of recursion or feedback process defines, generates, and maintains the autonomy of a system. The range of deviation this feedback seeks to control concerns the organization of the whole system itself. If the system should move beyond the limits of its own range of organization it would cease to be a system. Thus, autonomy refers to the maintenance of a systems wholeness. In biology, it becomes a definition of what maintains the variable called living." (Bradford P Keeney, "Aesthetics of Change", 1983)

"The emphasis in system(s) theory is on the dynamic behaviour of these phenomena, i.e. how do characteristic features (such as input and output) change in time and what are the relationships, also as functions of time. One tries to design control systems such that a desired behaviour is achieved. In this sense mathematical system(s) theory (and control theory) distinguishes itself from many other branches of mathematics in the sense that it is prescriptive rather than descriptive." (G J Olsder & J.W. van der Woude, "Mathematical Systems Theory" 2nd Ed., 1983)

"Ultimately, uncontrolled escalation destroys a system. However, change in the direction of learning, adaptation, and evolution arises from the control of control, rather than unchecked change per se. In general, for the survival and co-evolution of any ecology of systems, feedback processes must be embodied by a recursive hierarchy of control circuits." (Bradford P Keeney, "Aesthetics of Change", 1983)

"What is sometimes called 'positive feedback' or 'amplified deviation' is therefore a partial arc or sequence of a more encompassing negative feedback process. The appearance of escalating runaways in systems is a consequence of the frame of reference an observer has punctuated. Enlarging one's frame of reference enables the 'runaway' to be seen as a variation subject to higher orders of control." (Bradford P Keeney, "Aesthetics of Change", 1983)

"Conversely, once it was realized that the concept of policy was fundamental in control theory, the mathematicization of the basic engineering concept of 'feedback control', then the emphasis upon a state variable formulation became natural. We see then a very interesting interaction between dynamic programming and control theory. This reinforces the point that when working in the field of analysis it is exceedingly helpful to have some underlying physical processes clearly in mind." (Richard E Bellman, "Eye of the Hurricane: An Autobiography", 1984)

"Stability theory is the study of systems under various perturbing influences. Since there are many systems, many types of influences, and many equations describing systems, this is an open-ended problem. A system is designed so that it will be stable under external influences. However, one cannot predict all external influences, nor predict the magnitude of those that occur. Consequently, we need control theory. If one is interested in stability theory, a natural result is a theory of control." (Richard E Bellman, "Eye of the Hurricane: An Autobiography", 1984)

"The term closed loop-learning process refers to the idea that one learns by determining what s desired and comparing what is actually taking place as measured at the process and feedback for comparison. The difference between what is desired and what is taking place provides an error indication which is used to develop a signal to the process being controlled." (Harold Chestnut, 1984) 

"Perhaps the most exciting implication [of CA representation of biological phenomena] is the possibility that life had its origin in the vicinity of a phase transition and that evolution reflects the process by which life has gained local control over a successively greater number of environmental parameters affecting its ability to maintain itself at a critical balance point between order and chaos." ( Chris G Langton, "Computation at the Edge of Chaos: Phase Transitions and Emergent Computation", Physica D (42), 1990)

"There must be, however, cybernetic or homeostatic mechanisms for preventing the overall variables of the social system from going beyond a certain range. There must, for instance, be machinery for controlling the total numbers of the population; there must be machinery for controlling conflict processes and for preventing perverse social dynamic processes of escalation and inflation. One of the major problems of social science is how to devise institutions which will combine this overall homeostatic control with individual freedom and mobility." (Kenneth Boulding, "Economics of the coming spaceship Earth", 1994)

"The internet model has many lessons for the new economy but perhaps the most important is its embrace of dumb swarm power. The aim of swarm power is superior performance in a turbulent environment. When things happen fast and furious, they tend to route around central control. By interlinking many simple parts into a loose confederation, control devolves from the center to the lowest or outermost points, which collectively keep things on course. A successful system, though, requires more than simply relinquishing control completely to the networked mob." (Kevin Kelly, "New Rules for the New Economy: 10 radical strategies for a connected world", 1998)

"When everything is connected to everything in a distributed network, everything happens at once. When everything happens at once, wide and fast moving problems simply route around any central authority. Therefore overall governance must arise from the most humble interdependent acts done locally in parallel, and not from a central command.A mob can steer itself, and in the territory of rapid, massive, and heterogeneous change, only a mob can steer. To get something from nothing, control must rest at the bottom within simplicity. " (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"At present, there is far more to be gained by pushing the boundaries of what can be done by the bottom than by focusing on what can be done at the top. When it comes to control, there is plenty of room at the bottom. What we are discovering is that peer-based networks with millions of parts, minimal oversight, and maximum connection among them can do far more than anyone ever expected. We don’t yet know what the limits of decentralization are." (Kevin Kelly, "New Rules for the New Economy: 10 radical strategies for a connected world", 1998)

"Complexity theory began with an interest on how order spring from chaos. According to complexity theory, adaption is most effective in systems that are only partially connected. The argument is that too much structure creates gridlock, while too little structure creates chaos. […] Consequently, the key to effective change is to stay poised on this edge of chaos. Complexity theory focuses managerial thinking on the interrelationships among different parts of an organization and on the trade-off of less control for greater adaptation." (Shona Brown, "Competing on the Edge, 1998) 

