Graphical Representation: On Insights II (The Complexity Perspective)

Graphical Representation Series

Scientists attempt to discover laws and principles, and for this they conduct experiments, build theories and models rooted in the data they collect. In the business setup, data professionals analyze the data for identifying patterns, trends, outliers or anything else that can lead to new information or knowledge. On one side scientists chose the boundaries of the systems they study, while for data professionals even if the systems are usually given, they can make similar choices. 

In theory, scientists are more flexible in what data they collect, though they might have constraints imposed by the boundaries of their experiments and the tools they use. For data professionals most of the data they need is already there, in the systems the business uses, though the constraints reside in the intrinsic and extrinsic quality of the data, whether the data are fit for the purpose. Both parties need to work around limitations, or attempt to improve the experiments, respectively the systems. 

Even if the data might have different characteristics, this doesn't mean that the methods applied by data professionals can't be used by scientists and vice-versa. The closer data professionals move from Data Analytics to Data Science, the higher the overlap between the business and scientific setup. 

Conversely, the problems data professionals meet have different characteristics. Scientists outlook is directed mainly at the phenomena and processes occurring in nature and society, where randomness, emergence and chaos seem to feel at home. Business processes deal more with predefined controlled structures, cyclicity, higher dependency between processes, feedback and delays. Even if the problems may seem to be different, they can be modeled with systems dynamics. 

Returning to data visualization and the problem of insight, there are multiple questions. Can we use simple designs or characterizations to find the answer to complex problems? Which must be the characteristics of a piece of information or knowledge to generate insight? How can a simple visualization generate an insight moment? 

Appealing to complexity theory, there are several general approaches in handling complexity. One approach resides in answering complexity with complexity. This means building complex data visualizations that attempt to model problem's complexity. For example, this could be done by building a complex model that reflects the problem studied, and build a set of complex visualizations that reflect the different important facets. Many data professionals advise against this approach as it goes against the simplicity principle. On the other hand, starting with something complex and removing the nonessential can prove to be an approachable strategy, even if it involves more effort. 

Another approach resides in reducing the complexity of the problem either by relaxing the constraints, or by breaking the problem into simple problems and addressing each one of them with visualizations. Relaxing the constraints allow studying upon case a more general problem or a linearization of the initial problem. Breaking down the problem into problems that can be easier solved, can help to better understand the general problem though we might lose the sight of emergence and other behavior that characterize complex systems.

Providing simple visualizations to complex problems implies a good understanding of the problem, its solution(s) and the overall context, which frankly is harder to achieve the more complex a problem is. For its understanding a problem requires a minimum of knowledge that needs to be reflected in the visualization(s). Even if some important aspects are assumed as known, they still need to be confirmed by the visualizations, otherwise any deviation from assumptions can lead to a new problem. Therefore, its questionable that simple visualizations can address the complexity of the problems in a general manner. 

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Project Management: Projects' Dynamics II (Motion)

Motion is the action or process of moving or being moved between an initial and a final or intermediate point. From the tinniest endeavors to the movement of the planets and beyond, everything is governed by motion. If the laws of nature seem to reveal an inner structural perfection, the activities people perform are quite often far from perfect, which is acceptable if we consider that (almost) everything is a learning process. What is probably less acceptable is the volume of inefficient motion we can easily categorize sometimes as waste.

The waste associated with motion can take many forms: sorting through a pile of tools to find the right one, searching for information, moving back and forth to reach a destination or achieve a goal, etc. Suboptimal motion can have important effects for an organization resulting in reduced productivity, respectively higher costs.

If for repetitive activities that involve a certain degree of similarity can be found typically a way to optimize the motion, the higher the uncertainty of the steps involved, the more difficult it becomes to optimize it. It’s the case of discovery endeavors in which the path between start and destination can’t be traced beforehand, respectively when the destination or path in between can’t be depicted to the needed level of detail. A strategy’s implementation, ERP implementations and other complex projects, especially the ones dealing with new technologies and/or incomplete knowledge, tend to be exploratory in nature and thus fall under this latter type a motion.

In other words, one must know at minimum the starting point, the destination, how to reach it and what it takes to reach it – resources, knowledge, skillset. When one has all this information one can go on and estimate how long it will take to reach the destination, though the estimate reflects the information available as well estimator’s skills in translating the information into a realistic roadmap. Each new information has the potential of impacting considerably the whole process, in extremis to the degree that one must start the journey anew. The complexity of such projects and the volume of uncertainty can make estimation difficult if not impossible, no matter how good estimators' skills are. At best an estimator can come with a best- and worst-case estimation, both however dependent on the assumptions made.

Moreover, complex projects are sensitive to the initial conditions or auspices under which they start. This sensitivity can turn a project in a totally different direction or pace, that can be reinforced positively or negatively as the project progresses. It’s a continuous interplay between internal and external factors and components that can create synergies or have adverse effects with the potential of reaching tipping points.

Related to the initial conditions, as the praxis sometimes shows, for entities found in continuous movement (like organizations) it’s also important to know from where one’s coming (and at what speed), as the previous impulse (driving force) can be further used or stirred as needed. Metaphorically, a project will need a certain time to find the right pace if it lacks the proper impulse.

Unless the team is trained to play and plays like an orchestra, the impact of deviations from expectations can be hardly quantified. To minimize the waste, ideally a project’s journey should minimally deviate from the optimal path, which can be challenging to achieve as a project’s mass can pull the project in one direction or the other. The more the project advances the bigger the mass, fact which can make a project unstoppable. When such high-mass projects are stopped, their impulse can continue to haunt the organization years after.

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Project Management: Projects' Dynamics I (Introduction)

Despite the considerable collection of books on Project Management (PM) and related methodologies, and the fact that projects are inherent endeavors in professional as well personal life (setups that would give in theory people the environment and exposure to different project types), people’s understanding on what it takes to plan and execute a project seems to be narrow and questionable sometimes. Moreover, their understanding diverges considerably from common sense. It’s also true that knowledge and common sense are relative when considering any human endeavor in which there are multiple roads to the same destination, or when learning requires time, effort, skills, and implies certain prerequisites, however the lack of such knowledge can hurt when endeavor’s success is a must and a team effort. 

Even if the lack of understanding about PM can be considered as minor when compared with other challenges/problems faced by a project, when one’s running fast to finish a race, even a small pebble in one’s running shoes can hurt a lot, especially when one doesn’t have the luxury to stop and remove the stone, as it would make sense to do.

It resides in the human nature to resist change, to seek for information that only confirm own opinions, to follow the same approach in handling challenges, even if the attempts are far from optimal, even if people who walked the same path tell you that there’s a better way and even sketch the path and provide information about what it takes to reach there. As it seems, there’s the predisposition to learn on the hard way, if there’s significant learning involved at all. Unfortunately, such situations occur in projects and the solutions often overrun the boundaries of PM, where social and communication skills must be brought into play. 

On the other side, there’s still hope that change can be managed optimally once the facts are explained to a certain level that facilitates understanding. However, such an attempt can prove to be quite a challenge, given the various setups in which PM takes place. The intersection between technologies and organizational setups lead to complex scenarios which make such work more difficult, even if projects’ challenges are of organizational rather than technological nature. 

When the knowledge we have about the world doesn’t fit our expectation, a simple heuristic is to return to the basics. A solid edifice can be built only on a solid foundation and the best foundation in coping with reality is to establish common ground with other people. One can achieve this by identifying their suppositions and expectations, by closing the gap in perception and understanding, by establishing a basis for communication, in which feedback is a must if one wants to make significant progress.

