I got a useful lead in terms of my confusion from the first part of the second reference Bhobba gave. From that I have a cartoon under construction (as in it's a pile of mud and sticks) that the problem has to do with probing (the integral of all the Feynman thingamajigs) inside the plank scale where the energy domain is one that "creates" particles rather than observing them. If we are trying to count a set that our counting is creating, we will have a bit of a feedback loop.
I can imagine this is wildly flawed.
The idea of using a "cutoff" on the "observable" domain seems on the one hand just practical - to get at some useful answers. That paper on the MERA Ansatz is one I've tried to understand n times now. It invokes what I have learned about "re-normalization" from Sornette, just in terms of how it looks.
The part I am puzzling about... Today, is whether that threshold fixing process is only invented, or arguably natural. In Sornette's book "Critical Events in Complex Financial Systems" the idea of critical points was primary (obviously). But in hindsight their naturalness, as introduced, was as much about everyday intuition about the system he was using as an example (investor optimism), rather than a clearly demonstrated fundamental mechanism.
Had a bit of an epiphany diving back into "Evolutionary Dynamics" by Nowak this morning. In sec 7.1 "One Basic Model and One Third", he shows how critical points form as a pure function of N (size of finite population) under conditions of weak selection. According to the model he describes, the only thing required for real critical points of population "fixing" (where one of two species a and b disappears) would be expansion of the number of a and b, even at the same ratio. Other requirements are: Some non-flat payoff matrix and therefore fitness functions for a and b. That a and b are the best response to each other (strategically stable, or evenly matched for payoff). "Selection intensity" is weak (only some encounters induce selection). Pretty elegant and weird. At least I think that's what he said.
I need to see if I can find a paper by him, maybe on that chapter. And I need to revisit that MERA paper to see if I missed a similar natural, rather than introduced, re-normalization thresholding process they were proposing.
[Edit] the non-sequitur to Evolutionary Dynamics, goes-like "if space-time is discrete, are there mechanisms that could explain problematic observations, such as probabalistic irreversibility, and the fact that reality doesn't blow up, even though integrals over QM momenta suggest it should/could/would if we didn't somewhat arbitrarily re-normalize those integrals"
[Edit] There are a number of papers by M.A. Nowak on arxiv. I'll have to look to see if there is one on that particular model in his book.
http://arxiv.org/find/q-bio/1/au:+Nowak_M/0/1/0/all/0/1
[Edit] There are also a number of papers by D. Sornette on Arxiv. This one really grabbed me.
http://arxiv.org/abs/1408.1529
Self-organization in complex systems as decision making
V.I. Yukalov,
D. Sornette
(Submitted on 7 Aug 2014)
The idea is advanced that self-organization in complex systems can be treated as decision making (as it is performed by humans) and, vice versa, decision making is nothing but a kind of self-organization in the decision maker nervous systems. A mathematical formulation is suggested based on the definition of probabilities of system states, whose particular cases characterize the probabilities of structures, patterns, scenarios, or prospects. In this general framework, it is shown that the mathematical structures of self-organization and of decision making are identical. This makes it clear how self-organization can be seen as an endogenous decision making process and, reciprocally, decision making occurs via an endogenous self-organization. The approach is illustrated by phase transitions in large statistical systems, crossovers in small statistical systems, evolutions and revolutions in social and biological systems, structural self-organization in dynamical systems, and by the probabilistic formulation of classical and behavioral decision theories. In all these cases, self-organization is described as the process of evaluating the probabilities of macroscopic states or prospects in the search for a state with the largest probability. The general way of deriving the probability measure for classical systems is the principle of minimal information, that is, the conditional entropy maximization under given constraints. Behavioral biases of decision makers can be characterized in the same way as analogous to quantum fluctuations in natural systems.
Bhobba's statement is "We decide the cuttoff, which had to be there to make the calculation work and we based it on observation". I think there is actually a serious loop of truth to that way of describing it. The question of how... that decision got made, the whole chain of "fixing"... to me... is more than a little spooky, and vertiginous.
