HCI -> Theory -> Herb Simon
Simon, H. (1999) "The Sciences of the Artificial", 3rd. Ed. MIT Press.
Chapter 1. Understanding the Natural and the Artifical Worlds
Simon starts out by defining "natural science" as 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
On the surface this seems a reasonable definition. However, it is important to note that Simon makes sure to define the "natural science" as purely descriptive in nature. In fact, he goes on to say that the central task of a natural science is to make the woderful commonplace: to show that complexity, correctly viewed, is only a mask for simplicity; to find a pattern hidden in apparent chaos. I don't argue with this definition, but I do note the note of reductionism. For Simon, a way to dispense with complexity is to find a pattern, to make things "wonderful but not incomprehensible." I linger on this first page of the chapter because the book is about the "artificial" yet Simon starts out by defining the "natural" first.
Simon wrestles the concepts of "natural" and "artificial" apart in the next few pages of the book when he points out that most of our current world is artificial - created or engineered by some of us for most of us. Airplanes, cars and fast food may obey natural laws but they are fundamentally artificial. A forest may be natural but the farm is most certainly not. The corn we eat today has little to do with its wild ancestor. Simon seems to say that things that are artificial, while they obey natural laws, are ones that are adapted to human goals and purposes. So a tree is natural, but if I cut it down and carve a canoe out of it, the canoe would be artificial? Simon identifies the four criteria that set the boundaries for the sciences of the artificial:
1. Artificial things are syntehsized (though not always or usually wiht 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, particularily when they are being designed, in terms of imperatives as well as descriptives.
There is something complex in Simon's discussion of the environment in which things live (natural and artificial alike). He conceives of the artifact as an interface - the inner and the outer environments of the artifact may be "natural" but the thin boundary between the two is what defines the artificiality. This "interface" is a meeting point of the two environments, the point of interaction. In fact, we do not need to "know" much about the inner environment of artificial systems in order to predict their behavior in the outer environment if we have some knowledge of the system's goals and the parameters of the outer environment. This rests on the assumption that there is an invariant relationship between inner system and goal, independent of variations over a wide range in most paramters that characterize the outer environment. This is important, because in natural sciences, the characteristics of the "pattern", some ideas of the inner environment are often inferred through observation of interaction of a system and the outer environment. The best, most illuminating observations are gained when the environment is particularily and unexpectedly harsh (think neuroscience through brain damage). These same "taxing environments" allow us to see limits of adaptation of artificial systems.
The rest of the chapter, Simon focuses on computers as both the sources of the artificial and the tools to enable simulation that allows us to test the limits of artificial systems. Ok... simulation... we are all familiar with that. Yet Simon uses the concept of simulation as a cornerstone on which he balances his whole philosophy of "artificial science." The implications are staggering, as they are illuminating of the resultant field and it's interests.
Simon says that "there are two related wasy in which a simulation can provide new knowledge" - one obvious, the other subtle and often overlooked. The obvious way is simple and one where simulation comes to mind as a reasonable way to obtain knowledge - when we have correct premises and want to discover what they imply, we can build a simulation that can answer some of such questions. So if you are going to build a bridge, you may want to simulate some extreme conditions to see what kinds of designs hold up better than others. So... in the event that we know what the inner system is composed of, a simulation may help us discover how the system would behave in a series of outer environments. Or... in the event that we know the laws of behavior of componennts, we may want to find out how particular combinations of them would behave.
So the first way of getting knowledge from simulation is simple. What is the second?
Simon goes on to discuss the issue of simulation of poorly understood systems. It is here that things get really radical. He suggests that simulations can be of help even if we do not know very much about the natural laws that may govern the behavior of the inner systems. His premise rests on the assumption that we are seldom interested in explaining or predicting phenomena in all their particularity; we are usually interested only in a few properties abstracted from the complex reality.
In other words, although reality is complex, simplicity can be achieved with a high enough level of abstraction. So the more we abstract, the easier it is to simulate. Even the "hard" sciences generally attempt to understand the world through simulation of simplified models. Yet Simon goes farther. He suggests that simulations can remove the need to know the internal environment at all. In fact, it doesn't matter what the internal environment is as long as the simulation is capable of producing similar outcomes. Simon suggests that artificial science should satisfice! The question of "why?" is irrelevant in this context, only the question of "how?" bears any meaning. In Herb Simons opinion, it seems, only the question of "how?" belongs in the sciences of the artificial - psychology, computer science and design.
This idea, I think, was fundamentally embedded in HCI at its inception (the book was written in 1969). With this in mind, it is somewhat easier to come to terms with a field that is really hard to convince about this question of "why" and its importance. The simplest form of the denial of this question is expressed in the idea of - "build it and they will use" that permeates much of the geek-world.