Despite of being explorative and time-consuming, establishing common ground can be challenging when addressing to an imaginary audience, which is quite often the situation. The practice shows however that progress can be made by starting with a set of well-formulated definitions, simple models, principles, and heuristics that have the potential of helping in sense-making.

The goal is thus to identify first the definitions that reflect the basic concepts that need to be considered. Once the concepts defined, they can be related to each other with the help of a few models. Even if fictitious, as simplifications of the reality, the models should allow playing with the concepts, facilitating concepts’ understanding. Principles (set of rules for reasoning) can be used together with heuristics (rules of thumb methods or techniques) for explaining the ‘known’ and approaching the ‘unknown’. Even maybe not perfect, these tools can help building theories or explanatory constructs.

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Data Science: Data Mining (Just the Quotes)

"Data mining is the efficient discovery of valuable, nonobvious information from a large collection of data. […] Data mining centers on the automated discovery of new facts and relationships in data. The idea is that the raw material is the business data, and the data mining algorithm is the excavator, sifting through the vast quantities of raw data looking for the valuable nuggets of business information." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"Data mining is more of an art than a science. No one can tell you exactly how to choose columns to include in your data mining models. There are no hard and fast rules you can follow in deciding which columns either help or hinder the final model. For this reason, it is important that you understand how the data behaves before beginning to mine it. The best way to achieve this level of understanding is to see how the data is distributed across columns and how the different columns relate to one another. This is the process of exploring the data." (Seth Paul et al. "Preparing and Mining Data with Microsoft SQL Server 2000 and Analysis", 2002)

"Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algorithms are more scalable than statisticians ever thought possible. Formal statistical theory is more pervasive than computer scientists had realized." (Larry A Wasserman, "All of Statistics: A concise course in statistical inference", 2004)

"Most mainstream data-mining techniques ignore the fact that real-world datasets are combinations of underlying data, and build single models from them. If such datasets can first be separated into the components that underlie them, we might expect that the quality of the models will improve significantly. (David Skillicorn, "Understanding Complex Datasets: Data Mining with Matrix Decompositions", 2007)

"The name ‘data mining’ derives from the metaphor of data as something that is large, contains far too much detail to be used as it is, but contains nuggets of useful information that can have value. So data mining can be defined as the extraction of the valuable information and actionable knowledge that is implicit in large amounts of data. (David Skillicorn, "Understanding Complex Datasets: Data Mining with Matrix Decompositions", 2007)

"Compared to traditional statistical studies, which are often hindsight, the field of data mining finds patterns and classifications that look toward and even predict the future. In summary, data mining can (1) provide a more complete understanding of data by finding patterns previously not seen and (2) make models that predict, thus enabling people to make better decisions, take action, and therefore mold future events." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"Traditional statistical studies use past information to determine a future state of a system (often called prediction), whereas data mining studies use past information to construct patterns based not solely on the input data, but also on the logical consequences of those data. This process is also called prediction, but it contains a vital element missing in statistical analysis: the ability to provide an orderly expression of what might be in the future, compared to what was in the past (based on the assumptions of the statistical method)." (Robert Nisbet et al, "Handbook of statistical analysis and data mining applications", 2009)

"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)

"Data mining is a craft. As with many crafts, there is a well-defined process that can help to increase the likelihood of a successful result. This process is a crucial conceptual tool for thinking about data science projects. [...] data mining is an exploratory undertaking closer to research and development than it is to engineering." (Foster Provost, "Data Science for Business", 2013)

"There is another important distinction pertaining to mining data: the difference between (1) mining the data to find patterns and build models, and (2) using the results of data mining. Students often confuse these two processes when studying data science, and managers sometimes confuse them when discussing business analytics. The use of data mining results should influence and inform the data mining process itself, but the two should be kept distinct." (Foster Provost & Tom Fawcett, "Data Science for Business", 2013)

"Unfortunately, creating an objective function that matches the true goal of the data mining is usually impossible, so data scientists often choose based on faith and experience." (Foster Provost, "Data Science for Business", 2013)

"Data Mining is the art and science of discovering useful innovative patterns from data. (Anil K. Maheshwari, "Business Intelligence and Data Mining", 2015)

"Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more. Each of these is used by different communities and has different associations. Some have a long half-life, some less so." (Pedro Domingos, "The Master Algorithm", 2015)

"Today we routinely learn models with millions of parameters, enough to give each elephant in the world his own distinctive wiggle. It’s even been said that data mining means 'torturing the data until it confesses'." (Pedro Domingos, "The Master Algorithm", 2015)

"Data analysis and data mining are concerned with unsupervised pattern finding and structure determination in data sets. The data sets themselves are explicitly linked as a form of representation to an observational or otherwise empirical domain of interest. 'Structure' has long been understood as symmetry which can take many forms with respect to any transformation, including point, translational, rotational, and many others. Symmetries directly point to invariants, which pinpoint intrinsic properties of the data and of the background empirical domain of interest. As our data models change, so too do our perspectives on analysing data." (Fionn Murtagh, "Data Science Foundations: Geometry and Topology of Complex Hierarchic Systems and Big Data Analytics", 2018)

"The goal of data science is to improve decision making by basing decisions on insights extracted from large data sets. As a field of activity, data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting nonobvious and useful patterns from large data sets. It is closely related to the fields of data mining and machine learning, but it is broader in scope." (John D Kelleher & Brendan Tierney, "Data Science", 2018)

Data Science: Mathematical Model (Definitions)

"A mathematical model is any complete and consistent set of mathematical equations which are designed to correspond to some other entity, its prototype. The prototype may be a physical, biological, social, psychological or conceptual entity, perhaps even another mathematical model."  (Rutherford Aris, "Mathematical Modelling", 1978)

"The identification and selection of important descriptor variables to be used within an equation or process that can generate useful predictions." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"Mathematical model is an abstract model that describes a problem, environment, or system using a mathematical language." (Giusseppi Forgionne & Stephen Russell, "Unambiguous Goal Seeking Through Mathematical Modeling", 2008)

"A set of equations, usually ordinary differential equations, the solution of which gives the time course behaviour of a dynamical system." (Peter Wellstead et al, "Systems and Control Theory for Medical Systems Biology", 2009)

"An abstract model that uses mathematical language to describe the behaviour of a system. Mathematical models are used particularly in the natural sciences and engineering disciplines (such as physics, biology, and electrical engineering) but also in the social sciences (such as economics, sociology and political science). It can be defined as the representation of the essential aspects of an existing system (or a system to be constructed) which presents knowledge of that system in usable form." (Roberta Alfieri & Luciano Milanesi, "Multi-Level Data Integration and Data Mining in Systems Biology", Handbook of Research on Systems Biology Applications in Medicine, 2009)

"Mathematical description of a physical system. In the framework of this work mathematical models pursue the descriptions of mechanisms underlying stuttering, putting emphasis in the dynamics of neuronal regions involved in the disorder." (Manuel Prado-Velasco & Carlos Fernández-Peruchena "An Advanced Concept of Altered Auditory Feedback as a Prosthesis-Therapy for Stuttering Founded on a Non-Speech Etiologic Paradigm", 2011)

"Simplified description of a real world system in mathematical terms, e. g., by means of differential equations or other suitable mathematical structures." (Benedetto Piccoli, Andrea Tosin, "Vehicular Traffic: A Review of Continuum Mathematical Models" [Mathematics of Complexity and Dynamical Systems, 2012])

