❄️Systems Thinking: On Bounded Rationality (Quotes)

"The principle of bounded rationality [is] the capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problems whose solution is required for objectively rational behavior in the real world - or even for a reasonable approximation to such objective rationality." (Herbert A Simon, "Administrative Behavior", 1947)

"The first consequence of the principle of bounded rationality is that the intended rationality of an actor requires him to construct a simplified model of the real situation in order to deal with it. He behaves rationally with respect to this model, and such behavior is not even approximately optimal with respect to the real world. To predict his behavior we must understand the way in which this simplified model is constructed, and its construction will certainly be related to his psychological properties as a perceiving, thinking, and learning animal." (Herbert A Simon, "Models of Man", 1957)

[*]"A decision theory that rests on the assumptions that human cognitive capabilities are limited and that these limitations are adaptive with respect to the decision environments humans frequently encounter. Decision are thought to be made usually without elaborate calculations, but instead by using fast and frugal heuristics. These heuristics certainly have the advantage of speed and simplicity, but if they are well matched to a decision environment, they can even outperform maximizing calculations with respect to accuracy. The reason for this is that many decision environments are characterized by incomplete information and noise. The information we do have is usually structured in a specific way that clever heuristics can exploit." (E Ebenhoh, "Agent-Based Modelnig with Boundedly Rational Agents", 2007)

"Bounded rationality [...] is the rationality that takes into account the limitations of the decision maker in terms of information, cognitive capacity, and attention as opposed to substantive rationality, which is not limited to satisficing, but rather aims at fully optimized solutions." (Jean-Charles Pomerol & Frédéric Adam, "Understanding the Legacy of Herbert Simon to Decision Support Systems", 2008)

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

[*] "Conceptual model that assumes individuals are intentionally rational, i.e. they try to maximize their decisions. However, this ideal model is almost impossible to apply in practice: actions and decisions are taken and performed by individuals whose knowledge of the alternatives and the consequences is incomplete; in addition, preferences are subject to change and are not always clearly orderable." (Maddalena Sorrentino & Marco De Marco, "Developing an Interdisciplinary Approach to the Evaluation of E-Government Implementation", 2009)

[*] "The assumption that agents have limited ability to acquire and process information and to solve complex economic problems. These limitations imply that expectations can diverge from RE [Rational Expectationa]." (Sebastiano Manzan, Agent Based Modeling in Finance", 2009)

[*] "Refers to the difficulties faced by an individual in obtaining, memorizing, and processing information in an actionable manner. Although he/she may want to act rationally, the individual can only do so in a limited way, without being able to take into account all desirable information or all possible options. This limited way consists in acting on the basis of knowledge that is deemed acceptable and sufficient, rather than complete knowledge, and of simple rules, rather than a comprehensive method; and in taking shortcuts whenever possible." (Humbert Lesca & Nicolas Lesca, "Weak Signals for Strategic Intelligence: Anticipation Tool for Managers", 2011)

[*] "The theory that personal rationality is bounded by our ability to process information, our cognitive limitations, and the finite time we have to make a decision. Although our decisions are still rational, they are rational within these constraints and, therefore, may not always appear to be rational or optimal." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

[*] "The principle that the rationality of human beings is constrained ('bounded') by the limits of their cognition and capacity to process information." (Robert M Grant, "Contemporary Strategy Analysis" 10th Ed., 2018)

[*] "A situation in which people have a limited capacity to anticipate, solve complex problems, or enumerate all options." (Jeffrey M Perloff & James A Brander, "Managerial Economics and Strategy" 2nd Ed., 2016)

[*] "A concept that explains behavior that diverges from the standard assumption of a fully rational economic agent. It occurs due to limitations of cognitive ability and access to information for decision making." (Ashlesha Khedekar-Swaminathan, "Behavioral Strategies to Achieve Financial Stability in Uncertain Times", 2019)

"Bounded rationality means rationality within limits or bounds set by incomplete information, cognitive limitations of mind and limited time available for taking the decision." (Anubhuti Dwivedi, "Peace in Economic Equilibrium: A Micro-Perspective", 2019)

[*] "Paradigm that explains agents’ strategic decision-making based on the imperfect information available to them and the expectations they have that dictate whether they will view the results as satisfactory. It leads on to the idea of adaptive learning and trial-and-error processes." (César Camisón, "Neurostrategy", 2021)

[*] "The idea that decision making deviates from rationality due to such inherently human factors as limitations in cognitive capacity and willpower, and situational constraints." (Shaun Ruysenaar, "Thinking Critically About the Fourth Industrial Revolution as a Wicked Problem", 2021)

🏷️Knowledge Representation: On Heuristics (Quotes)

"Heuristic reasoning is reasoning not regarded as final and strict but as provisional and plausible only, whose purpose is to discover the solution of the present problem. We are often obliged to use heuristic reasoning. We shall attain complete certainty when we shall have obtained the complete solution, but before obtaining certainty we must often be satisfied with a more or less plausible guess. We may need the provisional before we attain the final. We need heuristic reasoning when we construct a strict proof as we need scaffolding when we erect a building." (George Pólya, "How to Solve It", 1945)

"The aim of heuristics is to study the methods and rules of discovery and invention. [...] Heuristic, as an adjective, means 'serving to discover'." (George Pólya, "How to Solve It", 1945)

"Heuristic (it is of Greek origin) means discovery. Heuristic methods are based on experience, rational ideas, and rules of thumb. Heuristics are based more on common sense than on mathematics. Heuristics are useful, for example, when the optimal solution needs an exhaustive search that is not realistic in terms of time. In principle, a heuristic does not guarantee the best solution, but a heuristic solution can provide a tremendous shortcut in cost and time." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"Heuristic methods may aim at local optimization rather than at global optimization, that is, the algorithm optimizes the solution stepwise, finding the best solution at each small step of the solution process and 'hoping' that the global solution, which comprises the local ones, would be satisfactory." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996)

"A heuristic is ecologically rational to the degree that it is adapted to the structure of an environment. Thus, simple heuristics and environmental structure can both work hand in hand to provide a realistic alternative to the ideal of optimization, whether unbounded or constrained." (Gerd Gigerenzer & Peter M Todd, "Fast and Frugal Heuristics: The Adaptive Toolbox" [in "Simple Heuristics That Make Us Smart"], 1999)

"Fast and frugal heuristics employ a minimum of time, knowledge, and computation to make adaptive choices in real environments. They can be used to solve problems of sequential search through objects or options, as in satisficing. They can also be used to make choices between simultaneously available objects, where the search for information (in the form of cues, features, consequences, etc.) about the possible options must be limited, rather than the search for the options themselves. Fast and frugal heuristics limit their search of objects or information using easily computable stopping rules, and they make their choices with easily computable decision rules." (Gerd Gigerenzer & Peter M Todd, "Fast and Frugal Heuristics: The Adaptive Toolbox" [in "Simple Heuristics That Make Us Smart"], 1999)

