🕸Systems Engineering: Invariance (Just the Quotes)

 "[...] the existence of any invariant over a set of phenomena implies a constraint, for its existence implies that the full range of variety does not occur. The general theory of invariants is thus a part of the theory of constraints. Further, as every law of nature implies the existence of an invariant, it follows that every law of nature is a constraint." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"Through all the meanings runs the basic idea of an 'invariant': that although the system is passing through a series of changes, there is some aspect that is unchanging; so some statement can be made that, in spite of the incessant changing, is true unchangingly." (W Ross Ashby, "An Introduction to Cybernetics", 1956)

"We know many laws of nature and we hope and expect to discover more. Nobody can foresee the next such law that will be discovered. Nevertheless, there is a structure in laws of nature which we call the laws of invariance. This structure is so far-reaching in some cases that laws of nature were guessed on the basis of the postulate that they fit into the invariance structure." (Eugene P Wigner, "The Role of Invariance Principles in Natural Philosophy", 1963)

"[..] principle of equipresence: A quantity present as an independent variable in one constitutive equation is so present in all, to the extent that its appearance is not forbidden by the general laws of Physics or rules of invariance. […] The principle of equipresence states, in effect, that no division of phenomena is to be laid down by constitutive equations." (Clifford Truesdell, "Six Lectures on Modern Natural Philosophy", 1966)

"It is now natural for us to try to derive the laws of nature and to test their validity by means of the laws of invariance, rather than to derive the laws of invariance from what we believe to be the laws of nature." (Eugene P Wigner, "Symmetries and Reflections", 1967)

"As a metaphor - and I stress that it is intended as a metaphor - the concept of an invariant that arises out of mutually or cyclically balancing changes may help us to approach the concept of self. In cybernetics this metaphor is implemented in the ‘closed loop’, the circular arrangement of feedback mechanisms that maintain a given value within certain limits. They work toward an invariant, but the invariant is achieved not by a steady resistance, the way a rock stands unmoved in the wind, but by compensation over time. Whenever we happen to look in a feedback loop, we find the present act pitted against the immediate past, but already on the way to being compensated itself by the immediate future. The invariant the system achieves can, therefore, never be found or frozen in a single element because, by its very nature, it consists in one or more relationships - and relationships are not in things but between them."  (Ernst von Glasersfeld German, "Cybernetics, Experience and the Concept of Self", 1970)

"The point of departure is the measurement problem, as it appears in physics; the manner in which measurements allow us to characterize subsystems; the role of such subsystems as tools in system analysis; and the relationships existing between different ways of perceiving or interacting with the same system. Our conclusions are: (1) there exists no universal family of of analytic units appropriate for the treatment of all interactions; (2) there are on the contrary many such families of analytic units, all of which are equally 'real' and entitled to be treated on the same footing; (3) the appropriate use of natural interactions can enormously extend the class of physical observables accessible to us; (4) the concept of a model must be formulated, in its most general terms, as the sharing of a subsystem by two otherwise distinct systems, capable of imposing the same dynamic on an appropriate system with which they can both interact. We establish these results through a variety of terminologies which turn out to be equivalent: stability, invariance, symmetry, homeostasis." (Robert Rosen, "Fundamentals of Measurement and Representation of Natural Systems", 1978)

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

"A smaller model with fewer covariates has two advantages: it might give better predictions than a big model and it is more parsimonious (simpler). Generally, as you add more variables to a regression, the bias of the predictions decreases and the variance increases. Too few covariates yields high bias; this called underfitting. Too many covariates yields high variance; this called overfitting. Good predictions result from achieving a good balance between bias and variance. […] finding a good model involves trading of fit and complexity." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Each of the most basic physical laws that we know corresponds to some invariance, which in turn is equivalent to a collection of changes which form a symmetry group. […] whilst leaving some underlying theme unchanged. […] for example, the conservation of energy is equivalent to the invariance of the laws of motion with respect to translations backwards or forwards in time […] the conservation of linear momentum is equivalent to the invariance of the laws of motion with respect to the position of your laboratory in space, and the conservation of angular momentum to an invariance with respect to directional orientation… discovery of conservation laws indicated that Nature possessed built-in sustaining principles which prevented the world from just ceasing to be." (John D Barrow, "New Theories of Everything", 2007)

"The concept of symmetry (invariance) with its rigorous mathematical formulation and generalization has guided us to know the most fundamental of physical laws. Symmetry as a concept has helped mankind not only to define ‘beauty’ but also to express the ‘truth’. Physical laws tries to quantify the truth that appears to be ‘transient’ at the level of phenomena but symmetry promotes that truth to the level of ‘eternity’." (Vladimir G Ivancevic & Tijana T Ivancevic,"Quantum Leap", 2008)

"The concept of symmetry is used widely in physics. If the laws that determine relations between physical magnitudes and a change of these magnitudes in the course of time do not vary at the definite operations (transformations), they say, that these laws have symmetry (or they are invariant) with respect to the given transformations. For example, the law of gravitation is valid for any points of space, that is, this law is in variant with respect to the system of coordinates." (Alexey Stakhov et al, "The Mathematics of Harmony", 2009)

"In dynamical systems, a bifurcation occurs when a small smooth change made to the parameter values (the bifurcation parameters) of a system causes a sudden 'qualitative' or topological change in its behaviour. Generally, at a bifurcation, the local stability properties of equilibria, periodic orbits or other invariant sets changes." (Gregory Faye, "An introduction to bifurcation theory",  2011)

♟️Strategic Management: The Ugly (Just the Quotes)

"The concept of leadership has an ambiguous status in organizational practice, as it does in organizational theory. In practice, management appears to be of two minds about the exercise of leadership. Many jobs are so specified in content and method that within very broad limits differences among individuals become irrelevant, and acts of leadership are regarded as gratuitous at best, and at worst insubordinate." (Daniel Katz & Robert L Kahn, "The Social Psychology of Organizations", 1966)

"Planning and management by objectives have their point as devices for compelling thought, so long as executives don't forget that any plan worth making is inaccurate; the longer a plan takes to write, the worse it is - just because of its consumption of time. And the more they change plans to suit events, the better they will manage - if you've made a mistake, you had better admit it." (Robert Heller, "The Naked Manager: Games Executives Play", 1972)

"[...] when a variety of tasks have all to be performed in cooperation, synchronization, and communication, a business needs managers and a management. Otherwise, things go out of control; plans fail to turn into action; or, worse, different parts of the plans get going at different speeds, different times, and with different objectives and goals, and the favor of the 'boss' becomes more important than performance." (Peter F Drucker, "People and Performance", 1977)

"The first rule is that a measurement - any measurement - is better than none. But a genuinely effective indicator will cover the output of the work unit and not simply the activity involved. […] If you do not systematically collect and maintain an archive of indicators, you will have to do an awful lot of quick research to get the information you need, and by the time you have it, the problem is likely to have gotten worse." (Andrew S Grove, "High Output Management", 1983)

"The obsession with methodologies in the workplace is another instance of the high-tech illusion. It stems from the belief that what really matters is the technology. [...] Whatever the technological advantage may be, it may come only at the price of a significant worsening of the team's sociology." (Tom DeMarco & Timothy Lister, "Peopleware: Productive Projects and Teams", 1987)