"Stated loosely, models are simplified, idealized and approximate representations of the structure, mechanism and behavior of real-world systems. From the standpoint of set-theoretic model theory, a mathematical model of a target system is specified by a nonempty set - called the model’s domain, endowed with some operations and relations, delineated by suitable axioms and intended empirical interpretation." (Zoltan Domotor, "Mathematical Models in Philosophy of Science" [Mathematics of Complexity and Dynamical Systems, 2012])

"The standard view among most theoretical physicists, engineers and economists is that mathematical models are syntactic (linguistic) items, identified with particular systems of equations or relational statements. From this perspective, the process of solving a designated system of (algebraic, difference, differential, stochastic, etc.) equations of the target system, and interpreting the particular solutions directly in the context of predictions and explanations are primary, while the mathematical structures of associated state and orbit spaces, and quantity algebras – although conceptually important, are secondary." (Zoltan Domotor, "Mathematical Models in Philosophy of Science" [Mathematics of Complexity and Dynamical Systems, 2012])

"They are a set of mathematical equations that explain the behaviour of the system under various operating conditions, and determine the dominant factors that govern the rules of the process. Mathematical modeling is also associated with data collection, data interpretation, parameter estimation, optimization, and provide tools for identifying possible approaches to control and for assessing the potential impact of different intervention measures." (Eldon R Rene et al, "ANNs for Identifying Shock Loads in Continuously Operated Biofilters", 2012)

"An abstract representation of the real-world system using mathematical concepts." (R Sridharan & Vinay V Panicker, "Ant Colony Algorithm for Two Stage Supply Chain", 2014)

"Is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modelling. Mathematical models can take many forms, including but not limited to dynamical systems, statistical models, differential equations, or game theoretic models. A model may help to explain a system and to study the effects of different components, and to make predictions about behaviour." (M T Benmessaoud et al, "Modeling and Simulation of a Stand-Alone Hydrogen Photovoltaic Fuel Cell Hybrid System", 2014)

"A mathematical model is a model built using the language and tools of mathematics. A mathematical model is often constructed with the aim to provide predictions on the future ‘state’ of a phenomenon or a system." (Crescenzio Gallo, "Artificial Neural Networks Tutorial", 2015)

"A mathematical model consists of an equation or a set of equations belonging to a certain class of mathematical models to describe the dynamic behavior of the corresponding system. The parameters involved in this mathematical model are related to a certain mathematical structure. This mathematical model is characterized by its class, its structure and its parameters." (Houda Salhi & Samira Kamoun, "State and Parametric Estimation of Nonlinear Systems Described by Wiener Sate-Space Mathematical Models", 2015)

"Description of a system using mathematical concepts and language." (Tomaž Kramberger, "A Contribution to Better Organized Winter Road Maintenance by Integrating the Model in a Geographic Information System", 2015)

"A description of a system using mathematical concepts and language." (Corrado Falcolini, "Algorithms for Geometrical Models in Borromini's San Carlino alle Quattro Fontane", 2016)

"A mathematical model is a mathematical description (often by means of a function or an equation) of a real-world phenomenon such as the size of a population, the demand for a product, the speed of a falling object, the concentration of a product in a chemical reaction, the life expectancy of a person at birth, or the cost of emission reductions. The purpose of the model is to understand the phenomenon and perhaps to make predictions about future behavior. [...] A mathematical model is never a completely accurate representation of a physical situation - it is an idealization." (James Stewart, "Calculus: Early Transcedentals" 8th Ed., 2016)

"Mathematical representation of a system to describe the behavior of certain variables for an indeterminate time." (Sergio S Juárez-Gutiérrez et al, "Temperature Modeling of a Greenhouse Environment", 2016)

"A mathematical model is a description of a system using mathematical concepts and language. The process of developing a mathematical model is termed mathematical modeling. Mathematical models are used not only in the natural sciences (such as physics, biology, earth science, meteorology) and engineering disciplines (e.g., computer science, artificial intelligence), but also in the social sciences (such as economics, psychology, sociology, and political science); physicists, engineers, statisticians, operations research analysts, and economists use mathematical models most extensively. A model may help to explain a system and to study the effects of different components, and to make predictions about behavior." (Addepalli V N Krishna & M Balamurugan, "Security Mechanisms in Cloud Computing-Based Big Data", 2019)

"A description of a system using mathematical symbols." (José I Gomar-Madriz et al, "An Analysis of the Traveling Speed in the Traveling Hoist Scheduling Problem for Electroplating Processes", 2020)

"An abstract mathematical representation of a process, device, or concept; it uses a number of variables to represent inputs, outputs and internal states, and sets of equations and inequalities to describe their interaction." (Alisher F Narynbaev, "Selection of an Information Source and Methodology for Calculating Solar Resources of the Kyrgyz Republic", 2020)

Strategic Management: Strategy & Tactics in Chess (Just the Quotes)

"If we assume that strategy consists in defining the object and making plans accordingly, and tactics in executing these plans, then we must look upon combination as the culmination of tactics. By combination we mean a short part of the game, within which a certain purpose is attained by force. Its sequence of moves forms a logical chain and cannot be divided up. When looked at one by one, they may seem to be purposeless or even mistakes, yet together they form an exceedingly beautiful unit. After a series of moves incomprehensible by themselves, the solution suddenly follows and their real purpose comes clearly to light. From this it follows that the aim must have already been conceived .from the first move of the combination. This is the difference between combinative and straightforward play. For a short space of time special and not general rules apply; as it were an exceptional state of things prevails." (Dr. Max Euwe, "Strategy & Tactices in chess", 1937)

"In chess we distinguish between Strategy and Tactics. Strategy is concerned with the setting of an aim and the forming of schemes. Tactics are concerned with the execution of the schemes. Strategy is abstract, tactics are concrete. Expressing it in a popular way: Strategy requires thought, tactics require observation." (Dr. Max Euwe, "Strategy & Tactices in chess", 1937)

"Notwithstanding the obviously great importance of tactics, nearly all existing manuals give greater prominence to strategica1 problems. This is principally due to two reasons: Firstly: the development of tactical capabilities is for the greater part a matter of practice and a question of routine. Secondly: the problems of tactics are so numerous and so varied in nature, that it seems an almost impossible undertaking to treat this domain systematically." (Dr. Max Euwe, "Strategy & Tactices in chess", 1937)

"We call games such as the preceeding one, in which strategy plays such an important part, positional games, in contrast to combinative games, in which the strategy is of secondary importance. One must not, however, identify strategy with positional play, for strategy is an aim and positional play represents a certain method of playing. The study of positional play teaches us the strategic lines." (Dr. Max Euwe, "Strategy & Tactices in chess", 1937)

"We have stated that strategy forms an indispensable element in the proper treatment of a game of chess; the same can be said, perhaps even with greater reason, about tactics. The chess-player who can judge a position very clearly and who can adapt his schemes to this position, will not be able to make use of these capabilities, if at the same time he is not well practised in tactics. As a rule a tactical mistake involves a much heavier punishment than a strategical one.
Nor are the conceptions of tactics and combinative play identical. One might say that tactics comprise all the moves in a game, and thus include also the combinations which occur in it." (Dr. Max Euwe, "Strategy & Tactices in chess", 1937)

"We make a difference between general and special principles of strategy. The general principles originate directly from the aim and the nature of Chess, and therefore they are constantly in force. It is, for instance, a general principle which goes without saying, that one has to procure the greatest possible freedom of action for one's men. The special principles apply only if the position shows certain peculiarities on account of which a special line of strategy has to be followed." (Dr. Max Euwe, "Strategy & Tactices in chess", 1937)

