Curt Doolittle

Theme: Measurement

  • (math) —“Curt: Someone said:”The closest you can get to objective truth is mat

    (math)

    —“Curt: Someone said:”The closest you can get to objective truth is maths, which is apriori, analytic and not empirical whatsoever” Is this a lie? I’m having difficulties understanding that since maths is axiomatic.”—

    This is one of the great intellectual problems that we must deal with. And it’s as old as the Greeks at least. It is better to say, that if you can describe something in *certain mathematical equations* then you have the lowest chance of misinterpretation of description.

    However, as we see in statistics daily, economics weekly, and physics monthly, mathematics is a tool limited to describing certain things. It does not for example, describe causality or sequence. And it can be misused more easily than used.

    Mathematics depends upon constant categories and constant relations, at scale independence. And so it is good for expression of deterministic phenomenon. However, in economics and in sentience, we have only inconstant categories, and fungible relations. We can think of it this way: the physical world can’t decide to change categories and relations; we can cause changes in the physical world if we want at some cost or other. The economic world can change categories and relations, but at some cost and effort; and the sentient world can change categories and relations with only exposure to information, and very near zero cost to the individual, but at very, very, very high cost to groups.

    This does not mean we cannot make true statements about economics at some degree of precision. Just as we cannot make true statements about subatomic world yet beyond some degree of precision. The reason being that at the subatomic level, and in the economic and sentient levels, the causal density is so high and categorical variation so high that mathematics has proven little use in direct prediction of consequences – and almost none at all. At the sentient level we have no way at all of expressing in mathematical terms the information necessary to change state.

    What we have seen is that there is a point at which we can model sufficient causal density of systems that we can observe intermediary phenomenon (patterns) that assist us in defining limits of consequent patterns (ends we want to observe). So we may not be able to predict the location of molecules of gas, prices of a good at a location, or the information necessary to form an idea. But that does not mean that we cannot make truthful (parsimonious and descriptive) statements about those phenomenon.

    And this is the current limit of our understanding of what we may be able to do with mathematics. In other words, while there may be an unmeasurable and unpredictable set of end states due to causal density and rapid heuristics that change our actions or associations, it appears that whatever limits humans are limited by, just as whatever limits the universe is limited by, cause patterns that appear, and these patterns may in fact assist us in predicting end states.

    The problem, as usual, will be at some point, the information necessary to perform a calculation is equal to reality itself.

    So, the response to your friend is that math is good at measuring simple things, that does not mean all things that we need measure are simple.

    Math works because it is trivial. But we have, until the 1800’s only used it to measure trivial things.

    We are just beginning to touch upon complicated things.

    -Curt

    Source date (UTC): 2017-04-02 15:48:00 UTC

  • Thou shalt make no statistical claim about mankind that does not survive constru

    Thou shalt make no statistical claim about mankind that does not survive construction from a sequence of rational actions within each quintile.

    Source date (UTC): 2017-03-31 17:02:00 UTC

  • YOUR ‘OPERATIONAL’ DEFINITION IS ‘PERSONAL’. (meaning, subjective nonsense) (oh

    YOUR ‘OPERATIONAL’ DEFINITION IS ‘PERSONAL’.

    (meaning, subjective nonsense) (oh the irony)

    Necessary definitions are what we call ‘truth’ statements. It is what it is. They are what they are. And yes I do need to do it. It’s my job: Sanitizing the informational commons. And exposing those who make excuses for people who conflate personal experiential emotions in the ignorance of possibility, cost, and consequence, possibility with aggregate possibility, cost, and consequence in order to promote and conduct thefts via the violence of government is one of the most moral services a man can provide to his people.

    Source date (UTC): 2017-03-29 14:54:00 UTC

  • compassion has nothing to do with either possibility or measurement. Instead, it

    compassion has nothing to do with either possibility or measurement. Instead, it’s intellectual laziness, status seeking, and virtue signaling.

    Regarding regulation. Unless you grasp the scale of the cost of compliance vs the returns on that compliance you are again making judgement out of intellectual laziness, and pseudo-morality rather than the science. While it may be one thing to punish the best dog owners whose dogs are fully trained because of those whose dogs require leashes, it is quite another to impose vast costs and the highest taxes on business and industry. While Foreign Affairs has traditionally been fairly conservative, the article you reference cherrypicks the regulatory nightmare of the EU where business is not growing, vs the remainder of the world where growth is continuing, regulation is non existent and corruption is rife.

    Why is it that ignorant people feel their opinions are anything but the fantastic impulses of the uneducated, uninformed, and unskillled implanted in them by critical theory and the ignorance of organizations and politiies at large scales?