"A model is an external and explicit representation of part of reality as seen by the people who wish to use that model to understand, to change, to manage, and to control that part of reality in some way or other." (Michael Pidd, "Just Modeling through: A Rough Guide to Modeling", Interfaces, Vol. 29, No. 2, 1999) 

"Cybernetics is the science of effective organization, of control and communication in animals and machines. It is the art of steersmanship, of regulation and stability. The concern here is with function, not construction, in providing regular and reproducible behaviour in the presence of disturbances. Here the emphasis is on families of solutions, ways of arranging matters that can apply to all forms of systems, whatever the material or design employed. [...] This science concerns the effects of inputs on outputs, but in the sense that the output state is desired to be constant or predictable – we wish the system to maintain an equilibrium state. It is applicable mostly to complex systems and to coupled systems, and uses the concepts of feedback and transformations (mappings from input to output) to effect the desired invariance or stability in the result." (Chris Lucas, "Cybernetics and Stochastic Systems", 1999)

"The central proposition in [realistic thinking] is that human actions and interactions are processes, not systems, and the coherent patterning of those processes becomes what it becomes because of their intrinsic capacity, the intrinsic capacity of interaction and relationship, to form coherence. That emergent form is radically unpredictable, but it emerges in a controlled or patterned way because of the characteristic of relationship itself, creation and destruction in conditions at the edge of chaos." (Ralph D Stacey et al, "Complexity and Management: Fad or Radical Challenge to Systems Thinking?", 2000)

"A model is an imitation of reality and a mathematical model is a particular form of representation. We should never forget this and get so distracted by the model that we forget the real application which is driving the modelling. In the process of model building we are translating our real world problem into an equivalent mathematical problem which we solve and then attempt to interpret. We do this to gain insight into the original real world situation or to use the model for control, optimization or possibly safety studies." (Ian T Cameron & Katalin Hangos, "Process Modelling and Model Analysis", 2001)

"Probably the first clear insight into the deep nature of control […] was that it is not about pulling levers to produce intended and inexorable results. This notion of control applies only to trivial machines. It never applies to a total system that includes any kind of probabilistic element - from the weather, to people; from markets, to the political economy. No: the characteristic of a non-trivial system that is under control, is that despite dealing with variables too many to count, too uncertain to express, and too difficult even to understand, something can be done to generate a predictable goal. Wiener found just the word he wanted in the operation of the long ships of ancient Greece. At sea, the long ships battled with rain, wind and tides - matters in no way predictable. However, if the man operating the rudder kept his eye on a distant lighthouse, he could manipulate the tiller, adjusting continuously in real-time towards the light. This is the function of steersmanship. As far back as Homer, the Greek word for steersman was kubernetes, which transliterates into English as cybernetes." (Stafford Beer, "What is cybernetics?", Kybernetes, 2002)

"We’re accustomed to thinking in terms of centralized control, clear chains of command, the straightforward logic of cause and effect. But in huge, interconnected systems, where every player ultimately affects every other, our standard ways of thinking fall apart. Simple pictures and verbal arguments are too feeble, too myopic." (Steven Strogatz, "Sync: The Emerging Science of Spontaneous Order", 2003)

"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 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) 

"A great deal of the results in many areas of physics are presented in the form of conservation laws, stating that some quantities do not change during evolution of the system. However, the formulations in cybernetical physics are different. Since the results in cybernetical physics establish how the evolution of the system can be changed by control, they should be formulated as transformation laws, specifying the classes of changes in the evolution of the system attainable by control function from the given class, i.e., specifying the limits of control." (Alexander L Fradkov, "Cybernetical Physics: From Control of Chaos to Quantum Control", 2007)

"To develop a Control, the designer should find aspect systems, subsystems, or constraints that will prevent the negative interferences between elements (friction) and promote positive interferences (synergy). In other words, the designer should search for ways of minimizing frictions that will result in maximization of the global satisfaction" (Carlos Gershenson, "Design and Control of Self-organizing Systems", 2007)

"[a complex system is] a system in which large networks of components with no central control and simple rules of operation give rise to complex collective behavior, sophisticated information processing, and adaptation via learning or evolution." (Melanie Mitchell, "Complexity: A Guided Tour", 2009)

"[…] in cybernetics, control is seen not as a function of one agent over something else, but as residing within circular causal networks, maintaining stabilities in a system. Circularities have no beginning, no end and no asymmetries. The control metaphor of communication, by contrast, punctuates this circularity unevenly. It privileges the conceptions and actions of a designated controller by distinguishing between messages sent in order to cause desired effects and feedback that informs the controller of successes or failures." (Klaus Krippendorff, "On Communicating: Otherness, Meaning, and Information", 2009)