"Strategy means abstract thinking and planning, as opposed to tactics, which are the individual operations used to implement strategy. Tactics are specific; strategy is general. Tactics tend to be immediate, strategy long-term." (Bruce Pandolfini, "Weapons of Chess: An omnibus of chess strategy", 1989)

"Castling may even be unnecessary. In some cases it might be prudent to keep the king in the center, possibly not castling at all. The center is often safer than the flank in closed positions. Strategy and long-term planning assume great importance . You usually have time to maneuver against your opponent's weaknesses, something hard to do when the center is open." (Bruce Pandolfini, "Weapons of Chess: An omnibus of chess strategy", 1989)

"A weakness can be tactical or positional. A tactical weakness is one based on immediate or temporary circumstances. Given time, a tactical weakness can be defended or eliminated: protection can be added, or the threatened man moved to safety. [...] When people talk about weakness, they usually mean positional, not tactical weakness. Positional weaknesses tend to have long-term ramifications. They are not subject to the shifting situation from move to move as are tactical weaknesses. It takes more than a few moves to correct a positional weakness, if it can be done at all." (Bruce Pandolfini, "Weapons of Chess: An omnibus of chess strategy", 1989)

"The strengths, often concealed below the surface, depend on the activity and interrelationship of the pieces, and on the tactical possibilities and threats that can be generated. Bringing these latent resources to the surface requires a deep and penetrating analysis and accurate calculation." (Bruce Pandolfini, "Weapons of Chess: An omnibus of chess strategy", 1989)

"[...] events on the chessboard are by no means accidental, on the contrary: everything is linked with everything and the dynamics involved in selecting a correct strategic plan reflects, delicately but also accurately, your dynamic thinking abilities!" (Vlastimil Jansa, "Dynamics of Chess Strategy", 2003)

"Every player appreciating the aesthetics of chess dreams about games where a single main strategical plan is carried through without distraction to a successful conclusion; these should go hand in hand with the competitive aspects of chess. Such a course is very difficult because our opponent usually has his own plan, his own strategy and intentions - which are very different from ours. A flawless game won by somebody probably does not exist, a mistake must creep into the game somewhere." (Vlastimil Jansa, "Dynamics of Chess Strategy", 2003)

"However, the dynamic changes and laws should be understood, not only from the point of view of strategy and tactics but also from that of the time of the development of ideas." (Vlastimil Jansa, "Dynamics of Chess Strategy", 2003)

"Opening presents opportunities for the most complicated but also the most fantastic strategic and tactical manoeuvres. In both cases the prerequisite is an accurate rhythm, first, a slow, wait-and-see type, then, at the right moment, a rapid one with surprising turns [...] An average dancer does not have the gusto for it - nor does a chess player without a sufficient sense of dynamics!" (Vlastimil Jansa, "Dynamics of Chess Strategy", 2003)

"Strategy and tactics often work hand in glove." (Vlastimil Jansa, "Dynamics of Chess Strategy", 2003)

"The ability to think dynamically in opening strategy may also be illustrated by a mastery of transferring experience and ideas from one opening to another. Seemingly incoherent courses of the game may sometimes have much in common. And sometimes a simple comparison brings new experience, surprising findings and thoughts!" (Vlastimil Jansa, "Dynamics of Chess Strategy", 2003)

"A tactician feels at home reacting to threats and seizing opportunities on the battlefield. When your opponent has blundered, a winning tactic can suddenly appear and serve as both means and end. […] Every time you make a move, you must consider your opponent’s response, your answer to that response, and so on. A tactic ignites an explosive chain reaction, a forceful sequence of moves that carries the players along on a wild ride. You analyze the position as deeply as you can, compute the dozens of variations, the hundreds of positions. If you don’t immediately exploit a tactical opportunity, the game will almost certainly turn against you; one slip and you are wiped out. But if you seize the opportunities that your strategy creates, you’ll play your game like a Grandmaster." (Garry Kasparov, "How Life Imitates Chess", 2007)

"Against solid strategy, diversionary tactics will either be insufficient, or flawed. If they are insufficient, you can and should ignore them, continuing along your path. If they are radical enough to force you from your path, they are likely flawed in some way - unless you have blundered. Often an opponent is so eager to get you to change your course that he fatally weakens his own position in the attempt." (Garry Kasparov, "How Life Imitates Chess", 2007)

"Effective tactics result from alertness and speed, this is obvious, but they also require an understanding of all the possibilities at hand. Experience allows us to instantly apply the patterns we have successfully used in the past." (Garry Kasparov, "How Life Imitates Chess", 2007)

"In chess we see many cases of good strategy failing due to bad tactics and vice versa. A single oversight can undo the most brilliant concepts. Even more dangerous in the long run are cases of bad strategy succeeding due to good tactics, or due to sheer good fortune. This may work once, but rarely twice. This is why it is so important to question success as vigorously as you question failure." (Garry Kasparov, "How Life Imitates Chess", 2007)

"Tactics involve calculations that can tax the human brain, but when you boil them down, they are actually the simplest part of chess and are almost trivial compared to strategy." (Garry Kasparov, "How Life Imitates Chess: Making the Right Moves, from the Board to the Boardroom", 2007)

"Whereas strategy is abstract and based on long-term goals, tactics are concrete and based on finding the best move right now. Tactics are conditional and opportunistic, all about threat and defense. No matter what pursuit you’re engaged in - chess, business, the military, managing a sports team - it takes both good tactics and wise strategy to be successful." (Garry Kasparov, "How Life Imitates Chess", 2007)

"A chess hypothesis is basically the equivalent to drawing up a strategic plan. Experimentation in chess is equivalent to the moves that are found to carry out each plan. Throughout the history of chess, both the plans (the hypotheses) as well as the moves (the experiments) have been evolving (thanks to results from the practice of the game and from analyses), and this knowledge is the patrimony of professional players." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"Finally, chess has a science - like special attraction since it lets the player first propose hypotheses of different strategic plans that are based on the game rules and possible moves of the pieces and then refute those hypotheses after careful investigation of the different lines of play. This process is analogous to the everyday work of a scientist." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"Still, it is good to realize how the synthesis between strategy and tactics is established. Therefore, we must always consider the activity of the pieces on both sides. A player may have a fantastic pawn formation, control a beautiful open file and possess a strong square, but if he goes down to a direct mating attack on the other side of the board, all his strategic advantages come to naught." (Herman Grooten, "Chess Strategy for Club Players: The Road to Positional Advantage", 2009)

"tactics and strategy hold together, as it were. With tactics, a combination is a 'random picture' in the game. For example, a piece is unprotected and this can immediately be exploited. Strategy means longer-term thinking." (Herman Grooten, "Chess Strategy for Club Players: The Road to Positional Advantage", 2009)

"The problem of identifying the subset of good moves is much more complicated than simply counting the total number of possibilities and falls completely into the domain of strategy and tactics of chess as a game." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"Vision is a capacity to understand a position and to generate solid strategic plans. And a good base of chess knowledge is needed to understand what it means to play with brilliance or elegance." (Diego Rasskin-Gutman, "Chess Metaphors: Artificial Intelligence and the Human Mind", 2009)

"[...] we can gather that strategy and tactics are in constant conflict with each other. For example, a strategically beautiful set-up can be spoilt by an 'accidental combination' at any moment. But it is also true that a well-built-up game in most cases needs to be crowned with a tactical turn. In practice it has turned out that many players find it difficult to combine long-term play with a timely discovery of tactical finesses. Only the strongest players manage to find a kind of balance between these two components of the game." (Herman Grooten, "Chess Strategy for Club Players: The Road to Positional Advantage", 2009)

