⚡️Jeroen ter Heerdt - Collected Quotes

"After the table is loaded or refreshed, the results for calculated tables and calculated columns are locked in and cannot be changed until the table is refreshed. The results are precomputed and aren’t dynamically determined. Most often, calculated tables are relatively easy to understand, precisely because you can inspect the DAX statement and predict the results. The same statement always returns the same result for the same parameters until the table is refreshed." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"Another issue is that it’s extremely difficult in DAX to reliably and definitively refer to a value from an earlier (or later) row. These relative or absolute shifts from the current evaluation position are possible but surprisingly hard to do. After all, you’re looking at a visual on your screen that shows data in a certain order, so it makes sense that you think that it should be easy to refer to an earlier (or later) row. However, if you try to do so, you’ll soon discover DAX doesn’t work that way. This is because DAX statements are evaluated in the model, which does not sort the data in the same way as the data is sorted in your visual. For all you know, that row that was at the top of your visual, is somewhere in the middle of the data when the DAX is evaluated, so the whole idea of referring to an earlier or later row is meaningless." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"Calculated columns are most often used to perform row-by-row calculations within a table - for example, to obtain the difference between two columns for each row. Calculated columns are static, meaning they’re calculated when the table is first loaded or refreshed, and their results cannot be changed until the table is refreshed again." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"Calculation groups can help reduce the number of redundant measures using the same filter expression. Calculation groups provide a way to change the type of calculation without adding another measure to the model. In this way, you can avoid adding more measures and duplicating logic in multiple measures."  (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"[....] context transition happens in calculated columns, where a row context is present, and whenever you’re using a function that iterates over multiple values, such as SUMX. This last group of functions are conveniently called iterators." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"Data may indeed be the new oil. But just like crude oil, data needs refining. It must be transformed into information. This is why we clean, combine, model, and visualize data. The output of all this work - whether you do it on your own, get some help, or use a (semi-)automatic process - includes reports and dashboards that provide insights into various aspects of the organization’s dealings, which decision-makers can then consume to make critical business decisions." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"DAX does not have any data connectors or ways to reach out to anything outside of Power BI to collect data. Therefore, all data must be connected to the semantic model first. After this has been done, you can use calculated tables to enrich your semantic model and apply calculations. Calculated tables let you add new tables based on data you loaded into the model. Instead of querying and loading values into your new table’s columns from a data source, you create a DAX formula to define the table’s values." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"Measures Unlike calculated columns and calculated tables, measures aren’t precalculated or static. Their output is dynamically calculated as needed and is determined not only by their definition but also by the filter context in which they’re executed. The same definition can have a different meaning based on the filter context. Measures are evaluated within that filter context and often summarize multiple rows."(Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"Power BI provides audit logs that contain information about all actions performed by users in the Power BI Service (cloud). Because this is limited to the web environment, nothing that occurs in Power BI Desktop is traced. The creation of visual calculations is also not traced as activity in audit logs but is covered in a generic activity named Update Report Content." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"Roche's Maxim of Data Transformation, expressed by Matthew Roche of Microsoft, states that 'Data should be transformed as far upstream as possible, and as far downstream as necessary'. Upstream data is source data (for example, data in a database), whereas downstream data is data that has been transformed in some way (for example, data in a report). We mention this maxim because you can apply calculations to data that is upstream, downstream, or anywhere in between. [...] The further upstream you go, the closer to the origin of the data you are. The further downstream you go, the closer to the visualization on the report you are, like the lake at the bottom of the waterfall." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"Row context can transform into filter context through a mechanism called context transition. Context transition takes any active row context and transforms it into a filter in the filter context. Multiple functions do this automatically." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"Using CALCULATE can feel like riding a wild bull. You ride it, but you never feel fully in control." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"Visual calculations are calculated in the context of the visual matrix. All model objects that are in the visual matrix can be used in a visual calculation. These can consist of columns from various tables, but also explicit measures saved in the model or implicit measures that are part of the visual." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"Visual calculations are DAX calculations executed in the scope of a visual. They are by default executed on a row-by-row basis, much like a calculated column, but are calculated on the fly, like a measure. In contrast to both calculated columns and measures, visual calculations aren’t part of the semantic model in Power BI but instead are part of a visual, such as a chart. This means visual calculations don’t have to worry about filter context as much as measures need to do. In fact, the filter context is seen as external to the visual calculation on a visual. This doesn’t mean the visual calculation isn’t affected by or would ignore the filter context but rather that it’s applied on a different level. The filter context dictates what the measures and fields on the visual return, and the visual calculation takes those values as input for its evaluation. In other words, a visual calculation is only indirectly affected by filter context, not directly, the way a measure or field reference is." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"Visual calculations share behaviors with calculated columns and measures but also have important differences, particularly in how they can be used, where they are stored, and when they are computed. " (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"What makes DAX hard has little to do with the functions themselves. The problem is also not its syntax - although DAX is a functional language, which means it uses an “inside-out” syntax instead of the more commonly used “top-to-bottom” syntax that is most used in programming. This requires some rethinking, particularly if you are coming from a procedural programming background or if, for example, you have written macros in Excel. To read and understand a DAX statement, you must find the innermost piece, parse it, then go to the next layer, which takes the innermost piece as a parameter, and work your way outward [...]" (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

"While it might be tempting to think about row context as a special filter on your table, we recommend not doing that. The row context is not a type of filter; it simply indicates to DAX which row is currently responsible for providing the values to perform the calculation and where the result of the calculation should go. Whether a column in the table is used in a particular calculation is irrelevant; all columns of the table are part of the row context when a calculation is performed." (Jeroen ter Heerdt et al, "Microsoft Power BI Visual Calculations: Simplifying DAX", 2026)

✒️Jay W Forrester - Collected Quotes

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

"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)

"Like all systems, the complex system is an interlocking structure of feedback loops [...] This loop structure surrounds all decisions public or private, conscious or unconscious. The processes of man and nature, of psychology and physics, of medicine and engineering all fall within this structure [...]" (Jay W Forrester, "Urban Dynamics", 1969)

"The structure of a complex system is not a simple feedback loop where one system state dominates the behavior. The complex system has a multiplicity of interacting feedback loops. Its internal rates of flow are controlled by non-linear relationships. The complex system is of high order, meaning that there are many system states (or levels). It usually contains positive-feedback loops describing growth processes as well as negative, goal-seeking loops." (Jay F Forrester, "Urban Dynamics", 1969)

"To model the dynamic behavior of a system, four hierarchies of structure should be recognized: closed boundary around the system; feedback loops as the basic structural elements within the boundary; level variables representing accumulations within the feedback loops; rate variables representing activity within the feedback loops." (Jay W Forrester, "Urban Dynamics", 1969)

"First, social systems are inherently insensitive to most policy changes that people choose in an effort to alter the behavior of systems. In fact, social systems draw attention to the very points at which an attempt to intervene will fail. Human intuition develops from exposure to simple systems. In simple systems, the cause of a trouble is close in both time and space to symptoms of the trouble. If one touches a hot stove, the burn occurs here and now; the cause is obvious. However, in complex dynamic systems, causes are often far removed in both time and space from the symptoms. True causes may lie far back in time and arise from an entirely different part of the system from when and where the symptoms occur. However, the complex system can mislead in devious ways by presenting an apparent cause that meets the expectations derived from simple systems." (Jay W Forrester, "Counterintuitive Behavior of Social Systems", 1995)

"Second, social systems seem to have a few sensitive influence points through which behavior can be changed. These high-influence points are not where most people expect. Furthermore, when a high-influence policy is identified, the chances are great that a person guided by intuition and judgment will alter the system in the wrong direction." (Jay W Forrester, "Counterintuitive Behavior of Social Systems", 1995)

