Posts Tagged ‘Mechanism’

Understanding Regularities 1: Some examples

August 30, 2015

One problem with the regularities framework is that, like other frameworks, it is an interlocking set of conceptual intuitions and hypotheses that do not lead to an easy definition. It is almost OK to say that regularities are not definable but we know one when we see one. I don’t quite agree with that conclusion, but let us first see if we can agree about some phenomena being regularities, so that we can at least say that we know one when we see one. Here are a few examples of what I would call regularities:

  1. The size of an animal predicts the pitch of it’s voice. Mice squeak and lions roar and not vice versa
  2. Clouds are puffy while water is runny.
  3. More controversially, the size of an animal predicts how smart it is. A bacterium can never be as smart as a dophin.

These three examples are all related to each other though not in any obvious way. The underlying mechanisms for mice squeaking, clouds puffing and dolphins thinking are all different. Even the evolutionary histories are different. However, at a thermodynamic level, we can see that all of them have to do with how energy and information flow through the respective systems. Physicists talk about “universality” i.e., that the macroscopic properties of a system can often be independent of it’s microscopic origins. The regularity approach takes this one step further, that the regularities of a system are not only independent of the underlying mechanism or causal features, they are the real thing. Especially when it comes to biological processes we can hypothesize that it is regularities and their graspability that is being selected for in natural selection, not the underlying mechanism. I see this as a biologically grounded version of the hardware/software distinction well known in AI and cognitive science. Just as an earlier generation of theorists argued that the same software can be instantiated in different hardware, we can argue that the same regularity can be instantiated via different mechanisms while remaining the same.

Understanding Regularities 2: what is not a regularity?

June 13, 2011

I am arguing that regularities are a powerful framework for understanding the world of organisms. That said, it is important when we are exploring a new concept or framework that we try not to capture everything under the universe within it’s ambit. So what is not a regularity?

  1. Mechanisms aren’t regularities. Consider the circulatory problem of trying to distribute nutrients and take away waste. We know that both nutrients and waste will scale as the volume of the organism; that is the regularity. The transportation problem could be solved by laying down pipes (as in humans) or diffusion through the entire cellular matrix in unicellular creatures, but these are mechanisms. They are not the regularities.
  2. Algorithms aren’t regularities. I can solve the same information processing problem using multiple algorithms, but it is the problem itself, such as recovering depth from stereo, that defines the regularity.

Algorithms are to information that mechanisms are to physical transport. In other words, we are trying to understand information or energy flow, but abstracting away from the details.

Understanding Regularities 1: Some examples

June 12, 2011

One problem with the regularities framework is that, like other frameworks, it is an interlocking set of conceptual intuitions and hypotheses that do not lead to an easy definition. It is almost OK to say that regularities are not definable but we know one when we see one. I don’t quite agree with that conclusion, but let us first see if we can agree about some phenomena being regularities, so that we can at least say that we know one when we see one. Here are a few examples of what I would call regularities:

  1. The size of an animal predicts the pitch of it’s voice. Mice squeak and lions roar and not vice versa
  2. Clouds are puffy while water is runny.
  3. More controversially, the size of an animal predicts how smart it is. A bacterium can never be as smart as a dophin.

These three examples are all related to each other though not in any obvious way. The underlying mechanisms for mice squeaking, clouds puffing and dolphins thinking are all different. Even the evolutionary histories are different. However, at a thermodynamic level, we can see that all of them have to do with how energy and information flow through the respective systems. Physicists talk about “universality” i.e., that the macroscopic properties of a system can often be independent of it’s microscopic origins. The regularity approach takes this one step further, that the regularities of a system are not only independent of the underlying mechanism or causal features, they are the real thing. Especially when it comes to biological processes we can hypothesize that it is regularities and their graspability that is being selected for in natural selection, not the underlying mechanism. I see this as a biologically grounded version of the hardware/software distinction well known in AI and cognitive science. Just as an earlier generation of theorists argued that the same software can be instantiated in different hardware, we can argue that the same regularity can be instantiated via different mechanisms while remaining the same.