"Strategy must show us how to fight against players of our own strength and how to make progress. It must help us play a bit better this week than last week, not just by studying opening variations because, as Petrosian pointed out: 'to study opening variations without reference to the strategic concepts that develop from them in the middlegame is, in effect, to separate the head from the body." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"A good strategy is one that takes into account not only the requirements of the position, but also the opponent's strategy and tactics. Strategy lies between science and art. It supports the ability to evaluate positions, recognize patterns and imagine adequate plans. Modern chess both offers and requires more 'move-to-move' combat." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"Chess strategy must lose some of its grandness, some of its sentential character, must come back to earth and become practical, so that we can make it work for us move by move." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"Discovering the truth about a specific variation is a trial-and-error process. A complete strategy must consider human reactions as part of it." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"Dynamic strategy is not only a reconciliation of classical strategy with the new problems raised in competitive chess, but also an escape from the circle of slow manoeuvres which rarely produce more than half points. It aims to promote fighting and uncompromising chess. The history of chess shows clearly that players who are afraid of losing rarely obtain outstanding results. Dynamic strategy is an attempt to revise the standard, static, conservative opinion on what strategy should be (a collection of rules about the centre, fixed pawn structure, open files, minority attack, queen- or kingside majority, two bishops, weak pawns and squares, and the standard plans to cope with these occurrences) and highlight strategy as a continuous process dealing with the present, the near future, and the distant future of modern chess positions." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"Some theoreticians define tactics as combinations with sacrifices." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"Strategy places an arsenal of efficient weapons in our conscious as well as (hopefully) our subconscious mind; the better this arsenal, the less superficial the assessment of the actual and emergent set-ups. By associating the general aspects of a position with some concepts, our strategic knowledge builds the foundations for suitable plans." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"Strategy should neither be imparted nor perceived as a surrogate for ultimate truth, but as an organizer which turns an amorphous mass of perceptual concepts into a collection." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"Strategy shouldn't set rules to be followed blindly; nonetheless, any theory can declare its stepping-stone concepts." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"The main goal of dynamic strategy is to develop the personality of a player, to discover everyone's uniqueness and turn it to good account. Social dynamism and the human personality are two assets of our day, and this is reflected in chess. Tactics and dynamics are becoming predominant in chess. (Mihai Suba, "Dynamic Chess Strategy", 2010)

"The true task of strategy is to help evaluate chess positions, their present state and their future possibilities. The ability to evaluate correctly is at least as important as tactical skill. Evaluation is, to an extent, subjective (it depends on which side of the board Tal is on!) and a strategy which claims 100% objectivity is utopian. A player must be flexible enough to adapt himself to the strategy, the strategy must be flexible enough to adapt itself to the player and both, in turn, must be flexible enough to adapt themselves to the position." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"The basic functions of strategy are to help the player: to reach a superficial evaluation of the position; to understand and/ or anticipate the opponent's plan; to understand and/ or anticipate the opponent's other weapons, especially when his play doesn't seem to obey a logical plan; to establish his own plan." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"In chess we use the term strategy as opposed to tactics and we assimilate it with positional play. I must show from the beginning that the use of these terms can be confusing. Strategy is not the opposite of tactics; it is the theory of tactics. It tries to clarify the general lines of tactics as well as its directions. In other words, it gives pointers for discovering plans, and plans, in their turn, direct the moves in given positions." (Mihai Suba, "Dynamic Chess Strategy", 2010)

"Appreciation of both strategy and tactics are the 'Ying and Yang' of a strong chess player. They sit side by side, in harmony with each other and you cannot hope to improve your play without working on both aspects of the game." (Adam Hunt, "Chess Strategy: Move by move", 2013)

"Chess strategy is concerned with the correct evaluation of a position and the formulation of an effective plan based upon its characteristic features. When the word 'strategy' is mentioned, people tend to jump to the conclusion that we are talking about long-term middlegame planning, and that you have to be some sort of psychic who can see the future, but that is rarely the case. Many features of a given position which are strategically important will evolve out of the opening and finish in the endgame, so it would be foolish to completely ignore these phases of the game. Plans can be short or long term, depending on how the landscape in front of you is changing as the game progresses. This is why the subject is so complex and so much material is available on it." (Adam Hunt, "Chess Strategy: Move by move", 2013)

Systems Engineering: Emergence (Just the Quotes)

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

"[…] self-organization is the spontaneous emergence of new structures and new forms of behavior in open systems far from equilibrium, characterized by internal feedback loops and described mathematically by nonlinear equations." (Fritjof Capra, "The web of life: a new scientific understanding of living systems", 1996)

"It may not be obvious at first, but the study of emergence and model-building go hand in hand. The essence of model-building is shearing away detail to get at essential elements. A model, by concentrating on selected aspects of the world, makes possible the prediction and planning that reveal new possibilities. That is exactly the problem we face in trying to develop a scientific understanding of emergence." (John H Holland, "Emergence" , Philosophica 59, 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)

"Emergent self-organization in multi-agent systems appears to contradict the second law of thermodynamics. This paradox has been explained in terms of a coupling between the macro level that hosts self-organization (and an apparent reduction in entropy), and the micro level (where random processes greatly increase entropy). Metaphorically, the micro level serves as an entropy 'sink', permitting overall system entropy to increase while sequestering this increase from the interactions where self-organization is desired." (H Van Dyke Parunak & Sven Brueckner, "Entropy and Self-Organization in Multi-Agent Systems", Proceedings of the International Conference on Autonomous Agents, 2001)

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

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

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

"Emergence is the phenomenon of properties, capabilities and behaviours evident in the whole system that are not exclusively ascribable to any of its parts." (Derek Hitchins, "Advanced Systems Thinking, Engineering and Management", 2003)

"Another typical feature of theories of emergence is the layered view of nature. On this view, all things in nature belong to a certain level of existence, each according to its characteristic properties. These levels of existence constitute a hierarchy of increasing complexity that also corresponds to their order of appearance in the course of evolution." (Markus Eronen, "Emergence in the Philosophy of Mind", 2004)

"The basic concept of complexity theory is that systems show patterns of organization without organizer (autonomous or self-organization). Simple local interactions of many mutually interacting parts can lead to emergence of complex global structures. […] Complexity originates from the tendency of large dynamical systems to organize themselves into a critical state, with avalanches or 'punctuations' of all sizes. In the critical state, events which would otherwise be uncoupled became correlated." (Jochen Fromm, "The Emergence of Complexity", 2004)

"Complexity arises when emergent system-level phenomena are characterized by patterns in time or a given state space that have neither too much nor too little form. Neither in stasis nor changing randomly, these emergent phenomena are interesting, due to the coupling of individual and global behaviours as well as the difficulties they pose for prediction. Broad patterns of system behaviour may be predictable, but the system's specific path through a space of possible states is not." (Steve Maguire et al, "Complexity Science and Organization Studies", 2006)

"The beauty of nature insists on taking its time. Everything is prepared. Nothing is rushed. The rhythm of emergence is a gradual, slow beat; always inching its way forward, change remains faithful to itself until the new unfolds in the full confidence of true arrival. Because nothing is abrupt, the beginning of spring nearly always catches us unawares. It is there before we see it; and then we can look nowhere without seeing it. (John O'Donohue, "To Bless the Space Between Us: A Book of Blessings", 2008)

