30 August 2006

Alan J Perlis - Collected Quotes

"A language that doesn’t affect the way you think about programming, is not worth knowing." (Alan J Perlis, "Epigrams on Programming", 1982)

"A program without a loop and a structured variable isn’t worth writing." (Alan J Perlis, "Epigrams on Programming", 1982)

"A programming language is low level when its programs require attention to the irrelevant." (Alan J Perlis, "Epigrams on Programming", 1982)

"Adapting old programs to fit new machines usually means adapting new machines to behave like old ones." (Alan J Perlis, "Epigrams on Programming", 1982)

"Computers don’t introduce order anywhere as much as they expose opportunities." (Alan J Perlis, "Epigrams on Programming", 1982)

"Documentation is like term insurance: It satisfies because almost no one who subscribes to it depends on its benefits." (Alan J Perlis, "Epigrams on Programming", 1982)

"Don’t have good ideas if you aren’t willing to be responsible for them." (Alan J Perlis, "Epigrams on Programming", 1982)

"Epigrams retrieve deep semantics from a data base that is all procedure." (Alan J Perlis, "Epigrams on Programming", 1982)

"Every program has (at least) two purposes: the one for which it was written, and another for which it wasn’t." (Alan J Perlis, "Epigrams on Programming", 1982)

"Functions delay binding; data structures induce binding. Moral: Structure data late in the programming process. " (Alan J Perlis, "Epigrams on Programming", 1982)

"If a program manipulates a large amount of data, it does so in a small number of ways." (Alan J Perlis, "Epigrams on Programming", 1982)

"If we believe in data structures, we must believe in independent (hence simultaneous) processing. For why else would we collect items within a structure? Why do we tolerate languages that give us the one without the other?" (Alan J Perlis, "Epigrams on Programming", 1982)

"In programming, everything we do is a special case of something more general — and often we know it too quickly." (Alan J Perlis, "Epigrams on Programming", 1982)

"In seeking the unattainable, simplicity only gets in the way." (Alan J Perlis, "Epigrams on Programming", 1982)

"Interfaces keep things tidy, but don’t accelerate growth: Functions do." (Alan J Perlis, "Epigrams on Programming", 1982)

"It is better to have 100 functions operate on one data structure than 10 functions on 10 data structures." (Alan J Perlis, "Epigrams on Programming", 1982)

"It is easier to change the specification to fit the program than vice versa. " (Alan J Perlis, "Epigrams on Programming", 1982)

"It is easier to write an incorrect program than understand a correct one." (Alan J Perlis, "Epigrams on Programming", 1982)

"It is not a language’s weakness but its strengths that control the gradient of its change: Alas, a language never escapes its embryonic sac." (Alan J Perlis, "Epigrams on Programming", 1982)

"Make no mistake about it: Computers process numbers — not symbols. We measure our understanding (and control) by the extent to which we can arithmetize an activity." (Alan J Perlis, "Epigrams on Programming", 1982)

"Making something variable is easy. Controlling duration of constancy is the trick." (Alan J Perlis, "Epigrams on Programming", 1982)

"Most people find the concept of programming obvious, but the doing impossible." (Alan J Perlis, "Epigrams on Programming", 1982)

"Often it is the means that justify the ends: Goals advance technique and technique survives even when goal structures crumble." (Alan J Perlis, "Epigrams on Programming", 1982)

"One can only display complex information in the mind. Like seeing, movement or flow or alteration of view is more important than the static picture, no matter how lovely." (Alan J Perlis, "Epigrams on Programming", 1982)

"Programmers are not to be measured by their ingenuity and their logic but by the completeness of their case analysis." (Alan J Perlis, "Epigrams on Programming", 1982)

"Prolonged contact with the computer turns mathematicians into clerks and vice versa." (Alan J Perlis, "Epigrams on Programming", 1982)

"Recursion is the root of computation since it trades description for time." (Alan J Perlis, "Epigrams on Programming", 1982)

"Simplicity does not precede complexity, but follows it." (Alan J Perlis, "Epigrams on Programming", 1982)

"Software is under a constant tension. Being symbolic it is arbitrarily perfectible; but also it is arbitrarily changeable." (Alan J Perlis, "Epigrams on Programming", 1982)

"Some programming languages manage to absorb change, but withstand progress." (Alan J Perlis, "Epigrams on Programming", 1982)

"Symmetry is a complexity-reducing concept (co-routines include subroutines); seek it everywhere." (Alan J Perlis, "Epigrams on Programming", 1982)

"Systems have sub-systems and sub-systems have sub-systems and so on ad infinitum - which is why we’re always starting over." (Alan J Perlis, "Epigrams on Programming", 1982)

"The cybernetic exchange between man, computer and algorithm is like a game of musical chairs: The frantic search for balance always leaves one of the three standing ill at ease." (Alan J Perlis, "Epigrams on Programming", 1982)

"The goal of computation is the emulation of our synthetic abilities, not the understanding of our analytic ones." (Alan J Perlis, "Epigrams on Programming", 1982)

"The string is a stark data structure and everywhere it is passed there is much duplication of process. It is a perfect vehicle for hiding information." (Alan J Perlis, "Epigrams on Programming", 1982)

"The use of a program to prove the 4-color theorem will not change mathematics - it merely demonstrates that the theorem, a challenge for a century, is probably not important to mathematics." (Alan J Perlis, "Epigrams on Programming", 1982)

"To understand a program you must become both the machine and the program." (Alan J Perlis, "Epigrams on Programming", 1982)

"We kid ourselves if we think that the ratio of procedure to data in an active data-base system can be made arbitrarily small or even kept small." (Alan J Perlis, "Epigrams on Programming", 1982)

"We will never run out of things to program as long as there is a single program around." (Alan J Perlis, "Epigrams on Programming", 1982)

"Wherever there is modularity there is the potential for misunderstanding: Hiding information implies a need to check communication." (Alan J Perlis, "Epigrams on Programming", 1982)

26 August 2006

Michael M Hammer - Collected Quotes

"At the heart of reengineering is the notion of discontinuous thinking—of recognizing and breaking away from the outdated rules and fundamental assumptions that underlie operations. Unless we change these rules, we are merely rearranging the deck chairs on the Titanic. We cannot achieve breakthroughs in performance by cutting fat or automating existing processes. Rather, we must challenge old assumptions and shed the old rules that made the business underperform in the first place." (Michael M Hammer, "Reengineering Work: Don't Automate, Obliterate", Magazine, 1990) [source]

"Conventional process structures are fragmented and piecemeal, and they lack the integration necessary to maintain quality and service. They are breeding grounds for tunnel vision, as people tend to substitute the narrow goals of their particular department for the larger goals of the process as a whole. When work is handed off from person to person and unit to unit, delays and errors are inevitable. Accountability blurs, and critical issues fall between the cracks." (Michael M Hammer, "Reengineering Work: Don't Automate, Obliterate", Magazine, 1990) [source]

