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Fra
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Fra said:I want to find an induction principle (like Ariel Caticha) that by construction works by the minimum speculation principle, that generates a guide for betting. This togther with the unavoidable element of uncertainty I want to use to infere the laws of physics and probably also to guess the most likely simplest possible AND distinguishable mictrostructure.
An addiotnal thing that may or many not clarify what I tried to convey:
The obvious approach I've found is bayesian reasoning and bayes rule or the "analog of bayes approach" in any non-probabilistic approach but there is still some unsatisfactory things there... The choice of initial prior and howto infer the probability space it self, this leads to inductive principles to the probability space itself / alternatively the microstructure... so we get like an induction principle, not only for the "probability distribution" or state of the microstructure (am speaking loosely here), but also for the probabilti pace / microstructure itself. Giving a non-linear and quite complex feedback.
Chris, do you have any direct pointers for such specific application, assuming I made myself understood in the first place :)
I have initiated a construct on my own, but progress is slow.
The idea is that the simplest answer to the question what is the microstructure of reality is that we don't know. Then I question that answer, and want a better one. It seems an arbitrary guess is better than no guess - so someone may exlaim STRINGS (better than no answer). Then again, I question that answer and want a motivation for the guess. How can we formalise an inductive reasoning when the underlying mictrostructure is part of the unknown? Not only is the state of the mictrostructure unknown, the mictrostructure itself is unknown!
That is my problem, made short.
Any ideas relating to that, would be highly appreciated.
/Fredrik
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