"Although the potential for chaos resides in every system, chaos, when it emerges, frequently stays within the bounds of its attractor(s): No point or pattern of points is ever repeated, but some form of patterning emerges, rather than randomness. Life scientists in different areas have noticed that life seems able to balance order and chaos at a place of balance known as the edge of chaos. Observations from both nature and artificial life suggest that the edge of chaos favors evolutionary adaptation." (Terry Cooke-Davies et al, "Exploring the Complexity of Projects", 2009)

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

"The notion of emergence is used in a variety of disciplines such as evolutionary biology, the philosophy of mind and sociology, as well as in computational and complexity theory. It is associated with non-reductive naturalism, which claims that a hierarchy of levels of reality exist. While the emergent level is constituted by the underlying level, it is nevertheless autonomous from the constituting level. As a naturalistic theory, it excludes non-natural explanations such as vitalistic forces or entelechy. As non-reductive naturalism, emergence theory claims that higher-level entities cannot be explained by lower-level entities." (Martin Neumann, "An Epistemological Gap in Simulation Technologies and the Science of Society", 2011)

"System theorists know that it's easy to couple simple-to-understand systems into a ‘super system’ that's capable of displaying behavioral modes that cannot be seen in any of its constituent parts. This is the process called ‘emergence’." (John L Casti, [interview with Austin Allen], 2012)

"Every system that has existed emerged somehow, from somewhere, at some point. Complexity science emphasizes the study of how systems evolve through their disorganized parts into an organized whole." (Lawrence K Samuels, "Defense of Chaos: The Chaology of Politics, Economics and Human Action", 2013)

"Things evolve to evolve. Evolutionary processes are the linchpin of change. These processes of discovery represent a complexity of simple systems that flux in perpetual tension as they teeter at the edge of chaos. This whirlwind of emergence is responsible for the spontaneous order and higher, organized complexity so noticeable in biological evolution - one–celled critters beefing up to become multicellular organisms." (Lawrence K Samuels, "Defense of Chaos: The Chaology of Politics, Economics and Human Action", 2013)

"This spontaneous emergence of order at critical points of instability, which is often referred to simply as 'emergence', is one of the hallmarks of life. It has been recognized as the dynamic origin of development, learning, and evolution. In other words, creativity-the generation of new forms-is a key property of all living systems." (Fritjof Capra, "The Systems View of Life: A Unifying Vision", 2014)

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

Systems Engineering: Sensitivity (Just the Quotes)

"An exceedingly small cause which escapes our notice determines a considerable effect that we cannot fail to see, and then we say the effect is due to chance. If we knew exactly the laws of nature and the situation of the universe at the initial moment, we could predict exactly the situation of that same universe at a succeeding moment. But even if it were the case that the natural laws had no longer any secret for us, we could still only know the initial situation 'approximately'. If that enabled us to predict the succeeding situation with 'the same approximation', that is all we require, and we should say that the phenomenon had been predicted, that it is governed by laws. But it is not always so; it may happen that small differences in the initial conditions produce very great ones in the final phenomena. A small error in the former will produce an enormous error in the latter. Prediction becomes impossible, and we have the fortuitous phenomenon. (Jules H Poincaré, "Science and Method", 1908)

"The predictions of physical theories for the most part concern situations where initial conditions can be precisely specified. If such initial conditions are not found in nature, they can be arranged." (Anatol Rapoport, "The Search for Simplicity", 1956)

"[...] the influence of a single butterfly is not only a fine detail - it is confined to a small volume. Some of the numerical methods which seem to be well adapted for examining the intensification of errors are not suitable for studying the dispersion of errors from restricted to unrestricted regions. One hypothesis, unconfirmed, is that the influence of a butterfly's wings will spread in turbulent air, but not in calm air." (Edward N Lorenz, [talk] 1972)

"Everywhere […] in the Universe, we discern that closed physical systems evolve in the same sense from ordered states towards a state of complete disorder called thermal equilibrium. This cannot be a consequence of known laws of change, since […] these laws are time symmetric- they permit […] time-reverse. […] The initial conditions play a decisive role in endowing the world with its sense of temporal direction. […] some prescription for initial conditions is crucial if we are to understand […]" (John D Barrow, "Theories of Everything: The Quest for Ultimate Explanation", 1991)

"In nonlinear systems - and the economy is most certainly nonlinear - chaos theory tells you that the slightest uncertainty in your knowledge of the initial conditions will often grow inexorably. After a while, your predictions are nonsense." (M Mitchell Waldrop, "Complexity: The Emerging Science at the Edge of Order and Chaos", 1992)

"In the everyday world of human affairs, no one is surprised to learn that a tiny event over here can have an enormous effect over there. For want of a nail, the shoe was lost, et cetera. But when the physicists started paying serious attention to nonlinear systems in their own domain, they began to realize just how profound a principle this really was. […] Tiny perturbations won't always remain tiny. Under the right circumstances, the slightest uncertainty can grow until the system's future becomes utterly unpredictable - or, in a word, chaotic." (M Mitchell Waldrop, "Complexity: The Emerging Science at the Edge of Order and Chaos", 1992)

"Symmetry breaking in psychology is governed by the nonlinear causality of complex systems (the 'butterfly effect'), which roughly means that a small cause can have a big effect. Tiny details of initial individual perspectives, but also cognitive prejudices, may 'enslave' the other modes and lead to one dominant view." (Klaus Mainzer, "Thinking in Complexity", 1994)

"How surprising it is that the laws of nature and the initial conditions of the universe should allow for the existence of beings who could observe it. Life as we know it would be impossible if any one of several physical quantities had slightly different values." (Steven Weinberg, "Life in the Quantum Universe", Scientific American, 1995)

"Unlike classical mathematics, net math exhibits nonintuitive traits. In general, small variations in input in an interacting swarm can produce huge variations in output. Effects are disproportional to causes - the butterfly effect." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

"Swarm systems generate novelty for three reasons: (1) They are 'sensitive to initial conditions' - a scientific shorthand for saying that the size of the effect is not proportional to the size of the cause - so they can make a surprising mountain out of a molehill. (2) They hide countless novel possibilities in the exponential combinations of many interlinked individuals. (3) They don’t reckon individuals, so therefore individual variation and imperfection can be allowed. In swarm systems with heritability, individual variation and imperfection will lead to perpetual novelty, or what we call evolution." (Kevin Kelly, "Out of Control: The New Biology of Machines, Social Systems and the Economic World", 1995)

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

"A sudden change in the evolutive dynamics of a system (a ‘surprise’) can emerge, apparently violating a symmetrical law that was formulated by making a reduction on some (or many) finite sequences of numerical data. This is the crucial point. As we have said on a number of occasions, complexity emerges as a breakdown of symmetry (a system that, by evolving with continuity, suddenly passes from one attractor to another) in laws which, expressed in mathematical form, are symmetrical. Nonetheless, this breakdown happens. It is the surprise, the paradox, a sort of butterfly effect that can highlight small differences between numbers that are very close to one another in the continuum of real numbers; differences that may evade the experimental interpretation of data, but that may increasingly amplify in the system’s dynamics." (Cristoforo S Bertuglia & Franco Vaio, "Nonlinearity, Chaos, and Complexity: The Dynamics of Natural and Social Systems", 2003)

"[…] we would like to observe that the butterfly effect lies at the root of many events which we call random. The final result of throwing a dice depends on the position of the hand throwing it, on the air resistance, on the base that the die falls on, and on many other factors. The result appears random because we are not able to take into account all of these factors with sufficient accuracy. Even the tiniest bump on the table and the most imperceptible move of the wrist affect the position in which the die finally lands. It would be reasonable to assume that chaos lies at the root of all random phenomena." (Iwo Białynicki-Birula & Iwona Białynicka-Birula, "Modeling Reality: How Computers Mirror Life", 2004)

