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The Sciences of the Artificial
Continuing his exploration of the organization of complexity and the science of design, this new edition of Herbert Simon's classic work on artificial intelligence adds a chapter that sorts out the current themes and tools -- chaos, adaptive systems, genetic algorithms -- for analyzing complexity and complex systems. There are updates throughout the book as well. These take into account important advances in cognitive psychology and the science of design while confirming and extending the book's basic thesis: that a physical symbol system has the necessary and sufficient means for intelligent action. The chapter "Economic Reality" has also been revised to reflect a change in emphasis in Simon's thinking about the respective roles of organizations and markets in economic systems.
"People sometimes ask me what they should read to find out about artificial intelligence. Herbert Simon's book The Sciences of the Artificial is always on the list I give them. Every page issues a challenge to conventional thinking, and the layman who digests it well will certainly understand what the field of artificial intelligence hopes to accomplish. I recommend it in the same spirit that I recommend Freud to people who ask about psychoanalysis, or Piaget to those who ask about child psychology: If you want to learn about a subject, start by reading its founding fathers." -- George A. Miller
"People sometimes ask me what they should read to find out about artificial intelligence. Herbert Simon's book The Sciences of the Artificial is always on the list I give them. Every page issues a challenge to conventional thinking, and the layman who digests it well will certainly understand what the field of artificial intelligence hopes to accomplish. I recommend it in the same spirit that I recommend Freud to people who ask about psychoanalysis, or Piaget to those who ask about child psychology: If you want to learn about a subject, start by reading its founding fathers." -- George A. Miller
248 pages, Paperback
First published January 1, 1969
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Displaying 1 - 30 of 57 reviews
July 21, 2011
Short version: absolutely, 100%, fucking brilliant.
Long version: this book is a brief glimpse into the incredibly well-organized mind of a genius, a man who could deftly tie together fields as diverse as biology, psychology, economics, design, computer science, and social science (and show with infuriating casualness how well he understands each of them) into a single grand exploration of a theme that pervades them all: apparent complexity emerging from simplicity in a complex environment. The expedition is framed as a study of the artificial, broadly defined as anything created by humans,-- a class into which business firms, machines, computer programs and human experience are all masterfully corralled-- but extends well beyond those bounds into the natural as well.
My favorite quote from the book, and one I will most certainly be using in my Masters thesis on a related idea, is, "Human beings, viewed as behaving systems, are quite simple. The apparent complexity of our behavior over time is largely a reflection of the complexity of the environment in which we find ourselves." While only a few chapters deal directly with this approach to human psychology, the remainder of the book hearkens strongly back to that idea, since both writer and reader are necessarily bound to a human brain.
Simon masterfully illustrates why we should all be thinking in terms of design and hierarchical systems, no matter what we do, and I can't imagine anyone reading the book from front to back and not agreeing. My intellectual life has been fundamentally changed in the reading, and definitely for the better.
If you are a cognitive scientist, designer, architect, psychologist, computer scientist, economist, biologist, or social scientist, you really can't afford to not read this boundary-breaking masterpiece.
Long version: this book is a brief glimpse into the incredibly well-organized mind of a genius, a man who could deftly tie together fields as diverse as biology, psychology, economics, design, computer science, and social science (and show with infuriating casualness how well he understands each of them) into a single grand exploration of a theme that pervades them all: apparent complexity emerging from simplicity in a complex environment. The expedition is framed as a study of the artificial, broadly defined as anything created by humans,-- a class into which business firms, machines, computer programs and human experience are all masterfully corralled-- but extends well beyond those bounds into the natural as well.
My favorite quote from the book, and one I will most certainly be using in my Masters thesis on a related idea, is, "Human beings, viewed as behaving systems, are quite simple. The apparent complexity of our behavior over time is largely a reflection of the complexity of the environment in which we find ourselves." While only a few chapters deal directly with this approach to human psychology, the remainder of the book hearkens strongly back to that idea, since both writer and reader are necessarily bound to a human brain.
Simon masterfully illustrates why we should all be thinking in terms of design and hierarchical systems, no matter what we do, and I can't imagine anyone reading the book from front to back and not agreeing. My intellectual life has been fundamentally changed in the reading, and definitely for the better.
If you are a cognitive scientist, designer, architect, psychologist, computer scientist, economist, biologist, or social scientist, you really can't afford to not read this boundary-breaking masterpiece.
February 3, 2020
In a nutshell, what is this book with a rather mysterious title about?
The task of an adaptive organism is to find the difference between an existing state and a desired state and then find the correlating process that will erase the difference. This is a means-end analysis. Simon’s ‘Sciences of the Artificial’ proposes a generic theory of ways in which humans conduct this means-end analysis. It is a theory of human problem solving. For Simon, problem solving is design, is tinkering with artifacts. The Sciences of the Artificial are therefore a meta-design theory.
We'll go into somewhat more depth now. Let me assure you that this book, which presents itself rather innocuously as a popularizing excursion by one of the previous century's most gifted polymaths, demands a lot from the reader. That friction is a result of the density, substance and organisation of the material that is collected in this book.
To start with the latter: the first edition was published in 1969 and included the three Karl Taylor Compton lectures delivered by Simon at MIT in the spring of the previous year. The book counted a mere 120 pages. The third edition, published in 1998, added a good hundred pages to the original. The reader cannot but notice that the book has grown by accretion, adding layers without substantive rewriting of the original material. And this does not make for the most clear or helpful line through the author's argument.
Qua substance, the book by its very nature encompasses a very wide scope. If we understand cybernetics to be the science that seeks to understand the 'adaptive brain' (Pickering), then Simon's 'Sciences of the Artificial' squarely fits into this tradition. But in doing so, it roams over a patchwork of disciplines, including organisational decision-making, economics, human cognition and artificial intelligence.