"In reengineering, managers break loose from outmoded business processes and the design principles underlying them and create new ones. [...] Reengineering requires looking at the fundamental processes of the business from a cross-functional perspective. [...] The reengineering team must keep asking Why? and What if? Why do we need to get a manager’s signature on a requisition? Is it a control mechanism or a decision point?" (Michael M Hammer, "Reengineering Work: Don't Automate, Obliterate", Magazine, 1990) [source]

"In short, a reengineering effort strives for dramatic levels of improvement. It must break away from conventional wisdom and the constraints of organizational boundaries and should be broad and cross-functional in scope. It should use information technology not to automate an existing process but to enable a new one." (Michael M Hammer, "Reengineering Work: Don't Automate, Obliterate", Magazine, 1990) [source]

"Information technology can capture and process data, and expert systems can to some extent supply knowledge, enabling people to make their own decisions. As the doers become self-managing and self-controlling, hierarchy - and the slowness and bureaucracy associated with it - disappears." (Michael M Hammer, "Reengineering Work: Don't Automate, Obliterate", Magazine, 1990) [source]

"Reengineering cannot be planned meticulously and accomplished in small and cautious steps. It's an all-or-nothing proposition with an uncertain result. Still, most companies have no choice but to muster the courage to do it. For many, reengineering is the only hope for breaking away from the antiquated processes that threaten to drag them down." (Michael M Hammer, "Reengineering Work: Don't Automate, Obliterate", Magazine, 1990) [source]

"Reengineering triggers changes of many kinds, not just of the business process itself. Job designs, organizational structures, management systems - anything associated with the process - must be refashioned in an integrated way. In other words, reengineering is a tremendous effort that mandates change in many areas of the organization." (Michael M Hammer, "Reengineering Work: Don't Automate, Obliterate", Magazine, 1990) [source]

"A business process is a collection of activities that takes one or more kinds of input and creates an output that is of value to the customer. A business process has a goal and is affected by events occurring in the external world or in other processes." (James A Champy & Michael M Hammer, "Reengineering the Corporation", 1993)

"A process perspective sees not individual tasks in isolation, but the entire collection of tasks that contribute to a desired outcome. Narrow points of view are useless in a process context. It just won't do for each person to be concerned exclusively with his or her own limited responsibility, no matter how well these responsibilities are met. When that occurs, the inevitable result is working at cross–purpose, misunderstanding, and the optimization of the part at the expense of the whole. Process work requires that everyone involved be directed toward a common goal; otherwise, conflicting objectives and parochial agendas impair the effort."  (James A Champy & Michael M Hammer, "Reengineering the Corporation", 1993)

"'Automating a mess yields an automated mess.' Unless an organization reconceptualized its operations, overlaying new technology on these operations accomplished little." (James A Champy & Michael M Hammer, "Reengineering the Corporation", 1993)

"Business reengineering isn't about fixing anything. Business reengineering means starting all over, starting from scratch. Business reengineering means putting aside much of the received wisdom of two hundred years of industrial management [...] How people and companies did things yesterday doesn't matter to the business reengineer." (James A Champy & Michael M Hammer, "Reengineering the Corporation", 1993)

"Reengineering is the fundamental rethinking and radical redesign of business processes to achieve dramatic improvements in critical contemporary measures of performance such as cost, quality, service and speed." (James A Champy & Michael M Hammer, "Reengineering the Corporation", 1993)

"Reengineering posits a radical new principle: that the design of work must be based not on hierarchical management and the specialization of labor but on end-to-end processes and the creation of value for the customer." (James A Champy & Michael M Hammer, "Reengineering the Corporation", 1993)

"To succeed at reengineering, you have to be a missionary, a motivator, and a leg breaker." (Michael M Hammer, Fortune, August 1993)

Robert C Martin - Collected Quotes

"A system that is comprehensively tested and passes all of its tests all of the time is a testable system. That’s an obvious statement, but an important one. Systems that aren’t testable aren’t verifiable. Arguably, a system that cannot be verified should never be deployed." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Any comment that forces you to look in another module for the meaning of that comment has failed to communicate to you and is not worth the bits it consumes." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008) 

"Clean code is focused. Each function, each class, each module exposes a single-minded attitude that remains entirely undistracted, and unpolluted, by the surrounding details."  (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Clean code is not written by following a set of rules. You don’t become a software craftsman by learning a list of heuristics. Professionalism and craftsmanship come from values that drive disciplines." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Code formatting is important. It is too important to ignore and it is too important to treat religiously. Code formatting is about communication, and communication is the professional developer’s first order of business."  (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Duplication is the primary enemy of a well-designed system. It represents additional work, additional risk, and additional unnecessary complexity."  (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Every system is built from a domain-specific language designed by the programmers to describe that system. Functions are the verbs of that language, and classes are the nouns."  (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Good software designs accommodate change without huge investments and rework. When we use code that is out of our control, special care must be taken to protect our investment and make sure future change is not too costly."  (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"If the discipline of requirements specification has taught us anything, it is that well-specified requirements are as formal as code and can act as executable tests of that code!"  (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"In an ideal system, we incorporate new features by extending the system, not by making modifications to existing code." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Indeed, the ratio of time spent reading versus writing is well over 10 to 1. We are constantly reading old code as part of the effort to write new code. [… Therefore,] making it easy to read makes it easier to write." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008) 

"It is a myth that we can get systems 'right the first time'. Instead, we should implement only today’s stories, then refactor and expand the system to implement new stories tomorrow. This is the essence of iterative and incremental agility. Test-driven development, refactoring, and the clean code they produce make this work at the code level." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"It is not enough for code to work. Code that works is often badly broken. Programmers who satisfy themselves with merely working code are behaving unprofessionally. They may fear that they don’t have time to improve the structure and design of their code, but I disagree. Nothing has a more profound and long-term degrading effect upon a development project than bad code." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"It is unit tests that keep our code flexible, maintainable, and reusable. The reason is simple. If you have tests, you do not fear making changes to the code! Without tests every change is a possible bug."  (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Nothing has a more profound and long-term degrading effect upon a development project than bad code. Bad schedules can be redone, bad requirements can be redefined. Bad team dynamics can be repaired. But bad code rots and ferments, becoming an inexorable weight that drags the team down." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"One difference between a smart programmer and a professional programmer is that the professional understands that clarity is king. Professionals use their powers for good and write code that others can understand." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008) 