"Managers sometimes justify the stick by pointing to better results, with the assumption that the threats caused the improvements. Alas, this is unlikely. One event coming before another does not automatically mean that the first is the cause of the second; the rooster does not make the sun rise every morning, although it may think it does. Bad results are much more likely to improve than get worse due to the simple law of statistics known as regression: results average out over time. Poor performance will eventually improve even when left to itself." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"A blame culture is corrosive, eroding the team ethos that is vital for success. If they fear that they will be pilloried or punished for their mistakes, your colleagues will start worrying more about how to protect their back than doing what’s best for the team and wider organization. In the worst cases, this can even lead to lying, setting up fall guys, and other dysfunctional behavior." (Paul Butcher, "Debug It! Find, Repair, and Prevent Bugs in Your Code", 2009)

"It is hard to avoid the conclusion that while strategy is undoubtedly a good thing to have, it is a hard thing to get right. […] So what turns something that is not quite strategy into strategy is a sense of actual or imminent instability, a changing context that induces a sense of conflict. Strategy therefore starts with an existing state of affairs and only gains meaning by an awareness of how, for better or worse, it could be different." (Lawrence Freedman, “Strategy: A history”, 2013) 

"Good decision-making is like playing chess and you must avoid making hasty decisions without thinking of how that particular decision will impact on different aspects of your work and organization. The worst kind of decision-making is to decide to delay a difficult decision until later or to pass it to someone else to have to make. You will never excel and be valued by your colleagues if you get into these habits of procrastination and passing responsibility to others." (Nigel Cumberland, "Secrets of Success at Work: 50 techniques to excel", 2014)

"The passage of time and the action of entropy bring about ever-greater complexity - a branching, blossoming tree of possibilities. Blossoming disorder" (things getting worse), now unfolding within the constraints of the physics of our universe, creates novel opportunities for spontaneous ordered complexity to arise." (D J MacLennan, "Frozen to Life", 2015)

🤖〽️Prompt Engineering: Copilot Unabridged (Part 122: Human–Machine Ecologies - Evolution over Next Decade)

 

Prompt Engineering Series

Prompt: "write a blog post of 600 words on the human-machine ecologies and their evolution over next decade focusing on the Foundations of Ambient Intelligence"

Introduction

Over the coming decade, human–machine ecologies will undergo a profound shift. We’re moving from a world where technology is something we use to one where it becomes something we live within. This transition - often described as the rise of ambient intelligence - marks the beginning of environments that sense, respond, and adapt to human presence with increasing subtlety. The next ten years will lay the groundwork for this transformation, shaping how we work, move, communicate, and care for one another.

The Quiet Embedding of Intelligence

Ambient intelligence doesn’t arrive with fanfare. It emerges quietly, through the gradual embedding of sensors, micro‑processors, and adaptive software into the spaces we inhabit. Over the next decade, this embedding will accelerate. Homes will learn daily rhythms and adjust lighting, temperature, and energy use without explicit commands. Offices will become responsive ecosystems that optimize collaboration, comfort, and focus. Public spaces will adapt to crowd flow, environmental conditions, and accessibility needs in real time.

What makes this shift ecological is the interplay between humans and machines. These systems won’t simply automate tasks; they’ll form feedback loops. Human behavior shapes machine responses, and machine responses shape human behavior. The ecology becomes a living system - dynamic, adaptive, and co‑evolving.

From Devices to Distributed Intelligence

One of the biggest changes ahead is the move away from device‑centric thinking. Today, we still treat phones, laptops, and smart speakers as discrete tools. Over the next decade, intelligence will diffuse across environments. Instead of asking a specific device to perform a task, people will interact with a distributed network that understands context. 

Imagine walking into your kitchen and having the room know whether you’re preparing a meal, grabbing a quick snack, or hosting friends. The intelligence isn’t in a single gadget; it’s in the relationships between sensors, data, and human intention. This shift will redefine how we design spaces, workflows, and even social interactions.

The Rise of Predictive and Adaptive Systems

Ambient intelligence thrives on prediction. As machine learning models become more sophisticated, environments will anticipate needs rather than simply respond to them. Over the next decade, predictive systems will become more accurate, more personalized, and more seamlessly integrated.

Transportation networks will anticipate congestion before it forms. Healthcare environments will detect subtle changes in behavior or physiology and prompt early interventions. Workspaces will adjust to cognitive load, offering focus‑enhancing conditions during deep work and collaborative cues during team sessions.

The challenge - and opportunity - lies in ensuring that these predictions enhance human autonomy rather than constrain it. The most successful systems will be those that support human choice, not replace it.

Ethical Foundations for a Machine‑Rich Ecology

As machines become more present and more perceptive, ethical questions will move to the forefront. The next decade will force societies to confront issues of privacy, consent, transparency, and agency in environments where machines are always listening, watching, and learning.

Who owns the data generated by ambient systems? How do we ensure that adaptive environments don’t reinforce bias or exclusion? What does autonomy mean when environments are constantly nudging behavior?

These questions won’t be solved by technology alone. They will require new governance models, new cultural norms, and new forms of digital literacy. The foundations of ambient intelligence must be ethical as well as technical.

Human Flourishing in Machine‑Enhanced Spaces

Despite the complexity, the promise of ambient intelligence is compelling. Done well, it can create environments that are more humane, more sustainable, and more responsive to individual and collective needs. It can reduce cognitive load, enhance creativity, support well‑being, and help societies use resources more wisely.

The next decade is not about machines taking over; it’s about machines becoming better partners. Human–machine ecologies will evolve toward balance - where technology amplifies human potential rather than overshadowing it.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🏗️Software Engineering: The Ugly (Just the Quotes)

"[...] when a variety of tasks have all to be performed in cooperation, synchronization, and communication, a business needs managers and a management. Otherwise, things go out of control; plans fail to turn into action; or, worse, different parts of the plans get going at different speeds, different times, and with different objectives and goals, and the favor of the 'boss' becomes more important than performance." (Peter F Drucker, "People and Performance", 1977)

"The obsession with methodologies in the workplace is another instance of the high-tech illusion. It stems from the belief that what really matters is the technology. [...] Whatever the technological advantage may be, it may come only at the price of a significant worsening of the team's sociology." (Tom DeMarco & Timothy Lister, "Peopleware", 1987)

"The longer we wait between integrations and acceptance tests, the worse things get. Wait twice as long and we'll have four or more times the hassle. The reason is that one bug written just yesterday is pretty easy to find, while ten or a hundred written weeks ago can become almost impossible." (Ron Jeffries, "Extreme Programming Installed", 2001)

"Obsolete comments are worse than no comments." (Douglas Crockford, "JavaScript: The Good Parts", 2008)

"Prototyping is always inspirational - not in the sense of a perfected artwork but just the opposite: because it inspires new ideas. Prototyping should start early in the life of a project, and we expect them to be numerous, quickly executed, and pretty ugly." (Tim Brown, "Change by Design: How Design Thinking Transforms Organizations and Inspires Innovation", 2009)