"In a complex society, individuals, organizations, and states require a high degree of confidence - even if it is misplaced - in the short-term future and a reasonable degree of confidence about the longer term. In its absence they could not commit themselves to decisions, investments, and policies. Like nudging the frame of a pinball machine to influence the path of the ball, we cope with the dilemma of uncertainty by doing what we can to make our expectations of the future self-fulfilling. We seek to control the social and physical worlds not only to make them more predictable but to reduce the likelihood of disruptive and damaging shocks (e.g., floods, epidemics, stock market crashes, foreign attacks). Our fallback strategy is denial." (Richard N Lebow, "Forbidden Fruit: Counterfactuals and International Relations", 2010)

"In chaotic deterministic systems, the probabilistic description is not linked to the number of degrees of freedom (which can be just one as for the logistic map) but stems from the intrinsic erraticism of chaotic trajectories and the exponential amplification of small uncertainties, reducing the control on the system behavior." (Massimo Cencini et al, "Chaos: From Simple Models to Complex Systems", 2010)

"Cyberneticists argue that positive feedback may be useful, but it is inherently unstable, capable of causing loss of control and runaway. A higher level of control must therefore be imposed upon any positive feedback mechanism: self-stabilising properties of a negative feedback loop constrain the explosive tendencies of positive feedback. This is the starting point of our journey to explore the role of cybernetics in the control of biological growth. That is the assumption that the evolution of self-limitation has been an absolute necessity for life forms with exponential growth." (Tony Stebbing, "A Cybernetic View of Biological Growth: The Maia Hypothesis", 2011)

"Cybernetics is the study of systems which can be mapped using loops (or more complicated looping structures) in the network defining the flow of information. Systems of automatic control will of necessity use at least one loop of information flow providing feedback." (Alan Scrivener, "A Curriculum for Cybernetics and Systems Theory", 2012)

"Without precise predictability, control is impotent and almost meaningless. In other words, the lesser the predictability, the harder the entity or system is to control, and vice versa. If our universe actually operated on linear causality, with no surprises, uncertainty, or abrupt changes, all future events would be absolutely predictable in a sort of waveless orderliness." (Lawrence K Samuels, "Defense of Chaos: The Chaology of Politics, Economics and Human Action", 2013)

[systems dynamics:] "A systems simulation methodology to study complex dynamic behavior of industrial and social systems based on control engineering and cybernetics." (Michael Mutingi & Charles Mbohwa, 2014)

"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", 2014)

"Cybernetics studies the concepts of control and communication in living organisms, machines and organizations including self-organization. It focuses on how a (digital, mechanical or biological) system processes information, responds to it and changes or being changed for better functioning (including control and communication)." (Dmitry A Novikov, "Cybernetics 2.0", 2016)

🦉Knowledge Management: On Critical Thinking (Quotes)

"Reflection upon situationality is reflection about the very condition of existence: critical thinking by means which people discover each other to be 'in a situation'. (Paulo Freire, "Pedagogia do oprimido" ["Pedagogy of the Oppressed"], 1968)

"Critical thinking, in short, offers the way to keep science and technology in harness." (Kenneth Ludmerer, "Learning to Heal: The Development of American Medical Education", 1985)

"Critical thinking is a type of thinking pattern that requires people to be reflective, and pay attention to decision-making which guides their beliefs and actions. Critical thinking allows people to deduct with more logic, to process sophisticated information and look at various sides of an issue so they can produce more solid conclusions." (Joan Baron & Robert Sternberg, "Book Reviews and Notes : Teaching Thinking Skills: Theory and Practice", 1987)

"Critical thinking does seem a superior sort of thinking because it seems as though the critic is actually going beyond the scope of what is being criticized in order to criticize it. That is only rarely a true assumption because, most often, the critic will seize on some little aspect that he or she understands and tackle only that." (Edward de Bono, "I Am Right, You are Wrong", 1990)

"Thinking about one's thinking in a manner designed to organize and clarify, raise the efficiency of, and recognize errors and biases in one's own thinking. Critical thinking is not 'hard' thinking nor is it directed at solving problems (other than 'improving' one's own thinking). Critical thinking is inward-directed with the intent of maximizing the rationality of the thinker. One does not use critical thinking to solve problems- one uses critical thinking to improve one's process of thinking." (Kirby Carmichael, [letter] 1997)

"The purpose of critical thinking [...] is rethinking: that is, reviewing, evaluating, and revising thought." (Jon Stratton, Critical Thinking for College Students, 1999)

[critical thinking:] "The evaluation of ideas, sources, or solutions that are proposed as potential resources or solutions to unusual problems or to product design." (Ruth C Clark, "Building Expertise: Cognitive Methods for Training and Performance Improvement", 2008)

"Critical thinking is essentially a questioning, challenging approach to knowledge and perceived wisdom. It involves ideas and information from an objective position and then questioning this information in the light of our own values, attitudes and personal philosophy." Brenda Judge et al, "Critical Thinking Skills for Education Students", 2009)

[critical thinking:] "Evaluation of products and ideas, such as critiquing an e-learning course or preparing an argument for a position." (Ruth C Clark & Richard E Mayer, "e-Learning and the Science of Instruction", 2011)

[critical thinking:] "Purposeful, self-regulatory judgment which results in interpretation, analysis, evaluation, and inference, as well as explanation of the evidential, conceptual, methodological, criteriological, or contextual considerations upon which that judgment is based" (Peter A Facione, "Critical Thinking: What It is and Why It Counts", 2011)