"One of the best ways to ruin a program is to make massive changes to its structure in the name of improvement. Some programs never recover from such “improvements.” The problem is that it’s very hard to get the program working the same way it worked before the 'improvement'." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Refactoring is a lot like solving a Rubik’s cube. There are lots of little steps required to achieve a large goal. Each step enables the next." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Standards make it easier to reuse ideas and components, recruit people with relevant experience, encapsulate good ideas, and wire components together. However, the process of creating standards can sometimes take too long for industry to wait, and some standards lose touch with the real needs of the adopters they are intended to serve." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"The majority of the cost of a software project is in long-term maintenance. In order to minimize the potential for defects as we introduce change, it’s critical for us to be able to understand what a system does. As systems become more complex, they take more and more time for a developer to understand, and there is an ever greater opportunity for a misunderstanding. Therefore, code should clearly express the intent of its author. The clearer the author can make the code, the less time others will have to spend understanding it. This will reduce defects and shrink the cost of maintenance." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"The problem isn’t the simplicity of the code but the implicity of the code (to coin a phrase): the degree to which the context is not explicit in the code itself." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"There are two parts to learning craftsmanship: knowledge and work. You must gain the knowledge of principles, patterns, practices, and heuristics that a craftsman knows, and you must also grind that knowledge into your fingers, eyes, and gut by working hard and practicing." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"We do not want to expose the details of our data. Rather we want to express our data in abstract terms. This is not merely accomplished by using interfaces and/or getters and setters. Serious thought needs to be put into the best way to represent the data that an object contains. The worst option is to blithely add getters and setters." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"When people look under the hood, we want them to be impressed with the neatness, consistency, and attention to detail that they perceive. We want them to be struck by the orderliness. We want their eyebrows to rise as they scroll through the modules. We want them to perceive that professionals have been at work. If instead they see a scrambled mass of code that looks like it was written by a bevy of drunken sailors, then they are likely to conclude that the same inattention to detail pervades every other aspect of the project." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Whether you are designing systems or individual modules, never forget to use the simplest thing that can possibly work." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"Yet attentiveness to detail is an even more critical foundation of professionalism than is any grand vision. First, it is through practice in the small that professionals gain proficiency and trust for practice in the large. Second, the smallest bit of sloppy construction, of the door that does not close tightly or the slightly crooked tile on the floor, or even the messy desk, completely dispels the charm of the larger whole. That is what clean code is about." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

"You should choose a set of simple rules that govern the format of your code, and then you should consistently apply those rules. If you are working on a team, then the team should agree to a single set of formatting rules and all members should comply." (Robert C Martin, "Clean Code: A Handbook of Agile Software Craftsmanship", 2008)

Cathy O'Neil - Collected Quotes

"A model, after all, is nothing more than an abstract representation of some process, be it a baseball game, an oil company’s supply chain, a foreign government’s actions, or a movie theater’s attendance. Whether it’s running in a computer program or in our head, the model takes what we know and uses it to predict responses in various situations. All of us carry thousands of models in our heads. They tell us what to expect, and they guide our decisions." (Cathy O'Neil, "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy", 2016)

"Big Data processes codify the past. They do not invent the future. Doing that requires moral imagination, and that’s something only humans can provide. We have to explicitly embed better values into our algorithms, creating Big Data models that follow our ethical lead. Sometimes that will mean putting fairness ahead of profit." (Cathy O'Neil, "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy", 2016)

"No model can include all of the real world’s complexity or the nuance of human communication. Inevitably, some important information gets left out. […] To create a model, then, we make choices about what’s important enough to include, simplifying the world into a toy version that can be easily understood and from which we can infer important facts and actions.[…] Sometimes these blind spots don’t matter. […] A model’s blind spots reflect the judgments and priorities of its creators. […] Our own values and desires influence our choices, from the data we choose to collect to the questions we ask. Models are opinions embedded in mathematics." (Cathy O'Neil, "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy", 2016)

"The first question: Even if the participant is aware of being modeled, or what the model is used for, is the model opaque, or even invisible? […] the second question: Does the model work against the subject’s interest? In short, is it unfair? Does it damage or destroy lives? […] The third question is whether a model has the capacity to grow exponentially. As a statistician would put it, can it scale?" (Cathy O'Neil, "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy", 2016)

"Whether or not a model works is also a matter of opinion. After all, a key component of every model, whether formal or informal, is its definition of success." (Cathy O'Neil, "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy", 2016)

"While Big Data, when managed wisely, can provide important insights, many of them will be disruptive. After all, it aims to find patterns that are invisible to human eyes. The challenge for data scientists is to understand the ecosystems they are wading into and to present not just the problems but also their possible solutions." (Cathy O'Neil, "Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy", 2016)

Carl von Clausewitz - Collected Quotes

"But when one comes to the effect of the engagement, where material successes turn into motives for further action, the intellect alone is decisive. In brief, tactics will present far fewer difficulties to the theorist than will strategy." (Carl von Clausewitz, "On War", 1832)

"In a tactical situation one is able to see at least half the problem with the naked eye, whereas in strategy everything has to be guessed at and presumed." (Carl von Clausewitz, "On War", 1832)

"Everything in strategy is very simple, but that does not mean everything is very easy." (Carl von Clausewitz, "On War", 1832)

"Such cases also occur in strategy, since strategy is directly linked to tactical action. In strategy too decisions must often be based on direct observation, on uncertain reports arriving hour by hour and day by day, and finally on the actual outcome of battles. It is thus an essential condition of strategic leadership that forces should be held in reserve according to the degree of strategic uncertainty." (Carl von Clausewitz, "On War", 1832)

"The function of theory is to put all this in systematic order, clearly and comprehensively, and to trace each action to an adequate, compelling cause. […] Theory should cast a steady light on all phenomena so that we can more easily recognize and eliminate the weeds that always spring from ignorance; it should show how one thing is related to another, and keep the important and the unimportant separate. If concepts combine of their own accord to form that nucleus of truth we call a principle, if they spontaneously compose a pattern that becomes a rule, it is the task of the theorist to make this clear." (Carl von Clausewitz, "On War", 1832)

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

25 August 2006

Paul Cilliers - Collected Quotes

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

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

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

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

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

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

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

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

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

24 August 2006

Richard W Hamming - Collected Quotes

“The purpose of computing is insight, not numbers […] sometimes […] the purpose of computing numbers is not yet in sight.” (Richard Hamming, “Numerical Methods for Scientists and Engineers”, 1962)

"Probability is the mathematics of uncertainty. Not only do we constantly face situations in which there is neither adequate data nor an adequate theory, but many modem theories have uncertainty built into their foundations. Thus learning to think in terms of probability is essential. Statistics is the reverse of probability (glibly speaking). In probability you go from the model of the situation to what you expect to see; in statistics you have the observations and you wish to estimate features of the underlying model." (Richard W Hamming, "Methods of Mathematics Applied to Calculus, Probability, and Statistics", 1985)

"Probability plays a central role in many fields, from quantum mechanics to information theory, and even older fields use probability now that the presence of 'noise' is officially admitted. The newer aspects of many fields start with the admission of uncertainty." (Richard W Hamming, "Methods of Mathematics Applied to Calculus, Probability, and Statistics", 1985)

"A model can not be proved to be correct; at best it can only be found to be reasonably consistant and not to contradict some of our beliefs of what reality is." (Richard W Hamming, "The Art of Probability for Scientists and Engineers", 1991)

"A model is often judged by how well it "explains" some observations. There need not be a unique model for a particular situation, nor need a model cover every possible special case. A model is not reality, it merely helps to explain some of our impressions of reality. [...] Different models may thus seem to contradict each other, yet we may use both in their appropriate places." (Richard W Hamming, "The Art of Probability for Scientists and Engineers", 1991)