    Source date (UTC): 2017-03-29 05:26:00 UTC

  • Don’t get it backwards. Math is so powerful precisely ’cause it’s so simple (dum

    Don’t get it backwards. Math is so powerful precisely ’cause it’s so simple (dumb). It’s easy to be correct when you choose your own causal density. It’s far harder to be correct when you can’t.

    Math is pretty simple for that reason, and we can delve into great complexity because of simplicity.

    But we are having problems in physics at higher causal density.

    And mathematics is all but useless in social science (say, in economics) because of causal density.

    And we can’t even figure out a unit of measure for sentience yet, which is an even higher causal density.

    So when people make statements like you just did, it sounds a little bit like someone saying chess is complicated. Actually it’s not. It’s a closed (ludic) game. It’s just hard for humans. There is math that is hard for humans for the same reason: mere scale of permutations. But it’s still trivial.

    Tell me how to measure the market value of a brand.

    Tell me how to measure the future rate of decline of iphone appeal.

    Tell me how to measure how much information it takes to change state from one idea to another?

    Doing puzzles is simple.

    Problems have high causal density.

    Source date (UTC): 2017-03-28 12:57:00 UTC

  • Simplicity is necessary in mathematics since mathematical symbols and operations

    Simplicity is necessary in mathematics since mathematical symbols and operations itself (state and operators) are necessary to allow us to remember state with sufficient precision that we can conduct comparisons between states.

    However, if we restated the foundations of mathematics operationally (constructively – analogous to gears), and we stated the foundations of mathematical deduction negatively, as geometry, we would be able to show that it is convergence between the via-positiva construction, and the via-negative deduction that leads us to truth.

    Unfortunately, man discovered (logically so) geometry prior to gears, and as such, we retain the ‘superstitious’ language of geometry (and algebra) of the superstitious era in which both were invented.

    Reality has only so many dimensions. By adding and removing dimensions from consideration we simplify the problem of describing the constant relations within it.

    Mathematics specializes in the removal of (a) scale, and (b) time, and (c) operations (and arguable (d) morality) from consideration, leaving only identity, quantity, and ratio, to which we add positional naming (numbers). We then construct general rules of arbitrary precision (scale independence) and apply those to reality wherein we must ‘hydrate’ (reconstitute) scale, time, and operations(actions).

    So just as philosophy is ‘stuck’ in non contradiction instead of increasing dimensions in order to test theories, mathematics is ‘stuck’ in non-contradiction instead of re-hydrating (restoring dimensions) to justify propositions.

    In other words, fancy words like ‘limits’ or ‘non-contradictory’ or ‘axiom of choice’ and various other terms in the field are just nonsense words that prevent the conversion of mathematics from a fictionalism into a science.

    Source date (UTC): 2017-03-28 07:10:00 UTC

  • Definitions: Post Euclidian Geometry

    I think that the scientific rather than platonic explanations are more truthful and less “magical” (and less ridiculous honestly). So try this: We can act in four dimensions of the physical universe, measure in four dimensions of the physical universe, and model four dimensions of the physical universe with mathematics. However, we can use the same techniques to model purely logical relationships, as we do to model physical relationships. It requires quite a bit of skill to keep track of what you’re doing, but when we are modeling very complex things, like waves, magnetism, forces, economic phenomenon, we can perform very complex calculations – not because these spaces exist, but because we can use the techniques we developed in the more simple physical spaces consisting of a small number of dimensions of change, to solve problems with many many, dimensions of change. It’s not that complicated really. It just sounds complicated because of the old fashioned (archaic) language we use to describe what we’re doing.

  • Definitions: Post Euclidian Geometry

    I think that the scientific rather than platonic explanations are more truthful and less “magical” (and less ridiculous honestly). So try this: We can act in four dimensions of the physical universe, measure in four dimensions of the physical universe, and model four dimensions of the physical universe with mathematics. However, we can use the same techniques to model purely logical relationships, as we do to model physical relationships. It requires quite a bit of skill to keep track of what you’re doing, but when we are modeling very complex things, like waves, magnetism, forces, economic phenomenon, we can perform very complex calculations – not because these spaces exist, but because we can use the techniques we developed in the more simple physical spaces consisting of a small number of dimensions of change, to solve problems with many many, dimensions of change. It’s not that complicated really. It just sounds complicated because of the old fashioned (archaic) language we use to describe what we’re doing.