[critical thinking:] "A process in which one applies observation, analysis, inference, context, reflective thinking, and the like, in order to reach judgments. Such judgments should be open to alternative perspectives that may not normally be otherwise considered." (Project Management Institute, "Navigating Complexity: A Practice Guide", 2014)

[critical thinking:] "The ability to use your personal experience, logical thought processes, and creativity to analyze and evaluate situations. Further, critical thinking allows you to use the information gathered to reach a conclusion or answer." (Darril Gibson, "Effective Help Desk Specialist Skills", 2014)

[critical thinking:] "Thinking that is characterized by careful evaluation and judgment; this involves thinking about one’s thinking (metacognition). Critical thinking may involve examining contradictory lines of reasoning and/or using different lines of reasoning to cross-examine alternatives." (Ken Sylvester, "Negotiating in the Leadership Zone", 2015)

[critical thinking:] "Evaluation of products and ideas such as critiquing an e-learning course or preparing an argument for a position." (Ruth C Clark & Richard E Mayer, "e-Learning and the Science of Instruction", 2016)

[critical thinking:] "The capacity of an individual to effectively engage in a process of making decisions or solving problems by analyzing and evaluating evidence, arguments, claims, beliefs, and alternative points of view; synthesizing and making connections between information and arguments; interpreting information; and making inferences using reasoning appropriate to the situation." (Yigal Rosen & Maryam Mosharraf, "Evidence-Centered Concept Map in Computer-Based Assessment of Critical Thinking", 2016) 

"A more detailed, but still uncontroversial comprehensive, definition is that philosophy is rationally critical thinking, of a more or less systematic kind about the general nature of the world (metaphysics or theory of existence), the justification of belief (epistemology or theory of knowledge), and the conduct of life (ethics or theory of value)." (Anthony Quinton)

🏷️Knowledge Representation: On Cognitive Maps (Quotes)

"[...] we believe that in the course of learning something like a field map of the environment gets established in the rat's brain [...] and it is this tentative map, indicating routes and paths and environmental relationships, which finally determines what responses, if any, the animal will finally release." (Edward C Tolman, "Cognitive maps in rats and men", Psychological Review 55(4), 1948)

"[…] learning consists not in stimulus-response connections but in the building up in the nervous system of sets which function like cognitive maps […] such cognitive maps may be usefully characterized as varying from a narrow strip variety to a broader comprehensive variety." (Edward C Tolman, "Cognitive maps in rats and men", Psychological Review 55(4), 1948)

"The cognitive map is a construct that has been proposed to explain how individuals know their environment. It assumes that people store information about their environment in a simplified form and in relation to other information they already have. It further assumes that this information is coded in a structure which people carry around in their heads, and that this structure corresponds, at least to a reasonable degree, to the environment it represents. It is as if an individual carried a map or model of the environment in his head." (Stephen Kaplan, "Cognitive maps, human needs and the designed environment", Environmental design research vol. 1, 1973)

"A person is changed by the contingencies of reinforcement under which he behaves; he does not store the contingencies. In particular, he does not store copies of the stimuli which have played a part in the contingencies. There are no 'iconic representations' in his mind; there are no 'data structures stored in his memory'; he has no 'cognitive map' of the world in which he has lived. He has simply been changed in such a way that stimuli now control particular kinds of perceptual behavior." (Burrhus F Skinner, "About behaviorism", 1974)

"A cognitive map is a specific way of representing a person's assertions about some limited domain, such as a policy problem. It is designed to capture the structure of the person's causal assertions and to generate the consequences that follow front this structure. […]  a person might use his cognitive map to derive explanations of the past, make predictions for the future, and choose policies in the present." (Robert M Axelrod, "Structure of Decision: The cognitive maps of political elites", 1976)

"The cognitive mapping approach promises to be more helpful to the decision maker for two reasons. First, since the advice can be expressed in terms of the person's own cognitive map, it can be solidly based in his own experience, using his own concepts, his own causal beliefs. and his own values. Equally important, when the cognitive map approach offers advice, it takes explicit account of the finite capacities of people and the way in which they simplify their images when dealing with a complex policy issue. Thus, with the cognitive mapping approach, a better understanding of how decisions are made can lead to the making of better decisions." (Robert M Axelrod, "Structure of Decision: The cognitive maps of political elites", 1976)

"Briefly, a cognitive map would consist of two major systems, a place system and a misplace system. The first is a memory system which contains information about places in the organism's environment, their spatial relations, and the existence of specific objects in specific places. The second, misplace, system signals changes in a particular place, involving either the presence of a new object or the absence of an old one. The place system permits an animal to locate itself in a familiar environment without reference to any specific sensory input, to go from one place to another independent of particular inputs (cues) or outputs (responses), and to link together conceptually parts of an environment which have never been experienced at the same time. The misplace system is primarily responsible for exploration, a species-typical behaviour which functions to build maps of new environments and to incorporate new information into existing maps." (John O'Keefe & Lynn Nadel, "The Hippocampus as a Cognitive Map", 1978)