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

"It is generally recognized that it is dangerous to apply any part of science without understanding what is behind the theory. This is especially true in the field of probability since in practice there is not a single agreed upon model of probability, but rather there are many widely different models of varying degrees of relevance and reliability. Thus the philosophy behind probability should not be neglected by presenting a nice set of postulates and then going forward; even the simplest applications of probability can involve the underlying philosophy." (Richard W Hamming, "The Art of Probability for Scientists and Engineers", 1991)

"Mathematics is not just a collection of results, often called theorems; it is a style of thinking. Computing is also basically a style of thinking. Similarly, probability is a style of thinking." (Richard W Hamming, "The Art of Probability for Scientists and Engineers", 1991)

23 August 2006

Richard Rumelt - Collected Quotes

"A leader’s most important job is creating and constantly adjusting this strategic bridge between goals and objectives." (Richard Rumelt, "Good Strategy/Bad Strategy", 2011)

"A strategy coordinates action to address a specific challenge. It is not defined by the pay grade of the person authorizing the action." (Richard Rumelt, "Good Strategy/Bad Strategy", 2011)

"Despite the roar of voices wanting to equate strategy with ambition, leadership, 'vision', planning, or the economic logic of competition, strategy is none of these. The core of strategy work is always the same: discovering the critical factors in a situation and designing a way of coordinating and focusing actions to deal with those factors." (Richard Rumelt, "Good Strategy Bad Strategy", 2011)

"Good strategy requires leaders who are willing and able to say no to a wide variety of actions and interests. Strategy is at least as much about what an organization does not do as it is about what it does." (Richard Rumelt, "Good Strategy/Bad Strategy", 2011)

"Having conflicting goals, dedicating resources to unconnected targets, and accommodating incompatible interests are the luxuries of the rich and powerful, but they make for bad strategy. Despite this, most organizations will not create focused strategies. Instead, they will generate laundry lists of desirable outcomes and, at the same time, ignore the need for genuine competence in coordinating and focusing their resources. Good strategy requires leaders who are willing and able to say no to a wide variety of actions and interests. Strategy is at least as much about what an organization does not do as it is about what it does." (Richard Rumelt, "Good Strategy/Bad Strategy", 2011)

"The kernel of a strategy contains three elements: a diagnosis, a guiding policy, and coherent action." (Richard Rumelt, "Good Strategy/Bad Strategy", 2011)

"When organizations are unable to make new strategies - when people evade the work of choosing among different paths in the future - then you get vague mom-and-apple-pie goals everyone can agree on. Such goals are direct evidence of leadership's insufficient will or political power to make or enforce hard choices." (Richard Rumelt, "Good Strategy/Bad Strategy", 2011)

22 August 2006

Judea Pearl - Collected Quotes

"Despite the prevailing use of graphs as metaphors for communicating and reasoning about dependencies, the task of capturing informational dependencies by graphs is not at all trivial." (Judea Pearl, "Probabilistic Reasoning in Intelligent Systems: Network of Plausible, Inference", 1988)

"Probabilities are summaries of knowledge that is left behind when information is transferred to a higher level of abstraction." (Judea Pearl, "Probabilistic Reasoning in Intelligent Systems: Network of Plausible, Inference", 1988)

"When loops are present, the network is no longer singly connected and local propagation schemes will invariably run into trouble. […] If we ignore the existence of loops and permit the nodes to continue communicating with each other as if the network were singly connected, messages may circulate indefinitely around the loops and process may not converges to a stable equilibrium. […] Such oscillations do not normally occur in probabilistic networks […] which tend to bring all messages to some stable equilibrium as time goes on. However, this asymptotic equilibrium is not coherent, in the sense that it does not represent the posterior probabilities of all nodes of the network." (Judea Pearl, "Probabilistic Reasoning in Intelligent Systems: Network of Plausible, Inference", 1988)

"Traditional statistics is strong in devising ways of describing data and inferring distributional parameters from sample. Causal inference requires two additional ingredients: a science-friendly language for articulating causal knowledge, and a mathematical machinery for processing that knowledge, combining it with data and drawing new causal conclusions about a phenomenon." (Judea Pearl, "Causal inference in statistics: An overview", Statistics Surveys 3, 2009)

"Again, classical statistics only summarizes data, so it does not provide even a language for asking [a counterfactual] question. Causal inference provides a notation and, more importantly, offers a solution. As with predicting the effect of interventions [...], in many cases we can emulate human retrospective thinking with an algorithm that takes what we know about the observed world and produces an answer about the counterfactual world." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

"Bayesian networks inhabit a world where all questions are reducible to probabilities, or (in the terminology of this chapter) degrees of association between variables; they could not ascend to the second or third rungs of the Ladder of Causation. Fortunately, they required only two slight twists to climb to the top." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

"Bayesian statistics give us an objective way of combining the observed evidence with our prior knowledge (or subjective belief) to obtain a revised belief and hence a revised prediction of the outcome of the coin’s next toss. [...] This is perhaps the most important role of Bayes’s rule in statistics: we can estimate the conditional probability directly in one direction, for which our judgment is more reliable, and use mathematics to derive the conditional probability in the other direction, for which our judgment is rather hazy. The equation also plays this role in Bayesian networks; we tell the computer the forward  probabilities, and the computer tells us the inverse probabilities when needed." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

"Deep learning has instead given us machines with truly impressive abilities but no intelligence. The difference is profound and lies in the absence of a model of reality." (Judea Pearl, "The Book of Why: The New Science of Cause and Effect", 2018)

"[…] deep learning has succeeded primarily by showing that certain questions or tasks we thought were difficult are in fact not. It has not addressed the truly difficult questions that continue to prevent us from achieving humanlike AI." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

"Some scientists (e.g., econometricians) like to work with mathematical equations; others (e.g., hard-core statisticians) prefer a list of assumptions that ostensibly summarizes the structure of the diagram. Regardless of language, the model should depict, however qualitatively, the process that generates the data - in other words, the cause-effect forces that operate in the environment and shape the data generated." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

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

"The main differences between Bayesian networks and causal diagrams lie in how they are constructed and the uses to which they are put. A Bayesian network is literally nothing more than a compact representation of a huge probability table. The arrows mean only that the probabilities of child nodes are related to the values of parent nodes by a certain formula (the conditional probability tables) and that this relation is sufficient. That is, knowing additional ancestors of the child will not change the formula. Likewise, a missing arrow between any two nodes means that they are independent, once we know the values of their parents. [...] If, however, the same diagram has been constructed as a causal diagram, then both the thinking that goes into the construction and the interpretation of the final diagram change." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

"The transparency of Bayesian networks distinguishes them from most other approaches to machine learning, which tend to produce inscrutable 'black boxes'. In a Bayesian network you can follow every step and understand how and why each piece of evidence changed the network’s beliefs." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

"When the scientific question of interest involves retrospective thinking, we call on another type of expression unique to causal reasoning called a counterfactual. […] Counterfactuals are the building blocks of moral behavior as well as scientific thought. The ability to reflect on one’s past actions and envision alternative scenarios is the basis of free will and social responsibility. The algorithmization of counterfactuals invites thinking machines to benefit from this ability and participate in this (until now) uniquely human way of thinking about the world." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