"Yet, with the discovery of the butterfly effect in chaos theory, it is now understood that there is some emergent order over time even in weather occurrence, so that weather prediction is not next to being impossible as was once thought, although the science of meteorology is far from the state of perfection." (Peter Baofu, "The Future of Complexity: Conceiving a Better Way to Understand Order and Chaos", 2007)

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

"The 'butterfly effect' is at most a hypothesis, and it was certainly not Lorenz’s intention to change it to a metaphor for the importance of small event. […] Dynamical systems that exhibit sensitive dependence on initial conditions produce remarkably different solutions for two initial values that are close to each other. Sensitive dependence on initial conditions is one of the properties to exhibit chaotic behavior. In addition, at least one further implicit assumption is that the system is bounded in some finite region, i.e., the system cannot blow up. When one uses expanding dynamics, a way of pull-back of too much expanded phase volume to some finite domain is necessary to get chaos." (Péter Érdi, "Complexity Explained", 2008)

"One of the remarkable features of these complex systems created by replicator dynamics is that infinitesimal differences in starting positions create vastly different patterns. This sensitive dependence on initial conditions is often called the butterfly - effect aspect of complex systems - small changes in the replicator dynamics or in the starting point can lead to enormous differences in outcome, and they change one’s view of how robust the current reality is. If it is complex, one small change could have led to a reality that is quite different." (David Colander & Roland Kupers, "Complexity and the art of public policy : solving society’s problems from the bottom up", 2014)

More quotes on the "Sensitivity of initial conditions" (aka "The Butterfly Effect") at the-web-of-knowledge.blogspot.com.

Systems Engineering: Systems (Just the Quotes)

"Systems in many respects resemble machines. A machine is a little system, created to perform, as well as to connect together, in reality, those different movements and effects which the artist has occasion for.  A system is an imaginary machine invented to connect together in the fancy those different movements and effects which are already in reality performed." (Adam Smith, "The Wealth of Nations", 1776)

"A good method of discovery is to imagine certain members of a system removed and then see how what is left would behave: for example, where would we be if iron were absent from the world: this is an old example." (Georg C Lichtenberg, Notebook J, 1789-1793)

"A system is a whole which is composed of various parts. But it is not the same thing as an aggregate or heap. In an aggregate or heap, no essential relation exists between the units of which it is composed. In a heap of grain, or pile of stones, one may take away part without the other part being at all affected thereby. But in a system, each part has a fixed and necessary relation to the whole and to all the other parts. For this reason we may say that a building, or a peace of mechanisme, is a system. Each stone in the building, each wheel in the watch, plays a part, and is essential to the whole." (James E Creighton, "An Introductory Logic"‎, 1909)

"The concept of an independent system is a pure creation of the imagination. For no material system is or can ever be perfectly isolated from the rest of the world. Nevertheless it completes the mathematician’s ‘blank form of a universe’ without which his investigations are impossible. It enables him to introduce into his geometrical space, not only masses and configurations, but also physical structure and chemical composition." (Lawrence J Henderson, "The Order of Nature: An Essay", 1917)

"The complexity of a system is no guarantee of its accuracy." (John P Jordan, "Cost accounting; principles and practice", 1920)

"Given a situation, a system with a Leerstelle [a gap], whether a given completion (Lueckenfuellung) does justice to the structure, is the 'right' one, is often determined by the structure of the system, the situation. There are requirements, structurally determined; there are possible in pure cases unambiguous decisions as to which completion does justice to the situation, which does not, which violates the requirements and the situation." (Max Wertheimer, "Some Problems in the Theory of Ethics", Social Research Vol. 2 (3), 1935)

"A system is difficult to define, but it is easy to recognize some of its characteristics. A system possesses boundaries which segregate it from the rest of its field: it is cohesive in the sense that it resists encroachment from without […]" (Marvin G Cline, "Fundamentals of a theory of the self: some exploratory speculations‎", 1950)

"Now a system is said to be at equilibrium when it has no further tendency to change its properties." (Walter J Moore, "Physical chemistry", 1950)

"Every part of the system is so related to every other part that any change in one aspect results in dynamic changes in all other parts of the total system." (Arthur D Hall & Robert E Fagen, "Definition of System", General Systems Vol. 1, 1956)

"Any pattern of activity in a network, regarded as consistent by some observer, is a system, Certain groups of observers, who share a common body of knowledge, and subscribe to a particular discipline, like 'physics' or 'biology' (in terms of which they pose hypotheses about the network), will pick out substantially the same systems. On the other hand, observers belonging to different groups will not agree about the activity which is a system." (Gordon Pask, "The Natural History of Networks", 1960)

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

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

"Roughly, by a complex system I mean one made up of a large number of parts that interact in a nonsimple way. In such systems, the whole is more than the sum of the parts, not in an ultimate, metaphysical sense, but in the important pragmatic sense that, given the properties of the parts and the laws of their interaction, it is not a trivial matter to infer the properties of the whole." (Herbert Simon, "The Architecture of Complexity", Proceedings of the American Philosophical Society Vol. 106 (6), 1962)

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

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

"A system has order, flowing from point to point. If something dams that flow, order collapses. The untrained might miss that collapse until it was too late. That's why the highest function of ecology is the understanding of consequences." (Frank Herbert, "Dune", 1965)

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

"To find out what happens to a system when you interfere with it you have to interfere with it (not just passively observe it)." (George E P Box, "Use and Abuse of Regression", 1966)

"That a system is open means, not simply that it engages in interchanges with the environment, but that this interchange is an essential factor underlying the system's viability, its reproductive ability or continuity, and its ability to change. [...] Openness is an essential factor underlying a system's viability, continuity, and its ability to change."  (Walter F Buckley, "Sociology and modern systems theory", 1967)

"You cannot sum up the behavior of the whole from the isolated parts, and you have to take into account the relations between the various subordinate systems which are super-ordinated to them in order to understand the behavior of the parts." (Ludwig von Bertalanffy, "General System Theory", 1968)

"[…] as a model of a complex system becomes more complete, it becomes less understandable. Alternatively, as a model grows more realistic, it also becomes just as difficult to understand as the real world processes it represents." (Jay M Dutton & William H Starbuck," Computer simulation models of human behavior: A history of an intellectual technology", IEEE Transactions on Systems, 1971)

"A system in one perspective is a subsystem in another. But the systems view always treats systems as integrated wholes of their subsidiary components and never as the mechanistic aggregate of parts in isolable causal relations." (Ervin László, "Introduction to Systems Philosophy", 1972)

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

"[The] system may evolve through a whole succession of transitions leading to a hierarchy of more and more complex and organized states. Such transitions can arise in nonlinear systems that are maintained far from equilibrium: that is, beyond a certain critical threshold the steady-state regime become unstable and the system evolves into a new configuration." (Ilya Prigogine, Gregoire Micolis & Agnes Babloyantz, "Thermodynamics of Evolution", Physics Today 25 (11), 1972) 

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

"When a system is considered in two different states, the difference in volume or in any other property, between the two states, depends solely upon those states themselves and not upon the manner in which the system may pass from one state to the other." (Rudolf Arnheim, "Entropy and Art: An Essay on Disorder and Order", 1974) 

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

"Synergy means behavior of whole systems unpredicted by the behavior of their parts taken separately." (R Buckminster Fuller, "Synergetics: Explorations in the Geometry of Thinking", 1975)