"In the language of mental models, such past experience provided the default assumptions necessary to fill the gaps in the emerging and necessarily incomplete framework of a relativistic theory of gravitation. It was precisely the nature of these default assumptions that allowed them to be discarded again in the light of novel information - provided, for instance, by the further elaboration of the mathematical formalism - without, however, having to abandon the underlying mental models which could thus continue to function as heuristic orientations." (Jürgen Renn, "Before the Riemann Tensor: The Emergence of Einstein’s Double Strategy", [in "The Universe of General Relativity"] 2000)

"Theories of choice are at best approximate and incomplete. One reason for this pessimistic assessment is that choice is a constructive and contingent process. When faced with a complex problem, people employ a variety of heuristic procedures in order to simplify the representation and the evaluation of prospects. These procedures include computational shortcuts and editing operations, such as eliminating common components and discarding nonessential differences. The heuristics of choice do not readily lend themselves to formal analysis because their application depends on the formulation of the problem, the method of elicitation, and the context of choice." (Amos Tversky & Daniel Kahneman, "Advances in Prospect Theory: Cumulative Representation of Uncertainty" [in "Choices, Values, and Frames"], 2000)

"Heuristics are rules of thumb that help constrain the problem in certain ways (in other words they help you to avoid falling back on blind trial and error), but they don't guarantee that you will find a solution. Heuristics are often contrasted with algorithms that will guarantee that you find a solution - it may take forever, but if the problem is algorithmic you will get there. However, heuristics are also algorithms." (S Ian Robertson, "Problem Solving", 2001)

"Models of bounded rationality describe how a judgement or decision is reached (that is, the heuristic processes or proximal mechanisms) rather than merely the outcome of the decision, and they describe the class of environments in which these heuristics will succeed or fail." (Gerd Gigerenzer & Reinhard Selten [Eds., "Bounded Rationality: The Adaptive Toolbox", 2001)

"Mental shortcuts, also called heuristic simplifications, help us analyze situations and make decisions quickly in our daily life. However, this process often leads us astray when analyzing decisions with risk and uncertainty. Because investing decisions involve substantial risk and uncertainty, our decisions are biased in predictable ways. The representativeness bias causes us to extrapolate the past and assume that good companies are good investments. The familiarity bias causes us to believe that firms we are familiar with are better investments than unfamiliar firms. Thus, we own more local firms and our employer’s stock and few international stocks. Thus, these biases lead to low diversification and higher risks." (John R Nofsinger, "The Psychology of Investing", 2002)

"Psychological research has shown that the brain uses shortcuts to reduce the complexity of analyzing information. Psychologists call these heuristic simplifications. These mental shortcuts allow the brain to generate an estimate of an answer before fully digesting all the available information. Two examples of shortcuts are known as representativeness and familiarity. Using these shortcuts allows the brain to organize and quickly process large amounts of information. However, these shortcuts also make it hard for investors to analyze new information correctly and can lead to inaccurate conclusions." (John R Nofsinger, "The Psychology of Investing", 2002)

"Most people give substantial weight to anecdotal evidence, perhaps so much that it will cancel out positive recommendations found in consumer reports. People's tendency to give undue weight to some types of information is called the availability heuristic. A heuristic is a rule of thumb, a mental shortcut." (Barry Schwartz, "The Paradox of Choice: Why More Is Less", 2004)

"A heuristic is defined as a simple rule that exploits both evolved abilities to act fast and struc- tures of the environment to act accurately and frugally. The complexity and uncertainty of an environment cannot be determined independently of the actor. What matters is the degree of complexity and uncertainty encountered by the decision maker." (Christoph Engel & Gerd Gigerenzer, "Law and Heuristics: An interdisciplinary venture" [in "Heuristics and the Law", 2006)

"Heuristics are needed in situations where the world does not permit optimization. For many real-world problems (as opposed to optimization-tuned textbook problems), optimal solutions are unknown because the problems are computationally intractable or poorly defined." (Christoph Engel & Gerd Gigerenzer, "Law and Heuristics: An interdisciplinary venture" [in "Heuristics and the Law", 2006)

"A heuristic is a rule applied to an existing solution represented in a perspective that generates a new (and hopefully better) solution or a new set of possible solutions." (Scott E Page, "The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools and Societies", 2008)

"A second class of metaphors - mathematical algorithms, heuristics, and models - brings us closer to the world of computer science programs, simulations, and approximations of the brain and its cognitive processes." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"A heuristic is a decision rule that utilizes a subset of the information set. Since in virtually all cases people must economize and cannot analyze all contingencies, we use heuristics without even realizing it." (Lucy F Ackert & Richard Deaves, "Behavioral Finance: Psychology, Decision-Making, and Markets", 2010)

"Perception and memory are imprecise filters of information, and the way in which information is presented, that is, the frame, influences how it is received. Because too much information is difficult to deal with, people have developed shortcuts or heuristics in order to come up with reasonable decisions. Unfortunately, sometimes these heuristics lead to bias, especially when used outside their natural domains." (Lucy F Ackert & Richard Deaves, "Behavioral Finance: Psychology, Decision-Making, and Markets", 2010)

"When heuristics don’t yield the results we expect, you’d think we would eventually realize that something’s wrong. Even if we don’t locate the biases, we should be able to see the discrepancy between what we wanted and what we got, right? Well, not necessarily. As it turns out, we have biases that support our biases! If we’re partial to one option - perhaps because it’s more memorable, or framed to minimize loss, or seemingly consistent with a promising pattern - we tend to search for information that will justify choosing that option. On the one hand, it’s sensible to make choices that we can defend with data and a list of reasons. On the other hand, if we’re not careful, we’re likely to conduct an imbalanced analysis, falling prey to a cluster of errors collectively known as 'confirmation biases'." (Sheena Iyengar, "The Art of Choosing", 2010)

"In particular, the accurate intuitions of experts are better explained by the effects of prolonged practice than by heuristics. We can now draw a richer and more balanced picture, in which skill and heuristics are alternative sources of intuitive judgments and choices." (Daniel Kahneman, "Thinking, Fast and Slow", 2011)

"This is the essence of intuitive heuristics: when faced with a difficult question, we often answer an easier one instead, usually without noticing the substitution." (Daniel Kahneman, "Thinking, Fast and Slow", 2011)

"Heuristics are an evolutionary solution to an ongoing problem: we have limited mental resources. As such, they have a very long and thoroughly time-tested history of helping us - on average - make better decisions." (Peter H Diamandis, "Abundance: The Future is Better Than You Think", 2012)

"Heuristics are simplified rules of thumb that make things simple and easy to implement. But their main advantage is that the user knows that they are not perfect, just expedient, and is therefore less fooled by their powers. They become dangerous when we forget that." (Nassim N Taleb, "Antifragile: Things that gain from disorder", 2012)