"System dynamics models are not derived statistically from time-series data. Instead, they are statements about system structure and the policies that guide decisions. Models contain the assumptions being made about a system. A model is only as good as the expertise which lies behind its formulation. A good computer model is distinguished from a poor one by the degree to which it captures the essence of a system that it represents. Many other kinds of mathematical models are limited because they will not accept the multiple-feedback-loop and nonlinear nature of real systems." (Jay W Forrester, "Counterintuitive Behavior of Social Systems", 1995)

"Third, social systems exhibit a conflict between short-term and long-term consequences of a policy change. A policy that produces improvement in the short run is usually one that degrades a system in the long run. Likewise, policies that produce long-run improvement may initially depress behavior of a system. This is especially treacherous. The short run is more visible and more compelling. Short-run pressures speak loudly for immediate attention. However, sequences of actions all aimed at short-run improvement can eventually burden a system with long-run depressants so severe that even heroic short-run measures no longer suffice. Many problems being faced today are the cumulative result of short-run measures taken in prior decades." (Jay W Forrester, "Counterintuitive Behavior of Social Systems", 1995)

"No plea about inadequacy of our understanding of the decision-making processes can excuse us from estimating decision making criteria. To omit a decision point is to deny its presence - a mistake of far greater magnitude than any errors in our best estimate of the process." (Jay W Forrester, "Perspectives on the modelling process", 2000)

✒️Albert-László Barabási - Collected Quotes

"Every network has its own fitness distribution, which tells us how similar or different the nodes in the network are. In networks where most of the nodes have comparable fitness, the distribution follows a narrowly peaked bell curve. In other networks, the range of fitnesses is very wide such that a few nodes are much more fit than most others. […] the mathematical tools developed decades earlier to describe quantum gases enabled us to see that, independent of the nature of links and nodes, a network's behavior and topology are determined by the shape of its fitness distribution. But even though each system, from the Web to Holywood, has a unique fitness distribution, Bianconi's calculation indicated that in terms of topology all networks fall into one of only two possible categories. In most networks the competition does not have an easily noticeable impact on the network's topology. In some networks, however, the winner takes all the links, a clear signature of Bose-Einstein condensation." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"In a random network the peak of the distribution implies that the vast majority of nodes have the same number of links and that nodes deviating from the average are extremely rare. Therefore, a random network has a characteristic scale in its node connectivity, embodied by the average node and fixed by the peak of the degree distribution. In contrast, the absence of a peak in a power-law degree distribution implies that in a real network there is no such thing as a characteristic node. We see a continuous hierarchy of nodes, spanning from rare hubs to the numerous tiny nodes. The largest hub is closely fol - lowed by two or three somewhat smaller hubs, followed by dozens that are even smaller, and so on, eventually arriving at the numerous small nodes." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"[…] most earlier attempts to construct a theory of complexity have overlooked the deep link between it and networks. In most systems, complexity starts where networks turn nontrivial. No matter how puzzled we are by the behavior of an electron or an atom, we rarely call it complex, as quantum mechanics offers us the tools to describe them with remarkable accuracy. The demystification of crystals-highly regular networks of atoms and molecules-is one of the major success stories of twentieth-century physics, resulting in the development of the transistor and the discovery of superconductivity. Yet, we continue to struggle with systems for which the interaction map between the components is less ordered and rigid, hoping to give self-organization a chance." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"Most systems displaying a high degree of tolerance against failures are a common feature: Their functionality is guaranteed by a highly interconnected complex network. A cell's robustness is hidden in its intricate regulatory and metabolic network; society's resilience is rooted in the interwoven social web; the economy's stability is maintained by a delicate network of financial and regulator organizations; an ecosystem's survivability is encoded in a carefully crafted web of species interactions. It seems that nature strives to achieve robustness through interconnectivity. Such universal choice of a network architecture is perhaps more than mere coincidences." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"Nature normally hates power laws. In ordinary systems all quantities follow bell curves, and correlations decay rapidly, obeying exponential laws. But all that changes if the system is forced to undergo a phase transition. Then power laws emerge-nature's unmistakable sign that chaos is departing in favor of order. The theory of phase transitions told us loud and clear that the road from disorder to order is maintained by the powerful forces of self-organization and is paved by power laws. It told us that power laws are not just another way of characterizing a system's behavior. They are the patent signatures of self-organization in complex systems." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"Networks are not en route from a random to an ordered state. Neither are they at the edge of randomness and chaos. Rather, the scale-free topology is evidence of organizing principles acting at each stage of the network formation process." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"[…] networks are the prerequisite for describing any complex system, indicating that complexity theory must inevitably stand on the shoulders of network theory. It is tempting to step in the footsteps of some of my predecessors and predict whether and when we will tame complexity. If nothing else, such a prediction could serve as a benchmark to be disproven. Looking back at the speed with which we disentangled the networks around us after the discovery of scale-free networks, one thing is sure: Once we stumble across the right vision of complexity, it will take little to bring it to fruition. When that will happen is one of the mysteries that keeps many of us going." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"[…] real networks not only are connected but are well beyond the threshold of one. Random network theory tells us that as the average number of links per node increases beyond the critical one, the number of nodes left out of the giant cluster decreases exponentially. That is, the more links we add, the harder it is to find a node that remains isolated. Nature does not take risks by staying close to the threshold. It well surpasses it."  (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002)

"Regular graphs are unique in that each node has exactly the same number of links. […] Such regularity is clearly absent from random graphs. The premise of the random network model is deeply egalitarian: We place the links completely randomly; thus all nodes have the same chance of getting one […] If the network is large, despite the links' completely random placement, almost all nodes will have approximately the same number of links." (Albert-László Barabási, "Linked: How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life", 2002) 

"The difference between human dynamics and data mining boils down to this: Data mining predicts our behaviors based on records of our patterns of activity; we don't even have to understand the origins of the patterns exploited by the algorithm. Students of human dynamics, on the other hand, seek to develop models and theories to explain why, when, and where we do the things we do with some regularity." (Albert-László Barabási, "Bursts: The Hidden Pattern Behind Everything We Do", 2010)

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

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

"And that’s what good networkers do. No matter the field, discipline, or industry, if we want to succeed, we must master the networks. Because as the First Law of Success reminds us, the harder it is to measure performance, the less performance matters." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

"[…] because performance is so bounded, it allows us to predict, with impressive accuracy, what our own ultimate limit." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

"Credit for teamwork isn’t based on performance. Credit is based on perception." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

"Diversity creates the best mix for success, but for that mix to be potent, it needs a leader. Indeed, the more successful a team is in the programming universe, the more lopsided the contributions are to the work. A single leader emerges who calls the shots and does most of the programming. To be sure, his fellow contributors also play a vital role, offering key expertise and filling in the holes. But it’s the leader who makes the project whole, correcting individual errors, rejecting pieces he considers subpar, and ensuring that the final product matches his vision and standards." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

"Networks brim with opportunity, partially because they’re held together by powerful hubs, people who are, well… really good at networking. These connectors are eager to utilize their relationships to support people and causes they find value in. They’re especially good at seeing opportunities in the social fabric that other people miss. Connect with them." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

"[…] once a project is successful, it will grow indefinitely, in proportion to previous success." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

"Performance drives success, but when performance can’t be measured, networks drive success." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

"Performance is bounded, no matter your profession, which makes distinguishing among top performers inevitably difficult." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

"Remember, performance needs to be empowered by opportunity. We need to reframe the all-too-frequent assumption that aiming for the top means scraping our way up from the bottom. " (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