Finally, the book provides some sort of 'summa' of a very long career and hence is saturated with ideas. But the introductory nature of 'Sciences' correlates with a rather informal writing style that skirts some of the underlying subtleties. Hence, even the foundational notion of 'the artificial' remains ambiguous in the end. Eventually it becomes clear that human beings themselves belong to the realm of the artificial. Indeed, they are probably the most important class of artifacts given that they are able not only to create other artifacts but also to re-engineer themselves (i.e. 'reconfigure the appreciative basis for their existence', according to Geoffrey Vickers) to fit changing circumstances. But this statement is buried somewhere deep in the argument.
So we concur with Saras Sarasvathy, who was tutored by Simon in her PhD research on effectual entrepreneurship, when she sums up the merits of the book as follows: "Sciences of the Artificial is one of the most exciting pieces Simon has ever published. In an oeuvre of over a thousand publications, that is saying a lot. But it is also, in my considered opinion, one of the most irritating. It bursts at its seams with brilliant ideas and mouth-watering possibilities for scholarship and pedagogy, but does not develop many of these into something readers can sink their teeth into, especially in the domains of management and economics. One is left with a sense of the enormity of work to be done, but not quite sure where to begin."
The book (Third Edition) is organised in eight chapters. However, in my mind I reorganised it in six parts to clarify the general flow of the argument. In Part I (Chapter 1), Simon introduces the scope of his project and the notion of 'the artificial'. As I indicated, although fascinating enough, this curtain raiser leaves important questions in suspension. Simon then goes on to discuss how human beings use certain artifacts - notably markets and corporations - to solve complex allocation problems (Chapter 2, Part II). These artifacts are more intelligent devices for means-ends analysis that centralised planning authorities by virtue of their decentralized sense-making and decision-making. It is in this context that Simon introduces the pivotal notion of 'bounded rationality'.
One of Simon’s key contributions, for which he was awarded a Nobel Prize, was to question the orthodox concept of economic rationality. Instead of an economic agent engaging in rational maximization he postulates the existence of fallible agents subject to bounded rationality. This notion expresses that human rationality is always limited by 1) the cognitive limitations of their minds, 2) the time available to make a decision, and 3) the complexity of the decision problem. As a result, we have to drop the illusion that we are in a position to choose an optimal course of action. Rather we have to find a way of assessing where a reasonable solution lies. Economic agents are not optimizers but satisficers.
In Part III (Chapter 3 and 4) the perspective shifts abruptly to human decision-making. Economic complexity is exchanged for the relative simplicity of short-range design challenges, i.e. well-defined, short-term, laboratory-style decision-making problems that reveal something of the inner workings of our own information-processing system. Based on experimental evidence, Simon hypothesizes a rather simple and crude artifact that suffers from stringent neurological limitations. Human beings' external environment is complex, but their inner environment, the hardware, is straightforward. It consists of a system that is basically serial in its operation, that can process only a few symbols at a time and that is relatively slow to transfer information to long-term memory. Superimposed on this are sets of generic control and search-guided mechanisms, and memory-based learning and discovery mechanisms that permit the system to adapt with gradually increasing effectiveness to the particular environment in which it finds itself.
Chapter 5 and 6 (Part IV and V) extrapolate these findings to progressively more complex decision-making problems (say, mid-range and long-range design challenges). Optimization problems offer a basic structure to reason about problem solving strategies in the way they align external constraints, alternatives for action and our subjective assessment of the value of these alternatives. This leads Simon to posit three activities as core elements of human problem solving skills: the ability to conduct a heuristic search for alternatives, the ability to evaluate solutions, and the the ability to allocate resources for search.
Enter one of Simon’s key metaphors: the maze. ”Human problem solving involves nothing more than varying mixtures of trial and error and selectivity. The selectivity derives from various rules of thumb, or heuristics, that suggest which paths should be tried first and which leads are promising.” Understanding this process of ‘heuristic search’ in human affairs is at the core of Simon’s life work. Considered as such, his work is pendant to Charles Darwin’s theory of evolution of natural species.
Throughout the argument, Simon discusses how these ideas found implementation into early AI concepts and algorithms. However, this conception of AI - as representational and symbol-processing in nature - has nowadays been overriden with a distributed and neurological way of operationalizing artificial intelligence.
It was Herbert Simon's key ambition to use these insights more generally as the basis for a design curriculum. Simon saw his design theory as a bridge to connect ‘epistemic communities’ that are usually disconnected. In essence, composers, medical professionals, engineers and managers are all doing the same thing. They are designing, i.e. they are ‘devising courses of action aimed at changing existing situations into preferred ones.’ Understanding the core underlying problem solving processes would enable these professionals to engage in meaningful conversation.
Chapter 7 offers a bridge into the argument of the final chapter (Part VI) where Simon discusses the properties of so-called 'hierarchical systems'. These are systems that are composed of interrelated subsystems, each of them being in turn hierarchic in structure until some lowest level of elementary subsystem is reached (for instance, animals including organs including tissues including cells). This particular architecture offers significant advantages in dealing with external complexity. The existence of subsystems (or 'intermediate stable forms') leads these systems to evolve more rapidly and hence allows them to cumulate the benefits of learning over time. This is why these systems are present everywhere around us. Markets and organizations, for instance, are hierarchically constructed artifacts created by human beings to navigate in a parsimonious way through the maze, in never-ending search for local optima. Also the brain is a hierarchical system, both in its neurological structure and in the symbolic complexes it relies on to solve problems.
'Sciences of the Artificial' offers a broad vista on a fascinating body of work. I see it as providing a necessary bridge between a 'hard', positivist and a 'soft', constructivist approach to human problem solving. These are not only dispassionate theories about thinking machines. There is humanity and realism in Simon's vision, as it is touchingly rendered by the book's envoi at the end of Chapter 6:
The task of an adaptive organism is to find the difference between an existing state and a desired state and then find the correlating process that will erase the difference. This is a means-end analysis. Simon’s ‘Sciences of the Artificial’ proposes a generic theory of ways in which humans conduct this means-end analysis. It is a theory of human problem solving. For Simon, problem solving is design, is tinkering with artifacts. The Sciences of the Artificial are therefore a meta-design theory.