"With Bayesian networks, we had taught machines to think in shades of gray, and this was an important step toward humanlike thinking. But we still couldn’t teach machines to understand causes and effects. [...] By design, in a Bayesian network, information flows in both directions, causal and diagnostic: smoke increases the likelihood of fire, and fire increases the likelihood of smoke. In fact, a Bayesian network can’t even tell what the 'causal direction' is." (Judea Pearl & Dana Mackenzie, "The Book of Why: The new science of cause and effect", 2018)

Joseph P Bigus - Collected 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)

"Like modeling, which involves making a static one-time prediction based on current information, time-series prediction involves looking at current information and predicting what is going to happen. However, with time-series predictions, we typically are looking at what has happened for some period back through time and predicting for some point in the future. The temporal or time element makes time-series prediction both more difficult and more rewarding. Someone who can predict the future based on what has occurred in the past can clearly have tremendous advantages over someone who cannot." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"Many of the basic functions performed by neural networks are mirrored by human abilities. These include making distinctions between items (classification), dividing similar things into groups (clustering), associating two or more things (associative memory), learning to predict outcomes based on examples (modeling), being able to predict into the future (time-series forecasting), and finally juggling multiple goals and coming up with a good- enough solution (constraint satisfaction)." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"More than just a new computing architecture, neural networks offer a completely different paradigm for solving problems with computers. […] The process of learning in neural networks is to use feedback to adjust internal connections, which in turn affect the output or answer produced. The neural processing element combines all of the inputs to it and produces an output, which is essentially a measure of the match between the input pattern and its connection weights. When hundreds of these neural processors are combined, we have the ability to solve difficult problems such as credit scoring." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"Neural networks are a computing model grounded on the ability to recognize patterns in data. As a consequence, they have many applications to data mining and analysis." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"Neural networks are a computing technology whose fundamental purpose is to recognize patterns in data. Based on a computing model similar to the underlying structure of the human brain, neural networks share the brains ability to learn or adapt in response to external inputs. When exposed to a stream of training data, neural networks can discover previously unknown relationships and learn complex nonlinear mappings in the data. Neural networks provide some fundamental, new capabilities for processing business data. However, tapping these new neural network data mining functions requires a completely different application development process from traditional programming." (Joseph P Bigus, "Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"People build practical, useful mental models all of the time. Seldom do they resort to writing a complex set of mathematical equations or use other formal methods. Rather, most people build models relating inputs and outputs based on the examples they have seen in their everyday life. These models can be rather trivial, such as knowing that when there are dark clouds in the sky and the wind starts picking up that a storm is probably on the way. Or they can be more complex, like a stock trader who watches plots of leading economic indicators to know when to buy or sell. The ability to make accurate predictions from complex examples involving many variables is a great asset." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"Unfortunately, just collecting the data in one place and making it easily available isn’t enough. When operational data from transactions is loaded into the data warehouse, it often contains missing or inaccurate data. How good or bad the data is a function of the amount of input checking done in the application that generates the transaction. Unfortunately, many deployed applications are less than stellar when it comes to validating the inputs. To overcome this problem, the operational data must go through a 'cleansing' process, which takes care of missing or out-of-range values. If this cleansing step is not done before the data is loaded into the data warehouse, it will have to be performed repeatedly whenever that data is used in a data mining operation." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"When training a neural network, it is important to understand when to stop. […] If the same training patterns or examples are given to the neural network over and over, and the weights are adjusted to match the desired outputs, we are essentially telling the network to memorize the patterns, rather than to extract the essence of the relationships. What happens is that the neural network performs extremely well on the training data. However, when it is presented with patterns it hasn't seen before, it cannot generalize and does not perform well. What is the problem? It is called overtraining." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

"While classification is important, it can certainly be overdone. Making too fine a distinction between things can be as serious a problem as not being able to decide at all. Because we have limited storage capacity in our brain (we still haven't figured out how to add an extender card), it is important for us to be able to cluster similar items or things together. Not only is clustering useful from an efficiency standpoint, but the ability to group like things together (called chunking by artificial intelligence practitioners) is a very important reasoning tool. It is through clustering that we can think in terms of higher abstractions, solving broader problems by getting above all of the nitty-gritty details." (Joseph P Bigus,"Data Mining with Neural Networks: Solving business problems from application development to decision support", 1996)

Kenneth E Boulding - Collected Quotes

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

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

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

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

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

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

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

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

21 August 2006

Richard L Daft - Collected Quotes

"A mental model can be thought of as an internal picture that affects a leader's actions and relationships with others. Mental models are theories people hold about specific systems in the world and their expected behavior." (Richard Daft, "The Leadership Experience" , 2002)

"Organizations are (1) social entities that (2) are goal-directed, (3) are designed as deliberately structured and coordinated activity systems, and (4) are linked to the external environment." (Richard Daft, "The Leadership Experience" , 2002)

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

"Systems thinking means the ability to see the synergy of the whole rather than just the separate elements of a system and to learn to reinforce or change whole system patterns. Many people have been trained to solve problems by breaking a complex system, such as an organization, into discrete parts and working to make each part perform as well as possible. However, the success of each piece does not add up to the success of the whole. to the success of the whole. In fact, sometimes changing one part to make it better actually makes the whole system function less effectively." (Richard L Daft, "The Leadership Experience", 2002)

"Systems thinking is a mental discipline and framework for seeing patterns and interrelationships. It is important to see organizational systems as a whole because of their complexity. Complexity can overwhelm managers, undermining confidence. When leaders can see the structures that underlie complex situations, they can facilitate improvement. But doing that requires a focus on the big picture." (Richard L Daft, "The Leadership Experience", 2002)

"Data are raw facts and figures that by themselves may be useless. To be useful, data must be processed into finished information, that is, data converted into a meaningful and useful context for specific users. An increasing challenge for managers is being able to identify and access useful information." (Richard L Daft & Dorothy Marcic, "Understanding Management" 5th Ed., 2006)

"Decision making is the process of identifying problems and opportunities and then resolving them. Decision making involves effort before and after the actual choice." (Richard L Daft & Dorothy Marcic, "Understanding Management" 5th Ed., 2006)

"A paradigm is a shared mindset that represents a fundamental way of thinking about, perceiving, and understanding the world." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

"Leaders should be aware of how their mental models affect their thinking and may cause 'blind spots' that limit understanding. Becoming aware of assumptions is a first step toward shifting one’s mental model and being able to see the world in new and different ways. Four key issues important to expanding and developing a leader’s mind are independent thinking, open-mindedness, systems thinking, and personal mastery." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

"Management can be defined as the attainment of organizational goals in an effective and efficient manner through planning, organizing, staffing, directing, and controlling organizational resources." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

"Strategy is the serious work of figuring out how to translate vision and mission into action. Strategy is a general plan of action that describes resource allocation and other activities for dealing with the environment and helping the organization reach its goals. Like vision, strategy changes, but successful companies develop strategies that focus on core competence, develop synergy, and create value for customers. Strategy is implemented through the systems and structures that are the basic architecture for how things get done in the organization." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