"Scientific analysis can illuminate seemingly deeply irrational puzzles, turning our assumptions on their heads. In other words, science can help us make sense of the randomness of the human world—unveiling the mechanisms at work when we’re passed over for a job, the underlying pattern that explains why some artists thrive while others fail, the lingering hunch that success is about more than just talent or how well we perform." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

"Since success is a collective phenomenon, measured by how our community reacts to a performance, it’s impossible to understand the phenomenon of success without also observing the network it takes place within. But networks are singularly important in areas like art, where performance and quality are hard to measure." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

"Successful teams require balance and diversity. But they also need a leader." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

"While team success requires diversity and balance, a single individual will receive credit for the group’s achievements." (Albert-László Barabási, "The Formula: The Universal Laws of Success", 2018)

✒️John Gall - Collected Quotes

"A complex system can fail in an infinite number of ways." (John Gall, "General Systemantics: How systems work, and especially how they fail", 1975)

"A complex system that works is invariably found to have evolved from a simple system that worked. The inverse proposition also appears to be true: A complex system designed from scratch never works and cannot be made to work. You have to start over, beginning with a working simple system." (John Gall, "General Systemantics: How systems work, and especially how they fail", 1975)

"A system represents someone's solution to a problem. The system doesn't solve the problem." (John Gall, "General Systemantics: How systems work, and especially how they fail", 1975)

"Loose systems last longer and function better." (John Gall, "General Systemantics: How systems work, and especially how they fail", 1975)

"Systems Are Seductive. They promise to do a hard job faster, better, and more easily than you could do it by yourself. But if you set up a system, you are likely to find your time and effort now being consumed in the care and feeding of the system itself. New problems are created by its very presence. Once set up, it won't go away, it grows and encroaches. It begins to do strange and wonderful things. Breaks down in ways you never thought possible. It kicks back, gets in the way, and opposes its own proper function. Your own perspective becomes distorted by being in the system. You become anxious and push on it to make it work. Eventually you come to believe that the misbegotten product it so grudgingly delivers is what you really wanted all the time. At that point encroachment has become complete. You have become absorbed. You are now a systems person." (John Gall, "General Systemantics: How systems work, and especially how they fail", 1975)

"The following four propositions, which appear to the author to be incapable of formal proof, are presented as Fundamental Postulates upon which the entire superstructure of General Systemantics [...] is based [...] (1) Everything is a system. (2) Everything is part of a larger system. (3) The universe is infinitely systematizable, both upward (larger systems) and downward (smaller systems) (4) All systems are infinitely complex. (The illusion of simplicity comes from focusing attention on one or a few variables.)" (John Gall, "General Systemantics: How systems work, and especially how they fail", 1975)

"The system always kicks back. - Systems get in the way - or, in slightly more elegant language: Systems tend to oppose their own proper functions. Systems tend to malfunction conspicuously just after their greatest triumph." (John Gall, "Systemantics: The underground text of systems lore", 1986)

"Alternating positive and negative feedback produces a special form of stability represented by endless oscillation between two polar states or conditions." (John Gall, "Systemantics: The Systems Bible", 2002)

"[...] the System may be so thoroughly organized around the familiar response strategy that a new response would require extensive restructuring - something that Systems do with the greatest reluctance and difficulty." (John Gall, "Systemantics: The Systems Bible", 2002)

"The function performed by a System is not operationally identical to the function of the same name performed by a person. In general, a function performed by a larger System is not operationally identical to the function of the same name as performed by a smaller System." (John Gall, "Systemantics: The Systems Bible", 2002)

"Systems-people everywhere share certain attributes, but each specific System tends to attract people with specific sets of traits. […] Systems attract not only Systems-people who have qualities making for success within the System; they also attract individuals who possess specialized traits adapted to allow them to thrive at the expense of the System; i.e., persons who parasitize the System." (John Gall, "Systemantics: The Systems Bible", 2002)

"We are accustomed to thinking that a System acts like a machine, and that if we only knew its mechanism, we could understand, even predict, its behavior. This is wrong. The correct orientation is:  - and if the machine is large and complex enough, it will act like a large System. We simply have our metaphors backwards." (John Gall, "Systemantics: The Systems Bible", 2002)

"When a system is set up to accomplish some goal, a new entity has come into being - the system itself. No matter what the 'goal' of the system, it immediately begins to exhibit systems-behavior, that is, to act according to the general laws that govern the operation of all systems." (John Gall, "Systemantics: The Systems Bible", 2002)

"Almost by definition, one is rarely privileged to 'control' a disaster. Yet the activity somewhat loosely referred to by this term is a substantial portion of Management, perhaps the most important part. […] It is the business of a good Manager to ensure, by taking timely action in the real world, that scenarios of disaster remain securely in the realm of Fantasy." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"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)

"Clearly, total feedback is Not a Good Thing. Too much feedback can over- whelm the response channels, leading to paralysis and inaction. Even in a system designed to accept massive feedback (such as the human brain), if the system is required to accommodate to all incoming data, equilibrium will never be reached. The point of decision will be delayed indefinitely, and no action will be taken." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"[…] even though a System may function very poorly, it can still tend to Expand to Fill the Known Universe, and Positive Feedback only encourages that tendency." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"Experts in the 'Problem' area proceed to elaborate its complexity. They design complex Systems to attack it. This approach guarantees failure, at least for all but the most pedestrian tasks. The problem is a Problem precisely because it is incorrectly conceptualized in the first place, and a large System for studying and attacking the Problem merely locks in the erroneous conceptualization into the minds of everyone concerned. What is required is not a large System, but a different approach. Trying to design a System in the hope that the System will somehow solve the Problem, rather than simply solving the Problem in the first place, is to present oneself with two problems in place of one." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"In point of fact, the System may be so thoroughly organized around the familiar response strategy that a new response would require extensive restructuring - something that Systems do with the greatest reluctance and difficulty." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"Information Theory is a mathematical treatment of what is left after the meanings have been removed from a Communication." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"Not only Nature, but Systems generally, cannot be wise when feedbacks are unduly delayed. Feedback is likely to cause trouble if it is either too slow or too prompt. It must be adjusted to the response rhythms of the system as well as to the tempo of the actual events - a double restriction." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"Nothing is more useless than struggling against a Law of Nature. On the other hand, there are circumstances (highly unusual and narrowly defined, of course) when one’s knowledge of Systems-functions will provide precisely the measure of extra added ability needed to tip the scales of a doubtful operation in one’s favor." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"Pragmatically, it is generally easier to aim at changing one or a few things at a time and then work out the unexpected effects, than to go to the opposite extreme. Attempting to correct everything in one grand design is appropriately designated as Grandiosity. […] A little Grandiosity goes a long way. […] The diagnosis of Grandiosity is quite elegantly and strictly made on a purely quantitative basis: How many features of the present System, and at what level, are to be corrected at once? If more than three, the plan is grandiose and will fail." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"Striving for Perfection produces a kind of tunnel-vision resembling a hypnotic state. Absorbed in the pursuit of perfecting the System at hand, the striver has no energy or attention left over for considering other, possibly better, ways of doing the whole thing." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"Systems are never dealing with the real world that the rest of us have to live in, but instead with a filtered, distorted, and censored version which is all that can get past the sensory organs of the System itself." (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

"[…] the System has its effects on the people within it. It isolates them, feeds them a distorted and partial version of the outside world, and gives them the illusion of power and effectiveness."  (John Gall, "The Systems Bible: The Beginner's Guide to Systems Large and Small"[Systematics 3rd Ed.], 2011)