"The cognitive map is not a picture or image which 'looks like' what it represents; rather, it is an information structure from which map-like images can be reconstructed and from which behaviour dependent upon place information can be generated." (John O'Keefe & Lynn Nadel, "The Hippocampus as a Cognitive Map", 1978)

"We would agree that organisms do not 'see' absolute space; cognitive maps are not pictures of the universe, they are schemata from which any portion of space can be constructed. The fact that we cannot perceive unified space does not mean we cannot conceive it; the latter potentiality derives from the possession of a structure which can be used to construct spaces that stretch endlessly in all dimensions." (John O'Keefe & Lynn Nadel, "The Hippocampus as a Cognitive Map", 1978)

"[...] cognitive maps can be seen as a picture or visual aid in comprehending the mappers' understanding of particular, and selective, elements of the thoughts (rather than thinking) of an individual, group or organization. They may also be seen as a representation that is amenable to analysis by both the mapper and others." (Colin Eden, "One the nature of cognitive maps", Journal of Management Studies 29 (3), 1992)

"Bounded rationality simultaneously constrains the complexity of our cognitive maps and our ability to use them to anticipate the system dynamics. Mental models in which the world is seen as a sequence of events and in which feedback, nonlinearity, time delays, and multiple consequences are lacking lead to poor performance when these elements of dynamic complexity are present. Dysfunction in complex systems can arise from the misperception of the feedback structure of the environment. But rich mental models that capture these sources of complexity cannot be used reliably to understand the dynamics. Dysfunction in complex systems can arise from faulty mental simulation - the misperception of feedback dynamics. These two different bounds on rationality must both be overcome for effective learning to occur. Perfect mental models without a simulation capability yield little insight; a calculus for reliable inferences about dynamics yields systematically erroneous results when applied to simplistic models." (John D Sterman, "Business Dynamics: Systems thinking and modeling for a complex world", 2000)

"Even if our cognitive maps of causal structure were perfect, learning, especially double-loop learning, would still be difficult. To use a mental model to design a new strategy or organization we must make inferences about the consequences of decision rules that have never been tried and for which we have no data. To do so requires intuitive solution of high-order nonlinear differential equations, a task far exceeding human cognitive capabilities in all but the simplest systems."  (John D Sterman, "Business Dynamics: Systems thinking and modeling for a complex world", 2000)

"The robustness of the misperceptions of feedback and the poor performance they cause are due to two basic and related deficiencies in our mental model. First, our cognitive maps of the causal structure of systems are vastly simplified compared to the complexity of the systems themselves. Second, we are unable to infer correctly the dynamics of all but the simplest causal maps. Both are direct consequences of bounded rationality, that is, the many limitations of attention, memory, recall, information processing capability, and time that constrain human decision making." (John D Sterman, "Business Dynamics: Systems thinking and modeling for a complex world", 2000)

"Eliciting and mapping the participant's mental models, while necessary, is far from sufficient [...] the result of the elicitation and mapping process is never more than a set of causal attributions, initial hypotheses about the structure of a system, which must then be tested. Simulation is the only practical way to test these models. The complexity of the cognitive maps produced in an elicitation workshop vastly exceeds our capacity to understand their implications. Qualitative maps are simply too ambiguous and too difficult to simulate mentally to provide much useful information on the adequacy of the model structure or guidance about the future development of the system or the effects of policies." (John D Sterman, "Learning in and about complex systems", Systems Thinking Vol. 3 2003)

[cognitive map:] "A mental representation of a portion of the physical environment and the relative locations of points within it." (Andrew M Colman, "A Dictionary of Psychology" 3rd Ed, 2008)

[cognitive map:] "A mental model (or map) of the external environment which may be constructed following exploratory behaviour." (Michael Allaby, "A Dictionary of Zoology" 3rd Ed., 2009)

"There is no reason to believe that cognitive maps are like iconic maps except, rather than being inscribed in the dirt, or on a rock, or imprinted on paper, they are somehow inscribed in neural tissue. They seem to be more like lists of significant places intertwined with bearings and headings between one place and another. The vital significance of these places is part and parcel of the map; the “map” is not a neutral spatial substrate to which vital significance is later attached. The space of cognitive maps is not merely about physical position; it is about needs and satisfiers, vantage points and opportunities for action." (William Benzon, "Maps, Iconic and Abstract", 2011)

[Cognitive Map:] "A representation of the conceptualization that the subject constructs of the system in which he evolves. The set of cognitive representations that emerge make it possible to understand his actions, the links between the factors structuring the cognitive patterns dictating his behaviors." (Henda E Karray & Souhaila Kammoun, "Strategic Orientation of the Managers of a Tunisian Family Group Before and After the Revolution", 2020)

🔖Knowledge Representation: Mental Models (Part II: Breaking of Complexity)

Mental Models Series

To understand how something complex works one has two main tools  - the mechanistic, respectively the holistic approach. The mechanistic approach assumes that something can be understood by breaking it into parts (aka analysis) and then by combining the parts to form the whole (aka synthesis). However, this approach doesn’t always account for everything as there’s behavior and/or characteristics not explainable by the parts themselves. Considering that the whole is more than its parts, the holistic approach studies the interactions of the parts that lead to such unexpected effects (aka synergies), the challenge being to identify those characteristics, circumstances or conditions that lead to or related to these effects. Thus, when these two tools are combined within multiple iterations one can get closer to the essence.