"Synergy is the combined action that occurs when people work together to create new alternatives and solutions. In addition, the greatest opportunity for synergy occurs when people have different viewpoints, because the differences present new opportunities. The essence of synergy is to value and respect differences and take advantage of them to build on strengths and compensate for weaknesses." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

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

"The other element of systems thinking is learning to infl uence the system with reinforcing feedback as an engine for growth or decline. [...] Without this kind of understanding, managers will hit blockages in the form of seeming limits to growth and resistance to change because the large complex system will appear impossible to manage. Systems thinking is a significant solution." (Richard L Daft, "The Leadership Experience" 4th Ed., 2008)

"An organization’s culture is the underlying set of key values, beliefs, understandings, and norms shared by employees. These underlying values and norms may pertain to ethical behavior, commitment to employees, efficiency, or customer service, and they provide the glue to hold organization members together. An organization’s culture is unwritten but can be observed in its stories, slogans, ceremonies, dress, and office layout." (Richard L Daft, "Organization Theory and Design", 3rd Ed., 2010)

"Efficiency refers to the amount of resources used to achieve the organization’s goals. It is based on the quantity of raw materials, money, and employees necessary to produce a given level of output. Effectiveness is a broader term, meaning the degree to which an organization achieves its goals." (Richard L Daft, "Organization Theory and Design", 3rd Ed., 2010)

"Organization theory focuses on the organizational level of analysis but with concern for groups and the environment. To explain the organization, one should look not only at its characteristics but also at the characteristics of the environment and of the departments and groups that make up the organization." (Richard L Daft, "Organization Theory and Design", 3rd Ed., 2010)

"The organization’s goals and strategy define the purpose and competitive techniques that set it apart from other organizations. Goals are often written down as an enduring statement of company intent. A strategy is the plan of action that describes resource allocation and activities for dealing with the environment and for reaching the organization’s goals. Goals and strategies define the scope of operations and the relationship with employees, customers, and competitors." (Richard L Daft, "Organization Theory and Design", 3rd Ed., 2010)

20 August 2006

C Anthony R Hoare - Collected Quotes

"The most important property of a program is whether it accomplishes the intention of its user." (C Anthony R Hoare, Communications of the ACM, 1969)

"A deterministic process may be defined in terms of a mathematical function from its input channels to its output channels. Each channel is identified with the indefinitely extensible sequence of messages which pass along it. Such functions are defined in the usual way by recursion on the structure of the input sequences, except that the case of an empty input sequence is not considered." (C Anthony R Hoare, "Communicating Sequential Processes", 1985)

"A process is defined by describing the whole range of its potential behaviour. Frequently, there will be a choice between several different actions [...]. On each such occasion, the choice of which event will actually occur can be controlled by the environment within which the process evolves. [...] . Fortunately, the environment of a process itself may be described as a process, with its behaviour defined by familiar notations. This permits investigation of the behaviour of a complete system composed from the process together with its environment, acting and interacting with each other as they evolve concurrently. The complete system should also be regarded as a process, whose range of behaviour is definable in terms of the behaviour of its component processes; and the system may in turn be placed within a yet wider environment. In fact, it is best to forget the distinction between processes, environments, and systems; they are all of them just processes whose behaviour may be prescribed, described, recorded and analysed in a simple and homogeneous fashion." (C Anthony R Hoare, "Communicating Sequential Processes", 1985)

"In constructing a mathematical model of a physical system, it is a good strategy to define the basic concepts in terms of attributes that can be directly or indirectly observed or measured." (C Anthony R Hoare, "Communicating Sequential Processes", 1985)

"In the design of a product, the designer has a responsibility to ensure that it will satisfy its specification; this responsibility may be discharged by the reasoning methods of the relevant branches of mathematics, for example, geometry or the differential and integral calculus." (C Anthony R Hoare, "Communicating Sequential Processes", 1985)

"Recognition of the idea that a programming language should have a precise mathematical meaning or semantics dates from the early 1960s. The mathematics provides a secure, unambiguous, precise and stable specification of the language to serve as an agreed interface between its users and its implementors. Furthermore, it gives the only reliable grounds for a claim that different implementations are implementations of the same language. So mathematical semantics are as essential to the objective of language standardisation as measurement and counting are to the standardisation of nuts and bolts." (C Anthony R Hoare, "Communicating Sequential Processes", 1985)

"Recursion permits the definition of a single process as the solution of a single equation. The technique is easily generalised to the solution of sets of simultaneous equations in more than one unknown. For this to work properly, all the right-hand sides must be guarded, and each unknown process must appear exactly once on the left-hand side of one of the equations." (C Anthony R Hoare, "Communicating Sequential Processes", 1985)

"There are two ways of constructing a software design. One way is to make it so simple that there are obviously no deficiencies. And the other way is to make it so complicated that there are no obvious deficiencies. It demands the same skill, devotion, insight, and even inspiration as the discovery of the simple physical laws which underlie the complex phenomena of nature." (C Anthony R Hoare, [lecture] 1987)

"The real value of tests is not that they detect bugs in the code, but that they detect inadequacies in the methods, concentration, and skills of those who design and produce the code." (C Anthony R Hoare, "How Did Software Get So Reliable Without Proof?", Lecture Notes in Computer Science Vol. 1051, 1996)

"Professional practice in a mature engineering discipline is based on relevant scientific theories, usually expressed in the language of mathematics. A mathematical theory of programming aims to provide a similar basis for specification, design and implementation of computer programs."  (C Anthony R Hoare, "Unified Theories of Programming", 1998)

"Programming languages on the whole are very much more complicated than they used to be: object orientation, inheritance, and other features are still not really being thought through from the point of view of a coherent and scientifically well-based discipline or a theory of correctness. My original postulate, which I have been pursuing as a scientist all my life, is that one uses the criteria of correctness as a means of converging on a decent programming language design - one which doesn’t set traps for its users, and ones in which the different components of the program correspond clearly to different components of its specification, so you can reason compositionally about it. [...] The tools, including the compiler, have to be based on some theory of what it means to write a correct program." (C Anthony R Hoare, [interview] 2002)

19 August 2006

Stephen G Haines - Collected Quotes

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

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

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

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

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

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

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

18 August 2006

George B Dantzig - Collected Quotes

"All such problems can be formulated as mathematical programming problems. Naturally, we can propose many sophisticated algorithms and a theory but the final test of a theory is its capacity to solve the problems which originated it." (George B Dantzig, "Linear Programming and Extensions", 1963)

"If the system exhibits a structure which can be represented by a mathematical equivalent, called a mathematical model, and if the objective can be also so quantified, then some computational method may be evolved for choosing the best schedule of actions among alternatives. Such use of mathematical models is termed mathematical programming." (George B Dantzig, "Linear Programming and Extensions", 1963)

"Linear programming is viewed as a revolutionary development giving man the ability to state general objectives and to find, by means of the simplex method, optimal policy decisions for a broad class of practical decision problems of great complexity. In the real world, planning tends to be ad hoc because of the many special-interest groups with their multiple objectives." (George B Dantzig, "Mathematical Programming: The state of the art", 1983)