Related work: important things to follow up, I think, would be
1. the rest of this book (coming up in more installments)
2. Marvin Minsky's writings, notably "Computation: Finite and Infinite Machines"
3. Newell & Simon (1976) Computer Science as Empirical Inquiry Communications of the ACM v. 19 (March)
Chapter 1. Understanding the Natural and the Artifical Worlds
Source: Powell's Books
Simon starts out by defining "natural science" as 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
On the surface this seems a reasonable definition. However, it is important to note that Simon makes sure to define the "natural science" as purely descriptive in nature. In fact, he goes on to say that the central task of a natural science is to make the woderful commonplace: to show that complexity, correctly viewed, is only a mask for simplicity; to find a pattern hidden in apparent chaos. I don't argue with this definition, but I do note the note of reductionism. For Simon, a way to dispense with complexity is to find a pattern, to make things "wonderful but not incomprehensible." I linger on this first page of the chapter because the book is about the "artificial" yet Simon starts out by defining the "natural" first.
Simon wrestles the concepts of "natural" and "artificial" apart in the next few pages of the book when he points out that most of our current world is artificial - created or engineered by some of us for most of us. Airplanes, cars and fast food may obey natural laws but they are fundamentally artificial. A forest may be natural but the farm is most certainly not. The corn we eat today has little to do with its wild ancestor. Simon seems to say that things that are artificial, while they obey natural laws, are ones that are adapted to human goals and purposes. So a tree is natural, but if I cut it down and carve a canoe out of it, the canoe would be artificial? Simon identifies the four criteria that set the boundaries for the sciences of the artificial:
1. Artificial things are syntehsized (though not always or usually wiht 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, particularily when they are being designed, in terms of imperatives as well as descriptives.
There is something complex in Simon's discussion of the environment in which things live (natural and artificial alike). He conceives of the artifact as an interface - the inner and the outer environments of the artifact may be "natural" but the thin boundary between the two is what defines the artificiality. This "interface" is a meeting point of the two environments, the point of interaction. In fact, we do not need to "know" much about the inner environment of artificial systems in order to predict their behavior in the outer environment if we have some knowledge of the system's goals and the parameters of the outer environment. This rests on the assumption that there is an invariant relationship between inner system and goal, independent of variations over a wide range in most paramters that characterize the outer environment. This is important, because in natural sciences, the characteristics of the "pattern", some ideas of the inner environment are often inferred through observation of interaction of a system and the outer environment. The best, most illuminating observations are gained when the environment is particularily and unexpectedly harsh (think neuroscience through brain damage). These same "taxing environments" allow us to see limits of adaptation of artificial systems.
The rest of the chapter, Simon focuses on computers as both the sources of the artificial and the tools to enable simulation that allows us to test the limits of artificial systems. Ok... simulation... we are all familiar with that. Yet Simon uses the concept of simulation as a cornerstone on which he balances his whole philosophy of "artificial science." The implications are staggering, as they are illuminating of the resultant field and it's interests.
Simon says that "there are two related wasy in which a simulation can provide new knowledge" - one obvious, the other subtle and often overlooked. The obvious way is simple and one where simulation comes to mind as a reasonable way to obtain knowledge - when we have correct premises and want to discover what they imply, we can build a simulation that can answer some of such questions. So if you are going to build a bridge, you may want to simulate some extreme conditions to see what kinds of designs hold up better than others. So... in the event that we know what the inner system is composed of, a simulation may help us discover how the system would behave in a series of outer environments. Or... in the event that we know the laws of behavior of componennts, we may want to find out how particular combinations of them would behave.
So the first way of getting knowledge from simulation is simple. What is the second?
Simon goes on to discuss the issue of simulation of poorly understood systems. It is here that things get really radical. He suggests that simulations can be of help even if we do not know very much about the natural laws that may govern the behavior of the inner systems. His premise rests on the assumption that we are seldom interested in explaining or predicting phenomena in all their particularity; we are usually interested only in a few properties abstracted from the complex reality.
In other words, although reality is complex, simplicity can be achieved with a high enough level of abstraction. So the more we abstract, the easier it is to simulate. Even the "hard" sciences generally attempt to understand the world through simulation of simplified models. Yet Simon goes farther. He suggests that simulations can remove the need to know the internal environment at all. In fact, it doesn't matter what the internal environment is as long as the simulation is capable of producing similar outcomes. Simon suggests that artificial science should satisfice! The question of "why?" is irrelevant in this context, only the question of "how?" bears any meaning. In Herb Simons opinion, it seems, only the question of "how?" belongs in the sciences of the artificial - psychology, computer science and design.
This idea, I think, was fundamentally embedded in HCI at its inception (the book was written in 1969). With this in mind, it is somewhat easier to come to terms with a field that is really hard to convince about this question of "why" and its importance. The simplest form of the denial of this question is expressed in the idea of - "build it and they will use" that permeates much of the geek-world.
Related work: important things to follow up, I think, would be
1. the rest of this book (coming up in more installments)
2. Marvin Minsky's writings, notably "Computation: Finite and Infinite Machines"
3. Newell & Simon (1976) Computer Science as Empirical Inquiry Communications of the ACM v. 19 (March)
posted by Q at 01:00
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