✒️Donella H Meadows - Collected Quotes

"Everything we think we know about the world is a model. Every word and every language is a model. All maps and statistics, books and databases, equations and computer programs are models. So are the ways I picture the world in my head - my mental models. None of these is or ever will be the real world. […] Our models usually have a strong congruence with the world. That is why we are such a successful species in the biosphere. Especially complex and sophisticated are the mental models we develop from direct, intimate experience of nature, people, and organizations immediately around us." (Donella H Meadows, "Limits to Growth", 1972)

"However, and conversely, our models fall far short of representing the world fully. That is why we make mistakes and why we are regularly surprised. In our heads, we can keep track of only a few variables at one time. We often draw illogical conclusions from accurate assumptions, or logical conclusions from inaccurate assumptions. Most of us, for instance, are surprised by the amount of growth an exponential process can generate. Few of us can intuit how to damp oscillations in a complex system." (Donella H Meadows, "Limits to Growth", 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 - and prevent us from taking effective action to solve it." (Donella H Meadows, "The Limits to Growth", 1972)

"Models can easily become so complex that they are impenetrable, unexaminable, and virtually unalterable." (Donella H Meadows, "The unavoidable a priori", 1980)

"The world is a complex, interconnected, finite, ecological–social–psychological–economic system. We treat it as if it were not, as if it were divisible, separable, simple, and infinite. Our persistent, intractable global problems arise directly from this mismatch." (Donella H Meadows, "Whole Earth Models and System", 1982)

"A quantity growing exponentially toward a limit reaches that limit in a surprisingly short time." (Donella Meadows, "Thinking in systems: A Primer", 2008)

"A system is a set of things – people, cells, molecules, or whatever – interconnected in such a way that they produce their own pattern of behavior over time. […] The system, to a large extent, causes its own behavior." (Donella H Meadows, “Thinking in Systems: A Primer”, 2008) 

"In fact, one of the most frustrating aspects of systems is that the purposes of subunits may add up to an overall behavior that no one wants." (Donella H Meadows, “Thinking in Systems: A Primer”, 2008)"

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

"Like resilience, self-organizazion is often sacrificed for purposes of short-term productivity and stability." (Donella H Meadows, “Thinking in Systems: A Primer”, 2008)

"Our culture, obsessed with numbers, has given us the idea that what we can measure is more important than what we can't measure. Think about that for a minute. It means that we make quantity more important than quality." (Donella H Meadows, "Thinking in Systems: A Primer", 2008)

"[…] our mental models fail to take into account the complications of the real world - at least those ways that one can see from a systems perspective. It is a warning list. Here is where hidden snags lie. 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. You are likely to mistreat, misdesign, or misread systems if you don’t respect their properties of resilience, self-organization, and hierarchy." (Donella H Meadows, “Thinking in Systems: A Primer”, 2008)

"The bounded rationality of each actor in a system may not lead to decisions that further the welfare of the system as a whole." (Donella H Meadows, “Thinking in Systems: A Primer”, 2008)

"The world is nonlinear. Trying to make it linear for our mathematical or administrative convenience is not usually a good idea even when feasible, and it is rarely feasible." (Donella H Meadow, "Thinking in Systems: A Primer", 2008)

"When there are long delays in feedback loops, some sort of foresight is essential." (Donella H Meadows, “Thinking in Systems: A Primer”, 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)

✒️Paul Cilliers - Collected Quotes

"A neural network consists of large numbers of simple neurons that are richly interconnected. The weights associated with the connections between neurons determine the characteristics of the network. During a training period, the network adjusts the values of the interconnecting weights. The value of any specific weight has no significance; it is the patterns of weight values in the whole system that bear information. Since these patterns are complex, and are generated by the network itself (by means of a general learning strategy applicable to the whole network), there is no abstract procedure available to describe the process used by the network to solve the problem. There are only complex patterns of relationships." (Paul Cilliers, "Complexity and Postmodernism: Understanding Complex Systems", 1998)

"Each element in the system is ignorant of the behavior of the system as a whole, it responds only to information that is available to it locally. This point is vitally important. If each element ‘knew’ what was happening to the system as a whole, all of the complexity would have to be present in that element." (Paul Cilliers, "Complexity and Postmodernism: Understanding Complex Systems" , 1998)

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

"From a more general philosophical perspective we can say that we wish to model complex systems because we want to understand them better.  The main requirement for our models accordingly shifts from having to be correct to being rich in information.  This does not mean that the relationship between the model and the system itself becomes less important, but the shift from control and prediction to understanding does have an effect on our approach to complexity: the evaluation of our models in terms of performance can be deferred. Once we have a better understanding of the dynamics of complexity, we can start looking for the similarities and differences between different complex systems and thereby develop a clearer understanding of the strengths and limitations of different models." (Paul Cilliers,"Complexity and Postmodernism: Understanding Complex Systems", 1998)

"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)

"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)

"The ability of neural networks to operate successfully on inputs that did not form part of the training set is one of their most important characteristics. Networks are capable of finding common elements in all the training examples belonging to the same class, and will then respond appropriately when these elements are encountered again. Optimising this capability is an important consideration when designing a network." (Paul Cilliers, "Complexity and Postmodernism: Understanding Complex Systems", 1998)

"The concept ‘complexity’ is not univocal either. Firstly, it is useful to distinguish between the notions ‘complex’ and ‘complicated’. If a system- despite the fact that it may consist of a huge number of components - can be given a complete description in terms of its individual constituents, such a system is merely complicated. […] In a complex system, on the other hand, the interaction among constituents of the system, and the interaction between the system and its environment, are of such a nature that the system as a whole cannot be fully understood simply by analysing its components. Moreover, these relationships are not fixed, but shift and change, often as a result of self-organisation. This can result in novel features, usually referred to in terms of emergent properties." (Paul Cilliers, "Complexity and Postmodernism: Understanding Complex Systems" , 1998)

"There is no over-arching theory of complexity that allows us to ignore the contingent aspects of complex systems. If something really is complex, it cannot by adequately described by means of a simple theory. Engaging with complexity entails engaging with specific complex systems. Despite this we can, at a very basic level, make general remarks concerning the conditions for complex behaviour and the dynamics of complex systems. Furthermore, I suggest that complex systems can be modelled." (Paul Cilliers," Complexity and Postmodernism", 1998)

✒️John M Bryson (Collected Quotes)

"A causal map is a word-and-arrow diagram in which ideas and actions are causally linked with one another through the use of arrows. The arrows indicate how one idea or action leads to another. Causal mapping makesit possible to articulate a large number of ideas and their interconnections in such a way that people can know what to do in an area of concern, how to do it and why, because the arrows indicate the causes and consequences of an idea or action. Causal mapping is therefore a technique for linking strategic thinking and acting, helping make sense of complex problems, and communicating to oneself and others what might be done about them." (John M Bryson et al, "Visible Thinking: Unlocking Causal Mapping For Practical Business Results", 2004)

"A statement of strategic aims is powerful when it represents the interaction between all of the goals [Strategy]: the goals are seen as a system where each goal helps deliver other high-level goals and may in turn be helped by the delivery of subordinate goals." (John M Bryson et al, "Visible Thinking: Unlocking Causal Mapping For Practical Business Results", 2004)

"Causal mapping is a simple and useful technique for addressing situations where thinking– as an individual or as a group–matters. A causal map is a word-and-arrow diagram in which ideas and actions are causally linked with one another through the use of arrows. The arrows indicate how one idea or action leads to another. Causal mapping makes it possible to articulate a large number of ideas and their interconnections in such a way that people can know what to do in an area of concern, how to do it and why, because the arrows indicate the causes and consequences of an idea or action." (John M Bryson et al, "Visible Thinking: Unlocking Causal Mapping For Practical Business Results", 2004)