We'll go into somewhat more depth now. Let me assure you that this book, which presents itself rather innocuously as a popularizing excursion by one of the previous century's most gifted polymaths, demands a lot from the reader. That friction is a result of the density, substance and organisation of the material that is collected in this book.
To start with the latter: the first edition was published in 1969 and included the three Karl Taylor Compton lectures delivered by Simon at MIT in the spring of the previous year. The book counted a mere 120 pages. The third edition, published in 1998, added a good hundred pages to the original. The reader cannot but notice that the book has grown by accretion, adding layers without substantive rewriting of the original material. And this does not make for the most clear or helpful line through the author's argument.
Qua substance, the book by its very nature encompasses a very wide scope. If we understand cybernetics to be the science that seeks to understand the 'adaptive brain' (Pickering), then Simon's 'Sciences of the Artificial' squarely fits into this tradition. But in doing so, it roams over a patchwork of disciplines, including organisational decision-making, economics, human cognition and artificial intelligence.
Finally, the book provides some sort of 'summa' of a very long career and hence is saturated with ideas. But the introductory nature of 'Sciences' correlates with a rather informal writing style that skirts some of the underlying subtleties. Hence, even the foundational notion of 'the artificial' remains ambiguous in the end. Eventually it becomes clear that human beings themselves belong to the realm of the artificial. Indeed, they are probably the most important class of artifacts given that they are able not only to create other artifacts but also to re-engineer themselves (i.e. 'reconfigure the appreciative basis for their existence', according to Geoffrey Vickers) to fit changing circumstances. But this statement is buried somewhere deep in the argument.
So we concur with Saras Sarasvathy, who was tutored by Simon in her PhD research on effectual entrepreneurship, when she sums up the merits of the book as follows: "Sciences of the Artificial is one of the most exciting pieces Simon has ever published. In an oeuvre of over a thousand publications, that is saying a lot. But it is also, in my considered opinion, one of the most irritating. It bursts at its seams with brilliant ideas and mouth-watering possibilities for scholarship and pedagogy, but does not develop many of these into something readers can sink their teeth into, especially in the domains of management and economics. One is left with a sense of the enormity of work to be done, but not quite sure where to begin."
The book (Third Edition) is organised in eight chapters. However, in my mind I reorganised it in six parts to clarify the general flow of the argument. In Part I (Chapter 1), Simon introduces the scope of his project and the notion of 'the artificial'. As I indicated, although fascinating enough, this curtain raiser leaves important questions in suspension. Simon then goes on to discuss how human beings use certain artifacts - notably markets and corporations - to solve complex allocation problems (Chapter 2, Part II). These artifacts are more intelligent devices for means-ends analysis that centralised planning authorities by virtue of their decentralized sense-making and decision-making. It is in this context that Simon introduces the pivotal notion of 'bounded rationality'.
One of Simon’s key contributions, for which he was awarded a Nobel Prize, was to question the orthodox concept of economic rationality. Instead of an economic agent engaging in rational maximization he postulates the existence of fallible agents subject to bounded rationality. This notion expresses that human rationality is always limited by 1) the cognitive limitations of their minds, 2) the time available to make a decision, and 3) the complexity of the decision problem. As a result, we have to drop the illusion that we are in a position to choose an optimal course of action. Rather we have to find a way of assessing where a reasonable solution lies. Economic agents are not optimizers but satisficers.
In Part III (Chapter 3 and 4) the perspective shifts abruptly to human decision-making. Economic complexity is exchanged for the relative simplicity of short-range design challenges, i.e. well-defined, short-term, laboratory-style decision-making problems that reveal something of the inner workings of our own information-processing system. Based on experimental evidence, Simon hypothesizes a rather simple and crude artifact that suffers from stringent neurological limitations. Human beings' external environment is complex, but their inner environment, the hardware, is straightforward. It consists of a system that is basically serial in its operation, that can process only a few symbols at a time and that is relatively slow to transfer information to long-term memory. Superimposed on this are sets of generic control and search-guided mechanisms, and memory-based learning and discovery mechanisms that permit the system to adapt with gradually increasing effectiveness to the particular environment in which it finds itself.
Chapter 5 and 6 (Part IV and V) extrapolate these findings to progressively more complex decision-making problems (say, mid-range and long-range design challenges). Optimization problems offer a basic structure to reason about problem solving strategies in the way they align external constraints, alternatives for action and our subjective assessment of the value of these alternatives. This leads Simon to posit three activities as core elements of human problem solving skills: the ability to conduct a heuristic search for alternatives, the ability to evaluate solutions, and the the ability to allocate resources for search.
Enter one of Simon’s key metaphors: the maze. ”Human problem solving involves nothing more than varying mixtures of trial and error and selectivity. The selectivity derives from various rules of thumb, or heuristics, that suggest which paths should be tried first and which leads are promising.” Understanding this process of ‘heuristic search’ in human affairs is at the core of Simon’s life work. Considered as such, his work is pendant to Charles Darwin’s theory of evolution of natural species.
Throughout the argument, Simon discusses how these ideas found implementation into early AI concepts and algorithms. However, this conception of AI - as representational and symbol-processing in nature - has nowadays been overriden with a distributed and neurological way of operationalizing artificial intelligence.
It was Herbert Simon's key ambition to use these insights more generally as the basis for a design curriculum. Simon saw his design theory as a bridge to connect ‘epistemic communities’ that are usually disconnected. In essence, composers, medical professionals, engineers and managers are all doing the same thing. They are designing, i.e. they are ‘devising courses of action aimed at changing existing situations into preferred ones.’ Understanding the core underlying problem solving processes would enable these professionals to engage in meaningful conversation.