"Mental models represent possibilities, and the theory of mental models postulates three systems of mental processes underlying inference: (0) the construction of an intensional representation of a premise’s meaning – a process guided by a parser; (1) the building of an initial mental model from the intension, and the drawing of a conclusion based on heuristics and the model; and (2) on some occasions, the search for alternative models, such as a counterexample in which the conclusion is false. System 0 is linguistic, and it may be autonomous. System 1 is rapid and prone to systematic errors, because it makes no use of a working memory for intermediate results. System 2 has access to working memory, and so it can carry out recursive processes, such as the construction of alternative models." (Sangeet Khemlania & P.N. Johnson-Laird, "The processes of inference", Argument and Computation, 2012)

"The art of reasoned persuasion is an iterative, recursive heuristic, meaning that we must go back and forth between the facts and the rules until we have a good fit. We cannot see the facts properly until we know what framework to place them into, and we cannot determine what framework to place them into until we see the basic contours of the facts." (Joel P Trachtman, "The Tools of Argument", 2013)

"A good heuristic decision is made by 1) knowing what to look for, 2) knowing when enough information is enough (the 'threshold of decision' ), and 3) knowing what decision to make." (Patrick Van Horne, "Left of Bang", 2014)

"A rule of thumb, or heuristic, enables us to make a decision fast, without much searching for information, but nevertheless with high accuracy. [...] A heuristic can be safer and more accurate than a calculation, and the same heuristic can underlie both conscious and unconscious decisions." (Gerd Gigerenzer, "Risk Savvy: How to make good decisions", 2014)

"Even if virtual worlds were tabula rasa, we are encumbered with a great deal of cognitive baggage. Our brains are hardwired with many mental shortcuts to help us make sense of the world. We simply do not have the time to carefully process every piece of information that comes our way. To cope with this inundation of information, our brains have developed automated heuristics that filter and preprocess this information for us. Thus, when we encounter new media and technological devices, we fall back on the existing rules and norms we know." (Nick Yee, "The Proteus Paradox", 2014)

"A heuristic is a strategy that ignores part of the information, with the goal of making decisions more quickly, frugally, and/or accurately than more complex methods." (Gerd Gigerenzer et al, "Simply Rational: Decision Making in the Real World", 2015)

"Because economists go through a similar training and share a common method of analysis, they act very much like a guild. The models themselves may be the product of analysis, reflection, and observation, but practitioners’ views about the real world develop much more heuristically, as a by-product of informal conversations and socialization among themselves. This kind of echo chamber easily produces overconfidence - in the received wisdom or the model of the day. Meanwhile, the guild mentality renders the profession insular and immune to outside criticism. The models may have problems, but only card-carrying members of the profession are allowed to say so. The objections of outsiders are discounted because they do not understand the models. The profession values smarts over judgment, being interesting over being right - so its fads and fashions do not always self-correct." (Dani Rodrik, "Economics Rules: The Rights and Wrongs of the Dismal Science", 2015)

"Heuristic decision making is fast and frugal and is often based on the evaluation of one or two salient bits of information." (Amitav Chakravarti, "Why People (Don’t) Buy: The Go and Stop Signals", 2015)

"Probability theory is not the only tool for rationality. In situations of uncertainty, as opposed to risk, simple heuristics can lead to more accurate judgments, in addition to being faster and more frugal. Under uncertainty, optimal solutions do not exist (except in hindsight) and, by definition, cannot be calculated. Thus, it is illusory to model the mind as a general optimizer, Bayesian or otherwise. Rather, the goal is to achieve satisficing solutions, such as meeting an aspiration level or coming out ahead of a competitor."  (Gerd Gigerenzer et al, "Simply Rational: Decision Making in the Real World", 2015)

"Judgments made in difficult circumstances can be based on a limited number of simple, rapidly-arrived-at rules ('heuristics'), rather than formal, extensive algorithmic calculus and programs. Often, even complex problems can be solved quickly and accurately using such 'quick and dirty' heuristics. However, equally often, such heuristics can be beset by systematic errors or biases." (Jérôme Boutang & Michel De Lara, "The Biased Mind", 2016)

"A heuristic is a strategy we derive from previous experience with a similar problem." (Darius Foroux, "Think Straight", 2017)

"In any analysis of any part of the world, it is mandatory to institute a general reasoning in which the whole - the Absolute - is also included. This is what science scrupulously avoids. Science is all about the parts, and ignoring the whole. Science is non-holistic, which is why it cannot arrive at a grand unified, final theory of everything. From the whole you can get to every part, because the whole defines the parts. If you start with the parts, as science does, you can never get to the whole because the parts are necessarily defined piecemeal, heuristically and with no regard to the whole, since the whole is unknown. A bottom-up approach can never work. Only top-down approaches have any chance of working. Empiricists are always parts people and bottom-up people. Rationalists are holistic and top-down. These are opposite worldviews. The PSR is an explanatory, top-down principle. Randomness is a non-explanatory, bottom-up speculation." (Thomas Stark, "God Is Mathematics: The Proofs of the Eternal Existence of Mathematics", 2018)

"The social world that humans have made for themselves is so complex that the mind simplifies the world by using heuristics, customs, and habits, and by making models or assumptions about how things generally work (the ‘causal structure of the world’). And because people rely upon (and are invested in) these mental models, they usually prefer that they remain uncontested." (Dr James Brennan, "Psychological  Adjustment to Illness and Injury", West of England Medical Journal Vol. 117 (2), 2018)

"We used the word 'heuristics' to describe aspects of software development that tip toward the liberal arts. Its counterpart, 'algorithms', was its alter ego on the technical side. Heuristics and algorithms are like two sides of the same coin. Both are specific procedures for making software do what it does: taking input, applying an operation, and producing output. Yet each had a different purpose." (Ken Kocienda, "Creative Selection: Inside Apple's Design Process During the Golden Age of Steve Jobs", 2018)

❄️Systems Thinking: On Technology (Quotes)

"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)

"Doing engineering is practicing the art of the organized forcing of technological change." (George Spencer-Brown, Electronics, Vol. 32 (47),  1959)

"Science is the reduction of the bewildering diversity of unique events to manageable uniformity within one of a number of symbol systems, and technology is the art of using these symbol systems so as to control and organize unique events. Scientific observation is always a viewing of things through the refracting medium of a symbol system, and technological praxis is always handling of things in ways that some symbol system has dictated. Education in science and technology is essentially education on the symbol level." (Aldous L Huxley, "Essay", Daedalus, 1962)