"Causal mapping is [...] a technique for linking strategic thinking and acting, helping make sense of complex problems, and communicating to oneself and others what might be done about them. With practice, the use of causal mapping can assist you in moving from 'winging it' when thinking matters to a more concrete and rigorous approach that helps you and others achieve success in an easy and far more reliable way" (John M Bryson et al, "Visible Thinking: Unlocking Causal Mapping For Practical Business Results", 2004)

"Causal mapping makes it possible to articulate a large number of ideas and their interconnections in such a way that we can better understand an area of concern. Causal mapping also helps us know what to do about the issue, what it would take to do those things, and what we would like to get out of having done so. Causal mapping is therefore a particularly powerful technique for making sense of complex problems, linking strategic thinking and acting, and helping to communicate to others what might or should be done. " (John M Bryson et al, "Visible Thinking: Unlocking Causal Mapping For Practical Business Results", 2004)

"Linking focuses attention not on topics but on action: what results might be achieved by doing something, and what things needed to be done to make other things happen." (John M Bryson et al, "Visible Thinking: Unlocking Causal Mapping For Practical Business Results", 2004)

"Positive feedback loops are very important. Even when afeedback loop is made up of competencies, because they are self-sustaining they may be very important resources for the future of the business [Strategy]. A positive loop means that each of the competencies in the loop feeds all of the others. When the feedback loop is distinctive, this will be even more important because the distinctiveness is self-sustaining." (John M Bryson et al, "Visible Thinking: Unlocking Causal Mapping For Practical Business Results", 2004)

"The process of constructing a map thus allows you to reflect on your own thinking, or that of someone else. Mapping helps you become conscious of your reasoning and to understand clearly what another person is thinking. In turn, that consciousness and understanding can help you reaffirm what you think or else change your mind. Shared understanding and mutual agreement also become possible." (John M Bryson et al, "Visible Thinking: Unlocking Causal Mapping For Practical Business Results", 2004)

"When an individual uses causal mapping to help clarify their own thinking, we call this technique cognitive mapping, because it is related to personal thinking or cognition. When a group maps their own ideas, we call it oval mapping, because we often use oval-shaped cards to record individuals’ ideas so that they can be arranged into a group’s map. Cognitive maps and oval maps can be used to create a strategic plan, because the maps include goals, strategies and actions, just like strategic plans." (John M Bryson et al, "Visible Thinking: Unlocking Causal Mapping For Practical Business Results", 2004)

"When people question assumptions, the map may clarify what they are. When logic is challenged, the map may help. When people want to know how goals and strategies are linked, the map may show how they are. The map does not make the decisions. Rather, it provides a record that preserves complexity, yet organizes and categorizes that complexity in such a way that people can understand and manage it. And if more mapping needs to be done, the map is there as a base on which to build." (John M Bryson et al, "Visible Thinking: Unlocking Causal Mapping For Practical Business Results", 2004)

✒️Robert M Axelrod (Collected Quotes)

"A cognitive map has only two basic types of elements: concepts and causal beliefs. The concepts are treated as variables, and the causal beliefs are treated as relationships between the variables." (Robert M Axelrod, "Structure of Decision: The cognitive maps of political elites", 1976)

"A cognitive map is a particular kind of mathematical model of a person's belief system; in actual practice, cognitive maps are derived from assertions of beliefs. [...] Like all mathematical models, a cognitive map can be useful in two quite distinct ways: as a normative model and as an empirical model. Interpreted as a normative model, a cognitive map makes no claims to reflect accurately how a person deduces new beliefs from old ones, how he makes decisions, and so on, but instead claims to show how he should do these things. Interpreted as an empirical model, a cognitive map claims to indicate how a person actually does perform certain cognitive operations, in the sense that the results of the various operations that are possible with the model do, in fact, correspond to the behavior of the per son who is being modeled." (Robert M Axelrod, "Structure of Decision: The cognitive maps of political elites", 1976)

"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)

"A cognitive map is designed to capture the structure of the causal assertions of a person with respect to a particular policy domain, and generate the consequences that follow from this structure." (Robert M Axelrod, "Structure of Decision: The cognitive maps of political elites", 1976)

"A decision maker must simplify the manifest complexities of the external world. He must be able to construct a manageable representation of the external world so that he can describe and cope with his environment. The use of this representation for the purposes of making reasoned decisions requires some beliefs that link possible choices with potential outcomes." (Robert M Axelrod, "Decision for Neoimperialism: The Deliberations of the British Eastern Committee in 1918", 1976)

"A mathematical model is a tremendous simplification of what it represents. But it does not simplify everything about its object, or there would be nothing left to model. Instead, it simplifies everything that is not to be examined, and leaves in the model what is to be examined." (Robert M Axelrod, "Structure of Decision: The cognitive maps of political elites", 1976)

"Cognitive mapping is one specific approach to belief systems. It focuses on causal beliefs and values and their structural relationships. Cognitive mapping is, therefore especially suitable to the study of the means-ends arguments people use when they try to evaluate the policy alternatives that they perceive are available to them." (Robert Axelrod, "Structure of Decision: The Cognitive Maps of Political Elites", 1976)

"The concepts a person uses are represented as points, and the causal links between these concepts are represented as arrows between these points. This gives a pictorial representation of the causal assertions of a person as a graph of points and arrows. This kind of representation of assertions as a graph will be called a cognitive map. The policy alternatives, all of the various causes and effects, the goals, and the ultimate utility of the decision maker can all be thought of as concept variables, and represented as points in the cognitive map. The real power of this approach ap pears when a cognitive map is pictured in graph form; it is then relatively easy to see how each of the concepts and causal relation ships relate to each other, and to see the overall structure of the whole set of portrayed assertions." (Robert Axelrod, "The Cognitive Mapping Approach to Decision Making" [in "Structure of Decision: The Cognitive Maps of Political Elites"], 1976)

"What cognitive mapping offers is a systematic way to proceed in our search for understanding how others will act. Its real strength (especially as compared to other formal approaches to decision making) is that it is able to employ the concepts of the decision maker who is being predicted, rather than the concepts of the person who is doing the predicting." (Robert Axelrod, "Structure of Decision: The Cognitive Maps of Political Elites", 1976)

"What difference does it make which cognitive map a person has on a policy issue? Why would we want to know about a person's cognitive map? There are two broad answers to these questions. The first is that we want to know about cognitive maps so that we can better understand the decision-making process. The second is that we want to know about cognitive maps so that we can improve the decision-making process." (Robert Axelrod, "Structure of Decision: The Cognitive Maps of Political Elites", 1976)

✒️Kenneth E Boulding - Collected Quotes

"Knowledge is not something which exists and grows in the abstract. It is a function of human organisms and of social organization. Knowledge, that is to say, is always what somebody knows: the most perfect transcript of knowledge in writing is not knowledge if nobody knows it. Knowledge however grows by the receipt of meaningful information - that is, by the intake of messages by a knower which are capable of reorganising his knowledge." (Kenneth E Boulding, "General Systems Theory: The Skeleton of Science", Management Science Vol. 2 (3), 1956)

"One advantage of exhibiting a hierarchy of systems in this way is that it gives us some idea of the present gaps in both theoretical and empirical knowledge. Adequate theoretical models extend up to about the fourth level, and not much beyond. Empirical knowledge is deficient at practically all levels." (Kenneth E Boulding, "General Systems Theory: The Skeleton of Science", 1956)

