Life, Mind and Engineering April 29, 2011
Posted by Michael in Cybernetics, Epistemology, Natural Philosophy.add a comment
The general view among materialists, physicalists and scientific realists has been that, while physics does ultimately explain everything, the physical explanations get exceedingly messy, sloppy and effectively incomprehensible once you get to life sciences, mind sciences and beyond. Life and mind may reduce to the dynamics of molecules, but that is not at all the same as reducing to molecules. A creature who has just died still has, for a while at least, all the same molecules as when it was alive, but it is not at all the same. Being alive consists of many high-level behaviors involving the coordinated dynamics of large aggregations of molecules, and when those behaviors cease, the molecules may remain but the life is gone.
Yet the notion that the dynamics of the inanimate world are simple and predictable while the dynamics of life are messy, complex and chaotic is simply false. For one thing, the inanimate world is also messy, complex and chaotic — consider the weather. For another, the fact that simple principles are indeed useful in understanding and predicting many inanimate behaviors, such as using Newton’s equations to calculate the trajectory of a projectile, does not rule out the possibility that life itself follows its own simple principles. My current proposal is exactly this.
The simple principle I have in mind is one which describes the behavior of cybernetic systems, which I formulate as follows:
Φ(r) & α(q) & ε(p) & ε(m) ⇒ q
where
- Φ(r) means that r is a goal, i.e., a desired state of the world;
- α(q) means that q is a choosable action;
- ε(p) means that the state of the world is known to be p; and
- ε(m) is short for ε(p & q ⇒ r), which means that it is known that when p, doing q leads to r.
In other words, if a system has a goal, and it knows what action will achieve its goal, and it has both the opportunity and the capacity to take the action, then the system will take the action.
To be sure, this is a simplification. For example, it doesn’t describe how a system might balance competing goals that call for contradictory actions. Nor does it discriminate between broad, long term, high level goals/actions and all the smaller, more immediate goals and actions they decompose into. But it’s straightforward to accommodate these and other complications by extending and elaborating on the formula without weakening the underlying principle.
The four terms in the formula represent the components that a system must have to be cybernetically successful, that is, to respond advantageously to opportunities in the environment and thereby achieve its goals.
The first term, Φ(r), is teleological; it defines the system’s purpose or goal. Generally this is a given, though some cybernetic systems have the ability to choose some goals for themselves.
The second and third terms, ε(p) and α(q), describe the system’s interaction with the world. They are functions of what the system is physically capable of sensing and doing. As such, they may vary widely from system to system, but the principle is always the same: to achieve its purpose, a cybernetic system must have the ability to obtain information about the state of the world, and the ability to perform useful actions.
The final term, ε(m), spells out the critical knowledge that is required for the system to achieve its goal. How this knowledge is acquired varies greatly; in my analysis, the nature of this knowledge acquisition is the primary characteristic that sets apart the main classes of cybernetic systems.
Let’s see how this applies in the case of a very simple cybernetic system: a furnace connected to a thermostat. We can describe the behavior of this system in two simple sentences:
- If the temperature is below the target level then if the furnace is off, turn the furnace on.
- If the temperature is above the target level then if the furnace is on, turn the furnace off.
Three of the four terms of the cybernetic formula are readily identifiable in these sentences:
- Φ(r), which means that r is the desired state, where r = “temperature is at the target level”
- ε(p), which means that p is known, where p has a different meaning in each of the two sentences:
- p1 = “the temperature is below the target level”
- p2 = “the temperature is above the target level”
- α(q) means that q is a choosable action, where q also has a different meaning in each sentence:
- q1 = “turn the furnace on”; α(q1) is true if the furnace is off.
- q2 = “turn the furnace off”; α(q2) is true if the furnace is on.
But what of the remaining term, ε(m)? This term refers to knowledge of the causal relationship between the action and the goal. In an engineered system such as a heating system, this knowledge is first and foremost in the mind of the engineer. From there it translates into interconnections between sensors and effectuators — ε(p) and (q if α(q)) — that the engineer builds into the system. In our heating system, the knowledge is embodied in the wiring between the thermostat and the furnace.
There exists another class of cybernetic systems where ε(m) comes to be embodied in the system without the assistance of an engineer. This is the class of systems whose designs are the product of Darwinian evolution, a category which includes all living organisms. Darwinian evolution emerges from the interplay of three processes: reproduction, mutation and selection. Selection operates on individual organisms, but reproduction and mutation are different: they depend on the existence of encoded information that persists beyond the individual and in fact accumulates and improves over generations.
In living organisms, this hard-won knowledge is encoded in nucleotide chains, that is, DNA. ε(m) is not explicitly encoded in the organism’s DNA. Rather, just as ε(m) in a heating system is embodied in the particular way the engineer connects the wires between the thermostat and the furnace, in a living creature ε(m) is embodied in the (primarily) biochemical interplay among the mechanisms that comprise it. The way these interactions are distributed and regulated is encoded in the organism’s DNA.
A third class of cybernetic systems is the class of systems capable of learning, which includes most animals to one degree or another. In such systems, a mechanism exists allowing the system to acquire and embody new knowledge, in addition to the knowledge it inherits. Such a system can potentially be much more adaptable than one whose behavior is hardcoded, whether by evolution or engineering.
Learning can take many forms. Essentially the task is to find actions (q) that succeed in a given circumstance (p). One way is to randomly generate candidates for q (perhaps by stringing together smaller, hardcoded actions), test them, and keep the ones that work. Another way is to start with q, make incremental changes, and reinforce or reject each change according to the result. Yet another way is to try a single behavior in different situations to find a beneficial combination — in other words, keep q fixed and vary p. In none of these cases does ε(m) need to be fully and explicitly represented, but at least some part of it (p or q) must be exposed in some fashion that translates into the ability to modify ε(m).
The fourth and final class of cybernetic systems are those capable of creating new ε(m) through an internal logical process of some kind, for example a mind. This class includes humans, possibly a few other species, and arguably some experimental computer systems. Because ε(m) in such a system is explicitly arrived at, it is explicitly represented. (This presumes a broad reading of the term “explicit”. For example, it includes algorithmic representations.)
The ability to synthesize ε(m) through internal processes is an enormous advantage. In contrast to acquiring information across generations, or over a lifetime of trial and error, a system with the ability to synthesize knowledge can generate new ε(m) on demand. In the case of humans, inference is so woven into our perception that we seldom acknowledge it. I walk into a home I’ve never been in before, and in the distance through a doorway I see just a sliver of a tall white object with two handles — a refrigerator. From a smidgen of evidence combined with knowledge I already possess I can now infer huge amounts of new knowledge. For example, if I get thirsty later in the evening and the host invites me to help myself to a beer, I instantly infer exactly where to look for one.
To sum up the different ways cybernetic systems get their ε(m), that is, usable knowledge of causal connections:
- burned — ε(m) is engineered into the system
- earned — ε(m) is acquired through evolution by natural selection
- learned — ε(m) is acquired through trial and error learning
- discerned — ε(m) is synthesized by an internal logical process
My contention is that the above processes, which are very different in the details of their operation, are all the same in terms of the principle that drives them and the nature of what they produce. For a single principle to underlie such varied phenomena — life, mind and engineering — it must be fundamental. And so it is: the physics of knowledge.
