The world as we perceive it is neither fully disorganized (Figure 1), beyond our ability to identify any overall pattern in it, nor fully organized, describable by us in terms of some single simple pattern (Figure 2). Instead, we are faced with, and find ourselves trying to make sense of, a world that most typically includes both disorganization and a variety of patterns that may (Figure 3) or may not (Figure 4) themselves obviously fall into larger patterns. In this exhibit, we describe, illustrate, and compare using computer models three general approaches to making sense of the world. Each provides a different way to think about pattern, disorganization, randomness, and their relationships.
Goal: to characterize the underlying pattern that everything actually relates to Perception of time/evolution: either absent or a parameter along which things get closer to the ideal Status of disorganization: a barrier to detecting the underlying pattern |
One general approach to making sense of such a world starts with the existence of patterns, and treats disorganization as something secondary: an inherent noisiness in the world that to varying degrees degrades the patterns or imperfections in our perceptual abilities that obscure the patterns. In either case, the task of the inquirer is to try and correct for the source of apparent disorganization so as to discern the actual underlying patterns. The history of research on celestial mechanics provides one good example of both the strength and the weaknesses of this approach. Random motion in the heavens gave way to celestial harmonies as humans recognized that some motions were more orderly than others. And celestial harmonies gave way to newtonian mechanics as perceptual abilities were sharpened by technological advances. At the same time, the approach proved inadequate to account for many of the details of the masses, orbits, and separations of our particular solar system. To account for the latter, one needs to move beyond the notion of ideal patterns in space and add a temporal and historical dimension: the reason for the currently observed distribution of large objects in our solar system is not that it fits a particular pattern but rather that a particular pattern emerged from several billions of years of successive interactions beginning with much smaller and simpler entities.
A contrasting general approach to making sense of the world begins not with primal spatial patterns as the explanation but rather as the problem to be accounted for. Inherent in this approach is both a temporal/historical perspective and the idea that patterns reflect interactions of smaller entities that make them up: a pattern on a larger scale exists not because a particular distribution of smaller entities fits a particular pre-existing template pattern but rather because the pattern itself has been created over time by the interactions of smaller entities. This "emergence" approach was used effectively by Keynes in economics, by Darwin in biology, and has become increasingly important in recent years in a variety of fields of inquiry.
The general emergence approach has the appealing feature of not being dependent on assumptions about primal template patterns, and so avoiding the question of what is responsible for the existence of such patterns. At the same time, it doesn't necessarily do away with infinite regress questions (what accounts for the the smaller scale entities and how they interact?). And it leaves open the question of what the relation is between disorganization and pattern. Why do we typically see a world that consists both of patterns and of disorganization? If disorganization is neither a perceptual artifact nor a degradation of pattern, what is it?
In his A New Kind of Science, Stephen Wolfram outlines a general approach to making sense of the world that is worth understanding and taking seriously, both for the possibilities it opens up and for others it neglects. Wolfram's program is an instance of the more general emergence approach, using the basic insight that one may indeed get a quite substantial array of patterns out of simple things interacting in simple ways. While this insight is not at all unique to Wolfram, he systematically explored it in a way that leads both to some important additional understandings and to some interesting new issues. We will refer to the Wolfram approach as "deterministic emergence" and use both the potentials and limitations of "deterministic emergence" to clarify and further characterize and explore an alternative approach to making sense of the world ("non-deterministic emergence") that we believe is also worth taking seriously.
Goal: to characterize the starting conditions and rules (initial pattern) from which everything else follows over time Perception of time/evolution: essential as the parameter underlying emergence Status of disorganization: along with later patterns, the product of the initial pattern |
Cellular automata are a widely explored instance of "simple things interacting in simple ways can yield surprisingly complex outcomes," with the additional feature that both the things and their interactions are "deterministic," ie any given set of things, interactions, and starting conditions will always yield the same result. Among the simplest of cellular automata is a linear array of elements each of which can exist in one of only two states, together with a rule set that transforms any given linear array into a new one based on the state of each element and its two neighbors. Since one element can be in either of two states, there are eight possible states for an element and its two neighbors. A rule set specifies what the subsequent state of the element will be for each of the eight possible states of that element and its neighbors. There are two possible outcomes for each of the eight possible states, and therefore 2x2x2x2x2x2x2x2 = 256 possible rule sets. Wolfram systematically explored the pattern generating capabilities of each of these 256 rule sets (as you can do yourself by clicking here) and a variety of others. From these came some important conclusions about deterministic systems:
With this understanding in mind, let's look a bit more at each of the conclusions.