"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)

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

"All nature is a continuum. The endless complexity of life is organized into patterns which repeat themselves at each level of system." (James G Miller, "Living Systems", 1978)

"An autopoietic system is organized (defined as a unity) as a network of processes of production (transformation and destruction) of components that produces the components that: (a) through their interactions and transformations continuously regenerate and realize the network of processes (relations) that produce them and, (b) constitute it (the machine) as a concrete unity in the space in which they exist by specifying the topological domain of its realization as such a network." (Francisco Varela, "Principles of Biological Autonomy", 1979)

"A system is an internally organised whole where elements are so intimately connected that they operate as one in relation to external conditions and other systems. An element may be defined as the minimal unit performing a definite function in the whole. Systems may be either simple or complex. A complex system is one whose elements may also be regarded as systems or subsystems." (Alexander Spirkin, "Dialectical Materialism", 1983)

"But structure is not enough to make a system. A system consists of something more than structure: it is a structure with certain properties. When a structure is understood from the standpoint of its properties, it is understood as a system." (Alexander Spirkin, "Dialectical Materialism", 1983)

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

"Organization denotes those relations that must exist among the components of a system for it to be a member of a specific class. Structure denotes the components and relations that actually constitute a particular unity and make its organization real." (Humberto Maturana, "The Tree of Knowledge", 1987)

"The dynamics of any system can be explained by showing the relations between its parts and the regularities of their interactions so as to reveal its organization. For us to fully understand it, however, we need not only to see it as a unity operating in its internal dynamics, but also to see it in its circumstances, i.e., in the context to which its operation connects it. This understanding requires that we adopt a certain distance for observation, a perspective that in the case of historical systems implies a reference to their origin. This can be easy, for instance, in the case of man-made machines, for we have access to every detail of their manufacture. The situation is not that easy, however, as regards living beings: their genesis and their history are never directly visible and can be reconstructed only by fragments."  (Humberto Maturana, "The Tree of Knowledge", 1987)

"A system of variables is 'interrelated' if an action that affects or meant to affect one part of the system will also affect other parts of it. Interrelatedness guarantees that an action aimed at one variable will have side effects and long-term repercussions. A large number of variables will make it easy to overlook them." (Dietrich Dorner, "The Logic of Failure: Recognizing and Avoiding Error in Complex Situations", 1989)

"Systems, acting dynamically, produce (and incidentally, reproduce) their own boundaries, as structures which are complementary (necessarily so) to their motion and dynamics. They are liable, for all that, to instabilities chaos, as commonly interpreted of chaotic form, where nowadays, is remote from the random. Chaos is a peculiar situation in which the trajectories of a system, taken in the traditional sense, fail to converge as they approach their limit cycles or 'attractors' or 'equilibria'. Instead, they diverge, due to an increase, of indefinite magnitude, in amplification or gain." (Gordon Pask, "Different Kinds of Cybernetics", 1992)

 "What is a system? A system is a network of interdependent components that work together to try to accomplish the aim of the system. A system must have an aim. Without an aim, there is no system. The aim of the system must be clear to everyone in the system. The aim must include plans for the future. The aim is a value judgment.” (William E Deming, "The New Economics for Industry, Government, Education”, 1993)

"The impossibility of constructing a complete, accurate quantitative description of a complex system forces observers to pick which aspects of the system they most wish to understand." (Thomas Levenson, "Measure for Measure: A musical history of science", 1994)

"Complex adaptive systems have the property that if you run them - by just letting the mathematical variable of 'time' go forward - they'll naturally progress from chaotic, disorganized, undifferentiated, independent states to organized, highly differentiated, and highly interdependent states. Organized structures emerge spontaneously. [...]A weak system gives rise only to simpler forms of self-organization; a strong one gives rise to more complex forms, like life. (J Doyne Farmer, "The Third Culture: Beyond the Scientific Revolution", 1995)

"Self-organization refers to the spontaneous formation of patterns and pattern change in open, nonequilibrium systems. […] Self-organization provides a paradigm for behavior and cognition, as well as the structure and function of the nervous system. In contrast to a computer, which requires particular programs to produce particular results, the tendency for self-organization is intrinsic to natural systems under certain conditions." (J A Scott Kelso, "Dynamic Patterns : The Self-organization of Brain and Behavior", 1995)

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

"By irreducibly complex I mean a single system composed of several well-matched, interacting parts that contribute to the basic function, wherein the removal of any one of the parts causes the system to effectively cease functioning. An irreducibly complex system cannot be produced directly (that is, by continuously improving the initial function, which continues to work by the same mechanism) by slight, successive modification of a precursor, system, because any precursors to an irreducibly complex system that is missing a part is by definition nonfunctional." (Michael Behe, "Darwin’s Black Box", 1996)

"Understanding ecological interdependence means understanding relationships. It requires the shifts of perception that are characteristic of systems thinking - from the parts to the whole, from objects to relationships, from contents to patterns. […] Nourishing the community means nourishing those relationships." (Fritjof Capra, "The Web of Life: A New Scientific Understanding of Living Systems", 1996)

"The notion of system we are interested in may be described generally as a complex of elements or components directly or indirectly related in a network of interrelationships of various kinds, such that it constitutes a dynamic whole with emergent properties." (Walter F. Buckley, "Society: A Complex Adaptive System - Essays in Social Theory", 1998)

"Formulation of a mathematical model is the first step in the process of analyzing the behaviour of any real system. However, to produce a useful model, one must first adopt a set of simplifying assumptions which have to be relevant in relation to the physical features of the system to be modelled and to the specific information one is interested in. Thus, the aim of modelling is to produce an idealized description of reality, which is both expressible in a tractable mathematical form and sufficiently close to reality as far as the physical mechanisms of interest are concerned." (Francois Axisa, "Discrete Systems" Vol. I, 2001)

"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 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)

"One of the key insights of the systems approach has been the realization that the network is a pattern that is common to all life. Wherever we see life, we see networks." (Fritjof Capra, "The Hidden Connections: A Science for Sustainable Living", 2002)

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

"The progress of science requires the growth of understanding in both directions, downward from the whole to the parts and upward from the parts to the whole." (Freeman Dyson, "The Scientist As Rebel", 2006)

"Humans have difficulty perceiving variables accurately […]. However, in general, they tend to have inaccurate perceptions of system states, including past, current, and future states. This is due, in part, to limited ‘mental models’ of the phenomena of interest in terms of both how things work and how to influence things. Consequently, people have difficulty determining the full implications of what is known, as well as considering future contingencies for potential systems states and the long-term value of addressing these contingencies." (William B. Rouse, "People and Organizations: Explorations of Human-Centered Design", 2007)

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

"A model is a representation in that it (or its properties) is chosen to stand for some other entity (or its properties), known as the target system. A model is a tool in that it is used in the service of particular goals or purposes; typically these purposes involve answering some limited range of questions about the target system." (Wendy S Parker, "Confirmation and Adequacy-for-Purpose in Climate Modelling", Proceedings of the Aristotelian Society, Supplementary Volumes, Vol. 83, 2009)

"System theorists know that it's easy to couple simple-to-understand systems into a ‘super system’ that's capable of displaying behavioral modes that cannot be seen in any of its constituent parts. This is the process called ‘emergence’." (John L Casti, [interview with Austin Allen], 2012)

"When some systems are stuck in a dangerous impasse, randomness and only randomness can unlock them and set them free." (Nassim N Taleb, "Antifragile: Things That Gain from Disorder", 2012) 

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

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

"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)

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