Chapter 7 offers a bridge into the argument of the final chapter (Part VI) where Simon discusses the properties of so-called 'hierarchical systems'. These are systems that are composed of interrelated subsystems, each of them being in turn hierarchic in structure until some lowest level of elementary subsystem is reached (for instance, animals including organs including tissues including cells). This particular architecture offers significant advantages in dealing with external complexity. The existence of subsystems (or 'intermediate stable forms') leads these systems to evolve more rapidly and hence allows them to cumulate the benefits of learning over time. This is why these systems are present everywhere around us. Markets and organizations, for instance, are hierarchically constructed artifacts created by human beings to navigate in a parsimonious way through the maze, in never-ending search for local optima. Also the brain is a hierarchical system, both in its neurological structure and in the symbolic complexes it relies on to solve problems.
'Sciences of the Artificial' offers a broad vista on a fascinating body of work. I see it as providing a necessary bridge between a 'hard', positivist and a 'soft', constructivist approach to human problem solving. These are not only dispassionate theories about thinking machines. There is humanity and realism in Simon's vision, as it is touchingly rendered by the book's envoi at the end of Chapter 6:
"Our age is one in which people are not reluctant to express their pessimism and anxieties. It is true that humanity is faced with many problems. It always has been but perhaps not always with such keen awereness of them as we have today. We might be more optimistic if we recognized that we do not have to solve all of these problems. Our essential task – a big enough one to be sure – is simply to keep open the options for the future or perhaps even to broaden them a bit by creating new variety and new niches. Our grandchildren cannot ask more of us than that we offer to them the same chance for adventure, for the pursuit of new and interesting designs, than we have had.”
October 12, 2020
Depending on your background, different parts of this book will strike very different chords. In any case, it's not easy to do justice to a great intellectual whose remarkable breadth and depth still resonate with readers in 21. century. Therefore, suffice it to say that his crystal clear language is like a breath of fresh air when it comes to technical writing, and every author should aspire to that level of standard in their own writing (which is easier said than done because it requires not only a very good command of the language, but also very clear thinking about the subject matter, and its associations).
October 2, 2022
Herb seems like that cool, calm, and wise mentor who'll wait for you to run around in circles, confused by your own methods, and then reassuringly pat you on the back and give his simpler, classical take that works 97% of the time. He's the kind of guy who's inordinately well-read across the sciences but can easily convince you with a back-of-napkin calculation.
Sadly, he could write a bit more coherently, especially between sections. This book is very much a weakly connected set of essays; some more tedious than others. In my opinion, it's, in his words, "a nearly decomposable system". I found it challenging to connect all his ideas together, though I know some central patterns exist... somewhere. I'll need to reread it.
Sadly, he could write a bit more coherently, especially between sections. This book is very much a weakly connected set of essays; some more tedious than others. In my opinion, it's, in his words, "a nearly decomposable system". I found it challenging to connect all his ideas together, though I know some central patterns exist... somewhere. I'll need to reread it.
Read
September 18, 2023Herbert Simon – a genius for his time, but now we live in a world run by vulgar Simonites (Simonians is already a word for another thing, right?). Neural networks, nudge theory, systems engineering concepts broadly (mis)applied, the linear programming revolution – you see his impact everywhere. And you see the disastrous results that happen when these tools are put in the hands of alienated technocrats and rapacious capitalists. Because Simon’s dream of a perfect and shockingly elegant system, while light years beyond some of the more simplistic models of A-then-B behaviorism and homo-economicus economic modeling, remains ungrounded. And so this read less as the persuasive intellectual journey of a forward-thinking mind, and more as a grim portent for things to come.
February 4, 2020
A classic and a must read for anyone interested in complexity. Dr. Simon is well-recognized as a brilliant thinker with a wide interest in topics. Wider than I knew of, for sure! I have always thought of him as a pioneer in Artificial Intelligence and it wasn't until reading this book that I learned he's more known as an economist and political scientist.
Wow.
All of that is quite obvious in this book. His study of the sciences of the artificial is wide-ranging, covering topics such as social order, business, economics, the human brain, evolution, and complexity. Seeing that I am not a man of such wide-ranging interests parts of this book were slow reading for me. It was hard to generate the urge to pick it up. But I kept going back to it. The early chapters and the final few, the last in particular, sparked my interest and I ate them up.
I picked it up because of my interest in Artificial Intelligence and I expected a treatise on the same from one of the founding fathers and early pioneers. This is not that book. While it touches on the topic it has far more to say on the study of complex systems than on the study of intelligence. Which is not a bad thing for the student of AI! Whatever your interest you will find in this book plenty to challenge your current way of thinking about things.
My one wish is that I had studied this in a group. Not only would they have kept my reading timely but mostly because these are the kinds of ideas that should be spoken aloud and bounced off of others in order to fully experience their richness. If you happen to be in college at the moment and notice a class that lists this as a text then take that class, no matter which department teaches it. Your brain will be the better for doing so.
Wow.
All of that is quite obvious in this book. His study of the sciences of the artificial is wide-ranging, covering topics such as social order, business, economics, the human brain, evolution, and complexity. Seeing that I am not a man of such wide-ranging interests parts of this book were slow reading for me. It was hard to generate the urge to pick it up. But I kept going back to it. The early chapters and the final few, the last in particular, sparked my interest and I ate them up.
I picked it up because of my interest in Artificial Intelligence and I expected a treatise on the same from one of the founding fathers and early pioneers. This is not that book. While it touches on the topic it has far more to say on the study of complex systems than on the study of intelligence. Which is not a bad thing for the student of AI! Whatever your interest you will find in this book plenty to challenge your current way of thinking about things.
