Registered: Feb 2004
I've read your post on ANKoS forum. I think you have misunderstood randomness here. Random doesn't mean casual.
"However, in Medicine (like in Love) nothing is really random."
In fact nothing is completely random. That's why probability theory has not only uniform variables but all kinds of distributions, and that's why two different random processes can be correlated or not. I have a certain probability of getting in love, but that probability can be higher if I meet the right girl P(in love|right girl). It's a over simplification to say that random in medicine means "without any rule". In fact, Bayesian modelled diagnosis tools perform quite well, in some cases better than the human doctors that instructed it.
"Nature presents itself to us as change. We distinguish between two kinds of change: Change which is explained by a theory, and unexplained change, which we call Randomness. Randomness is not an inherent (ontological) property of nature. It is our way to describe it."
Randomness is implicit in nature. Quantum physics and the Heisenberg's indetermination principle say that.
"Neural Nets start from a random initial state and converge to a (non random) solution. Thus Neural Nets are processes (algorithms) which eliminate randomness."
Neural nets are usually deterministic you're right (mostly). But what does it mean? They still deal with randomness. The probability theory rules that we use to manipulate random variables aren't random and so? Tools can be random or deterministic, this says nothing. Neural networks are strongly based on probability theory, in fact they are derived from Bayesian classifiers. Randomness does converge, every randomized algorithm for example converges (otherwise we couldn't get d a solution) and they are fundamental in computer science. The CLT say something on that too. But this is not an argument against randomness! It just says that random does not mean that we can't get results from it, and that random doesn't mean casual, inpredictable.
"Life never starts from randomness. Two cells, the sperm and the ovum unite to form a (non random) zygote, which evolves into what we are. This is why Life and Randomness is a medical oxymoron, and a meaningful model of Life has to exclude Randomness."
??? Yes when the sperm and the ovum met they form a zygote. And so? Do they always met? No! They met with some random distribution. By the way without randomness the darwinian evolution simply does not work, so it's lucky that we have randomness all the way down :D
"Life also lacks the two prerequisites of CLT. Neither are its elements isolated nor independent. Unfortunately, epidemiologists ignore this common wisdom and base their statistics on the CLT. They take the human being and simplify his attributes until the CLT requirements are met. Yet this simplified creature is a far cry from that which was created in Genesis. Epidemiology thus nurtures medically induced diseases known as Iatrogenesis. By now you might understand why I dislike randomness."
Sometimes you can misuse a tool and what will that say? That the tool itself is wrong? No maybe it's wrong in that model, maybe the model is oversimplified but that's something that you'll find everywhere in science, we always make simplifications, with random processes and without. Determinism that you advocate is no better. Equations are not more powerful than random models, they are usually worse. Chaotic system could be but it's hard to say. Even Wolfram that was wrote a big book saying many things that where known for decades (he is not the only one, or the first, or the main, CA researcher, and CA are only a subset of all the chaotic models we could use) hasn't found a good way to use them to solve some kind of scientific problem as far as I know. He haven't applied them in the real world, they're just toys, interesting, unoriginal toys. Not a 'new kind of science'. Not even a 'new kind of model' as the model was already widely known.
"The Random Walk is another manifestation of the Randomness concept. It is a stochastic process like Brownian motion, and serves among other to describe the stock market and exchange rates. The Efficient Market Theory says that the prices of many financial assets, such as shares, follow a random walk."
See the reply above... if a model doesn't fit a problem that doesn't make the underlying framework bad. We should only elaborate a more complex model. That more complex model could be random or non random, it depends but that doesn't say anything about how good are random models in general. The fact that they are so widely used, that they are so successful and that there aren't other models in many fields should tell you that they shouldn't be so bad after all... If even with all those simplifications they manage to make good predictions, that means that there is some inherent randomness, who knows what we can make with the same framework and more accurate models!
"The crudeness of Darwin’s model is evident in Genetic Algorithms (GA) which apply it for classifying and generating various solutions,. They manipulate their objects in the same way as Darwinists would suggest. Despite some impressive achievements, GA hardly ever generalize. Above all they are not creative."
Eh!!! GA do generalize, GA are global optimizators, GA are creative! And if we don't use GA we use other random processes to solve global optimization problems, not deterministic ones! There's no useful deterministic global optimization procedure (except maybe tabu search). All the deterministic ones are local, they converge to a local minimum and fail to find a global one. We should be happy that life is dominated by the Darwinian process, otherwise we could fail in one of those local minimums and never evolve further.
"I dislike Darwinism for two main reasons: 1. Social Darwinism promotes discrimination, and 2. Medicine applies Darwinism to describe cancer progression. As cancer evolves, it becomes fitter than its host (the patient), and gradually destroys him . Yet Cancer is more than that! It is a creative process operating in a creative host."
Wrong, wrong, wrong, WRONG. Again and again the same argument. You dislike something because you find that it fails sometimes, in a particular contest (I don't know how good are Social Darwinism or Darwinian cancer evolution, I assume that they are bad, I trust you). If someone writes a bad model using linear equations would you say that linear equations are bad?
Report this post to a moderator | IP: Logged