  • The State of Mathematical Economics

      Understanding advanced mathematics of economics and physics for ordinary people. The Mengerian revolution, which we call the Marginalist revolution, occurred when the people of the period applied calculus ( the mathematics of “relative motion”) to what had been largely a combination of accounting and algebra. 20th century economics can be seen largely as an attempt to apply the mathematics of relative motion (constant change) from mathematics of constant categories that we use in perfectly constant axiomatic systems, and the relatively constant mathematics of physical systems, to the mathematics of inconstant categories that we find in economics – because things on the market have a multitude of subsequent yet interdependent uses that are determined by ever changing preferences, demands, availability, and shocks. Physics is a much harder problem than axiomatic mathematics. Economics is a much harder problem than mathematical physics, and before we head down this road (which I have been thinking about a long time) Sentience (the next dimension of complexity) is a much harder problem than economics. And there have been questions in the 20th century whether mathematics as we understand it can solve the hard problem of economics. But this is, as usual, a problem of misunderstanding the very simple nature of mathematics as the study of constant relations. Most human use of mathematics consists of the study of trivial constant relations such as quantities of objects, physical measurements. Or changes in state over time. Or relative motion in time. And this constitutes the four dimensions we can conceive of when discussing real world physical phenomenon. So in our simplistic view of mathematics, we think in terms of small numbers of causal relations. But, it does not reflect the number of POSSIBLE causal relations. In other words, we change from the position of observing change in state by things humans can observe and act upon, to a causal density higher than humans can observe and act upon, to a causal density such that every act of measurement distorts what humans can observe and act upon, by distorting the causality. One of our discoveries in mathematical physics, is that as things move along a trajectory, they are affected by high causal density, and change through many different states during that time period. Such that causal density is so high that it is very hard to reduce change in state of many dimensions of constant relations to a trivial value: meaning a measurement or state that we can predict. Instead we fine a range of output constant relations, which we call probabilistic. So that instead of a say, a point as a measurement, we fined a line, or a triangle, or a multi dimensional geometry that the resulting state will fit within. However, we can, with some work identify what we might call sums or aggregates (which are simple sets of relationships) but what higher mathematicians refer to as patterns, ‘symmetries’ or ‘geometries’. And these patterns refer to a set of constant relations in ‘space’ (on a coordinate system of sorts) that seem to emerge regardless of differences in the causes that produce them. These patterns, symmetries, or geometries reflect a set of constant relationships that are the product of inconstant causal operations. And when you refer to a ‘number’, a pattern, a symmetry, or a geometry, or what is called a non-euclidian geometry, we are merely talking about the number of dimensions of constant relations we are talking about, and using ‘space’ as the analogy that the human mind is able to grasp. Unfortunately, mathematics has not ‘reformed’ itself into operational language as have the physical sciences – and remains like the social sciences and philosophy a bastion of archaic language. But we can reduce this archaic language into meaningful operational terms as nothing more than sets of constant relations between measurements, consisting of a dimension per measurement, which we represent as a field (flat), euclidian geometry (possible geometry), or post Euclidian geometry (physically impossible but logically useful) geometry of constant relations. And more importantly, once we can identify these patterns, symmetries, or geometries that arise from complex causal density consisting of seemingly unrelated causal operations, we have found a constant by which to measure that which is causally dense but consequentially constant. So think of the current need for reform in economics to refer to and require a transition from the measurement of numeric (trivial) values, to the analysis of (non-trivial) consequent geometries. These constant states (geometries) constitute the aggregate operations in economies. The unintended but constant consequences of causally dense actions. Think of it like using fingers to make a shadow puppet. If you put a lot of people together between the light and the shadow, you can form the same pattern in the shadow despite very different combinations of fingers, hands, and arms. But because of the limits of the human anatomy, there are certain patterns more likely to emerge than others. Now imagine we do that in three dimensions. Now (if you can) four, and so on. At some point we can’t imagine these things. Because we have moved beyond what is possible to that which is only analogous to the possible: a set of constant relations in multiple dimensions. So economics then can evolve from the study of inputs and outputs without intermediary state which allows prediction, to the study of the consequence of inputs and the range of possible outputs that will likely produce predictability. in other words, it is possible to define constant relations in economics. And of course it is possible to define constant relations in sentience. The same is true for the operations possible by mankind. There are many possible, but there are only so many that produce a condition of natural law: reciprocity. Like I’ve said. Math isn’t complicated if you undrestand that it’s nothing more than saying “this stone represents one of our sheep”. And in doing so produce a constant relation. all we do is increase the quantity of constant relations we must measure. And from them deduce what we do not know, but is necessary because of those constant relations. Math is simple. That’s why it works for just about everything: we can define a correspondence with anything. Curt Doolittle The Propertarian Institute Kiev Ukraine