"But the history of large systems demonstrates that, once the hurdle of stability has been cleared, a more subtle challenge appears. It is the challenge of remaining stable when the rules change. Machines, like organizations or organisms, that fail to meet this challenge find that their previous stability is no longer of any use. The responses that once were life-saving now just make things worse. What is needed now is the capacity to re-write the procedure manual on short notice, or even" (most radical change of all) to change goals." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"A boat without a captain is nothing more than a floating waiting room: unless someone grabs the rudder and starts the engine, it's just going to drift along aimlessly with the current. A piece of software is just like that boat: if no one pilots it, you're left with a group of engineers burning up valuable time, just sitting around waiting for something to happen (or worse, still writing code that you don't need)." (Titus Winters, "Software Engineering at Google: Lessons Learned from Programming Over Time", 2020)

🤖〽️Prompt Engineering: Copilot Unabridged (Part 121: Human–Machine Ecologies - A 50‑Year Evolution, Decade by Decade)

 

Prompt Engineering Series

Prompt: "write a blog post of 600 words on the human-machine ecologies and their evolution overnext 50 years split by decade"

Introduction

Human–machine ecologies - our intertwined systems of people, technologies, environments, and data - are entering a period of rapid transformation. Over the next half‑century, these ecologies will evolve from today’s loosely connected networks into deeply interdependent, adaptive ecosystems. Looking decade by decade helps reveal how this evolution might unfold and what it could mean for society, culture, and the planet.

2025–2035: Foundations of Ambient Intelligence

The next decade will be defined by the normalization of ambient, always‑present computational systems. Sensors, AI models, and connected devices will fade into the background of everyday life, forming the early scaffolding of human–machine ecologies.

Homes, workplaces, and public spaces will become context‑aware environments that adjust to human needs without explicit commands. Energy systems will self‑optimize, transportation networks will coordinate autonomously, and personal devices will collaborate rather than compete for attention.

This period will also bring the first major societal debates about autonomy, privacy, and data stewardship. As machines become more embedded in daily life, people will begin to question not just what these systems do, but how they shape behavior, choices, and relationships. Governance frameworks will emerge, though often reactively, as societies grapple with the implications of pervasive machine agency.

2035–2045: Cognitive Symbiosis and Shared Intelligence

By the mid‑2030s, human–machine ecologies will shift from environmental intelligence to cognitive partnership. AI systems will increasingly function as co‑thinkers - augmenting memory, creativity, and decision‑making.

Interfaces will evolve beyond screens and voice. Neural‑signal‑based interaction, gesture‑driven control, and adaptive conversational agents will blur the line between internal thought and external computation. People will begin to treat machine intelligence as an extension of their own cognitive toolkit.

At the societal level, organizations will restructure around hybrid teams of humans and AI systems. Knowledge work will become more fluid, with machines handling pattern recognition and humans focusing on interpretation, ethics, and meaning‑making.

This decade will also see the rise of 'ecology designers' - professionals who shape the interactions between humans, machines, and environments. Their work will be less about building tools and more about cultivating balanced, resilient ecosystems.

2045–2055: Ecological Integration and Adaptive Cities

As human–machine ecologies mature, they will expand from personal and organizational contexts into full urban and planetary systems. Cities will operate as adaptive organisms, using real‑time data to regulate energy, transportation, waste, and public health.

Infrastructure will become self‑healing and self‑optimizing. Buildings will negotiate energy loads with one another, autonomous vehicles will coordinate traffic flow dynamically, and environmental sensors will guide urban planning with unprecedented precision.

Human behavior will feed directly into these systems, creating feedback loops that allow cities to evolve continuously. The challenge will be ensuring that these ecologies remain inclusive and equitable. Without careful governance, adaptive systems could reinforce existing inequalities or create new forms of digital exclusion.

Culturally, machines will become collaborators in art, science, and design. Hybrid creativity - where humans and AI co‑produce ideas - will become a mainstream mode of expression.

2055–2075: Co‑Evolution and Ecological Maturity

By the final decades of this 50‑year arc, human–machine ecologies will reach a stage of co‑evolution. Machines will not simply adapt to humans; humans will adapt to machine‑mediated environments in return.

Education will shift toward ecological literacy - understanding how to navigate, shape, and sustain complex human–machine systems. Social norms will evolve around shared agency, where responsibility is distributed across humans, machines, and institutions.

At this stage, the most successful societies will be those that embrace diversity - of people, cultures, and machine systems - and allow for continuous adaptation rather than rigid control. Human–machine ecologies will feel less like technologies and more like living environments we inhabit, influence, and co‑create.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

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🤖〽️Prompt Engineering: Copilot Unabridged (Part 120: Human–Machine Ecologies - The Next 50 Years)

 

Prompt Engineering Series

Prompt: "write a blog post of 600 words on the human-machine ecologies and their evolution overnext 50 years"

Introduction

The relationship between humans and machines has always been dynamic, but we’re now entering a period where that relationship becomes ecological - interdependent, adaptive, and constantly evolving. Over the next 50 years, human–machine ecologies will shift from simple tool‑use to deeply integrated systems that shape how we live, work, and even understand ourselves.

The Rise of Symbiotic Systems

Today’s machines already sense, predict, and respond, but the coming decades will push this much further. Instead of isolated devices, we’ll inhabit environments where machines form distributed networks that learn from and adapt to human behavior. Homes, workplaces, and public spaces will function like living systems, adjusting lighting, temperature, information flow, and even social dynamics based on subtle cues.

This won’t be about convenience alone. As climate pressures intensify, these ecologies will help optimize energy use, reduce waste, and coordinate resources across entire cities. Think of buildings that negotiate energy loads with one another or transportation systems that self‑organize to minimize congestion. Humans will remain central, but machines will increasingly handle the orchestration.

Cognitive Ecosystems

The next half‑century will also redefine cognition. Instead of viewing intelligence as something that resides in individual humans or machines, we’ll see it as a property of networks. People will collaborate with AI systems that augment memory, creativity, and decision‑making. These systems won’t simply answer questions - they’ll help shape the questions worth asking.

As interfaces become more natural - voice, gesture, neural signals - the boundary between internal thought and external computation will blur. This doesn’t mean machines will replace human thinking; rather, they’ll extend it. The most successful societies will be those that treat intelligence as a shared resource, cultivated across human–machine collectives.

Ethical and Social Adaptation

Ecologies evolve not just through technology but through norms, values, and governance. Over the next 50 years, we’ll grapple with questions about autonomy, privacy, and agency in environments where machines are always present. Who controls the data that fuels these ecologies? How do we ensure that machine‑mediated environments remain inclusive and equitable?

Expect new professions to emerge - ecology designers, algorithmic ethicists, cognitive architects - whose job is to shape these systems with human flourishing in mind. The challenge won’t be building the technology; it will be aligning it with the messy, diverse, and sometimes contradictory needs of human communities.

Emotional and Cultural Integration

Machines will also become part of our emotional and cultural landscapes. Not as replacements for human relationships, but as companions, collaborators, and creative partners. We’ll see AI co‑authors, co‑musicians, and co‑inventors. Cultural production will become a hybrid process, blending human intuition with machine‑driven exploration.

This raises fascinating questions about authorship and authenticity. When a poem emerges from a dialogue between a human and an AI, who 'owns' the voice? Over time, society will likely shift from thinking in terms of ownership to thinking in terms of participation-valuing the interplay itself.