When breaking things into parts we need first to look at the thing or object of study from a bird’s eyes view and identify the things that might look like parts. Even if the object of study looks amorphous, the experience doubled by intuition and perseverance can offer a starting point, and from there one can start iteratively to take things apart until one decides to stop. When and where one stops is a question of possible depth, as allowed by the object itself, by the techniques available or our grasping, respectively by the intended depth  -  the level chosen for approximation.

Between the whole and the lowest perceived components, one has the luxury of experimenting by breaking things apart (physically and/or mentally) and putting things together to form unitary parts  - parts that typically explain one or more functions or characteristics, respectively the whole. In addition, one can play with the object, consider it in a range of contexts, extrapolate its characteristics, identify behavior not explainable by the parts themselves. In the process one arrives to a set of facts (things known or proved to be true), respectively suppositions expressed as beliefs (things hold as true without proof), assumptions (things accepted as true without proof) or hypotheses (things who’s value of truth is not known, typically because of limited evidence).

One builds thus a (mental) model, an abstraction of the object of study. The parts and relations existing between the parts form the skeleton of the model, while the facts and suppositions attempt to give the model form. Unfortunately, models seldom accommodate all the facts, therefore what one ignores or considers into the model can make an important difference on whether the model is of any use. One is forced thus to advance theories on how the skeleton can accommodate the form, how form reflects the facts and suppositions.

Simple models can prove to be useful, especially when they allow approximating the real thing within the considered context. However, the better approximations one needs and/or the broader the context is, more complex the models can become, especially when the number of facts considered as important increases. This can mean that two models or theories can be useful or correct when considered in different contexts but lose their applicability when considered in another context.

Having a repository of models to choose from is usually a helpful thing, especially in understanding more about the object studied. The appropriate usage of a model depends also on understanding its range of applicability within a context or across contexts, the advantages, and disadvantages of using the model. Knowing when to use a model is as important as knowing when not to use it, while understanding the measure of the error associated with a model can make us aware of the risks associated with a model and decisions made based on it.

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🕸️Web x.0: The Wisdom of the Crowds (Part I - Is there any Wisdom?)

I haven’t managed to read until today “The Wisdom of the Crowds”, James Surowiecki’s well mediatized book, though it’s almost impossible to read an article or book on collaboration without meeting at least a small reference to it, if not a quote from it. Through the quotes met on the web I arrived to grasp a little from Surowiecki’s philosophy, and even if I it’s not the same as the read itself, I decided to write this post before reading the book, attempting to see how much my ideas changed after reading it.

I have the feeling that many have misunderstood maybe what knowledge and wisdom is about, how things are harnessed and evolve in life. Many bloggers, reputed writers and philosophers, believe that the crowds, sometimes referred as the masses or the mob, can’t be wise but rather stupid, thus appeared terms like stupidity of the masses, madness of the masses, etc. They are not so far away from the truth, especially when they are referring to the uneducated masses or to the panic-like effects, however the reality is that even a simple person who worked the land all his life or any did any other type of work and had no time for school could have more wisdom than a phony intellectual who spent all his life on the banks of schools without achieving a grain of wisdom or even knowledge. 

Again, with the risk of repeating myself, and as many have stressed, data is not information, information is not knowledge, and knowledge is not wisdom, the scale in this order implying a refinement and evolution of the thought process. Wisdom is a Holy Grail, and this not only in respect to spiritual life, but also to daily professional life, as it implies going above the knowledge existing in a certain field, either managerial, engineering, a simple job that pays the rent or any other activity. A simple person could hold his own grain of wisdom valid in his world, and with the eyes of an open mind and some chunk of knowledge from other domains, he might see the patterns a more educated person can’t grasp. For sure such a statement can’t be digested by researchers, especially when it’s almost impossible to express and measure wisdom, being more like a Morgan le Fay. It seems that is more important to touch the rays of wisdom, however it’s difficult to delimit where ends knowledge and begins wisdom, plus the various facets of the two, and from here the almighty confusion.  

Even if it’s difficult to believe that wisdom could be found at the fingertips of everybody, in definitive each person brings a different range of experiences, data, information and knowledge, different perspective of the same story, different cultural, social, geographical and cognitive aspects, in other words diversity of a wide range and richness. So on one side we are having the wisdom of the individual, even if it doesn’t fit the academic benchmarking for wisdom, but there is still some wisdom in there, and we have a network of people between each exists different grades of relationship (blood relation, friends, co-workers, neighbors, readers of the same material, etc.) with about 6 or less degrees of connectivity. 

An old English proverb says that "two’s a company, three’s a crowd", and above its relevant meaning, it’s actually more important its value as output of masses’ wisdom. Sayings, stories, verses, songs and other type of cultural manifestation of the crowds, are examples of wisdom, condensed wisdom I might say. Somebody was saying that all the great ideas of mankind were thought with many ages before, nothing more truly, and we are just rediscovering them now through the advances of modern techniques and thought.