"Engineering is the art of skillful approximation; the practice of gamesmanship in the highest form. In the end it is a method broad enough to tame the unknown, a means of combing disciplined judgment with intuition, courage with responsibility, and scientific competence within the practical aspects of time, of cost, and of talent. This is the exciting view of modern-day engineering that a vigorous profession can insist be the theme for education and training of its youth. It is an outlook that generates its strength and its grandeur not in the discovery of facts but in their application; not in receiving, but in giving. It is an outlook that requires many tools of science and the ability to manipulate them intelligently In the end, it is a welding of theory and practice to build an early, strong, and useful result. Except as a valuable discipline of the mind, a formal education in technology is sterile until it is applied." (Ronald B Smith, "Professional Responsibility of Engineering", Mechanical Engineering Vol. 86 (1), 1964)

"It is a commonplace of modern technology that there is a high measure of certainty that problems have solutions before there is knowledge of how they are to be solved." (John K Galbraith, "The New Industrial State", 1967)

"Technological invention and innovation are the business of engineering. They are embodied in engineering change." (Daniel V DeSimone & Hardy Cross, "Education for Innovation", 1968)

"It follows from this that man's most urgent and pre-emptive need is maximally to utilize cybernetic science and computer technology within a general systems framework, to build a meta-systemic reality which is now only dimly envisaged. Intelligent and purposeful application of rapidly developing telecommunications and teleprocessing technology should make possible a degree of worldwide value consensus heretofore unrealizable." (Richard F Ericson, "Visions of Cybernetic Organizations", 1972)

"Technology can relieve the symptoms of a problem without affecting the underlying causes. Faith in technology as the ultimate solution to all problems can thus divert our attention from the most fundamental problem - the problem of growth in a finite system." (Donella A Meadows, "The Limits to Growth", 1972)

"Modern scientific principle has been drawn from the investigation of natural laws, technology has developed from the experience of doing, and the two have been combined by means of mathematical system to form what we call engineering." (George S Emmerson, "Engineering Education: A Social History", 1973)

"The system of nature, of which man is a part, tends to be self-balancing, self-adjusting, self-cleansing. Not so with technology." (Ernst F Schumacher, "Small is Beautiful", 1973)

"People’s views of the world, of themselves, of their own capabilities, and of the tasks that they are asked to perform, or topics they are asked to learn, depend heavily on the conceptualizations that they bring to the task. In interacting with the environment, with others, and with the artifacts of technology, people form internal, mental models of themselves and of the things with which they are interacting. These models provide predictive and explanatory power for understanding the interaction." (Donald A Norman, "Some observations on Mental Models", 1983)

"With the changes in technological complexity, especially in information technology, the leadership task has changed. Leadership in a networked organization is a fundamentally different thing from leadership in a traditional hierarchy." (Edgar Schein, "Organizational Culture and Leadership", 1985)

"The new information technologies can be seen to drive societies toward increasingly dynamic high-energy regions further and further from thermodynamical equilibrium, characterized by decreasing specific entropy and increasingly dense free-energy flows, accessed and processed by more and more complex social, economic, and political structures." (Ervin László, "Information Technology and Social Change: An Evolutionary Systems Analysis", Behavioral Science 37, 1992) 

"Now that knowledge is taking the place of capital as the driving force in organizations worldwide, it is all too easy to confuse data with knowledge and information technology with information." (Peter Drucker, "Managing in a Time of Great Change", 1995)

"Commonly, the threats to strategy are seen to emanate from outside a company because of changes in technology or the behavior of competitors. Although external changes can be the problem, the greater threat to strategy often comes from within. A sound strategy is undermined by a misguided view of competition, by organizational failures, and, especially, by the desire to grow." (Michael E Porter, "What is Strategy?", Harvard Business Review, 1996)

"Networks constitute the new social morphology of our societies, and the diffusion of networking logic substantially modifies the operation and outcomes in processes of production, experience, power, and culture. While the networking form of social organization has existed in other times and spaces, the new information technology paradigm provides the material basis for its pervasive expansion throughout the entire social structure." (Manuel Castells, "The Rise of the Network Society", 1996)

"Beauty is more important in computing than anywhere else in technology because software is so complicated. Beauty is the ultimate defense against complexity." (David Gelernter, "Machine Beauty: Elegance And The Heart Of Technolog", 1998)

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

"Networks have existed in every economy. What’s different now is that networks, enhanced and multiplied by technology, penetrate our lives so deeply that 'network' has become the central metaphor around which our thinking and our economy are organized. Unless we can understand the distinctive logic of networks, we can’t profit from the economic transformation now under way." (Kevin Kelly, "New Rules for the New Economy: 10 radical strategies for a connected world", 1998)

"The more interconnected a technology is, the more opportunities it spawns for both use and misuse. [… The law of plentitude is most accurately rendered thus: In a network, the more opportunities that are taken, the faster new opportunities arise." (Kevin Kelly, "New Rules for the New Economy: 10 radical strategies for a connected world", 1998)

"A primary reason that evolution - of life-forms or technology - speeds up is that it builds on its own increasing order." (Ray Kurzweil, "The Age of Spiritual Machines: When Computers Exceed Human Intelligence", 1999) 

"As systems became more varied and more complex, we find that no single methodology suffices to deal with them. This is particularly true of what may be called information intelligent systems - systems which form the core of modern technology. To conceive, design, analyze and use such systems we frequently have to employ the totality of tools that are available. Among such tools are the techniques centered on fuzzy logic, neurocomputing, evolutionary computing, probabilistic computing and related methodologies. It is this conclusion that formed the genesis of the concept of soft computing." (Lotfi A Zadeh, "The Birth and Evolution of Fuzzy Logic: A personal perspective", 1999)

"We do not learn much from looking at a model - we learn more from building the model and manipulating it. Just as one needs to use or observe the use of a hammer in order to really understand its function, similarly, models have to be used before they will give up their secrets. In this sense, they have the quality of a technology - the power of the model only becomes apparent in the context of its use." (Margaret Morrison & Mary S Morgan, "Models as mediating instruments", 1999)

"Periods of rapid change and high exponential growth do not, typically, last long. A new equilibrium with a new dominant technology and/or competitor is likely to be established before long. Periods of punctuation are therefore exciting and exhibit unusual uncertainty. The payoff from establishing a dominant position in this short time is therefore extraordinarily high. Dominance is more likely to come from skill in marketing and positioning than from superior technology itself." (Richar Koch, "The Power Laws", 2000)

"The business changes. The technology changes. The team changes. The team members change. The problem isn't change, per se, because change is going to happen; the problem, rather, is the inability to cope with change when it comes." (Kent Beck, Extreme Programming Explained, 2000)

"Technology can relieve the symptoms of a problem without affecting the underlying causes. Faith in technology as the ultimate solution to all problems can thus divert our attention from the most fundamental problem - the problem of growth in a finite system - and prevent us from taking effective action to solve it." (Donella H Meadows & Dennis L Meadows, "The Limits to Growth: The 30 Year Update", 2004)