A Living, Evolving Ecology

By 2075, human–machine ecologies will feel less like tools and more like ecosystems we inhabit. They’ll evolve continuously, shaped by feedback loops between human behavior, machine learning, and environmental constraints. The most resilient ecologies will be those that embrace diversity - of people, cultures, and machine systems - and allow for adaptation rather than rigid control.

If the last 50 years were about digitizing the world, the next 50 will be about ecological integration. The future won’t be dominated by machines, nor will it be a nostalgic return to pre‑digital life. It will be something new: a co‑evolutionary dance where humans and machines learn, adapt, and grow together.

Disclaimer: The whole text was generated by Copilot (under Windows 11) at the first attempt. This is just an experiment to evaluate feature's ability to answer standard general questions, independently on whether they are correctly or incorrectly posed. Moreover, the answers may reflect hallucinations and other types of inconsistent or incorrect reasoning.

Previous Post <<||>> Next Post

🕸Systems Engineering: Self-Similarity (Just the Quotes)

"[…] a pink (or white, or brown) noise is the very paradigm of a statistically self-similar process. Phenomena whose power spectra are homogeneous power functions lack inherent time and frequency scales; they are scale-free. There is no characteristic time or frequency -whatever happens in one time or frequency range happens on all time or frequency scales. If such noises are recorded on magnetic tape and played back at various speeds, they sound the same […]" (Manfred Schroeder, "Fractals, Chaos, Power Laws Minutes from an Infinite Paradise", 1990)

"All physical objects that are 'self-similar' have limited self-similarity - just as there are no perfectly periodic functions, in the mathematical sense, in the real world: most oscillations have a beginning and an end (with the possible exception of our universe, if it is closed and begins a new life cycle after every 'big crunch' […]. Nevertheless, self-similarity is a useful  abstraction, just as periodicity is one of the most useful concepts in the sciences, any finite extent notwithstanding." (Manfred Schroeder, "Fractals, Chaos, Power Laws Minutes from an Infinite Paradise", 1990)

"Percolation is a widespread paradigm. Percolation theory can therefore illuminate a great many seemingly diverse situations. Because of its basically geometric character, it facilitates the analysis of intricate patterns and textures without needless physical complications. And the self-similarity that prevails at critical points permits profitably mining the connection with scaling and fractals." (Manfred Schroeder, "Fractals, Chaos, Power Laws Minutes from an Infinite Paradise", 1990)

"[…] power laws, with integer or fractional exponents, are one of the most fertile fields and abundant sources of self-similarity." (Manfred Schroeder, "Fractals, Chaos, Power Laws Minutes from an Infinite Paradise", 1990)

"The only prerequisite for a self-similar law to prevail in a given size range is the absence of an inherent size scale." (Manfred Schroeder, "Fractals, Chaos, Power Laws Minutes from an Infinite Paradise", 1990)

"The unifying concept underlying fractals, chaos, and power laws is self-similarity. Self-similarity, or invariance against changes in scale or size, is an attribute of many laws of nature and innumerable phenomena in the world around us. Self-similarity is, in fact, one of the decisive symmetries that shape our universe and our efforts to comprehend it." (Manfred Schroeder, "Fractals, Chaos, Power Laws Minutes from an Infinite Paradise", 1990)

"[…] the world is not complete chaos. Strange attractors often do have structure: like the Sierpinski gasket, they are self-similar or approximately so. And they have fractal dimensions that hold important clues for our attempts to understand chaotic systems such as the weather." (Manfred Schroeder, "Fractals, Chaos, Power Laws Minutes from an Infinite Paradise", 1990)

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

"Chaos appears in both dissipative and conservative systems, but there is a difference in its structure in the two types of systems. Conservative systems have no attractors. Initial conditions can give rise to periodic, quasiperiodic, or chaotic motion, but the chaotic motion, unlike that associated with dissipative systems, is not self-similar. In other words, if you magnify it, it does not give smaller copies of itself. A system that does exhibit self-similarity is called fractal. [...] The chaotic orbits in conservative systems are not fractal; they visit all regions of certain small sections of the phase space, and completely avoid other regions. If you magnify a region of the space, it is not self-similar." (Barry R Parker, "Chaos in the Cosmos: The stunning complexity of the universe", 1996)

"What is renormalization? First of all, if scaling is present we can go to smaller scales and get exactly the same result. In a sense we are looking at the system with a microscope of increasing power. If you take the limit of such a process you get a stability that is not otherwise present. In short, in the renormalized system, the self-similarity is exact, not approximate as it usually is. So renormalization gives stability and exactness." (Barry R Parker, "Chaos in the Cosmos: The stunning complexity of the universe", 1996)

"The self-similarity of fractal structures implies that there is some redundancy because of the repetition of details at all scales. Even though some of these structures may appear to teeter on the edge of randomness, they actually represent complex systems at the interface of order and disorder. " (Edward Beltrami, "What is Random?: Chaos and Order in Mathematics and Life", 1999)

"The self-similarity on different scales arises because growth often involves iteration of simple, discrete processes (e.g. branching). These repetitive processes can often be summarized as sets of simple rules." (David G Green, 2000)

"Fractals are self-similar objects. However, not every self-similar object is a fractal, with a scale-free form distribution. If we put identical cubes on top of each other, we get a self-similar object. However, this object will not have scale-free statistics: since it has only one measure of rectangular forms, it is single-scaled. We need a growing number of smaller and smaller self-similar objects to satisfy the scale-free distribution." (Péter Csermely, "Weak Links: The Universal Key to the Stabilityof Networks and Complex Systems", 2009)

"The concept of bifurcation, present in the context of non-linear dynamic systems and theory of chaos, refers to the transition between two dynamic modalities qualitatively distinct; both of them are exhibited by the same dynamic system, and the transition (bifurcation) is promoted by the change in value of a relevant numeric parameter of such system. Such parameter is named 'bifurcation parameter', and in highly non-linear dynamic systems, its change can produce a large number of bifurcations between distinct dynamic modalities, with self-similarity and fractal structure. In many of these systems, we have a cascade of numberless bifurcations, culminating with the production of chaotic dynamics." (Emilio Del-Moral-Hernandez, "Chaotic Neural Networks", Encyclopedia of Artificial Intelligence, 2009)

"In the telephone system a century ago, messages dispersed across the network in a pattern that mathematicians associate with randomness. But in the last decade, the flow of bits has become statistically more similar to the patterns found in self-organized systems. For one thing, the global network exhibits self-similarity, also known as a fractal pattern. We see this kind of fractal pattern in the way the jagged outline of tree branches look similar no matter whether we look at them up close or far away. Today messages disperse through the global telecommunications system in the fractal pattern of self-organization." (Kevin Kelly, "What Technology Wants", 2010)

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

"Laws of complexity hold universally across hierarchical scales (scalar, self-similarity) and are not influenced by the detailed behavior of constituent parts." (Jamshid Gharajedaghi, "Systems Thinking: Managing Chaos and Complexity A Platform for Designing Business Architecture" 3rd Ed., 2011)

"Fractals are different from chaos. Fractals are self-similar geometric objects, while chaos is a type of deterministic yet unpredictable dynamical behavior. Nevertheless, the two ideas or areas of study have several interesting and important links. Fractal objects at first blush seem intricate and complex. However, they are often the product of very simple dynamical systems. So the two areas of study - chaos and fractals - are naturally paired, even though they are distinct concepts." (David P Feldman,"Chaos and Fractals: An Elementary Introduction", 2012)