  • The State of Mathematical Economics

      Understanding advanced mathematics of economics and physics for ordinary people. The Mengerian revolution, which we call the Marginalist revolution, occurred when the people of the period applied calculus ( the mathematics of “relative motion”) to what had been largely a combination of accounting and algebra. 20th century economics can be seen largely as an attempt to apply the mathematics of relative motion (constant change) from mathematics of constant categories that we use in perfectly constant axiomatic systems, and the relatively constant mathematics of physical systems, to the mathematics of inconstant categories that we find in economics – because things on the market have a multitude of subsequent yet interdependent uses that are determined by ever changing preferences, demands, availability, and shocks. Physics is a much harder problem than axiomatic mathematics. Economics is a much harder problem than mathematical physics, and before we head down this road (which I have been thinking about a long time) Sentience (the next dimension of complexity) is a much harder problem than economics. And there have been questions in the 20th century whether mathematics as we understand it can solve the hard problem of economics. But this is, as usual, a problem of misunderstanding the very simple nature of mathematics as the study of constant relations. Most human use of mathematics consists of the study of trivial constant relations such as quantities of objects, physical measurements. Or changes in state over time. Or relative motion in time. And this constitutes the four dimensions we can conceive of when discussing real world physical phenomenon. So in our simplistic view of mathematics, we think in terms of small numbers of causal relations. But, it does not reflect the number of POSSIBLE causal relations. In other words, we change from the position of observing change in state by things humans can observe and act upon, to a causal density higher than humans can observe and act upon, to a causal density such that every act of measurement distorts what humans can observe and act upon, by distorting the causality. One of our discoveries in mathematical physics, is that as things move along a trajectory, they are affected by high causal density, and change through many different states during that time period. Such that causal density is so high that it is very hard to reduce change in state of many dimensions of constant relations to a trivial value: meaning a measurement or state that we can predict. Instead we fine a range of output constant relations, which we call probabilistic. So that instead of a say, a point as a measurement, we fined a line, or a triangle, or a multi dimensional geometry that the resulting state will fit within. However, we can, with some work identify what we might call sums or aggregates (which are simple sets of relationships) but what higher mathematicians refer to as patterns, ‘symmetries’ or ‘geometries’. And these patterns refer to a set of constant relations in ‘space’ (on a coordinate system of sorts) that seem to emerge regardless of differences in the causes that produce them. These patterns, symmetries, or geometries reflect a set of constant relationships that are the product of inconstant causal operations. And when you refer to a ‘number’, a pattern, a symmetry, or a geometry, or what is called a non-euclidian geometry, we are merely talking about the number of dimensions of constant relations we are talking about, and using ‘space’ as the analogy that the human mind is able to grasp. Unfortunately, mathematics has not ‘reformed’ itself into operational language as have the physical sciences – and remains like the social sciences and philosophy a bastion of archaic language. But we can reduce this archaic language into meaningful operational terms as nothing more than sets of constant relations between measurements, consisting of a dimension per measurement, which we represent as a field (flat), euclidian geometry (possible geometry), or post Euclidian geometry (physically impossible but logically useful) geometry of constant relations. And more importantly, once we can identify these patterns, symmetries, or geometries that arise from complex causal density consisting of seemingly unrelated causal operations, we have found a constant by which to measure that which is causally dense but consequentially constant. So think of the current need for reform in economics to refer to and require a transition from the measurement of numeric (trivial) values, to the analysis of (non-trivial) consequent geometries. These constant states (geometries) constitute the aggregate operations in economies. The unintended but constant consequences of causally dense actions. Think of it like using fingers to make a shadow puppet. If you put a lot of people together between the light and the shadow, you can form the same pattern in the shadow despite very different combinations of fingers, hands, and arms. But because of the limits of the human anatomy, there are certain patterns more likely to emerge than others. Now imagine we do that in three dimensions. Now (if you can) four, and so on. At some point we can’t imagine these things. Because we have moved beyond what is possible to that which is only analogous to the possible: a set of constant relations in multiple dimensions. So economics then can evolve from the study of inputs and outputs without intermediary state which allows prediction, to the study of the consequence of inputs and the range of possible outputs that will likely produce predictability. in other words, it is possible to define constant relations in economics. And of course it is possible to define constant relations in sentience. The same is true for the operations possible by mankind. There are many possible, but there are only so many that produce a condition of natural law: reciprocity. Like I’ve said. Math isn’t complicated if you undrestand that it’s nothing more than saying “this stone represents one of our sheep”. And in doing so produce a constant relation. all we do is increase the quantity of constant relations we must measure. And from them deduce what we do not know, but is necessary because of those constant relations. Math is simple. That’s why it works for just about everything: we can define a correspondence with anything. Curt Doolittle The Propertarian Institute Kiev Ukraine