"Although the Singularity has many faces, its most important implication is this: our technology will match and then vastly exceed the refinement and suppleness of what we regard as the best of human traits."  (Ray Kurzweil, "The Singularity is Near", 2005)

"The Singularity will represent the culmination of the merger of our biological thinking and existence with our technology, resulting in a world that is still human but that transcends our biological roots. There will be no distinction, post-Singularity, between human and machine or between physical and virtual reality. If you wonder what will remain unequivocally human in such a world, it’s simply this quality: ours is the species that inherently seeks to extend its physical and mental reach beyond current limitations." (Ray Kurzweil, "The Singularity is Near", 2005)

"Chance is just as real as causation; both are modes of becoming.  The way to model a random process is to enrich the mathematical theory of probability with a model of a random mechanism. In the sciences, probabilities are never made up or 'elicited' by observing the choices people make, or the bets they are willing to place.  The reason is that, in science and technology, interpreted probability exactifies objective chance, not gut feeling or intuition. No randomness, no probability." (Mario Bunge, "Chasing Reality: Strife over Realism", 2006)

"Synergy occurs when organizational parts interact to produce a joint effect that is greater than the sum of the parts acting alone. As a result the organization may attain a special advantage with respect to cost, market power, technology, or employee." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

"The butterfly effect demonstrates that complex dynamical systems are highly responsive and interconnected webs of feedback loops. It reminds us that we live in a highly interconnected world. Thus our actions within an organization can lead to a range of unpredicted responses and unexpected outcomes. This seriously calls into doubt the wisdom of believing that a major organizational change intervention will necessarily achieve its pre-planned and highly desired outcomes. Small changes in the social, technological, political, ecological or economic conditions can have major implications over time for organizations, communities, societies and even nations." (Elizabeth McMillan, "Complexity, Management and the Dynamics of Change: Challenges for practice", 2008)

"The evolution of science and technology parallels the evolution of nature. The major technological transitions are also passages from one level of organization to another."(Kevin Kelly, "What Technology Wants", 2010) 

"What’s next for technology and design? A lot less thinking about technology for technology’s sake, and a lot more thinking about design. Art humanizes technology and makes it understandable. Design is needed to make sense of information overload. It is why art and design will rise in importance during this century as we try to make sense of all the possibilities that digital technology now affords." (John Maeda, "Why Apple Leads the Way in Design", 2010) 

"Today, technology has lowered the barrier for others to share their opinion about what we should be focusing on. It is not just information overload; it is opinion overload." (Greg McKeown, "Essentialism: The Disciplined Pursuit of Less", 2014)

🦉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)

❄️Systems Thinking: On Systems Theory (Quotes)

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

"The aim of systems theory for business is to develop an objective, understandable environment for decision making; that is, if the system within which managers make the decisions can be provided as an explicit framework, then such decision making should be easier to handle." (Richard A Johnson et al, "Systems Theory and Management", Management Science Vol. 10 (2), 1964)

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

"Whereas traditional reductionism sought to find the commonality underlying diversity in reference to a shared substance, such as material atoms, contemporary systems theory seeks to find common features in terms of shared aspects of organization." (Ervin László, "The Systems View of the World: A Holistic Vision for Our Time", 1972)

"A system may be specified in either of two ways. In the first, which we shall call a state description, sets of abstract inputs, outputs and states are given, together with the action of the inputs on the states and the assignments of outputs to states. In the second, which we shall call a coordinate description, certain input, output and state variables are given, together with a system of dynamical equations describing the relations among the variables as functions of time. Modern mathematical system theory is formulated in terms of state descriptions, whereas the classical formulation is typically a coordinate description, for example a system of differential equations." (E S Bainbridge, "The Fundamental Duality of System Theory", 1975)

"Systems theory is a scientific discipline concerned with the explanations of various phenomena, regardless of their specific nature, in terms of the formal relationships between the factors involved and the ways they are transformed under different conditions; the observations are explained in terms of the relationships between the components, i.e., in reference to the organization and functioning rather than with an explicit reference to the nature of the mechanisms involved (e.g., physical, biological, social, or even purely conceptual)." (Mihajlo D Mesarovic & Y Takahara, "Foundations for the mathematical theory of general systems", 1975)

"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 most general form of systems theory is a set of logical or mathematical statements about all conceptual systems. A subset of this concerns all concrete systems. A subsubset concerns the very special and very important living systems, i. e., general living systems theory." (James G Miller, "Living systems", 1978)

"Systems theory looks at the world in terms of the interrelatedness and interdependence of all phenomena, and in this framework an integrated whole whose properties cannot be reduced to those of its parts is called a system. Living organisms, societies, and ecosystems are all systems." (Fritjof Capra, "The Turning Point: Science, Society, and the Turning Culture", 1982)

"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)

"For a long time, people have been trying to characterize or define the notion of system. After all, ‘systems’ are supposed to be what System Theory is about. The results so far have been contradictory and unsatisfactory. This confusion at the foundations has led many to conclude that there is no such thing as a ‘system’ and hence to deny that System Theory is about anything. Even those most sympathetic to the notion have difficulties at this level. The very founders of System Theory did not try to say what a system was; and as for System Theory, they characterized it only obliquely, by saying it comprised all studies of interest to more than one discipline. They thereby begged the entire question." (Robert Rosen, "Some comments on systems and system theory", 1986) 

"Systems theory pursues the scientific exploration and understanding of systems that exist in the various realms of experience, in order to arrive at a general theory of systems: an organized expressing of sets of interrelated concepts and principles that apply to all systems." (Béla H Bánáthy, "Systems Design of Education", 1991)

"Systems theory is antireductionist; it asserts that no system can be adequately understood or totally explained once it has been broken down into its component parts." (Charles Zastrow, "Introduction to Social Work and Social Welfare: Empowering People", 1993)

"Systems theory, in its concern for the whole and its emergent properties, ignores the components." (Walter F Buckley, "A Complex Adaptive System: Essays in Social Theory", 1998) 

"Systems theory is an interdisciplinary field of science concerned with the nature of complex systems, be they physical or natural or purely mathematical." (Thomas B Sheridan, The System Perspective on Human Factors in Aviation, 2010) 

"Systems theory is the interdisciplinary study of systems in general, with the goal of elucidating principles that can be applied to all types of systems at all nesting levels in all fields of research. The term does not yet have a well-established, precise meaning, but systems theory can reasonably be considered a specialization of systems thinking; alternatively as a goal output of systems science and systems engineering, with an emphasis on generality useful across a broad range of systems (versus the particular models of individual fields)." (Gabriela Walker & Elizabeth Pattison, "Using Bronfenbrenner's Ecological Framework to Design Support Systems for Education and Special Education: Learning About Thought Systems", 2016)

"The assumption that quality of a part of a system can only be understood in its relationship to the whole and investigating the parts in isolation cannot explain their combined effect on the whole system." (Margaret S Suubi, "Education for Sustainable Development (ESD) in Higher Education", 2019) 

[Systems Theory:] "Framework of describing how smaller, multiple units and components work together to create a larger system that is designed to carry out a particular function or meet a certain goal." (RaMonda Horton, "Systems-Based Approaches to Speech-Language Pathology Service Delivery for School Age Children", 2020)

[Systems Theory:]"Is an interdisciplinary study of systems that takes a holistic approach to analysis that focuses on the elements within a system, how they interrelate, how they work over time and within the context of larger systems (e.g., natural or man-made)." (Tatiana C Valencia & Stephanie J Valencia, "Cultivating Flow and Happiness in Children", 2020)

"Systems theory is an interdisciplinary theory about the nature of complex systems in nature, society, and science. It is a framework by which one can use to study, investigate and describe any group of objects that work in collaboration towards a common purpose/goal."