People might be illiterates but could be masters in manual work or in any field that doesn’t involve writing, people might be shy but find incredible creative force when they are talking about or expressing their passions, they could come with unexpected solutions to complex problems when the problems are broken down to their language. Sometimes only by having a real partner of discussion or having somebody to address to, a person arrives to discover new perspectives in the process of externalizing knowledge, come with a new idea or find the solution to a problem. As Mark Zuckerberg remarks, "people have really gotten comfortable not only sharing more information and different kinds, but more openly and with more people - and that social norm is just something that has evolved over time". And very important, people are willing to give their knowledge free if the right context is met, but how do we achieve that?

Coming back to the before mentioned proverb, it gives a minimal definition for what it means a company, with direct and figurative meaning, an association of two people, respectively a crowd, as an association of at least three people. Witty definition in common sense language, much less than the definitions given by the academic literature, isn’t it? It is necessary to say that here the meaning of association is quite rich, implying any type of association made between people who come in contact in a form or another – organizations, ways of transportation, in queue lines, in chat rooms, forums or within the boundaries of a social network, in fact any place in which some type of communication occurs.

Communication is the backbone on which our society is based, and there are so many aspects, but what is important to retain, is the sharing of ideas which occurs between two or more people, the mode in which the communicated data, information or knowledge shape us. There is a micro scale, in which the communication happens between any two people of a group, and a macro scale, in which the communication is regarded as a whole, though the interactions between all the members of a group. In both cases is important how the input is transformed, loosing or gaining content, what each person retains or contributes.  

Researching the exchange of information occurred at multiple levels in our society is almost impossible, we only observe its effects, how markets change, how fast we receive important or irrelevant information, how meaning is changed voluntarily or involuntarily, altruistically or looking for profit. I’m mentioning this, because without understanding the whole process of communication and all its aspects we can’t use adequately the communication channels and harness the skills of people.

We could say that we are having the potentiality of crowds’ wisdom, but it depends how we harness it. Harvesting doesn’t resume in the ultimate action of taking the results of nature’s work, you have to invest some time in cultivating the seeds, take care of the plants, providing water and the needed care, the right temperature and fertilizer, consider the appropriate time for performing each action in the process. In addition you have to come also with some additional knowledge about the plants themselves, but also about the context, which is the best soil, what it takes to be in agriculture, to build an infrastructure for optimal work, etc. Who says that the same doesn’t apply to individuals and groups too?!

Individuals and groups need to be brought to the required level of education and knowledge in order to rise to the level of the demands. Wisdom involves some degree of knowledge and implicitly of information, the volume depending on the nature of the task at hand. Same you educate a kid upbringing him to a certain level of autonomy by repetitive task increasing in difficulty, upon its degree of understanding and pace, the same should apply to a group too, based on its intrinsic qualities and requirements. In the past years have been attempted to use the masses in order to solve several types of tasks, some with positive, but also many with negative results. 

If the masses can’t solve a certain type or types of problems, this doesn’t mean that they are dumb and posses no wisdom. An example of such a “failure” is the attempt to predict the trends of the various types of markets by using the masses, though it’s hard to think that such experiments could come with positive results as long there are too many interests and people who want to make profit by influencing the masses. This aspect stresses especially the need for independence, which adds to autonomy and diversity, other two characteristics that needs to be met by the crowds.

It’s also important to address a problem to the right community. I doubt, for example, that a complex problem of physics could be solved by addressing it to a group of sportives, excepting the cases when you talk about sportive physicists. Mentioning people who have knowledge coming from two or more domains, they are quite important, but not necessarily the most important. Depending on the problem at hand, the group should have a set of given properties that would allow it to approach and solve a problem.

There are many more aspects that need to be considered in relation to the crowds, hopefully I will manage to develop the ideas in a series of other posts.

Learning: Random Thoughts on Knowledge and Learning

Kristen Weatherby, sharing her thoughts after Reform event held in UK, writes on “The Teachers Blog” in “Knowledge versus Learning” post:

“I believe very strongly that education is about the transfer of knowledge from one generation to the next.”

“Knowledge is the basic building block for a successful life…. What is to be criticised is an education system which has relegated the importance of knowledge in favour of ill-defined learning skills.”

 I’m not a teacher, even if I coquetted with the idea of teaching, and it’s not (yet) in my attributions to criticize or defend the past or future of education, however as a simple mortal who spent some time in schools, I hope I’m allowed a few observations.

Taking the first quote from above I would like to start with two other quotes that worth to meditate on:

“Where is the Life we have lost in living?
Where is the wisdom we have lost in knowledge?
Where is the knowledge we have lost in information?” (T.S. Eliot,  "The Rock")

 “Information is not knowledge. Knowledge is not wisdom. Wisdom is not truth. Truth is not beauty. Beauty is not love. Love is not music. Music is the best.” (Frank Zappa)

Knowledge is not the most important asset that we have to transmit to further generations, but the wisdom we’ve acquired, teaching them how not to make the same mistakes that we did. Philosophers and spiritual leaders attempted in many books to reveal the importance and the many facets of wisdom, highlighting in parallel the limitations of knowledge. Spending our lives on the quest of knowledge has quite a high price that might cost us the future of our children. Also wisdom might not be enough, but together with love, beauty and music, could make a difference.