My one wish is that I had studied this in a group. Not only would they have kept my reading timely but mostly because these are the kinds of ideas that should be spoken aloud and bounced off of others in order to fully experience their richness. If you happen to be in college at the moment and notice a class that lists this as a text then take that class, no matter which department teaches it. Your brain will be the better for doing so.
November 12, 2019
It is daring to write a review from such a low starting point of knowledge as mine about one of the most renowned and influential works in the world of complex systems, computer science and design. I genuinely enjoyed the most the first and fourth chapters of the book, Understanding the Natural and the Artificial Worlds, and The Architecture of Complexity, respectively, since they cover the topics I am most engaged in, and I have adequate understanding.
Overall, I truly enjoyed the clear structure of the subchapters and of most of its definitions. Sometimes certain terms conveyed manifold meanings which are up to the reader to interpret what the author intended to express, so what I will say is my own interpretation. I do not agree with some definitions that the author made, and this will probably look pretentious. At times, he sounded like bearing a reductionist thinking about what a complex system is and how we can achieve its understanding. In a way, he seemed to have a profound faith for science (or humans) to wholly comprehend what complexity is, which I disagree with. However, it is difficult for me to criticise this, due to not knowing if it is due to being a relatively "old" book, or if the author actually had such thinking. Related to the former, I read more recent books about System's Thinking and Complexity that influenced me to a great extent, and now it is hard for me to see Complex Systems differently.
I recommend this book to anyone interested in artificiality, intelligence, computer science, design, complexity, and system's thinking. Just for how influential this book has been, it is already worth it to read it.
I will include here some quotes that I particularly like. Not that I necessarily agree with them, but I believe they generate food for thought, especially considering that they were written in 1969:
"The central task of a natural science is to make the wonderful commonplace: to show that complexity, correctly viewed, is only a mask for simplicity; to find pattern hidden in apparent chaos." (Chapter 1)
"The aesthetics of natural science and mathematics is at one with aesthetics of music and painting -both inhere in the discovery of a partially concealed pattern." (Chapter 1)
"The world we live today is much more a man-made, or artificial, world than it is a natural world. [...] We must be careful about equating "biological" with "natural": a forest may be a phenomenon of nature; a farm certainly is not". (Chapter 1)
"Too little is know about their (computer's) task environments to permit accurate prediction of how well they will perform, how selectively they will be able to search for problem solutions." (Chapter 1)
"The proper study of mankind has been said to be man. But I have argued that man -or at least the intellective component of man- may be relatively simple; that most of the complexity of his behaviour may be drawn from his environment, from his search for good designs." (Chapter 3)
"The task of science is to make use of the world's redundancy to describe that world simply." (Chapter 4)
"If there are important systems in the world that are complex without being hierarchic, they may to a considerable extent escape our observation and our understanding. Analysis of their behavior would involve such detailed knowledge and calculation of the interactions of their elementary parts that it would be beyond our capacities of memory or computation." (Chapter 4)
********************
Update Nov/2019: I have just finished reading the reissue of the third edition with a new introduction by John E. Laird (2019) that made me enjoy even more the book. New Chapters 6 (Social Planning: Designing the Evolving Artifact) and 7 (Alternative Views of Complexity) were a great addendum to get better insights into Simon's mind (my review above was on the first edition, which consisted of four chapters). After reading more on the history of Computer Science, the second read of the book left me with a good feeling. I definitely embrace its reading to people interested in computer science, AI, complexity and even social issues in general.
Overall, I truly enjoyed the clear structure of the subchapters and of most of its definitions. Sometimes certain terms conveyed manifold meanings which are up to the reader to interpret what the author intended to express, so what I will say is my own interpretation. I do not agree with some definitions that the author made, and this will probably look pretentious. At times, he sounded like bearing a reductionist thinking about what a complex system is and how we can achieve its understanding. In a way, he seemed to have a profound faith for science (or humans) to wholly comprehend what complexity is, which I disagree with. However, it is difficult for me to criticise this, due to not knowing if it is due to being a relatively "old" book, or if the author actually had such thinking. Related to the former, I read more recent books about System's Thinking and Complexity that influenced me to a great extent, and now it is hard for me to see Complex Systems differently.
I recommend this book to anyone interested in artificiality, intelligence, computer science, design, complexity, and system's thinking. Just for how influential this book has been, it is already worth it to read it.
I will include here some quotes that I particularly like. Not that I necessarily agree with them, but I believe they generate food for thought, especially considering that they were written in 1969:
"The central task of a natural science is to make the wonderful commonplace: to show that complexity, correctly viewed, is only a mask for simplicity; to find pattern hidden in apparent chaos." (Chapter 1)
"The aesthetics of natural science and mathematics is at one with aesthetics of music and painting -both inhere in the discovery of a partially concealed pattern." (Chapter 1)
"The world we live today is much more a man-made, or artificial, world than it is a natural world. [...] We must be careful about equating "biological" with "natural": a forest may be a phenomenon of nature; a farm certainly is not". (Chapter 1)
"Too little is know about their (computer's) task environments to permit accurate prediction of how well they will perform, how selectively they will be able to search for problem solutions." (Chapter 1)
"The proper study of mankind has been said to be man. But I have argued that man -or at least the intellective component of man- may be relatively simple; that most of the complexity of his behaviour may be drawn from his environment, from his search for good designs." (Chapter 3)
"The task of science is to make use of the world's redundancy to describe that world simply." (Chapter 4)
"If there are important systems in the world that are complex without being hierarchic, they may to a considerable extent escape our observation and our understanding. Analysis of their behavior would involve such detailed knowledge and calculation of the interactions of their elementary parts that it would be beyond our capacities of memory or computation." (Chapter 4)
********************
Update Nov/2019: I have just finished reading the reissue of the third edition with a new introduction by John E. Laird (2019) that made me enjoy even more the book. New Chapters 6 (Social Planning: Designing the Evolving Artifact) and 7 (Alternative Views of Complexity) were a great addendum to get better insights into Simon's mind (my review above was on the first edition, which consisted of four chapters). After reading more on the history of Computer Science, the second read of the book left me with a good feeling. I definitely embrace its reading to people interested in computer science, AI, complexity and even social issues in general.