"Linear programming and its generalization, mathematical programming, can be viewed as part of a great revolutionary development that has given mankind the ability to state general goals and lay out a path of detailed decisions to be taken in order to 'best' achieve these goals when faced with practical situations of great complexity. The tools for accomplishing this are the models that formulate real-world problems in detailed mathematical terms, the algorithms that solve the models, and the software that execute the algorithms on computers based on the mathematical theory."  (George B Dantzig & Mukund N Thapa, "Linear Programming" Vol I, 1997)

"Linear programming is concerned with the maximization or minimization of a linear objective function in many variables subject to linear equality and inequality constraints."  (George B Dantzig & Mukund N Thapa, "Linear Programming" Vol I, 1997)

"Mathematical programming (or optimization theory) is that branch of mathematics dealing with techniques for maximizing or minimizing an objective function subject to linear, nonlinear, and integer constraints on the variables."  (George B Dantzig & Mukund N Thapa, "Linear Programming" Vol I, 1997)

"Models of the real world are not always easy to formulate because of the richness, variety, and ambiguity that exists in the real world or because of our ambiguous understanding of it." (George B Dantzig & Mukund N Thapa, "Linear Programming" Vol I, 1997)

"The linear programming problem is to determine the values of the variables of the system that (a) are nonnegative or satisfy certain bounds, (b) satisfy a system  of linear constraints, and (c) minimize or maximize a linear form in the variables called an objective." (George B Dantzig & Mukund N Thapa, "Linear Programming" Vol I, 1997)

"The mathematical model of a system is the collection of mathematical relationships which, for the purpose of developing a design or plan, characterize the set of feasible solutions of the system." (George B Dantzig & Mukund N Thapa, "Linear Programming" Vol I, 1997)

17 August 2006

Peter F Drucker - Collected Quotes

"What the worker needs is to see the plant as if he were a manager. Only thus can he see his part, from his part he can reach the whole. This ‘seeing’ is not a matter of information, training courses, conducted plant tours, or similar devices. What is needed is the actual experience of the whole in and through the individual's work." (Peter F Drucker, "The New Society", 1950)

"A manager sets objectives - A manager organizes - A manager motivates and communicates - A manager, by establishing yardsticks, measures." (Peter F Drucker, "The Practice of Management", 1954)

"Business is a process which converts a resource, distinct knowledge, into a contribution of economic value in the market place. The purpose of a business is to create a customer. The purpose is to provide something for which an independent outsider, who can choose not to buy, is willing to exchange his purchasing power. And knowledge alone (excepting only the case of the complete monopoly) gives the products of any business that leadership position on which success and survival ultimately depend." (Peter F Drucker, "Managing for Results: Economic Tasks and Risk-taking Decisions", 1964)

"But waste is often hard to find. The costs of not-doing tend to be hidden in the figures. […] Waste runs high in any business. Man, after all, is not very efficient. Special efforts to find waste are therefore always necessary." (Peter F Drucker, "Managing for Results: Economic Tasks and Risk-taking Decisions", 1964)

"Costs - their identification, measurement, and control - are the most thoroughly worked, if not overworked, business area. […] Altogether focusing resources on results is the best and most effective cost control. Cost, after all, does not exist by itself. It is always incurred - in intent at least - for the sake of a result. What matters therefore is not the absolute cost level but the ratio between efforts and their results." (Peter F Drucker, "Managing for Results: Economic Tasks and Risk-taking Decisions", 1964)

"Results are obtained by exploiting opportunities, not by solving problems. [...] Resources, to produce results, must be allocated to opportunities rather than to problems." (Peter F Drucker, "Managing for Results: Economic Tasks and Risk-taking Decisions", 1964)

"The best way to come to grips with one’s own business knowledge is to look at the things the business has done well, and the things it apparently does poorly. […] Knowledge is a perishable commodity. It has to be reaffirmed, relearned, repracticed all the time. One has to work constantly at regaining one’s specific excellence. […] The right knowledge is the knowledge needed to exploit the market opportunities." (Peter F Drucker, "Managing for Results: Economic Tasks and Risk-taking Decisions", 1964)

"There are three different dimensions to the economic task: (1) The present business must be made effective; (2) its potential must be identified and realized; (3) it must be made into a different business for a different future. Each task requires a distinct approach. Each asks different questions. Each comes out with different conclusions. Yet they are inseparable. All three have to be done at the same time: today." (Peter F Drucker, "Managing for Results: Economic Tasks and Risk-taking Decisions", 1964)

"To be able to control costs, a business therefore needs a cost analysis which: Identifies the cost centers - that is, the areas where the significant costs are, and where effective cost reduction can really produce results. Finds what the important cost points are in each major cost center. Looks at the entire business as one cost stream. Defines ‘cost’ as what the customer pays rather than as what the legal or tax unit of accounting incurs. Classifies costs according to their basic characteristics and thus produces a cost diagnosis." (Peter F Drucker, "Managing for Results: Economic Tasks and Risk-taking Decisions", 1964)

"Modern organization makes demands on the individual to learn something he has never been able to do before: to use organization intelligently, purposefully, deliberately, responsibly [...] to manage organization [...] to make [...] his job in it serve his ends, his values, his desire to achieve." (Peter F Drucker, The Age of Discontinuity, 1968)

"Effectiveness is the foundation of success - efficiency is a minimum condition for survival after success has been achieved. Efficiency is concerned with doing things right. Effectiveness is doing the right things." (Peter Drucker, "Management: Tasks, Responsibilities, Challenges", 1973)

"Leadership is lifting a person's vision to higher sights, the raising of a person's performance to a higher standard, the building of a personality beyond its normal limitations." (Peter Drucker, "Management: Tasks, Responsibilities, Challenges", 1973)

"[Management] has authority only as long as it performs." (Peter F Drucker, "Management: Tasks, Responsibilities, Practices", 1973)

"Managers, therefore, need to be skilled in making decisions with long futurity on a systematic basis. Management has no choice but to anticipate the future, to attempt to mold it, and to balance short-range and long-range goals.[…] 'Short range' and 'long range' are not determined by any given time span. A decision is not short range because it takes only a few months to carry it out. What matters is the time span over which it is effective. […] The skill we need is not long-range planning. It is strategic decision-making, or perhaps strategic planning." (Peter F Drucker, "Management: Tasks, Responsibilities, Practices", 1973)

"Organizationally what is required - and evolving - is systems management." (Peter F Drucker, "Management: Tasks, Responsibilities, Practices", 1973)

"[…] strategic planning […] is the continuous process of making present entrepreneurial (risk-taking) decisions systematically and with the greatest knowledge of their futurity; organizing systematically the efforts needed to carry out these decisions; and measuring the results of these decisions against the expectations through organized, systematic feedback." (Peter F Drucker, "Management: Tasks, Responsibilities, Practices", 1973)

"Strategic planning is not the 'application of scientific methods to business decision' […] . It is the application of thought, analysis, imagination, and judgment. It is responsibility, rather than technique. […] Strategy planning is not forecasting. […] Strategic planning is necessary precisely because we cannot forecast. […] Strategic planning does nor deal with future decisions. It deals with the futurity of present decisions. […] Strategic planning is not an attempt to eliminate risk. It is not even an attempt to minimize risk." (Peter F Drucker, "Management: Tasks, Responsibilities, Practices", 1973)