"The study of chaos shows that simple systems can exhibit complex and unpredictable behavior. This realization both suggests limits on our ability to predict certain phenomena and that complex behavior may have a simple explanation. Fractals give scientists a simple and concise way to qualitatively and quantitatively understand self-similar objects or phenomena. More generally, the study of chaos and fractals hold many fun surprises; it challenges one’s intuition about simplicity and complexity, order and disorder." (David P Feldman,"Chaos and Fractals: An Elementary Introduction", 2012

"Chaos theory is a branch of mathematics focusing on the study of chaos - dynamical systems whose random states of disorder and irregularities are governed by underlying patterns and deterministic laws that are highly sensitive to initial conditions. Chaos theory is an interdisciplinary theory stating that, within the apparent randomness of complex, chaotic systems, there are underlying patterns, interconnectedness, constant feedback loops, repetition, self-similarity, fractals, and self-organization. The butterfly effect, an underlying principle of chaos, describes how a small change in one state of a deterministic nonlinear system can result in large differences in a later state (meaning that there is a sensitive dependence on initial conditions)." (Nima Norouzi, "Criminal Policy, Security, and Justice in the Time of COVID-19", 2022)

🏗️Software Engineering: Relationships (Just the Quotes)

"Since software construction is inherently a systems effort - an exercise in complex interrelationships - communication effort is great, and it quickly dominates the decrease in individual task time brought about by partitioning [increasing the workers]. Adding more people then lengthens, not shortens, the schedule." (Frederick Brook, "The Mythical Man-Month", 1975)

"By pulling together all of the decisions affecting the choice of modules and interrelationships in a system, we necessarily affect the way in which other decisions are organized and resolved. Thus, some issues which have traditionally been approached in a certain way during the earliest phase of a project may have to be dealt with in an entirely different manner at a much later stage once the designer graduates to a structured design approach." (Edward Yourdon & Larry L Constantine, "Structured Design: Fundamentals of a discipline of computer program and systems design", 1978)

"Elements" (lines of code) in a coincidentally-cohesive module have no relationship. Typically occurs as the result of modularizing existing code, to separate out redundant code." (Edward Yourdon & Larry L Constantine, "Structured Design: Fundamentals of a discipline of computer program and systems design", 1978)

"Module cohesion may be conceptualized as the cement that holds the processing elements of a module together. It is a most crucial factor in structured design, and it is a major constituent of effective modularity. The concept represents the principal technical handle" that a designer has on the relationship of his system to the original problem structure. In a sense, a high degree of module cohesion is an indication of close approximation of inherent problem structure." (Edward Yourdon & Larry L Constantine, "Structured Design: Fundamentals of a discipline of computer program and systems design", 1978)

"Architecture is defined as a clear representation of a conceptual framework of components and their relationships at a point in time [���] a discussion of architecture must take into account different levels of architecture. These levels can be illustrated by a pyramid, with the business unit at the top and the delivery system at the base. An enterprise is composed of one or more Business Units that are responsible for a specific business area. The five levels of architecture are Business Unit, Information, Information System, Data and Delivery System. The levels are separate yet interrelated. [...] The idea if an enterprise architecture reflects an awareness that the levels are logically connected and that a depiction at one level assumes or dictates that architectures at the higher level." (W Bradford Rigdon, "Architectures and Standards", 1989)

"Object-oriented programming is a method of implementation in which programs are organized as cooperative collections of objects, each of which represents an instance of some class, and whose classes are all members of a hierarchy of classes united via inheritance relationships." (Grady Booch, "Object-oriented design: With Applications", 1991)

"Visual thinking is necessary in engineering. A major portion of engineering information is recorded and transmitted in a visual language that is in effect the lingua franca of engineers in the modern world. It is the language that permits 'readers' of technologically explicit and detailed drawings to visualise the forms, the proportions, and the interrelationships of the elements that make up the object depicted. It is the language in which designers explain to makers what they want them to construct." (Eugene S Ferguson, "Engineering and the Mind's Eye", 1992)

"Although the concept of an enterprise architecture (EA) has not been well defined and agreed upon, EAs are being developed to support information system development and enterprise reengineering. Most EAs differ in content and nature, and most are incomplete because they represent only data and process aspects of the enterprise. [...] An EA is a conceptual framework that describes how an enterprise is constructed by defining its primary components and the relationships among these components." (M A Roos, "Enterprise architecture: definition, content, and utility", Enabling Technologies: Infrastructure for Collaborative Enterprises, 1994)

"Meaning is conferred not by a one-to-one correspondence of a symbol with some external concept or object, but by the relationships between the structural components of the system itself." (Paul Cilliers, "Complexity and Postmodernism: Understanding Complex Systems", 1998)

"Complexity is that property of a model which makes it difficult to formulate its overall behaviour in a given language, even when given reasonably complete information about its atomic components and their inter-relations." (Bruce Edmonds, "Syntactic Measures of Complexity", 1999)

"Computer programs are complex by nature. Even if you could invent a programming language that operated exactly at the level of the problem domain, programming would be complicated because you would still need to precisely define relationships between real-world entities, identify exception cases, anticipate all possible state transitions, and so on. Strip away the accidental work involved in representing these factors in a specific programming language and in a specific computing environment, and you still have the essential difficulty of defining the underlying real-world concepts and debugging your understanding of them." (Steve C McConnell," After the Gold Rush : Creating a True Profession of Software Engineering", 1999)

"Enterprise architecture is a family of related architecture components. This include information architecture, organization and business process architecture, and information technology architecture. Each consists of architectural representations, definitions of architecture entities, their relationships, and specification of function and purpose. Enterprise architecture guides the construction and development of business organizations and business processes, and the construction and development of supporting information systems." (Gordon B Davis, "The Blackwell encyclopedic dictionary of management information systems", 1999)

"Generically, an architecture is the description of the set of components and the relationships between them. [...] A software architecture describes the layout of the software modules and the connections and relationships among them. A hardware architecture can describe how the hardware components are organized. However, both these definitions can apply to a single computer, a single information system, or a family of information systems. Thus 'architecture' can have a range of meanings, goals, and abstraction levels, depending on who's speaking." (Frank J Armour et al, "A big-picture look at enterprise architectures", IT professional Vol 1 (1), 1999)

"The fundamental organization of a system embodied in its components, their relationships to each other, and to the environment, and the principles guiding its design and evolution." (ANSI/IEEE Std 1471: 2000)

"On small, informal projects, a lot of design is done while the programmer sits at the keyboard. 'Design' might be just writing a class interface in pseudocode before writing the details. It might be drawing diagrams of a few class relationships before coding them. It might be asking another programmer which design pattern seems like a better choice. Regardless of how it's done, small projects benefit from careful design just as larger projects do, and recognizing design as an explicit activity maximizes the benefit you will receive from it." (Steve C McConnell, "Code Complete: A Practical Handbook of Software Construction" 2nd Ed., 2004)

"In fact, I'm a huge proponent of designing your code around the data, rather than the other way around, and I think it's one of the reasons git has been fairly successful. [...] I will, in fact, claim that the difference between a bad programmer and a good one is whether he considers his code or his data structures more important. Bad programmers worry about the code. Good programmers worry about data structures and their relationships." (Linus Torvalds, [email] 2006)