Conclusions 1 and 2. " ... yield patterns that one doesn't expect ..." and " ... yield results that could not be determined in advance"
The "surprise" here is of two sorts. The first has to do with what an observer expects and doesn't expect of simple interactions of simple things. If one thinks that pattern depends on a designer or architect, one is surprised to see it appear in the absence of either. Once one gets used to the emergence perspective, there is less surprise on this count.
The other source of surprise is that there exist processes for which every step is fully defined ("algorithmic processes) but where the outcome can't be known except by carrying out each step. If one has, for one reason or another, come to believe that there is an "equation" for all well-defined processes, one that will allow one to determine the state of the system at all future times by plugging in a value of time, this is not only a surprising but an important new understanding. If one has a greater familiarity with the work of Turing and others, the point is not less important but is less surprising.
Wolfram makes more of this point, saying he suspects it "is the ultimate origin of the apparent freedom of human will ... even though all the components of our brains presumably follow definite laws, I strongly suspect that their overall behavior corresponds to an irreducible computation whose outcome can never in effect be found by reasonable laws." We'll return to this issue below.
3. "... produce both patterns and what appears to be disorganization ..."
At this point, we need to be more explicit about what one means by "pattern" as well as by "disorganization." Most people looking briefly at Figure 1 would agree that it is "disorganized" in the sense that it exhibits no pattern. But others may see patterns in it from the outset, and others may begin to see patterns if they look at it long enough. The human brain is designed (by evolution) to find patterns, and so one's judgement that something is disorganized because it has no pattern is not a very reliable one.
Figure 1 is, however, "disorganized" or "unpatterned" in a less subjective sense. Each element that makes up the figure can be in either of two states (black or white), each has a 50% probability of being on or off, and whether one is on or off is completely independent of the state of any other element. To put it differently, there is no correlation whatsoever between the state of one element in the image and the state of any other element. Knowing the state of one element does not give one any information useful in predicting the state of any other. In this sense, figure 1 is fully disorganized, lacking any pattern; the distribution of black and white is random. Figure 2 is at the other end of a spectrum. Once one knows the distribution of blacks and whites over a small part of the figure, one knows it over the entire figure. There's a pattern. The same is true of Figure 3. Figure 4, on the other hand, seems to have local areas of pattern as well as local areas lacking pattern.
The surprise here is in important ways the converse of the surprise associated with point 1. Simple interactions of simple things can yield patterns that one doesn't expect" AND yield disorganization, absence of pattern. Not one or the other but both. It is not only order that can result from simple interactions of simple things but lack of order, statistical randomness, as well. Perhaps then, as Wolfram suggests, simple deterministic interactions of simple deterministic things can produce everything we experience around us, everything we could experience? Perhaps deterministic emergence actually could be a sufficient basis for making sense of the world?
At this point we need to flag the notion of "statistical" randomness. What one can conclude from observations on cellular automata (and in other mathematical explorations), is that deterministic systems are capable of producing results that look to be indistinguishable from the results of non-deterministic systems (ones in which the current state allows several different possible next states). Another way to say the same thing is that any known output of a system, no matter how much pattern it has or doesn't have, can be generated by a deterministic system. That is not quite the same thing as saying that there is in principle no distinction whatsoever between deterministic and non-deterministic systems. We'll discuss this further below; its a critical issue in deciding whether indeed "deterministic" emergence is an adequate basis for making sense of the world.