Coming back to the actual subject of this post, I strongly believe that it's important to make the educational system adaptable to the changes occurring in our society, to create an infrastructure that sustains and embraces change. In many countries the educational systems remained the same for years, even decades, or the changes are insignificant when compared to the changes occurred in our society, especially in the area of technologies, business’ dynamics or culture. Change is inevitable, and there is a time for change, propitious for change, and when this time passes, the system is forced to change from outside, and then there are also victims. It’s not a personal theory, but the laws of nature - an educational system is part of another ecosystem, which at his turn is part of a bigger system, and so on, thus a change occurring in an upper system has greater impact in lower ecosystems if the respective ecosystems are not prepared to support the change. The actual economical crisis is maybe the best example in this direction.

     Something needs to change, but what? We are living in a world overloaded of information, we are attempting to derive knowledge from information, but the sea of information is so deep that we risk to sink, we are attempting to process information using the processing power of computers and that’s not so easy, as the attempt of mimicking meaning, which has an important role in the identification of knowledge, it’s still in its baby steps. Einstein was saying something like "a problem cannot be solved at the same level (of consciousness) it was created" ( I hope is the right quote).  Until we find the right solution to the big problem, we could try to solve the common small issues we deal with.

    The debate between memorization of facts and figures, respectively personalized learning, in fact could be reduced to the attempt to find a balance between quantity and quality – quantity as a measure of what is learned, and quality as a measure of what remained after. (Maybe it’s not the best formulation, but the meaning is close to what I intended to say.)  In theory all we have to do is to optimize the equation based on the two, and most probably many other aspects (e.g. financial, personal or technological resources). Normally the solution is somewhere in between, and rarely at extremes. In this case, again, at least in theory, should resume in using effectively and efficiently what we have, in strengthening the weaknesses.

    For me as a student and later as hobbyist “researcher”, the most time consuming activities are searching for information, indexing and mapping the information found. I’ve been spending lot of time for searching simple definitions, definitions that should help us define the world we live in. In addition, often it’s not easy to fish for the facts, having to read a whole book in order to find 2-3 important ideas. For sure we need to simplify the content, to personalize it to the degree of interest and understanding for each person, but in order to reveal the networked structure of network, I think that we need to provide the same type of structural knowledge navigability. Wikipedia is somehow providing that, but it’s just a start.  Only relatively I was introduced in the world of visual representation of knowledge. 

Various types of simple tabular diagrams, circle, trees or brace maps have been used for decades in almost all fields, but I have hardly seen in manuals or teachers to refer to more complex knowledge maps like Mind Maps, Cognitive Map, semantic networks or Vee-diagrams, and I feel that they better reflect the structural patterns of knowledge than the text. The promoters of such maps insist on the importance of associations, visual rhythm, patterns, colour or spatial awareness, on the role of left brain vs. right brain, on the cognitive processes, the methods of memorizing, how knowledge is created, stored, processed, mixed and remixed, on how to collaborate in order to solve problems. Making people aware of these is a step, and it starts with learning people how to learn, on how to use the tools and resources effectively and efficiently, on how and where to find information, how to take notes and map knowledge. I don’t think this requires a huge amount of investment, and there are schools that made important steps in this direction.

The personalization of the learning process is required by the different learning needs, of the different aptitudes and attitudes toward learning. For many people learning could be regarded as an unpleasant endeavor, I think that some effort must be spent in order to make learning pleasant, to make things "attractive to learn" – games work well for small and mature alltogether. In addition many students don't see the necessity of learning so many things, there should be a trade between theory, utility and applicability. For example in Mathematics behind each theory there is a problem that the respective theory attempts to solve, graduating in Math, I found the applicability of many of the theories learned only many years later. 

Talking about University, I've seen that the professors were often avoiding, most probably from lack of time, to explain people the grounds of theories, but expecting people to understand the grounds even if they are not so simple to see. From time to time I was hearing a professor asking himself how is it possible that the students don't understand the grounds behind the theories, how they can't perceive such basic stuff. Of course, the true is somewhere in between, what I'm trying to point here is that not for everybody all the facts are evident. Especially in Mathematics we are talking about several hundred/thousands years of evolution in mathematical thought, in teaching of Mathematics being avoided subjects like: why, when, where, how, by what means, questions essential in acquiring of knowledge.  There were also professors who preferred that students learn less material, that they understood the principles behind each theory, that they can make use of the knowledge they have. I think is important to invest effort in making knowledge, implicitly also knowledge resources, available at the level required by a student.

 Looking into the future, at the impact technologies have in our life, I expect that the role of a teacher changes from knowledge provider to enabler/facilitator of the learning process, of course this most probably can't be done starting from the first grade but introduced gradually. This doesn't mean that there will be no more need for skilled teachers, only the roles will change, and I feel that will be a win-win outcome.