February 12, 2023
A masterpiece of a work, a collection of essays that touches on computing, information, and complex systems. Goes from political theory to economics to computing in one fell swoop, much to learn (and to agree and disagree with) that this book can provide.
January 29, 2021
This is a work of genius.
August 2, 2020
Wow. If it had been written today it would be impressively insightful, but it was written 40+ years ago - truly an incredible achievement.
August 6, 2021
must-read
February 5, 2013
While I'm glad to have this book now, I can't help but think how my life might be different if I had it at age 17, 22, or 25. As I grew into adulthood I became interested in computer programming, AI, engineering, architecture, economics, geography, and complexity science which over the years have been tempered with psychology, foresight, social science, design, and governance. What is it these subjects have in common? These are all the study of what human beings choose to build for themselves. This volume, the most known work by a man who won the highest prizes of computer science and economics, lays down a vision integrating these topics in the original text laying out many of their directions influencing us yet today.
This book recognizes its most formidable limit, which is that the objectives placed on design problems is taken as coming from the outside world. That the book makes this assumption is of little surprise, as finding a steady structure to satisfying our needs, at the vast simplification of finding a role in a firm or economy, is a behavior central to human achievement. Nonetheless, without a designer's preference, the search for innovation is rather a more random matter, more resembling the random recombination of genetic search and optimization, than what it is in practice, the directed learning of situations aimed at finding human travails and leverage spots for change.
Nonetheless, despite limits, this book does offer a great deal to all of the subjects I mention above. For example, it begins to paint a picture of the cognitive limits that we have, limits that our brain elides for us, suggesting that by finding representations that work with the grain of our thinking and our problems, we can do better with tools shaped to us than we are prepared to admit. That the tools of information sciences have given us new resources for organizing the way we communicate does not mean we have come to the end, or really even the beginning, in providing ourselves these tools of representation.
This book recognizes its most formidable limit, which is that the objectives placed on design problems is taken as coming from the outside world. That the book makes this assumption is of little surprise, as finding a steady structure to satisfying our needs, at the vast simplification of finding a role in a firm or economy, is a behavior central to human achievement. Nonetheless, without a designer's preference, the search for innovation is rather a more random matter, more resembling the random recombination of genetic search and optimization, than what it is in practice, the directed learning of situations aimed at finding human travails and leverage spots for change.
Nonetheless, despite limits, this book does offer a great deal to all of the subjects I mention above. For example, it begins to paint a picture of the cognitive limits that we have, limits that our brain elides for us, suggesting that by finding representations that work with the grain of our thinking and our problems, we can do better with tools shaped to us than we are prepared to admit. That the tools of information sciences have given us new resources for organizing the way we communicate does not mean we have come to the end, or really even the beginning, in providing ourselves these tools of representation.
May 10, 2021
329-The Sciences of the Artificial-Herbert Simon-Science-1970
Barack
2021/05/09
" The Sciences of the Artificial ", first edition in 1970. It explores topics such as complexity, design science, chaos theory, adaptive systems, and genetic algorithms.
Herbert Simon was born in Milwaukee, Wisconsin, US in 1916 and died in 2001. Studied at the University of Chicago. He is an economist, political scientist, and cognitive psychologist. His main research direction is decision-making within the organization, and he is well-known for his " bounded rationality " and " satisficing " theories. He won the Turing Prize in 1975 and the Nobel Prize in Economics in 1978. Representative works: " The Sciences of the Artificial ", " Human Problem Solving ", etc.
Table of Contents
1 Understanding the Natural and Artificial Worlds
2 Economic Rationality: Adaptive Artifice
3 The Psychology of Thinking: Embedding Artifice in Nature
4 Remembering and Learning: Memory As Environment for
Thought
5 The Science of Design: Creating the Artificial
6 Social Planning: Designing the Evolving Artifact
7 Alternative Views of Complexity
8 The Architecture of Complexity: Hierarchic Systems
" About three centuries after Newton we are thoroughly familiar with the concept
of natural science most unequivocally with physical and biological science.
Natural science is a body of knowledge about some class of things objects or
phenomena in the world: about the characteristics and properties that they have;
about how they behave and interact with each other. ”
One of the great things about Newton is that he made the objective laws of using human rational thinking to understand the world gradually widely recognized, which means that the laws of nature are no longer the patent of gods.
" Stevin was so pleased with his construction that he incorporated it into a
vignette, inscribing above it
Wonder, en is gheen wonder
that is to say: "Wonderful, but not incomprehensible." ”
Nature is amazing, but for a scholar, and we can not just stop at the top of wonder, we have to be able to explore it, understand it, and eventually the use of its law behind it.
" The world we live in today is much more a man-made,1 or artificial, world than it
is a natural world. Almost every element in our environment shows evidence of
human artifice. ”
I think, 21 century may be from humans have been written records, the most amazing period. Most of the items used in our daily lives are not natural products, but some form of industrial products. Today we are on this for granted, you had better know that even 100 years ago, this is also not the case as it should be.
" So too we must be careful about equating "biological" with "natural." A forest
may be a phenomenon of nature; a farm certainly is not. The very species upon
which we depend for our food our corn and our cattle are artifacts of our
ingenuity. A plowed field is no more part of nature than an asphalted street and
no less. "
Some people are opposed to modern technology, but in fact, even if we return to the agricultural era, we are still living in an era full of man-made objects. If you really going completely to embrace the natural way of life, probably only returned like animals barbarism of life. But if that's the case, how are humans different from beasts?