(Cheryl M Cordeiro et al, "Culture From a Value Systems Perspective", 2020)

[Systems Theory:] "The domain of systems inquiry that explores the principles and the description models of the abstract organization of phenomena, in an interdisciplinary manner and independently of their nature (natural or social systems) or scale of existence." (Evangelos C Papakitsos et al, "The Challenges of Work-Based Learning via Systemic Modelling in the European Union", 2020)

[Systems Theory:] "Theory that holds that systems in nature are holistic, interconnected and interdependent. If a change occurs in one part of a system, other parts of the system are affected as well." (Joe Monaco & Edward W Schneider, "Building Performance Systems That Last", 2020)

❄️Systems Thinking: On Systems Engineering (Quotes)

"The engineer must be able not only to design, but to execute. A draftsman may be able to design, but unless he is able to execute his designs to successful operation he cannot be classed as an engineer. The production engineer must be able to execute his work as he has planned it. This requires two qualifications in addition to technical engineering ability: He must know men, and he must have creative ability in applying good statistical, accounting, and 'system' methods to any particular production work he may undertake." (Hugo Diemer, "Industrial Engineering", 1905)

"The analysis of engineering systems and the understanding of economic structure have advanced since then, and the time is now more ripe to bring these topics into a potentially fruitful marriage." (Arnold Tustin, "The Mechanism of Economic Systems", 1953)

"The term 'systems engineering' is a term with an air of romance and of mystery. The romance and the mystery come from its use in the field of guided missiles, rockets, artificial satellites, and space flight. Much of the work being done in these areas is classified and hence much of it is not known to the general public or to this writer. […] From a business point of view, systems engineering is the creation of a deliberate combination of human services, material services, and machine service to accomplish an information processing job. But this is also very nearly a definition of business system analysis. The difference, from a business point of view, therefore, between business system analysis and systems engineering is only one of degree. In general, systems engineering is more total and more goal-oriented in its approach [...]." ("Computers and People" Vol. 5, 1956)

"By some definitions 'systems engineering' is suggested to be a new discovery. Actually it is a common engineering approach which has taken on a new and important meaning because of the greater complexity and scope of problems to be solved in industry, business, and the military. Newly discovered scientific phenomena, new machines and equipment, greater speed of communications, increased production capacity, the demand for control over ever-extending areas under constantly changing conditions, and the resultant complex interactions, all have created a tremendously accelerating need for improved systems engineering. Systems engineering can be complex, but is simply defined as 'logical engineering within physical, economic and technical limits' — bridging the gap from fundamental laws to a practical operating system." (Instrumentation Technology, 1957)

"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 more likely to be closely associated with top management of an enterprise than the engineering of the components of the system. If an engineering task is large and complex enough, the arrangement-making problem is especially difficult. Commonly, in a large job, the first and foremost problem for the systems engineers is to relate the objectives to the technical art. [...] Systems engineering is a highly technical pursuit and if a nontechnical man attempts to direct the systems engineering as such, it must end up in a waste of technical talent below." (Aeronautical Engineering Review Vol. 16, 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)

"There are two types of systems engineering - basis and applied. [...] Systems engineering is, obviously, the engineering of a system. It usually, but not always, includes dynamic analysis, mathematical models, simulation, linear programming, data logging, computing, optimating, etc., etc. It connotes an optimum method, realized by modern engineering techniques. Basic systems engineering includes not only the control system but also all equipments within the system, including all host equipments for the control system. Applications engineering is - and always has been - all the engineering required to apply the hardware of a hardware manufacturer to the needs of the customer. Such applications engineering may include, and always has included where needed, dynamic analysis, mathematical models, simulation, linear programming, data logging, computing, and any technique needed to meet the end purpose - the fitting of an existing line of production hardware to a customer's needs. This is applied systems engineering." (Instruments and Control Systems Vol. 31, 1958)

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

"In a society which is producing more people, more materials, more things, and more information than ever before, systems engineering is indispensable in meeting the challenge of complexity." (Harold Chestnut, "Systems Engineering Tools," 1965)

"Systems engineering is most effectively conceived of as a process that starts with the detection of a problem and continues through problem definition, planning and designing of a system, manufacturing or other implementing section, its use, and finally on to its obsolescence. Further, Systems engineering is not a matter of tools alone; It is a careful coordination of process, tools and people." (Arthur D. Hall, "Systems Engineering from an Engineering Viewpoint" In: Systems Science and Cybernetics. Vol.1 Issue.1, 1965)

"Systems Engineering Methods is directed towards the development of a broad systems engineering approach to help such people improve their decision-making capability. Although the emphasis is on engineering, the systems approach can also has validity for many other areas in which emphasis may be social, economic, or political." (Harold Chestnut, "Systems Engineering Methods", 1965) 

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

"In the minds of many writers systems engineering is synonymous with component selection and interface design; that is, the systems engineer does not design hardware but decides what types of existing hardware shall be coupled and how they shall be coupled. Complete agreement that this function is the essence of systems engineering will not be found here, for, besides the very important function of systems engineering in systems analysis, there is the role played by systems engineering in providing boundary conditions for hardware design." (A Wayne Wymore, "A Mathematical Theory of Systems Engineering", 1967)

"Systems Engineering is the science of designing complex systems in their totality to ensure that the component sub-systems making up the system are designed, fitted together, checked and operated in the most efficient way." (Gwilym Jenkins, "The Systems Approach", 1969) 

"The purpose and real value of systems engineering is [...] to keep going around the loop; find inadequacies and make improvements." (Robert E Machol, "Mathematicians are useful", 1971)

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

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

"With the subsequent strong support from cybernetics, the concepts of systems thinking and systems theory became integral parts of the established scientific language, and led to numerous new methodologies and applications - systems engineering, systems analysis, systems dynamics, and so on." (Fritjof Capra, "The Web of Life", 1996)

