The Emergent Mind

The Emergent Mind Defining Emergence in the Cognitive Realm

How can a bundle of neurons, let alone silicon logic gates, arranged in any clever and complex fashion ever give rise to the phenomenon of mind and consciousness? The question is regularly put to neuroscientists and artificial intelligence researchers alike. One recently proposed answer is that intelligence is an emergent property—one that can arise from relatively simple rules and units arranged in a complex form.

At first glance, this may seem like a very satisfactory answer. Surely there are neurons and are in themselves rather simple. Equally certain, we know that somehow from the dizzyingly complex arrangement of these cells a mind arises or is at least somehow related to this fleshy machine. One would not argue with the idea that a human persona seems to emerge from a child as he or she grows into adulthood. So using the same word to describe how mind and life arises from biology appears natural. But what exactly do these philosophers and scientists mean by an “emergent property?” What is emergent and what isn’t? Are emotions emergent? Is consciousness emergent? Is there some sort of mathematical or scientific definition by which we can identity emergent behavior or are we just using a convenient word to describe (and perhaps dismiss) a complexity which we simply don’t understand?

To define emergence we first need to discover were this concept entered into the sciences. We then will look at various definitions of emergent behavior and discuss the relationship between these concepts and our current theories of the mind. To begin then we need to look at the science of complexity.

The study of complex adaptive systems (CAS) is a relatively new discipline and some may still argue whether it is a field in its own right. The beginnings are intertwined with the mathematical study of chaos. Chaos defines a nonlinear mathematical system whose behavior is essential indeterminate given arbitrary starting points. In other words, a chaotic system is one in which given a very simple set of governing rules it is impossible to predict how the system will behave over a period of time. A simple example would be your leaky kitchen faucet. Predicting when the next drip will drop or if a drop will occur at a particular time tomorrow, even if you know almost everything about the faucet, is nigh impossible. The same can be said about the behavior of two connected pendulums or grains of sand piling up in the bottom of an hour glass. Some apparently simple systems show startlingly chaotic behavior.

In an effort to better understand chaos, researchers began studying a large class of simple systems. What they found was that systems generally end up in one of three categories. The first category is stability—the system will eventually either reach a point of no change or become locked into a definite cycle. Alternatively, a system could never properly settle down and forever tumble in chaos. Yet the third group of systems showed to be the most interesting. These systems fall on the edge between stability and chaos, never quite settling down but also never falling into a truly chaotic state. Such systems have been understandably dubbed complex adaptive systems

Complex adaptive systems can be seen around us each day, in the behavior of animal populations, in the economies and markets of the world, and in many biological systems. Such systems have several common properties of which the two most important are spontaneous self-organization and adaptively. These properties separate complexity from stability and chaos.

Finally, every one of these complex, self-organizing, adaptive systems possess a kind of dynamism that makes them qualitatively different from static objects such as computer chips or snowflakes, which are merely complicated. Complex systems are more spontaneous, more disorderly, more alive than that. At the same time, however, their particular dynamism is also a far cry from the weird gyrations known as chaos.

—M. Michell Waldrop, Complexity

The complex behavior of these systems does not appear to be built into the rules by which they operate. For example, with some very simple rules that say nothing about flocking, such group behavior appears almost out of no where in Craig Reynolds’ “boids” program. These global properties of complex systems are often referred to as emergent behaviors. Which global properties actually count as emergent is still somewhat debated. As Andy Clark points out in Mindware there are four major proposals for defining emergence:

1. Collective Self-Organization: the idea that the properties of organization itself is emergent.
2. Unprogrammed Functionality: in this case, emergent behavior describes some interesting side-effect which is not explicitly stated in the rules of the system.
3. Interactive Complexity: describes the process by which the interaction of complex processes results in globally stable patterns.
4. Uncompressed Unfolding: emergence is also used to describe systems in which prediction is impossible and the only way of knowing what will happen is to actually let the system (or simulation) run its course.

One of the many debates in current complexity studies is refining this definition. With a proper theory, researchers will not only be better equipped to identify such emergence in nature, but also be able to better understand what, if any, rules govern such behavior.

The principles of complexity and emergence are currently used in simulations of artificial life. Computer simulations of biological populations show that with a few rules, surprising life-like qualities arise. Such studies in artificial life have successfully replicated a number of complex properties exhibited in real-life populations. This success has encouraged scientists to believe that other complex phenomenon might also be simulated in like manor, specifically, the property of mind and consciousness that arises from our complex brains.

When researchers suggest that the mind or consciousness is an emergent property they mean that the phenomenon of intelligence will not be found explicitly programmed in the neurons and synapses of the brain. Instead, they suggest that mind arises from the complex interplay of these simple units and that given the right set of rules and conditions, consciousness will naturally emerge. This emergent mind is no more mystic than the property of flocking or market fluctuations and in fact exists due to the very same principles.

However, while many of the properties of life seem emergent, whether or not the mind is one of them is still debated. There is much that we currently do not understand about complex adaptive systems and whether such science will prove to be robust enough to describe intelligence has yet to be determined. One of the leading researchers in this field, John Holland, described the situation as follows:

Our understanding of the universe will be severely limited until we have a more definitive view of how much life and consciousness can be explained as emergent phenomena. We must know how far we can go with explanations based on the interactions of a few well-understood mechanisms (say biomolecules and neurons, respectively). We are a long way from knowing the limitations (if any) of such explanations. But until we have made a sustained effort at such an explanation, we will not know what must be explained in other ways.

—John Holland, Emergence: From Chaos To Order

Thus, while emergence may not completely illuminate the mystery of the mind, it will certainly be key in framing our understanding. Stating the mind is an emergent property will have clear and explicit implications about its inner workings. Even should we find that emergence does not fully capture the subtleties of conciousness, we will better come to appreciate the vast complexity of the universe and how it can arise from the simple.