" A
natural science is a body of knowledge about some class of things objects or
phenomena in the world: about the characteristics and properties that they have;
about how they behave and interact with each other. ”
Natural science studies the objective law that does not pass through the world, and this objective law is not shifted by anyone's will. Therefore, for those trying to understand these laws, it is necessary to be calm and objective.
" If science is to encompass these objects and phenomena in which human purpose, as well as natural law, is embodied, it must have means for relating these two
disparate components. ”
Engineering Research of Shi, how to do, science is, what is? The differences in research purposes have resulted in differences in research methods and focus in the research process.
" We have now identified four indicia that distinguish the artificial from the
natural; hence we can set the boundaries for sciences of the artificial:
1. Artificial things are synthesized (though not always or usually with full
forethought) by human beings.
2. Artificial things may imitate appearances in natural things while lacking, in
one or many respects, the reality of the latter.
3. Artificial things can be characterized in terms of functions, goals, adaptation.
4. Artificial things are often discussed, particularly when they are being
designed, in terms of imperatives as well as descriptives. ”
AI concept, recently 1 0 years is fire. We want more in-depth to understand artificial intelligence, we may first want to know what, what is natural and what is artificial is. Where is the boundary between natural and artificial?
" An artifact can be thought of as a
meeting point an "interface" in today's terms between an "inner" environment,
the substance and organization of the artifact itself, and an outer" environment,
the surroundings in which it operates. ”
Artifact actually can be seen as the internal world and the external world of a combination of points. Internal environment, is an internal environment, internal environment out of this man-made system. It is the objective entity it wants to operate.
" Analogous to the role played by natural selection in evolutionary biology is the
role played by rationality in the sciences of human behavior. If we know of a
business organization only that it is a profit-maximizing system, we can often
predict how its behavior will change if we change its environment how it will
alter its prices if a sales tax is levied on its products. ”
The world is not benevolent, and everything is a dog. In the process of research, it may be necessary to adopt an objective or even indifferent attitude, rationally analyze the optimal solution, instead of being led by one's own biological nature.
" Analogous to the role played by natural selection in evolutionary biology is the
role played by rationality in the sciences of human behavior. If we know of a
business organization only that it is a profit-maximizing system, we can often
predict how its behavior will change if we change its environment how it will
alter its prices if a sales tax is levied on its products. ”
If we know its behavior, we can judge its output and predict its behavior. This is a stable system, and only a stable system can function better.
May 15, 2019
Phew.
This was a tedious read. The book starts off so great. The first 50 pages are pure gold about what design is in the name of problem solving. And how all that is artificial is so perticular, because it is something we have created. And if it's created by us: should've we study it as what it should be and not as what it is?
But then the last 200 pages. They are not bad at all. On the contrary, they are very well thought out and intellectually challenging. Too challenging I would say.
Where as Herbert Simon starts of as a nice narrative about what design is how we should approach it, to book just flies off to outer space. What you have in the end, is whole essay about complex systems in general.
And well, the answer is just that: complex.
The book is still great and I would encourage anybody interested in design, complex systems, human mind or AI to read it.
But it's not something to just casually stroll by when lying in your hammock.
This was a tedious read. The book starts off so great. The first 50 pages are pure gold about what design is in the name of problem solving. And how all that is artificial is so perticular, because it is something we have created. And if it's created by us: should've we study it as what it should be and not as what it is?
But then the last 200 pages. They are not bad at all. On the contrary, they are very well thought out and intellectually challenging. Too challenging I would say.
Where as Herbert Simon starts of as a nice narrative about what design is how we should approach it, to book just flies off to outer space. What you have in the end, is whole essay about complex systems in general.
And well, the answer is just that: complex.
The book is still great and I would encourage anybody interested in design, complex systems, human mind or AI to read it.
But it's not something to just casually stroll by when lying in your hammock.
March 6, 2015
This is a good introduction to some of the topics in the field of complexity. However, the book seems to be written for a reader who loves tracking developments in science. This is true even for a reader in 2015. The author proceeds with each topic very carefully. Several times I found myself correctly predicting what author will be saying in a following paragraph. This, I attribute to the author's writing. The book is a good starting point for someone who wants to dwell into reading about artificial intelligence, complexity and consciousness. Of course, the book itself had made contributions to these areas. Finally, on a personal note, the author has become my inspiration due to his interest in different subjects.
September 28, 2012
I wanted to really like this book, but it didn't capture the attention of my inner geek like I hoped. I think it is because there was too much discussion of economic and organizational systems and not enough of computer and engineering systems. Still, it was heady and full of interesting ideas.
October 8, 2014
One of the more painful textbooks to read. Interesting concepts and eye-opening for a book written so long ago.
Nonetheless, the words are a tad archaic and left me behind a couple of times.
Nonetheless, the words are a tad archaic and left me behind a couple of times.
June 19, 2017
I read this book after Kurzweil's "Singularity is near", and found Herbert's argument extremely under-researched and weak. Nothing to gain.
June 3, 2019
This is my first exposure to "systems thinking" from a member of the generation which kicked that term off, along with its sister term "cybernetics". The book reads not so much as a thesis, but as a way of thinking applied to a variety of closely related systems. He uses his new theory of the artificial to exploring his research on the internal environment of the human brain, and how it makes decisions, as well as the economy, and government.
The book includes a window into the research going on in Simon's cohort in the 1960s, from computational brain models such as SOAR, to chess playing bots, to highway planners, to theses on management decision-making. Simon draws on these papers to find the underlying constants that tie the common attributes of the systems together, such as the structure as hierarchical or almost-hierarchical. This is a mix of obvious and misleading. I would say that taking such a fundamental view on systems will give us profound ideas only if we can see them, it will show us obvious ideas which were non-obvious at the time, and will show us gaps in our thought that might be good candidates for exploration. One of these is a learning system which can search a tree of concepts MCTS style but applies an idea learned on one leaf immediately to other leaves which might contain similar ideas. Basically, an MCTS which tries to learn and apply patterns.