"[…] systems engineering is a process - a process that transforms a functional need, a mission capability requirement, or market opportunity into a complete description for a system that meets the need." (John Boardman & Brian Sauser, "Systems Thinking: Coping with 21st Century Problems", 2008)

"Systems engineering should be, first and foremost, a state of mind and an attitude taken when dealing with complexity." (Dominique Luzeaux et al, "Complex Systems and Systems of Systems Engineering", 2013) 

"If all the theories pertinent to systems engineering could be discussed within a common framework by means of a standard set of nomenclature and definitions, many separate courses might not be required." (A Wayne Wymore)

"The central activity of engineering, as distinguished from science, is the design of new devices, processes and systems." (Myron Tribus, "Rational Descriptions, Decisions and Designs", 2016)

♾️Cognitive Science: On Collective Intelligence (Quotes)

"We must therefore establish a form of decision-making in which voters need only ever pronounce on simple propositions, expressing their opinions only with a yes or a no. […] Clearly, if anyone’s vote was self-contradictory (intransitive), it would have to be discounted, and we should therefore establish a form of voting which makes such absurdities impossible." (Nicolas de Condorcet, "On the form of decisions made by plurality vote", 1788)

"Collective wisdom, alas, is no adequate substitute for the intelligence of individuals. Individuals who opposed received opinions have been the source of all progress, both moral and intellectual. They have been unpopular, as was natural." (Bertrand Russell, "Why I Am Not a Christian", 1927)

"The collective intelligence of any group of people who are thinking as a 'herd' rather than individually is no higher than the intelligence of the stupidest members." (Mary Day Winn, "Adam's Rib", 1931)

"Learning is a property of all living organisms. […] Since organized groups can be looked upon as living entities, they can be expected to exhibit learning […]" (Winfred B. Hirschmann, "Profit from the Learning Curve", Harvard Business Review, 1964)

"A cardinal principle in systems theory is that all parties that have a stake in a system should be represented in its management." (Malcolm S Knowles, "The Adult Learner", 1973)

"Collective intelligence emerges when a group of people work together effectively. Collective intelligence can be additive (each adds his or her part which together form the whole) or it can be synergetic, where the whole is greater than the sum of its parts." (Trudy and Peter Johnson-Lenz, "Groupware: Orchestrating the Emergence of Collective Intelligence", cca. 1980)

"Cybernetic information theory suggests the possibility of assuming that intelligence is a feature of any feedback system that manifests a capacity for learning." (Paul Hawken et al, "Seven Tomorrows", 1982)

"The concept of organizational learning refers to the capacity of organizational complexes to develop experiential knowledge, instincts, and 'feel' or intuition which are greater than the combined knowledge, skills and instincts of the individuals involved." (Don E. Kash, "Perpetual Innovation", 1989)

"We haven't worked on ways to develop a higher social intelligence […] We need this higher intelligence to operate socially or we're not going to survive. […] If we don't manage things socially, individual high intelligence is not going to make much difference. [...] Ordinary thought in society is incoherent - it is going in all sorts of directions, with thoughts conflicting and canceling each other out. But if people were to think together in a coherent way, it would have tremendous power." (David Bohm, "New Age Journal", 1989)

"Civilization is to groups what intelligence is to individuals. It is a means of combining the intelligence of many to achieve ongoing group adaptation. […] Civilization, like intelligence, may serve well, serve adequately, or fail to serve its adaptive function. When civilization fails to serve, it must disintegrate unless it is acted upon by unifying internal or external forces." (Octavia E Butler, "Parable of the Sower", 1993)

"Great leaders reinforce the idea that accomplishment in our society comes from great individual acts. We credit individuals for outcomes that required teams and communities to accomplish." (Peter Block, "Stewardship", 1993)

"We must learn to think together in an integrated, synergistic fashion, rather than in fragmented and competitive ways." (Joanna Macy, Noetic Sciences Bulletin, 1994-1995)

"The leading edge of growth of intelligence is at the cultural and societal level. It is like a mind that is struggling to wake up. This is necessary because the most difficult problems we face are now collective ones. They are caused by complex global interactions and are beyond the scope of individuals to understand and solve. Individual mind, with its isolated viewpoints and narrow interests, is no longer enough." (Jeff Wright, "Basic Beliefs", [email] 1995)

"It [collective intelligence] is a form of universally distributed intelligence, constantly enhanced, coordinated in real time, and resulting in the effective mobilization of skills. I'll add the following indispensable characteristic to this definition: The basis and goal of collective intelligence is mutual recognition and enrichment of individuals rather than the cult of fetishized or hypostatized communities." (Pierre Levy, "Collective Intelligence", 1999)

The three basic mechanisms of averaging, feedback and division of labor give us a first idea of a how a CMM [Collective Mental Map] can be developed in the most efficient way, that is, how a given number of individuals can achieve a maximum of collective problem-solving competence. A collective mental map is developed basically by superposing a number of individual mental maps. There must be sufficient diversity among these individual maps to cover an as large as possible domain, yet sufficient redundancy so that the overlap between maps is large enough to make the resulting graph fully connected, and so that each preference in the map is the superposition of a number of individual preferences that is large enough to cancel out individual fluctuations. The best way to quickly expand and improve the map and fill in gaps is to use a positive feedback that encourages individuals to use high preference paths discovered by others, yet is not so strong that it discourages the exploration of new paths." (Francis Heylighen, "Collective Intelligence and its Implementation on the Web", 1999)

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

"Cultures are never merely intellectual constructs. They take form through the collective intelligence and memory, through a commonly held psychology and emotions, through spiritual and artistic communion." (Tariq Ramadan, "Islam and the Arab Awakening", 2012)

"[…] recent researchers in artificial intelligence and computational methods use the term swarm intelligence to name collective and distributed techniques of problem solving without centralized control or provision of a global model. […] the intelligence of the swarm is based fundamentally on communication. […] the member of the multitude do not have to become the same or renounce their creativity in order to communicate and cooperate with each other. They remain different in terms of race, sex, sexuality and so forth. We need to understand, then, is the collective intelligence that can emerge from the communication and cooperation of such varied multiplicity." (Antonio Negri, "Multitude: War and Democracy in the Age of Empire", 2004)

"Collective Intelligence (CI) is the capacity of human collectives to engage in intellectual cooperation in order to create, innovate, and invent." (Pierre Levy, "Toward a Self-referential Collective Intelligence", 2009)

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

"By analyzing patterns in crowd behavior and honing methods of gauging fitness - or at least extricating it from popularity - the market can effectively use collective intelligence so that the cream always rises to the top." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

🔖Knowledge Representation: Mental Models’ Fallacies (Part I: Professional Defects and Thinking Models)

Mental Models

Professional Defects

I heard of “professional defect” almost 15-20 years back, in a colloquial discussion with an acquaintance. I don’t remember anymore the context entirely, though as I used it then, it became part of my vocabulary. With time “professional defect” meant for me “seeing and judging the world through the knowledge of our profession, and ignoring or misinterpreting thus some (important) aspects of our quotidian life”.  I’m not sure whether the term is actually in use or a similar term is used to reflect this aspect. I met occasionally similar constructs later, some of my German colleagues stressing the fact that sometimes we have to take out our professional glasses in order to see things differently (e.g. take out or put our managerial glasses). A generalization can be met in De Bono’s six thinking hats – the hats representing in De Bono’s perception the distinct six ways in which people think about the world, each of them stressing an import aspect of reality and our thinking patterns.