"It is important to realize that the exercise of any skill depends on the ability to create an abstract system of some kind out of the totality of the world around us." (Kenneth E Boulding, "The Skills of the Economist", 1958)

"The idea of knowledge as an improbable structure is still a good place to start. Knowledge, however, has a dimension which goes beyond that of mere information or improbability. This is a dimension of significance which is very hard to reduce to quantitative form. Two knowledge structures might be equally improbable but one might be much more significant than the other." (Kenneth E Boulding, "Beyond Economics: Essays on Society", 1968)

"It [knowledge] is clearly related to information, which we can now measure; and an economist especially is tempted to regard knowledge as a kind of capital structure, corresponding to information as an income flow. Knowledge, that is to say, is some kind of improbable structure or stock made up essentially of patterns - that is, improbable arrangements, and the more improbable the arrangements, we might suppose, the more knowledge there is." (Kenneth E Boulding, "Beyond Economics: Essays on Society", 1968)

"The human condition can almost be summed up in the observation that, whereas all experiences are of the past, all decisions are about the future. It is the great task of human knowledge to bridge this gap and to find those patterns in the past which can be projected into the future as realistic images." (Kenneth E Boulding, [foreword] 1972)

"We never like to admit to ourselves that we have made a mistake. Organizational structures tend to accentuate this source of failure of information." (Kenneth E Boulding, "Toward a General Social Science", 1974)

"Prediction of the future is possible only in systems that have stable parameters like celestial mechanics. The only reason why prediction is so successful in celestial mechanics is that the evolution of the solar system has ground to a halt in what is essentially a dynamic equilibrium with stable parameters. Evolutionary systems, however, by their very nature have unstable parameters. They are disequilibrium systems and in such systems our power of prediction, though not zero, is very limited because of the unpredictability of the parameters themselves. If, of course, it were possible to predict the change in the parameters, then there would be other parameters which were unchanged, but the search for ultimately stable parameters in evolutionary systems is futile, for they probably do not exist… Social systems have Heisenberg principles all over the place, for we cannot predict the future without changing it." (Kenneth E Boulding, "Evolutionary Economics", 1981)

✒️John L Casti - Collected Quotes

"[…] a complex system is incomprehensible unless we can simplify it by using alternative levels of description." (John L Casti, "On System Complexity: Identification, Measurement, and Management" [in "Complexity, Language, and Life: Mathematical Approaches"] 1986)

"Coping with complexity involves the creation of faithful models of not only the system to be managed. but also of the management system itself." (John L Casti, "On System Complexity: Identification, Measurement, and Management" [in "Complexity, Language, and Life: Mathematical Approaches"] 1986)

"[…] complexity emerges from simplicity when alternative descriptions of a system are not reducible to each other. For a given observer, the more such inequivalent descriptions he or she generates, the more complex the system appears. Conversely, a complex system can be simplified in one of two ways: reduce the number of potential descriptions (by restricting the observer's means of interaction with the system) and/or use a coarser notion of system equivalence, thus reducing the number of equivalence classes." (John L Casti, "On System Complexity: Identification, Measurement, and Management" [in "Complexity, Language, and Life: Mathematical Approaches"] 1986)

"Simple systems generally involve a small number of components. with self-interaction dominating the mutual interaction of the variables. […] Besides involving only a few variables. simple systems generally have very few feedback/feedforward loops. Such loops enable the system to restructure. or at least modify. the interaction pattern of its variables. thereby opening-up the possibility of a wider range of potential behavior patterns." (John L Casti, "On System Complexity: Identification, Measurement, and Management" [in "Complexity, Language, and Life: Mathematical Approaches"] 1986)

"[…] a model is a mathematical representation of the modeler's reality, a way of capturing some aspects of a particular reality within the framework of a mathematical apparatus that provides us with a means for exploring the properties of the reality mirrored in the model." (John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"Basically, the point of making models is to be able to bring a measure of order to our experiences and observations, as well as to make specific predictions about certain aspects of the world we experience." (John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"Reliable information processing requires the existence of a good code or language, i.e., a set of rules that generate information at a given hierarchical level, and then compress it for use at a higher cognitive level. To accomplish this, a language should strike an optimum balance between variety (stochasticity) and the ability to detect and correct errors (memory)."(John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"Skewness is a measure of symmetry. For example, it's zero for the bell-shaped normal curve, which is perfectly symmetric about its mean. Kurtosis is a measure of the peakedness, or fat-tailedness, of a distribution. Thus, it measures the likelihood of extreme values." (John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"[…] the complexity of a given system is always determined relative to another system with which the given system interacts. Only in extremely special cases, where one of these reciprocal interactions is so much weaker than the other that it can be ignored, can we justify the traditional attitude regarding complexity as an intrinsic property of the system itself." (John L Casti, "Reality Rules: Picturing the world in mathematics", 1992)

"The core of a decision problem is always to find a single method that can be applied to each question, and that will always give the correct answer for each individual problem." (John L Casti, "Mathematical Mountaintops: The Five Most Famous Problems of All Time", 2001)

"[…] according to the bell-shaped curve the likelihood of a very-large-deviation event (a major outlier) located in the striped region appears to be very unlikely, essentially zero. The same event, though, is several thousand times more likely if it comes from a set of events obeying a fat-tailed distribution instead of the bell-shaped one." (John L Casti, "X-Events: The Collapse of Everything", 2012)

"[…] both rarity and impact have to go into any meaningful characterization of how black any particular [black] swan happens to be." (John L Casti, "X-Events: The Collapse of Everything", 2012)

"Due to the problem of predicting outlier events, they are not usually factored into the design of systems." (John L Casti, "X-Events: The Collapse of Everything", 2012)

"Forecasting models […] ordinarily are based only on past data, which is generally a tiny sample of the total range of possible outcomes. The problem is that those 'experts' who develop the models often come to believe they have mapped the entire space of possible system behaviors, which could not be further from the truth. Worse yet, when outliers do crop up, they are often discounted as 'once in a century' events and are all but ignored in planning for the future. […] the world is much more unpredictable than we’d like to believe."(John L Casti, "X-Events: The Collapse of Everything", 2012)

"If you want a system - economic, social, political, or otherwise - to operate at a high level of efficiency, then you have to optimize its operation in such a way that its resilience is dramatically reduced to unknown - and possibly unknowable - shocks and/or changes in its operating environment. In other words, there is an inescapable price to be paid in efficiency in order to gain the benefits of adaptability and survivability in a highly uncertain environment. There is no escape clause!" (John L Casti, "X-Events: The Collapse of Everything", 2012)

"Sustainability is a delicate balancing act calling upon us to remain on the narrow path between organization and chaos, simplicity and complexity." (John L Casti, "X-Events: The Collapse of Everything", 2012)

"The first path of increasing complexity via innovation often faces limits as to how much complexity can be added or reduced in a given system. This is because if you change the complexity level in one place, a compensating change in the opposite direction generally occurs somewhere else." (John L Casti, "X-Events: The Collapse of Everything", 2012)

"[…] the law of requisite complexity […] states that in order to fully regulate/control a system, the complexity of the controller has to be at least as great as the complexity of the system that’s being controlled. To put it in even simpler terms, only complexity can destroy complexity." (John L Casti, "X-Events: The Collapse of Everything", 2012)

"What is and isn’t complex depends to a large degree not only on a target system, but also on the system(s) the target interacts with, together with the overall context in which the interacting systems are embedded." (John L Casti, "X-Events: The Collapse of Everything", 2012)