4. "Making things and their interactions more complex doesn't increase the complexity of the resulting patterns"
Here too we need to be more explicit about terms, in this case what is meant by "complex." Wolfram's point is that something as simple as a cellular automaton is capable of doing exactly the same set of computations as any serial computer, no matter how sophisticated. And that certainly may surprise people unless again, they are familiar with the work of Turing and others following him. A "Turing machine" is also a deterministic interaction of simple elements interacting deterministically in simple ways and, like a cellular automaton, it is also capable of doing exactly the same set of computations as any contemporary (or conceivable) serial computer. The point is an important one if one is thinking about computers, or about information processing more generally. And it is an even more important one if one thinks that the brain is subject to the same limitations as a cellular automaton or a Turing machine. But do we need to presume it is? Or can there be things in the world, our world, beyond those that a cellular automaton or Turing machine is capable of creating? That's the question to which we now turn.
Goal: to contribute to the ongoing creation and exploration of possibilities Perception of time/evolution: essential as the parameter underlying emergence Status of disorganization: the starting point and continuing contributor to exploration |
"... we know all atoms to perform all the time a completely disorderly heat motion, which, so to speak, opposes itself to their orderly behavior and does not allow the events that happen between a small number of atoms to enrol themselves according to any recognizable laws. Only in the co-operation of an enormously large number of atoms do statistical laws begin to operate ... All the physical and chemical laws that are known to play an important part in the life of organisms are of this statistical kind; any other kind of lawfulness and orderliness that one might think of is being perpetually disturbed and made inoperative by the unceasing heat motion of the atoms" ....... Erwin Schrdinger, What Is Life?, 1944
Perhaps what's important about the world isn't any particular pattern that it somewhat imperfectly reflects any particular pattern that it started with but instead an ongoing exploration of possible forms of existence, an exploration that both originates and continues to depend on randomness? If so, perhaps it is randomness that yields pattern rather than randomness being something that obscures patterns? And the reason why we see both patterns and disorganization is that without randomness there would be no patterns?
This idea is not so far-fetched as it might at first seem. It actually is random motion that drives, for example, the spreading out of cream in coffee. And it is random motion and interactions that produce pattern in chemical reactions. And the random falling apart of the sun that drives life on the earth. Furthermore, randomness in the form of mutations underpins the diversity of life on the brain, perhaps the best example of the mix of pattern and disorganization that we see all around us.
The idea that the world is about exploration, and hence that randomness rather than pattern is fundamental also opens some intriguing new questions. Wolfram classified his cellular automata in terms of their differing abilities to create pattern and disorganization. But one might instead ask of them, how good are they at exploring? And would they be better if instead of being fully deterministic systems they incorporated some degree of randomness in their behavior?
This question can be answered with a further useful simplification of the cellular automata phenomena explored by Wolfram. Let's focus attention not on their two dimensional appearance but rather on the successive one-dimensional states they generate. Each of these is a linear array of light and dark patches. If n is the number of elements in the array, then there are 2 expt n possible different arrays. If one is interested in the exploratory abilities of cellular automata, what one wants to know is how many of the 2 expt n possibilities is realized by any given cellular automaton?
The applet to the right makes it possible to answer this question, by keeping track of the number of different one dimensional arrays that are generated by a cellular automaton operating on a nine element array using any of the 128 possible rules discussed earlier. Several points quickly become clear by playing with it ...
Though we cannot yet prove this, nor yet provide a compelling reason why it should hold, it suggests the interesting possible conclusion that deterministic systems may be fundamentally limited in their exploratory capacity relative to non-deterministic ones. A linear array of elements in which the state of each element is independently and randomly varied will over time generate all possible linear arrays. That a deterministic cellular automaton won't may well be related to the limitations on logical systems first described by Godel and to the similar limit on computational systems established by Turing. And provide a further reason to approach understanding the world not as a problem of locating a basic pattern but rather of understanding (and participating in) the process of exploration and creation.
If making sense of the world involves ongoing exploration/creation, an interesting question is what methods/procedures facilitate that process? Random variation will generate all linear patterns but will take a long time to do it. Will some combination of deterministic and non-deterministic processes do it more rapidly? One can try that out using the applet to the right. In this case, one can add to the cellular automaton varying amounts of indeterminacy and see whether that enhances their exploratory capacity and whether there is some optimal level of indeterminacy that does so in different cases.
a cellular automaton can do anything but it can't do everything
randomness is not something to be accounted for but something to be used to account for things business of inquiry isn't to "explain" but to participate |