Thus, clearly, quantum mechanics drops out as a fundamental category. It only remains as a research strategy in the face of apparent difficulties, but it still is on the road to 100% accuracy.
Admittedly, quantum mechanics itself seems to pose that nature would have random properties at the micro particle level. Some even argue that this would be the basic example of true probability - while all other ‘examples of probability’ (like throwing dice) are basically deterministic (and we only use probability techniques to make up for our lack of knowledge or laziness in measurement). In particular, Richard Gill, professor in mathematical statistics at Utrecht university, gives this argument at a roundtable discussion:
“We should be collectively ashamed not to know anything about quantum mechanics. I would like to see all introductory texts in probability theory going a little into the physical (quantum) theory behind the geiger counter before using some data of alpha particle counts as an illustration of the Poisson process; I would like a discussion of the Bell inequalities together with a modicum of quantum mechanical background to show how elegant probabilistic reasoning shows that the quantum world is truly random (unless you would like to go for an even more weird non-local deterministic theory).” (1997b)
Indeed, also economists are familiar with the concept of Brownian movement, or the random walk, and use this model for example in analysis of the stock markets. Or in the labour market, with labour supply LS and employment LE, unemployment is u = 1 - LE/LS: but u then basically is a probability, since the model does not provide an additional explanation why one person works and the other doesn’t.
But Gill’s argument does not convince me. The point is: you may pose that nature would be such, but you don’t know for sure. You are still using only a model. The scientific challenge remains to develop a model that increases accuracy.
Yes, there is the Heisenberg uncertainty model that if you measure position then you no longer know speed, and if you measure speed then you no longer know position: and this model nicely captures a basic notion of uncertainty. But, try for a better model then - and take some thousands years more to do so. [ [64] [ [65] [ [66]
As a corollary, we can take a position on path-dependency (hysteresis) and chaos.
Some authors use the word ‘chaos’ in the sense of path-dependency. For example, a small variation in first conditions (starting point, parameter) can cause a widely different result - a butterfly flapping a wing can cause a tropical storm. Since we already have the term ‘path-dependency’ for this, we better reserve ‘chaos’ for the meaning of ‘seemingly random’. A chaotic system, in this proper sense, then gives a fully deterministic description, but the outward appearance that some variables would be random. Here it is strange that people who are in favor of ‘chaotic modeling’ also use this to be against determinism.
Path-dependent and chaotic models can be useful. The orbit of Earth around the sun looks solid, but over the billion years it seems pretty random. There is Schrödinger’s cat model that shows the macro world depending upon a micro state. There are the strange models in history and biology, where for example a meteor wipes out dinosaurs. OK, all these models exist, and they can be real good descriptions of true states of nature. But all this does not disprove the definitory deterministic predisposition of science. If you would run the movie again from the start (which is currently said to be a Big Bang, but I don’t know about that), then you would get, by the models that science tries to develop, the same result. If you would argue that anything else might pop up, and your mother could be a dinosaur with a pig’s head, and if you would develop models that would show this, then you are quite in danger of being out of science. (You would drop out on this definition, but could be in on the other criteria.)
Concluding this section, we find that definitions indeed guide our understanding of nature. The definition of science itself guides our perceptions - for example when it guides us into taking quantum mechanics as a model only instead of as ‘reality itself’.