✒️Stephen G Haines - Collected Quotes

"Delay time, the time between causes and their impacts, can highly influence systems. Yet the concept of delayed effect is often missed in our impatient society, and when it is recognized, it’s almost always underestimated. Such oversight and devaluation can lead to poor decision making as well as poor problem solving, for decisions often have consequences that don’t show up until years later. Fortunately, mind mapping, fishbone diagrams, and creativity/brainstorming tools can be quite useful here." (Stephen G Haines, "The Managers Pocket Guide to Systems Thinking & Learning", 1998)

"Our simplistic cause-effect analyses, especially when coupled with the desire for quick fixes, usually lead to far more problems than they solve - impatience and knee-jerk reactions included. If we stop for a moment and take a good look our world and its seven levels of complex and interdependent systems, we begin to understand that multiple causes with multiple effects are the true reality, as are circles of causality-effects." (Stephen G Haines, "The Managers Pocket Guide to Systems Thinking & Learning", 1998)

"Strategic planning and strategic change management are really 'strategic thinking'. It’s about clarity and simplicity, meaning and purpose, and focus and direction." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"Systems thinking is based on the theory that a system is, in essence, circular. Using a systems approach in your strategic management, therefore, provides a circular implementing structure that can evolve, with continuously improving, self-checking, and learning capabilities [...]" (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"The systems approach, on the other hand, provides an expanded structural design of organizations as living systems that more accurately reflects reality." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"This is what systems thinking is all about: the idea of building an organization in which each piece, and partial solution of the organization has the fit, alignment, and integrity with your overall organization as a system, and its outcome of serving the customer." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

"True systems thinking, on the other hand, studies each problem as it relates to the organization’s objectives and interaction with its entire environment, looking at it as a whole within its universe. Taking your organization from a partial systems to a true systems state requires effective strategic management and backward thinking." (Stephen G Haines, "The Systems Thinking Approach to Strategic Planning and Management", 2000)

✒️Yaneer Bar-Yamm - Collected Quotes

"A complex system is a system formed out of many components whose behavior is emergent, that is, the behavior of the system cannot be simply inferred from the behavior of its components. The amount of information necessary to describe the behavior of such a system is a measure of its complexity." (Yaneer Bar-Yamm, "Dynamics of Complexity", 1997)

"A dictionary definition of the word ‘complex’ is: ‘consisting of interconnected or interwoven parts’ […] Loosely speaking, the complexity of a system is the amount of information needed in order to describe it. The complexity depends on the level of detail required in the description. A more formal definition can be understood in a simple way. If we have a system that could have many possible states, but we would like to specify which state it is actually in, then the number of binary digits (bits) we need to specify this particular state is related to the number of states that are possible." (Yaneer Bar-Yamm, "Dynamics of Complexity", 1997)

"Many of the systems that surround us are complex. The goal of understanding their properties motivates much if not all of scientific inquiry. […] all scientific endeavor is based, to a greater or lesser degree, on the existence of universality, which manifests itself in diverse ways. In this context, the study of complex systems as a new endeavor strives to increase our ability to understand the universality that arises when systems are highly complex." (Yaneer Bar-Yamm, "Dynamics of Complexity", 1997)

"There are two approaches to organizing the properties of complex systems that will serve as the foundation of our discussions. The first of these is the relationship between elements, parts and the whole. Since there is only one property of the complex system that we know for sure - that it is complex - the primary question we can ask about this relationship is how the complexity of the whole is related to the complexity of the parts. […] The second approach to the study of complex systems begins from an understanding of the relationship of systems to their descriptions. The central issue is defining quantitatively what we mean by complexity."  (Yaneer Bar-Yamm, "Dynamics of Complexity", 1997)

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

"A fundamental reason for the difficulties with modern engineering projects is their inherent complexity. The systems that these projects are working with or building have many interdependent parts, so that changes in one part often have effects on other parts of the system. These indirect effects are frequently unanticipated, as are collective behaviors that arise from the mutual interactions of multiple components. Both indirect and collective effects readily cause intolerable failures of the system. Moreover, when the task of the system is intrinsically complex, anticipating the many possible demands that can be placed upon the system, and designing a system that can respond in all of the necessary ways, is not feasible. This problem appears in the form of inadequate specifications, but the fundamental issue is whether it is even possible to generate adequate specifications for a complex system." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"Complex problems are the problems that persist - the problems that bounce back and continue to haunt us. People often go through a series of stages in dealing with such problems - from believing they are beyond hope, to galvanizing collective efforts of many people and dollars to address the problem, to despair, retreat, and rationalization." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"Emergence refers to the relationship between the details of a system and the larger view. Emergence does not emphasize the primary importance of the details or of the larger view; it is concerned with the relationship between the two. Specifically, emergence seeks to discover: Which details are important for the larger view, and which are not? How do collective properties arise from the properties of parts? How does behavior at a larger scale of the system arise from the detailed structure, behavior and relationships on a finer scale?" (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"Engineers use abstraction to simplify the description or specification of the system, extracting the properties of the system they find most relevant and ignoring other details. While this is a useful tool, it assumes that the details that will be provided to one part of the system (module) can be designed independently of details in other parts."  (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"Modularity, an approach that separates a large system into simpler parts that are individually designed and operated, incorrectly assumes that complex system behavior can essentially be reduced to the sum of its parts. A planned decomposition of a system into modules works well for systems that are not too complex. […] However, as systems become more complex, this approach forces engineers to devote increasing attention to designing the interfaces between parts, eventually causing the process to break down."  (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"The basic idea of systems engineering is that it is possible to take a large and highly complex system that one wants to build, separate it into key parts, give the parts to different groups of people to work on, and coordinate their development so that they can be put together at the end of the process. This mechanism is designed to be applied recursively, so that we separate the large system into parts, then the parts into smaller parts, until each part is small enough for one person to execute. Then we put all of the parts together until the entire system works." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"The collapse of a particular project may appear to have a specific cause, but an overly high intrinsic complexity of these systems is a problem common to many of them. A chain always breaks first in one particular link, but if the weight it is required to hold is too high, failure of the chain is guaranteed." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"The complexity of each individual or organization must match the complexity of the task each is to perform. When we think about a highly complex problem, we are generally thinking about tasks that are more complex than a single individual can understand. Otherwise, complexity is not the main issue in solving it. If a problem is more complex than a single individual, the only way to solve it is to have a group of people - organized appropriately - solve it together. When an organization is highly complex it can only function by making sure that each individual does not have to face the complexity of the task of the organization as a whole. Otherwise failure will occur most of the time." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"The complexity of engineering projects has been increasing, but this is not to say that this complexity is new. Engineers and managers are generally aware of the complexity of these projects and have developed systematic techniques that are often useful in addressing it. Notions like modularity, abstraction, hierarchy and layering allow engineers to usefully analyze the complex systems they are working with. At a certain level of interdependence, though, these standard approaches become ineffective." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"The most basic issue for organizational success is correctly matching a system’s complexity to its environment. When we want to accomplish a task, the complexity of the system performing that task must match the complexity of the task. In order to perform the matching correctly, one must recognize that each person has a limited level of complexity. Therefore, tasks become difficult because the complexity of a person is not large enough to handle the complexity of the task. The trick then is to distribute the complexity of the task among many individuals." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"There is a dual nature to engineering. Engineers are responsible for careful quantitative evaluation of how to achieve objectives, what to do to achieve them, and even (a task that most people find almost impossible) how long it will take to do the task. The other side of engineering is an independent creative 'cowboy'-type attitude characteristic of people breaking out of the mold, coming up with novel ideas, implementing them, and changing the world through new technology. This is the culture of high-tech innovation." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"There is no doubt that science has made great progress by taking things apart, but it has become increasingly clear that many important questions can only be addressed by thinking more carefully about relationships between and amongst the parts. Indeed, one of the main difficulties in answering questions or solving problems - any kind of problem - is that we think the problem is in the parts, when it is really in the relationships between them." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"What do people do today when they don’t understand 'the system'? They try to assign responsibility to someone to fix the problem, to oversee 'the system', to coordinate and control what is happening. It is time we recognized that 'the system' is how we work together. When we don’t work together effectively putting someone in charge by its very nature often makes things worse, rather than better, because no one person can understand 'the system' well enough to be responsible. We need to learn how to improve the way we work together, to improve 'the system' without putting someone in charge, in order to make things work." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"What is the solution to coordinating people to perform complex tasks? Analyzing the flows of information and the way tasks are distributed through the system can help. ultimately, however, the best solution is to create an environment where evolution can take place. Organizations that learn by evolutionary change create an environment of ongoing innovation. Evolution by competition and cooperation and the creation of composites of patterns of behavior is the way to synthesize effective systems to meet the complex challenges of today’s world." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004)