"A conceptual model of an interactive application is, in summary: the structure of the application - the objects and their operations, attributes, and relation-ships; an idealized view of the how the application works - the model designers hope users will internalize; the mechanism by which users accomplish the tasks the application is intended to support." (Jeff Johnson & Austin Henderson, "Conceptual Models", 2011)

"How does a smell manifest in design? A smell occurs as a result of a combination of one or more design decisions. In other words, the design ecosystem itself is responsible for the creation of the smell. The presence of the smell in turn impacts the ecosystem in several ways. First, it is likely that the presence of the smell triggers new design decisions that are needed to address the smell! Second, the smell can potentially influence or constrain future design decisions as a result of which one or more new smells may manifest in the ecosystem. Third, smells also tend to have an effect on other smells. For instance, some smells amplify the effects of other smells, or co-occur with or act as precursors to other smells. Clearly, smells share a rich relationship with the ecosystem in which they occur." (Girish Suryanarayana et al, "Refactoring for Software Design Smells: Managing Technical Debt", 2015)

"Once we understand our user's mental model, we can capture it in a conceptual model. The conceptual model is a representation of the mental model using elements, relationships, and conditions. Our design and final system will be the tangible result of this conceptual model." (Pau Giner & Pablo Perea, "UX Design for Mobile, 2017)

"The calculus of causation consists of two languages: causal diagrams, to express what we know, and a symbolic language, resembling algebra, to express what we want to know. The causal diagrams are simply dot-and-arrow pictures that summarize our existing scientific knowledge. The dots represent quantities of interest, called 'variables', and the arrows represent known or suspected causal relationships between those variables - namely, which variable 'listens' to which others." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

♟️Strategic Management: Relationships (Just the Quotes)

"The insights gained and garnered by the mind in its wanderings among basic concepts are benefits that theory can provide. Theory cannot equip the mind with formulas for solving problems, nor can it mark the narrow path on which the sole solution is supposed to lie by planting a hedge of principles on either side. But it can give the mind insight into the great mass of phenomena and of their relationships, then leave it free to rise into the higher realms of action." (Carl von Clausewitz, "On War", 1832)

"An Organization Chart is a cross section picture covering every relationship in the bank. It is a schematic survey showing department functions and interrelations, lines of authority, responsibility, communication and counsel. Its purpose is 'to bring the various human parts of the organization into effective correlation and co-operation'." (John W Schulze, "Office Administration", 1919)

"The pattern of personal characteristics of the leader must bear some relevant relationship to the characteristics, activities, and goals of the followers. [...] It becomes clear that an adequate analysis of leadership involves not only a study of leadership but also of situations." (R M Stodgill, "Journal of Psychology", 1948)

"The most elementary aspect of administration is organization the structure of social institutions and their constituent parts, the composition of economic enterprises and their various branches, the organization of governmental agencies and their numerous departments. As it is mainly a matter of structure, organization bears the same rudimentary relationship to administration as does the science of anatomy or skeletology to the field of medicine. An administrative organization can be sketched and charted just as the human body can be physically depicted. Apart from its graphic convenience and its 'teachable' quality, however, what intrinsic relationship does organization bear to administration?" (Albert Lepawsky, "Administration: the art and science of organization and management", 1949)

"Organization planning is the process of defining and grouping the activities of the enterprise so that they may be most logically assigned and effectively executed. It is concerned with the establishment of relationships among the units so as to further the objectives of the enterprise." (Ernest Dale, "Planning and developing the company organization structure", 1952)

"[...] authority - the right by which superiors are able to require conformity of subordinates to decisions - is the basis for responsibility and the force that binds organization together. The process of organizing encompasses grouping of activities for purposes of management and specification of authority relationships between superiors and subordinates and horizontally between managers. Consequently, authority and responsibility relationships come into being in all associative undertakings where the superior-subordinate link exists. It is these relationships that create the basic character of the managerial job." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"If charts do not reflect actual organization and if the organization is intended to be as charted, it is the job of effective management to see that actual organization conforms with that desired. Organization charts cannot supplant good organizing, nor can a chart take the place of spelling out authority relationships clearly and completely, of outlining duties of managers and their subordinates, and of defining responsibilities." (Harold Koontz & Cyril O Donnell, "Principles of Management", 1955)

"It is probable that one day we shall begin to draw organization charts as a series of linked groups rather than as a hierarchical structure of individual 'reporting' relationships." (Douglas McGregor, "The Human Side of Enterprise", 1960)

"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 leadership and other processes of the organization must be such as to ensure a maximum probability that in all interactions and all interactions and all relationships with the organization each member will, in the light of his background, values, and expectations, view the experience as supportive and one which builds and maintains his sense of personal worth and importance." (Rensis Likert, "New patterns of management", 1961)

"In some firms role relationships prescribed by the chart seemed to be of secondary importance to personal relationships between individuals." (Joan Woodward, Industrial Organization: Theory and practice", 1965)

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

"The systems approach to problems focuses on systems taken as a whole, not on their parts taken separately. Such an approach is concerned with total - system performance even when a change in only one or a few of its parts is contemplated because there are some properties of systems that can only be treated adequately from a holistic point of view. These properties derive from the relationship between parts of systems: how the parts interact and fit together." (Russell L Ackoff, "Towards a System of Systems Concepts", 1971) 

"Managing upward relies on informal relationships, timing, exploiting ambiguity, and implicit communication. And the irony of it all is that these most subtle skills must be learned and mastered by younger managers who not only lack education and directed experience in benign guerilla warfare but are further misguided by management myths which contribute to false expectations and a misleading perception of reality." (Richard T Pascale & Anthony G Athos, "The Art of Japanese Management", 1981)

"Every company has two organizational structures: the formal one is written on the charts; the other is the everyday living relationship of the men and women in the organization." (Harold Geneen & Alvin Moscow, "Managing", 1984)

"Most managers are reluctant to comment on ineffective or inappropriate interpersonal behavior. But these areas are often crucial for professional task success. This hesitancy is doubly felt when there is a poor relationship between the two. [...] Too few managers have any experience in how to confront others effectively; generally they can more easily give feedback on inadequate task performance than on issues dealing with another's personal style." (David L Bradford & Allan R Cohen, "Managing for Excellence", 1984)

"It seems to me that we too often focus on the inside aspects of the job of management, failing to give proper attention to the requirement for a good manager to maintain those relationships between his organization and the environment in which it must operate which permits it to move ahead and get the job done." (Breene Kerr, Giants in Management, 1985) 

"Operating managers should in no way ignore short-term performance imperatives [when implementing productivity improvement programs.] The pressures arise from many sources and must be dealt with. Moreover, unless managers know that the day-to-day job is under control and improvements are being made, they will not have the time, the perspective, the self-confidence, or the good working relationships that are essential for creative, realistic strategic thinking and decision making." (Robert H Schaefer, Harvard Business Review, 1986)

