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snowflakes come to have the intricate shapes they do. But I am not concerned, for example, with details such as what the precise curvature of the tips of the arms of the snowflake will be.

In most cases the basic approach I take is to try to construct the very simplest possible model for each system. From the intuition of traditional science we might think that if the behavior of a system is complex, then any model for the system must also somehow be correspondingly complex.

But one of the central discoveries of this book is that this is not in fact the case, and that at least if one thinks in terms of programs rather than traditional mathematical equations, then even models that are based on extremely simple underlying rules can yield behavior of great complexity. And in fact in the course of this chapter, I will construct a whole sequence of remarkably simple models that do rather well at reproducing the main features of complex behavior in a wide range of everyday natural and other systems.

Any model is ultimately an idealization in which only certain aspects of a system are captured, and others are ignored. And certainly in each kind of system that I consider here there are many details that the models I discuss do not address. But in most cases there have in the past never really been models that can even reproduce the most obvious features of the behavior we see. So it is already major progress that the models I discuss yield pictures that look even roughly right.

In many traditional fields of science any model which could yield such pictures would immediately be considered highly successful. But in some fields—especially those where traditional mathematics has been used the most extensively—it has come to be believed that in a sense the only truly objective or scientific way to test a model is to look at certain rather specific details.

Most often what is done is to extract a small set of numbers from the observed behavior of a system, and then to see how accurately these numbers can be reproduced by the model. And for systems whose overall behavior is fairly simple, this approach indeed often works quite well. But when the overall behavior is complex, it becomes impossible to characterize it in any complete way by just a few numbers.

snowflakes come to have the intricate shapes they do. But I am not concerned, for example, with details such as what the precise curvature of the tips of the arms of the snowflake will be.

In most cases the basic approach I take is to try to construct the very simplest possible model for each system. From the intuition of traditional science we might think that if the behavior of a system is complex, then any model for the system must also somehow be correspondingly complex.

But one of the central discoveries of this book is that this is not in fact the case, and that at least if one thinks in terms of programs rather than traditional mathematical equations, then even models that are based on extremely simple underlying rules can yield behavior of great complexity. And in fact in the course of this chapter, I will construct a whole sequence of remarkably simple models that do rather well at reproducing the main features of complex behavior in a wide range of everyday natural and other systems.

Any model is ultimately an idealization in which only certain aspects of a system are captured, and others are ignored. And certainly in each kind of system that I consider here there are many details that the models I discuss do not address. But in most cases there have in the past never really been models that can even reproduce the most obvious features of the behavior we see. So it is already major progress that the models I discuss yield pictures that look even roughly right.

In many traditional fields of science any model which could yield such pictures would immediately be considered highly successful. But in some fields—especially those where traditional mathematics has been used the most extensively—it has come to be believed that in a sense the only truly objective or scientific way to test a model is to look at certain rather specific details.

Most often what is done is to extract a small set of numbers from the observed behavior of a system, and then to see how accurately these numbers can be reproduced by the model. And for systems whose overall behavior is fairly simple, this approach indeed often works quite well. But when the overall behavior is complex, it becomes impossible to characterize it in any complete way by just a few numbers.


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From Stephen Wolfram: A New Kind of Science [citation]