While in school, preparing for our profession, and especially later when we arrive to practice it, we acquire a knowledge base, the facts about the world, a set of thinking models that provide an approximation of the world in which our profession is anchored, and heuristics, “algorithms” of thinking and decision making that form with time thinking patterns. They help us understand situations and solve problems in our professional life. They add up to our previous personal experience accumulated over the years. 

In fact we’ll more likely attempt at first to use some of the experience we acquired before attempting a profession, some of the knowledge, models and heuristics being validated or invalidated in our professional life. With time we arrive to use some of the models we exclusively acquired and verified in professional life also in our personal life. This translation may seem natural as professional life extends itself sometimes beyond the borders of our profession – some coworkers become our friends, we spend more and more time in communities related to our profession, we meet similar problems in the two areas as of life, etc.

This extension from professional to personal life can have positive as well negative implications. It is true that some models from professional life can help us figure out some aspects from personal life, we arrive maybe to validate and improve our models, reaching somehow a deeper understanding of life. The problem comes when we ignore that we deal with a different type of environment, with its own characteristics, with different groups of people having higher diversity and other characteristics than the ones we meet in professional life. As the premises, the environment and its actors change, some of the models need some tweaking or they become unusable in our personal life. Maybe the number of such cases is incomparable small when we regard other type of thinking fallacies we fall into on a daily basis, though as long they arrive to define us and our choices in one way or another, we need to take a step back and look at them.

Some Examples

There could be found multiple examples in which the thinking patterns and models about the world we “learned” in our profession are used outside the professional life. Sometimes they look at the world by generalizing the characteristics of the individuals, focusing typically on a single aspect of the human nature, making totally or partially abstraction of the whole.  It’s also a sequel of the analytic way of thinking about the world we’ve been exposed from the early years of school. A more appropriate way of studying, understanding and modeling the human nature is by following the systemic thinking view in which the human being is more than the components that builds it – the body, the psychological, the language, the character, the behavior ranging from simple movements to complex forms of expression, the creativity, the ways of thinking transposed in decision making and the philosophy about life, that include the religious and the spiritual, the experience, the community the person belongs to, and so on. Some of these aspects are interrelated and mold together, and as the human they are a part of the complex web of life we belong to.

Unfortunately, our models about the world are quite simplistic and inflexible, as long we keep them so and as long we can’t discover and build more complex models. Before jumping to further aspects about models let’s look at a few examples. They might not be representative, they might be a little brought to extreme, though they are met in life and affect us in one form or another.

For example, a mathematician or somebody with strong mathematical background arrives to think of the world around him in terms of probability for an event to happen. On one side this can prove as a powerful tool to quantify and predict such events, though might make the person ignore the area of impossible, expressed maybe in opportunities with small chances to happen, opportunities that when considered could have a huge impact on his live. It’s a trivial example of the attempt to quantify and simplify the world to a degree that makes it understandable and predictable, mathematician’s luggage of models being more complex than that. Judging the words of wisdom of some mathematicians, they seemed to arrive to a deeper understanding of life - spirituality, philosophy, literature and wit melting in mathematician's life. Still anyone can be entrapped in the fallacies of the cold mathematical thinking about life in which everything is reduced to a representational model.

Not far from the previous example, a philosopher builds in his mind a world of ideas, and sooner or later arrives to evaluate the world around and his personal interactions with the world based on the philosophical currents he adheres to. No matter how complete and well-established a philosophy is, it’s a human based system of beliefs and models, with its loops and wholes. Sooner or later the philosopher will find himself trapped in the threads of his own philosophy as long he applies it too deep und unaware into his personal life. From this perspective it will be hard for a philosopher to be happy and content with his life.

A psychologist or somebody with a similar background may arrive to judge the people outside his praxis based on the traits they reveal. A chemist or biologist may see human’s constitution governed by a chain of chemical reactions, reducing thus feelings and behavior to them. A doctor may start to see in people the predispositions to or existence of a given disease, arriving maybe to judge people based on certain predispositions. A dentist might start evaluate people based on the denture they have. A sales person might start to acquire new friends (exclusively) based on the predisposition of buying a product. Accountants and the other financial professionals might see the world around them through financial models and wealth. An artist may treat as inferior people those who don’t exhibit any artistic skills. A priest may see the people around him as sinners.

There are many other examples, each profession comes with own type of similar fallacies. Think about your profession or the professional environment you belong to! More likely you’ll discover similar examples. How deep have they penetrated in the human society? How much have they influenced or still influence you? How could you escape them?

A Jump to Thinking Models Fallacies

Models are a good way to represent and approximate the world around us and predict behavior, though paraphrasing George Box, all models are to some degree wrong. Sometimes is enough if a model is right once in a while, other times we need to have reliable models. In general acceptance the success of a particular model is tied to its ability to cope capture the behavior of the real world, while its reliability is proportional to the number of cases it does so. When dealing with people often we need reliable models. It’s a question of ethics, but primordially of chances we lose to meet new interesting people, new experiences, and why not in overcoming our current condition.

Relying too much on a model make us vulnerable to a range of events that are not addressed by our models. Some of the presumptions on which such models are built upon, same as the limitations of our thinking models can get easily forgotten in our decision making process. We become then the prisoners of a set of (thinking) patterns from which will be impossible to exit unless we realize their traps. This actually applies to all type of models we built along the years. Some of them are rooted deeply in our beliefs on how the world (environment) around us works or is supposed to work.

Not only the beliefs we have, but also the models we acquired and use make us filter the signals, events or information from our environment. What we consider in a model is as important as what we leave out. The components removed by reducing the world to a model can be quintessential in what concerns the understanding and why not the living of life as a whole – the religious, the spiritual, the favorable chance, the free will, the tipping point, the random, the potential of people or groups to raise above some condition(s). No matter how well established some models, there will always be exceptions. We should expect for such exceptions to happen. They should make us challenge and enhance our models.

Going beyond the limitations of our models can prove to be a considerable challenge. We need to challenge our models on a daily basis, their visible and hidden premises, recognize their advantages, disadvantages and area of applicability. We need to detach ourselves from the models we’ve built, adapt them or learn new similar models that complement or counterbalance the existing models. Each model offers in the end a glimpse of truth, though we’ll mistake if we consider it the ultimate truth.

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