"Architecture is defined as a clear representation of a conceptual framework of components and their relationships at a point in time […] a discussion of architecture must take into account different levels of architecture. These levels can be illustrated by a pyramid, with the business unit at the top and the delivery system at the base. An enterprise is composed of one or more Business Units that are responsible for a specific business area. The five levels of architecture are Business Unit, Information, Information System, Data and Delivery System. The levels are separate yet interrelated. [...] The idea if an enterprise architecture reflects an awareness that the levels are logically connected and that a depiction at one level assumes or dictates that architectures at the higher level." (W Bradford Rigdon, "Architectures and Standards", 1989)

"Leadership is always dependent upon the context, but the context is established by the relationships." (Margaret J Wheatley, "Leadership and the New Science: Discovering Order in a Chaotic World", 1992)

"Although the concept of an enterprise architecture (EA) has not been well defined and agreed upon, EAs are being developed to support information system development and enterprise reengineering. Most EAs differ in content and nature, and most are incomplete because they represent only data and process aspects of the enterprise. […] An EA is a conceptual framework that describes how an enterprise is constructed by defining its primary components and the relationships among these components." (M A Roos, "Enterprise architecture: definition, content, and utility", Enabling Technologies: Infrastructure for Collaborative Enterprises, 1994)

"Trust is the glue of life. It's the most essential ingredient in effective communication. It's the foundational principle that holds all relationships - marriages, families, and organizations of every kind - together." (Stephen Covey, "First Things First", 1994)

"A strategy is a set of hypotheses about cause and effect. The measurement system should make the relationships (hypotheses) among objectives" (and measures) in the various perspectives explicit so that they can be managed and validated. The chain of cause and effect should pervade all four perspectives of a Balanced Scorecard." (Robert S Kaplan & David P Norton, "The Balanced Scorecard", Harvard Business Review, 1996)

"[Schemata are] knowledge structures that represent objects or events and provide default assumptions about their characteristics, relationships, and entailments under conditions of incomplete information." (Paul J DiMaggio, "Culture and Cognition", Annual Review of Sociology No. 23, 1997)

"The Enterprise Architecture is the explicit description of the current and desired relationships among business and management process and information technology. It describes the 'target' situation which the agency wishes to create and maintain by managing its IT portfolio." (Franklin D Raines, 1997)

"Leadership has long been associated with authority - we tend to concentrate on the leader, to think of them as innately superior in some way, and take the followers for granted. But formal authority is only one possible part of leadership. Many leaders do not have it. In some cases, perhaps ‘companionship’ better describes the relationship between leader and followers." (Joseph O’Connor, "Leading With NLP: Essential Leadership Skills for Influencing and Managing People", 1998)

"An information system architecture typically encompasses an overview of the entire information system - including the software, hardware, and information architectures" (the structure of the data that systems will use). In this sense, the information system architecture is a meta-architecture. An enterprise architecture is also a meta-architecture in that it comprises many information systems and their relationships (technical infrastructure). However, because it can also contain other views of an enterprise - including work, function, and information - it is at the highest level in the architecture pyramid. It is important to begin any architecture development effort with a clear definition of what you mean by 'architecture'." (Frank J Armour et al, "A big-picture look at enterprise architectures", IT professional Vol 1" (1), 1999)

"Enterprise architecture is a family of related architecture components. This include information architecture, organization and business process architecture, and information technology architecture. Each consists of architectural representations, definitions of architecture entities, their relationships, and specification of function and purpose. Enterprise architecture guides the construction and development of business organizations and business processes, and the construction and development of supporting information systems." (Gordon B Davis, "The Blackwell encyclopedic dictionary of management information systems"‎, 1999)

"The key element of an organization is not a building or a set of policies and procedures; organizations are made up of people and their relationships with one another. An organization exists when people interact with one another to perform essential functions that help attain goals." (Richard Daft, "The Leadership Experience", 2002)

"Organization charts are subject to important limitations. A chart shows only formal authority relationships and omits the many significant informal and informational relationships." (Harold Koontz and Heinz Weihrich, "Essentials Of Management", 2006)

"Enterprise architecture is the process of translating business vision and strategy into effective enterprise change by creating, communicating and improving the key requirements, principles and models that describe the enterprise's future state and enable its evolution. The scope of the enterprise architecture includes the people, processes, information and technology of the enterprise, and their relationships to one another and to the external environment. Enterprise architects compose holistic solutions that address the business challenges of the enterprise and support the governance needed to implement them." (Anne Lapkin et al, "Gartner Clarifies the Definition of the Term 'Enterprise Architecture", 2008)

"Systems thinking is a mental discipline and framework for seeing patterns and interrelationships. It is important to see organizational systems as a whole because of their complexity. Complexity can overwhelm managers, undermining confidence. When leaders can see the structures that underlie complex situations, they can facilitate improvement. But doing that requires a focus on the big picture." (Richard L Daft, "The Leadership Experience", 2008)

"One advantage that decision tree modeling has over other pattern recognition techniques lies in the interpretability of the decision model. Due to this interpretability, information relating to the identification of important features and interclass relationships can be used to support the design of future experiments and data analysis." (S D Brown, A J Myles, in Comprehensive Chemometrics, 2009)

💎💫SQL Reloaded: Schema Differences between Database Versions - Part I: INFORMATION_SCHEMA version

During data migrations and other similar activities it's important to check what changed in the database at the various levels. Usually, it's useful to check when schemas, object names or table definitions changed, even if the changes are thoroughly documented. One can write a script to point out all the differences in one output, though it's recommended to check the differences at each level of detail. 

For this purpose one can use the INFORMATION_SCHEMA available for many of the RDBMS implementing it. This allows to easily port the scripts between platforms. The below queries were run on SQL Server 2025 in combination with Dynamics 365 schemas, though they should run on the earlier versions, incl. (Azure) SQL Databases. 

Such comparisons must be done from the both sides, this implying a FULL OUTER JOIN when writing a single SELECT statement, however the results can become easily hard to read and even interpret when the number of columns in output increases. Therefore, it's recommended to keep the number of columns at a minimum while addressing the scope, respectively break the FULL OUTER JOIN in two LEFT JOINs.

The simplest check is at schema level, and this can be easily done from both sides (note that database names needed to be replaced accordingly):

-- difference schemas (objects not available in the new schema)
SELECT *
FROM ( -- comparison
	SELECT DB1.CATALOG_NAME
	, DB1.SCHEMA_NAME
	, DB1.SCHEMA_OWNER
	, DB1.DEFAULT_CHARACTER_SET_NAME
	, DB2.SCHEMA_OWNER NEW_SCHEMA_OWNER
	, DB2.DEFAULT_CHARACTER_SET_NAME NEW_DEFAULT_CHARACTER_SET_NAME
	, CASE 
		WHEN DB2.SCHEMA_NAME IS NULL THEN 'schema only in old db'
		WHEN DB1.SCHEMA_OWNER <> IsNull(DB2.SCHEMA_OWNER, '') THEN 'different table type'
	  END Comment
        , CASE WHEN DB1.DEFAULT_CHARACTER_SET_NAME <> DB2.DEFAULT_CHARACTER_SET_NAME THEN 'different character sets' END Character_sets
	FROM [old database_name].INFORMATION_SCHEMA.SCHEMATA DB1