"'Structure follows strategy' is one of the fundamental insights we have acquired in the last twenty years. Without understanding the mission, the objectives, and the strategy of the enterprise, managers cannot be managed, organizations cannot be designed, managerial jobs cannot be made productive. [...] Strategy determines what the key activities are in a given business. And strategy requires knowing 'what our business is and what it should be'." (Peter F Drucker, "Management: Tasks, Responsibilities, Practices", 1973)

"There is a point of complexity beyond which a business is no longer manageable." (Peter F Drucker, "Management: Tasks, Responsibilities, Practices", 1973)

"The manager is a servant. His master is the institution he manages and his first responsibility must therefore be to it." (Peter F Drucker, "Management: Tasks, Responsibilities, Practices", 1973)

"The worker's effectiveness is determined largely by the way he is being managed. (Peter F Drucker, Management: Tasks, Responsibilities, Practices", 1973)

"Above all, innovation is not invention. It is a term of economics rather than of technology. [...] The measure of innovation is the impact on the environment. [...] To manage innovation, a manager has to be at least literate with respect to the dynamics of innovation." (Peter F Drucker, "People and Performance", 1977)

"'Management' means, in the last analysis, the substitution of thought for brawn and muscle, of knowledge for folkways and superstition, and of cooperation for force. It means the substitution of responsibility for obedience to rank, and of authority of performance for authority of rank. (Peter F Drucker, "People and Performance", 1977)

"Objectives are not fate; they are direction. They are not commands; they are commitments. They do not determine the future; they are means to mobilize the resources and energies of the business for the making of the future." (Peter F Drucker, "People and Performance", 1977)

"[...] the first criterion in identifying those people within an organization who have management responsibility is not command over people. It is responsibility for contribution. Function rather than power has to be the distinctive criterion and the organizing principle." (Peter F Drucker, "People and Performance", 1977)

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

"Knowledge work, unlike manual work, cannot be replaced by capital investment. On the contrary, capital investment creates the need for more knowledge work." (Peter F Drucker, "Management in Turbulent Times", 1980)

"The productivity of work is not the responsibility of the worker but of the manager." (Peter F Drucker, "Management in Turbulent Times", 1980)

"Top management work is work for a team rather than one man." (Peter F Drucker, "Memos for Management: Leadership", 1983)

"Knowledge has to be improved, challenged, and increased constantly, or it vanishes." (Peter F Drucker) 

"So much of what we call management consists in making it difficult for people to work." (Peter F Drucker)

Henry Mintzberg - Collected Quotes

"Five coordinating mechanisms seem to explain the fundamental ways in which organizations coordinate their work: mutual adjustment, direct supervision, standardization of work processes, standardization of work outputs, and standardization of worker skills." (Henry Mintzberg, "The Structuring of Organizations", 1979)

"We find that the manager, particularly at senior levels, is overburdened with work. With the increasing complexity of modern organizations and their problems, he is destined to become more so. He is driven to brevity, fragmentation, and superficiality in his tasks, yet he cannot easily delegate them because of the nature of his information. And he can do little to increase his available time or significantly enhance his power to manage. Furthermore, he is driven to focus on that which is current and tangible in his work, even though the complex problems facing many organizations call for reflection and a far-sighted perspective." (Henry Mintzberg, "The Structuring of Organizations", 1979)

"[…] the most successful strategies are visions, not plans. Strategic planning isn’t strategic thinking. One is analysis, and the other is synthesis." (Henry Mintzberg, "The Fall and Rise of Strategic Planning", Harvard Business Review, 1994) [source] 

"Sometimes strategies must be left as broad visions, not precisely articulated, to adapt to a changing environment." (Henry Mintzberg, "The Fall and Rise of Strategic Planning", Harvard Business Review, 1994) [source] 

"Strategy making needs to function beyond the boxes to encourage the informal learning that produces new perspectives and new combinations. […] Once managers understand this, they can avoid other costly misadventures caused by applying formal techniques, without judgement and intuition, to problem solving." (Henry Mintzberg, 1994)

"Strategy-making is an immensely complex process involving the most sophisticated, subtle, and at times subconscious of human cognitive and social processes." (Henry Mintzberg, "Strategy Safari: A Guided Tour Through The Wilds of Strategic Mangement", 2005)

"Theory is a dirty word in some managerial quarters. That is rather curious, because all of us, managers especially, can no more get along without theories than libraries can get along without catalogs - and for the same reason: theories help us make sense of incoming information." (Henry Mintzberg," Managers Not MBAs", 2005) 

"The real challenge in crafting strategy lies in detecting subtle discontinuities that may undermine a business in the future. And for that there is no technique, no program, just a sharp mind in touch with the situation." (Henry Mintzberg, "Tracking Strategies: Toward a General Theory", 2007)

"Strategic planning is not strategic thinking. Indeed, strategic planning often spoils strategic thinking, causing managers to confuse real vision with the manipulation of numbers." (Henry Mintzberg)

16 August 2006

A Stafford Beer - Collected Quotes

"[…] cybernetics studies the flow of information round a system, and the way in which this information is used by the system as a means of controlling itself: it does this for animate and inanimate systems indifferently. For cybernetics is an interdisciplinary science, owing as much to biology as to physics, as much to the study of the brain as to the study of computers, and owing also a great deal to the formal languages of science for providing tools with which the behaviour of all these systems can be objectively described." (A Stafford Beer, 1966)

"If cybernetics is the science of control, management is the profession of control." (A Stafford Beer, "Decision and Control", 1966)

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

"Policy-making, decision-taking, and control: These are the three functions of management that have intellectual content." (A Stafford Beer, "Management Science" , 1968)

"Management is not founded on observation and experiment, but on a drive towards a set of outcomes. These aims are not altogether explicit; at one extreme they may amount to no more than an intention to preserve the status quo, at the other extreme they may embody an obsessional demand for power, profit or prestige. But the scientist's quest for insight, for understanding, for wanting to know what makes the system tick, rarely figures in the manager's motivation. Secondly, and therefore, management is not, even in intention, separable from its own intentions and desires: its policies express them. Thirdly, management is not normally aware of the conventional nature of its intellectual processes and control procedures. It is accustomed to confuse its conventions for recording information with truths-about-the-business, its subjective institutional languages for discussing the business with an objective language of fact and its models of reality with reality itself." (A Stafford Beer, "Decision and Control", 1994)

"Industrial managers faced with a problem in production control invariably expect a solution to be devised that is simple and unidimensional. They seek the variable in the situation whose control will achieve control of the whole system: tons of throughput, for example. Business managers seek to do the same thing in controlling a company; they hope they have found the measure of the entire system when they say 'everything can be reduced to monetary terms'." (A Stafford Beer, "Decision and Control", 1994)

"The trouble is that no manager can really handle the full-scale isomorph of his enterprise unless he is the only employee. To delegate is to embark on a series of one-many transformations. The manager can at best settle for a homomorph consisting of all the ones." (A Stafford Beer, "Decision and Control", 1994)

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