"When parts are acting independently, the fine scale behavior is more complex. When they are working together, the fine scale complexity is much smaller, but the behavior is on a larger scale. This means that complexity is always a trade-off, more complex at a large scale means simpler at a fine scale. This trade-off is a basic conceptual tool that we need in order to understand complex systems." (Yaneer Bar-Yam, "Making Things Work: Solving Complex Problems in a Complex World", 2004) 

✒️Karl E Weick - Collected Quotes

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

"In a loosely coupled system there is more room available for self-determination by the actors. If it is argued that a sense of efficacy is crucial for human beings. when a sense of efficacy might be greater in a loosely coupled system with autonomous units than it would be in a tightly coupled system where discretion is limited." (Karl E Weick, "Educational organizations as loosely coupled systems", 1976)

"Managers construct, rearrange, single out, and demolish many objective features of their surroundings. When people act they unrandomize variables, insert vestiges of orderliness, and literally create their own constraints." (Karl E Weick, "Social Psychology of Organizing", 1979)

"The typical coupling mechanisms of authority of office and logic of the task do not operate in educational organizations." (Karl E Weick, "Educational organizations as loosely coupled systems", 1976)

"Any approach to the study of organizations is built on specific assumptions about the nature of organizations and how they are designed and function." (Richard L Daft & Karl E Weick, "Toward a model of organizations as interpretation systems", Academy of Management Review Vol 9 (2), 1984)

"Action often creates the orderly relations that originally were mere presumptions summarized in a cause map. Thus language trappings of organizations such as strategic plans are important components in the process of creating order. They hold events together long enough and tightly enough in people's heads so that they act in the belief that their actions will be influential and make sense." (Karl E. Weick, "Organizational culture as a source of high reliability", 1987)

"An ordered set of assertions about a generic behavior or structure assumed to hold throughout a significantly broad range of specific instances." (Karl E Weick, "Theory construction as disciplined imagination", 1989)

"Experience is the consequence of activity. The manager literally wades into the swarm of 'events' that surround him and actively tries to unrandomize them and impose some order: The manager acts physically in the environment, attends to some of it, ignores most of it, talks to other people about what they see and are doing."  (Karl E Weick, "Sensemaking in Organizations", 1995)

"Organizations are presumed to talk to themselves over and over to find out what they are thinking." (Karl E Weick, "Sensemaking in Organizations", 1995)

"Sensemaking is about the enlargement of small cues. It is a search for contexts within which small details fit together and make sense. It is people interacting to flesh out hunches. It is a continuous alternation between particulars and explanations with each cycle giving added form and substance to the other." (Karl E Weick, "Sensemaking in Organizations", 1995)

"Sensemaking tends to be swift, which means we are more likely to see products than processes." (Karl E Weick, Sensemaking in Organizations, 1995)

"The organism or group enacts equivocal raw talk, the talk is viewed retrospectively, sense is made of it, and then this sense is stored as knowledge in the retention process. The aim of each process has been to reduce equivocality and to get some idea of what has occurred." (Karl E Weick, "Sensemaking in Organizations", 1995)

"The point we want to make here is that sensemaking is about plausibility, coherence, and reasonableness. Sensemaking is about accounts that are socially acceptable and credible... It would be nice if these accounts were also accurate. But in an equivocal, postmodern world, infused with the politics of interpretation and conflicting interests and inhabited by people with multiple shifting identities, an obsession with accuracy seems fruitless, and not of much practical help, either." (Karl E Weick, "Sensemaking in Organizations", 1995)

"To talk about sensemaking is to talk about reality as an ongoing accomplishment that takes form when people make retrospective sense of the situations in which they find themselves and their creations. There is a strong reflexive quality to this process. People make sense of things by seeing a world on which they already imposed what they believe. In other words, people discover their own inventions. This is why sensemaking can be understood as invention and interpretations understood as discovery. These are complementary ideas. If sensemaking is viewed as an act of invention, then it is also possible to argue that the artifacts it produces include language games and texts." (Karl E Weick, "Sensemaking in Organizations", 1995)

"When people perform an organized action sequence and are interrupted, they try to make sense of it. The longer they search, the higher the arousal, and the stronger the emotion. If the interruption slows the accomplishment of an organized sequence, people are likely to experience anger. If the interruption has accelerated accomplishment, then they are likely to experience pleasure. If people find that the interruption can be circumvented, they experience relief. If they find that the interruption has thwarted a higher level plan, then anger is likely to turn into rage, and if they find that the interruption has thwarted a minor behavioural sequence, they are likely to feel irritated." (Karl E Weick, "Sensemaking in Organizations", 1995)

"The basic idea of sensemaking is that reality is an ongoing accomplishment that emerges from efforts to create order and make retrospective sense of what occurs." (Karl E Weick, "The collapse of sensemaking in organizations: The Mann Gulch disaster", Administrative Science Quarterly 3, 1993)

✒️Fernando J Corbató - Collected Quotes

"Systems with unknown behavioral properties require the implementation of iterations which are intrinsic to the design process but which are normally hidden from view. Certainly when a solution to a well-understood problem is synthesized, weak designs are mentally rejected by a competent designer in a matter of moments. On larger or more complicated efforts, alternative designs must be explicitly and iteratively implemented. The designers perhaps out of vanity, often are at pains to hide the many versions which were abandoned and if absolute failure occurs, of course one hears nothing. Thus the topic of design iteration is rarely discussed. Perhaps we should not be surprised to see this phenomenon with software, for it is a rare author indeed who publicizes the amount of editing or the number of drafts he took to produce a manuscript." (Fernando J Corbató, "A Managerial View of the Multics System Development", 1977)

"Because one has to be an optimist to begin an ambitious project, it is not surprising that underestimation of completion time is the norm." (Fernando J Corbató, "On Building Systems That Will Fail", 1991)

"Design bugs are often subtle and occur by evolution with early assumptions being forgotten as new features or uses are added to systems." (Fernando J Corbató, "On Building Systems That Will Fail", 1991)

"It is important to emphasize the value of simplicity and elegance, for complexity has a way of compounding difficulties and as we have seen, creating mistakes. My definition of elegance is the achievement of a given functionality with a minimum of mechanism and a maximum of clarity." (Fernando J Corbató, "On Building Systems That Will Fail", 1991)

"The value of metaphors should not be underestimated. Metaphors have the virtue of an expected behavior that is understood by all. Unnecessary communication and misunderstandings are reduced. Learning and education are quicker. In effect metaphors are a way of internalizing and abstracting concepts allowing one's thinking to be on a higher plane and low-level mistakes to be avoided." (Fernando J Corbató, "On Building Systems That Will Fail", 1991)