	     LEFT JOIN [new database name].INFORMATION_SCHEMA.SCHEMATA DB2

	       ON DB1.SCHEMA_NAME = DB2.SCHEMA_NAME
 ) DAT
WHERE DAT.Comment IS NOT NULL
ORDER BY DAT.CATALOG_NAME
, DAT.SCHEMA_NAME


-- difference schemas (new objects)
SELECT *
FROM ( -- comparison
	SELECT DB1.CATALOG_NAME
	, DB1.SCHEMA_NAME
	, DB1.SCHEMA_OWNER
	, DB1.DEFAULT_CHARACTER_SET_NAME
	, DB2.SCHEMA_OWNER OLD_SCHEMA_OWNER
	, DB2.DEFAULT_CHARACTER_SET_NAME OLD_DEFAULT_CHARACTER_SET_NAME
	, CASE 
		WHEN DB2.SCHEMA_NAME IS NULL THEN 'schema only in old db'
		WHEN DB1.SCHEMA_OWNER <> IsNull(DB2.SCHEMA_OWNER, '') THEN 'different table type'
	  END Comment
        , CASE WHEN DB1.DEFAULT_CHARACTER_SET_NAME <> DB2.DEFAULT_CHARACTER_SET_NAME THEN 'different character sets' END Character_sets
	FROM [new database name].INFORMATION_SCHEMA.SCHEMATA DB1
	     LEFT JOIN [old database name].INFORMATION_SCHEMA.SCHEMATA DB2
	       ON DB1.SCHEMA_NAME = DB2.SCHEMA_NAME
 ) DAT
WHERE DAT.Comment IS NOT NULL
ORDER BY DAT.CATALOG_NAME
, DAT.SCHEMA_NAME

Comments:
1) The two queries can be easily combined via a UNION ALL, though it might be a good idea then to add a column to indicate the direction of the comparison. 

The next step would be to check which objects has been changed:

-- table-based objects only in the old schema (tables & views)
SELECT *
FROM ( -- comparison
	SELECT DB1.TABLE_CATALOG
	, DB1.TABLE_SCHEMA
	, DB1.TABLE_NAME
	, DB1.TABLE_TYPE
	, DB2.TABLE_CATALOG NEW_TABLE_CATALOG
	, DB2.TABLE_TYPE NEW_TABLE_TYPE
	, CASE 
		WHEN DB2.TABLE_NAME IS NULL THEN 'objects only in old db'
		WHEN DB1.TABLE_TYPE <> IsNull(DB2.TABLE_TYPE, '') THEN 'different table type'
		--WHEN DB1.TABLE_CATALOG <> IsNull(DB2.TABLE_CATALOG, '') THEN 'different table catalog'
	  END Comment
	FROM [old database name].INFORMATION_SCHEMA.TABLES DB1
	    LEFT JOIN [new database name].INFORMATION_SCHEMA.TABLES DB2
	      ON DB1.TABLE_SCHEMA = DB2.TABLE_SCHEMA
	     AND DB1.TABLE_NAME = DB2.TABLE_NAME
 ) DAT
WHERE DAT.Comment IS NOT NULL
ORDER BY DAT.TABLE_SCHEMA
, DAT.TABLE_NAME

Comments:
1) If the database was imported under another name, then the TABLE_CATALOG will have different values as well.

At column level, the query increases in complexity, given the many aspects that must be considered:

-- difference columns (columns not available in the new scheam, respectively changes in definitions)
SELECT *
FROM ( -- comparison
	SELECT DB1.TABLE_CATALOG
	, DB1.TABLE_SCHEMA
	, DB1.TABLE_NAME
	, DB1.COLUMN_NAME 
	, DB2.TABLE_CATALOG NEW_TABLE_CATALOG
	, CASE WHEN DB2.TABLE_NAME IS NULL THEN 'column only in old db' END Comment
	, DB1.DATA_TYPE
	, DB2.DATA_TYPE NEW_DATA_TYPE
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.DATA_TYPE, '') <> IsNull(DB2.DATA_TYPE, '') THEN 'Yes' END Different_data_type
	, DB1.CHARACTER_MAXIMUM_LENGTH
	, DB2.CHARACTER_MAXIMUM_LENGTH NEW_CHARACTER_MAXIMUM_LENGTH
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.CHARACTER_MAXIMUM_LENGTH, '') <> IsNull(DB2.CHARACTER_MAXIMUM_LENGTH, '') THEN 'Yes' END Different_maximum_length
	, DB1.NUMERIC_PRECISION
	, DB2.NUMERIC_PRECISION NEW_NUMERIC_PRECISION
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.NUMERIC_PRECISION, '') <> IsNull(DB2.NUMERIC_PRECISION, '') THEN 'Yes' END Different_numeric_precision
	, DB1.NUMERIC_SCALE
	, DB2.NUMERIC_SCALE NEW_NUMERIC_SCALE
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.NUMERIC_SCALE, '') <> IsNull(DB2.NUMERIC_SCALE,'') THEN 'Yes' END Different_numeric_scale
	, DB1.CHARACTER_SET_NAME
	, DB2.CHARACTER_SET_NAME NEW_CHARACTER_SET_NAME
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.CHARACTER_SET_NAME, '') <> IsNull(DB2.CHARACTER_SET_NAME, '') THEN 'Yes' END Different_character_set_name 
	, DB1.COLLATION_NAME
	, DB2.COLLATION_NAME NEW_COLLATION_NAME
	, CASE WHEN DB2.TABLE_NAME IS NOT NULL AND IsNull(DB1.COLLATION_NAME, '') <> IsNull(DB2.COLLATION_NAME, '') THEN 'Yes' END Different_collation_name
	, DB1.ORDINAL_POSITION
	, DB2.ORDINAL_POSITION NEW_ORDINAL_POSITION
	, DB1.COLUMN_DEFAULT
	, DB2.COLUMN_DEFAULT NEW_COLUMN_DEFAULT
	, DB1.IS_NULLABLE
	, DB2.IS_NULLABLE NEW_IS_NULLABLE
	FROM [old database name].INFORMATION_SCHEMA.COLUMNS DB1
	    LEFT JOIN [new database name].INFORMATION_SCHEMA.COLUMNS DB2

	      ON DB1.TABLE_SCHEMA = DB2.TABLE_SCHEMA
	     AND DB1.TABLE_NAME = DB2.TABLE_NAME
	     AND DB1.COLUMN_NAME = DB2.COLUMN_NAME
 ) DAT
WHERE DAT.Comment IS NOT NULL
  OR IsNull(DAT.Different_data_type,'') = 'Yes'
  OR IsNull(DAT.Different_maximum_length,'') = 'Yes'
  OR IsNull(DAT.Different_numeric_precision,'') = 'Yes'
  OR IsNull(DAT.Different_numeric_scale,'') = 'Yes'
  OR IsNull(DAT.Different_character_set_name,'') = 'Yes'
  OR IsNull(DAT.Different_collation_name,'') = 'Yes'
ORDER BY DAT.TABLE_SCHEMA
, DAT.TABLE_NAME
, DAT.COLLATION_NAME

Comments:
1) The query targets only the most common scenarios, therefore must be changed to handle further cases (e.g. different column defaults, different attributes like nullable, etc.)!
2) The other perspective can be obtained by inverting the table names (without aliases) and changing the name of the columns from "NEW_' to "OLD_" (see the queries for schemas).
3) One can move the column-based conditions for the differences in the main query, though then is needed to duplicate the logic, which will make the code more challenging to change and debug. 

Happy coding!