Hi A/, Nonsense or not, it's a fun read. I have some comments which I will place in your thesis(?) below. -Mark
[A/] To Ham/Mark and others. To me that's just a way of categorization, and in MoQ, MoQ itself i sorted under the same category. You shouldn't be surprised to find such maps. For my part, I would probably have dismissed MoQ hadn't I been able to see any such connections. You can't really explain why science "works", if you reduce it to an intellectual pattern not corresponding to anything. [Mark's attitude] MOQ is an analogy. It should be used for ones own positive purposes, and dismissed if one does not find it useful. I have gained much from the notion of Quality especially during my formative years. Personally, I use it as a spring board for all sorts of nonsense. This do doubt annoys the purists, but I do feel I have something to add, just in my own way. [A/] Anyway. I actually expanded on the topic today. I had some different trains of thought, but this was the analogy that seemed to work best. In creating map between the two SYSTEMS OF THOUGHT (which is the way I conceive them, such systems span statement spaces), we should first find the analogy to the concept of Quality as a whole. After ponding some time, I found it to be negentropy, which is also information (in the Shannon concept). [Mark] Seems like a good starting point. I believe Pirsig used this kind of analogy and then was accused of being Theological. Some discuss posts I contributed to in the past dealt with thermodynamics, and the apparent well of negative entropy (the spark of life). [A/] Why this? Well, first of all, in ordinary science information is what the brain, as a concrete system is working with. A sound, for example, having the highest entropy (the largest variation), would be perceived as noise. The same with light, which would just be a "whiteness" and no colors. In a word of "total entropy", there wouldn't be anything at all: everything would be totally shattered and there would be no motion at all. You don't perceive the Brownian motion of the molecules in the air, which is high entropic, but you do perceive a wind, which is a low entropic motion. And so on. So I just state the relation as axiomatic: Quality -> Negentropy. Now, how should we map the division between static and dynamic? In mathematic systems theory all dynamics are functions of time. How could we conceive this? If we have a space-time (and the "space" doesn't have to consist of spatial dimensions only), then anything dynamic, is moving along some curve not parallel to the time dimension in that non-time dimension(s) considered. [Mark] Interesting use of information simplification. I have considered an interpretation of the levels as harmonics, with noise in between. I believe in your in your last sentence above you meant "static" instead of "dynamic". If not, then I do not follow. [A/] Then of course, what is static and what is dynamic depends on which dimension(s) you are considering (it's quite easy to imagine a three-dimensional Cartesian space, were a curve is parallel to the time axis in one but not the other of the other two). This would, then, be the description of static and dynamic in the frame of "static physical patterns". Which scale you chose to measure, would in any case depend upon just what kind of pattern you study. Because by measuring you are gaining some quantity of information, which it just would be cumbersome to try to compare to some absolute scale. It's better to choose some relative scale useful in the context. The amount of some dynamic pattern, would straight of be the amount of information/energy gained or paid for the measurement. A amount of a static pattern would be the different ways it could be decomposed. [Mark] I makes sense to separate DQ from SQ in terms of dimensional characteristics, such as the two dimensional world from the three. I am not quite sure where this would lead however. A similar notion could be seen in the difference between a point and a vector. (SQ and DQ). [A/] If I allow myself to oversimplify, consider the central nervous system for instance. It consists of neurons and connections between those, called synapses. The probability that a signal is propagated through any specific synapse when a neuron fire is a number between zero and one. The total number of possible connections between neurons would be a function of the number of neurons, N, f(N)=(N(N-1)/2 In this simplified particular instance, I would term this number of a maximally connected network, the amount of static patterns of the system considered. The amount of change in the probabilities of the synapses, on the other hand, would be the dynamics. To reduce the number of static patterns, you have to remove neurons, and to reduce the dynamic patterns, the system must approach the state when all probabilities are either one or zero (if you make it a discrete model, for instance, you could make a probability increase every time the synapse carries a signal and make it decrease every time the synapse hasn't carried a signal for three iterations or more). [Mark] It would be nice if the brain were that overly simple. I spent a few years studying the brain (in rats), even got some publications out. The digital analogy falls apart due to summation of input which is a result of density of connections, constant changes (adaptations) of the synaptic cleft, and so forth. There is also a chemical gradation between synapses that may delay firing, and a transmitter clean-up which affects signal transduction. But yes, either a nerve propagates an action potential or it doesn't, so a binary system is appropriate, and I see your point of making the nerves static for descriptive purposes. How information is stored by the brain is a whole 'nother subject. Probabilities of one or zero, doesn't sound like statistics to me, but what do I know. [A/] Perhaps this is just nonsense, but anyway :-) [Mark] Ditto Moq_Discuss mailing list Listinfo, Unsubscribing etc. http://lists.moqtalk.org/listinfo.cgi/moq_discuss-moqtalk.org Archives: http://lists.moqtalk.org/pipermail/moq_discuss-moqtalk.org/ http://moq.org/md/archives.html