I'm really happy I discovered this, because it launched my exploration of design as a field.
The book includes a window into the research going on in Simon's cohort in the 1960s, from computational brain models such as SOAR, to chess playing bots, to highway planners, to theses on management decision-making. Simon draws on these papers to find the underlying constants that tie the common attributes of the systems together, such as the structure as hierarchical or almost-hierarchical. This is a mix of obvious and misleading. I would say that taking such a fundamental view on systems will give us profound ideas only if we can see them, it will show us obvious ideas which were non-obvious at the time, and will show us gaps in our thought that might be good candidates for exploration. One of these is a learning system which can search a tree of concepts MCTS style but applies an idea learned on one leaf immediately to other leaves which might contain similar ideas. Basically, an MCTS which tries to learn and apply patterns.
I'm really happy I discovered this, because it launched my exploration of design as a field.
July 17, 2023
I read this book at the behest of a professor (and potential/probably advisor) I'm working for - John Horton. We had a slight disagreement about what it means for a human to be rational (which I have now unquestionably switched to his side) in that all humans have goals and constraints. These goals might be irrational, but we undoubtedly have them. He suggested this book as a starting point and a great piece to dive into some of these ideas a bit more as they are likely going to apply to our work.
A few things I learned:
1. Human memory is one of the few psychological features that is remarkably consistent across people (although you could probably call it neuro-scientific). Humans have a limited capacity to remember things short term - around 7 "chunks" of memory pieces at a time. The cool part about this is that the chunks can change with expertise! So if the memory task is something you know a lot about, those "chunks" are bigger.
2. One of our best skills as humans it taking computationally impossible tasks and finding a fairly good (although usually not the best) way to solve them. For example, solving puzzles with many pieces or a route between multiple destinations.
3. It's really cool that the human sensory organs are at least somewhat parallel- eyes and ears take in information at the same time for us to act upon even if other things are serial!
A few things I learned:
1. Human memory is one of the few psychological features that is remarkably consistent across people (although you could probably call it neuro-scientific). Humans have a limited capacity to remember things short term - around 7 "chunks" of memory pieces at a time. The cool part about this is that the chunks can change with expertise! So if the memory task is something you know a lot about, those "chunks" are bigger.
2. One of our best skills as humans it taking computationally impossible tasks and finding a fairly good (although usually not the best) way to solve them. For example, solving puzzles with many pieces or a route between multiple destinations.
3. It's really cool that the human sensory organs are at least somewhat parallel- eyes and ears take in information at the same time for us to act upon even if other things are serial!
July 17, 2019
I found this book to be extremely thought provoking & empowering.
This book & its author, by & far, lived up to their reputation, providing well thought-out practical principles to both question & reconstruct one's perception of thought itself, setting a clear agenda based on sound logic as how one may construct & advance thinking.
The subject matter I found to be predominantly technical based, but I urge all whom possess an interest in thinking & decision-making to overcome any sense of finding themselves overwhelmed to experience & explore the available thought principles within.
This book & its author, by & far, lived up to their reputation, providing well thought-out practical principles to both question & reconstruct one's perception of thought itself, setting a clear agenda based on sound logic as how one may construct & advance thinking.
The subject matter I found to be predominantly technical based, but I urge all whom possess an interest in thinking & decision-making to overcome any sense of finding themselves overwhelmed to experience & explore the available thought principles within.
November 7, 2022
"All correct reasoning is a grand system of tautologies, but only God can make direct use of that fact."
"Solving a problem simply means representing it so as to make the solution transparent."
"The proper study of mankind has been said to be man. But I have argued that people or at least their intellective component may be relatively simple, that most of the complexity of their behavior may be drawn from their environment, from their search for good designs."
"Solving a problem simply means representing it so as to make the solution transparent."
"The proper study of mankind has been said to be man. But I have argued that people or at least their intellective component may be relatively simple, that most of the complexity of their behavior may be drawn from their environment, from their search for good designs."
July 2, 2023
Looking back, this book was an impressive feat.
Through rigorous analysis and clear reasoning, Simon arrives at insights that still today feel highly relevant for understanding our world.
He taken on "articifical" (anything man-made, intentionally or not) sheds some light on AI as a subclass of that broader category.
Interesting discussions of learning and decissionmaking are usefull and just enough as a takeaway from a challenging read.
Through rigorous analysis and clear reasoning, Simon arrives at insights that still today feel highly relevant for understanding our world.
He taken on "articifical" (anything man-made, intentionally or not) sheds some light on AI as a subclass of that broader category.
Interesting discussions of learning and decissionmaking are usefull and just enough as a takeaway from a challenging read.
March 6, 2024
I was expecting to get some examples of "sciences of the artificial," how they are studied, discussed, particularly how the scientific method is applied to them. While this book is analytical about various examples of "study of the artificial," it's not a survey of different sciences. It seemed like the covered topics could feed into some forms of mathematics, in most cases.
Nevertheless, I got a few valuable nuggets out of it.
Nevertheless, I got a few valuable nuggets out of it.
February 17, 2018
I don't really feel up to the task yet of describing my feelings about this book, but I absolutely love it and highly recommend it. If you're interested in psychology, economics, or science of just about any kind, this is worth reading.
April 25, 2018
Livro publicado em 1969 primeira edição, não perde a atualidade e importância. A visão sistêmica do Herbert Simon está presente no livro, que li pela segunda vez. Recomendo, sem sombra de dúvidas. É um clássico.
December 30, 2022
4/5 I really appreciate this book. It is a thought-provoking read. I would say it should be a classic reading for those who are interested in AI and Design Science. It spans across a variety of subject areas.
July 10, 2017
Utterly brilliant...
December 15, 2019
Shit's good, it's just that I am a bit too uneducated to appreciate it fully.
Read
April 20, 2020stopped reading at 11%.
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