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Signal-to-noise ratio: Difference between revisions
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{{a|systems| | {{a|systems|{{image|Infinity|png|Where<br>“n” is the data in which you trust; and<br>“x” is the data you haven’t got yet.}}}} | ||
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''Caught in a mesh of living veins,<br> | ''Caught in a mesh of living veins,<br> | ||
''In cell of padded bone,<br> | ''In cell of padded bone,<br> | ||
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All of these are another way of attacking a familiar problem: the universe, the world, the nation, your market, your workplace and even your interpersonal relationships are [[complex]], not just [[complicated]]. Mere [[complication]] is a ''function'' of a [[paradigm]]. It is part of the game. It is within the rules. It is soluble, by sufficiently skilled application of the rules. Complication can be beaten by an algorithm. You ''can'' brute force it. | All of these are another way of attacking a familiar problem: the universe, the world, the nation, your market, your workplace and even your interpersonal relationships are [[complex]], not just [[complicated]]. Mere [[complication]] is a ''function'' of a [[paradigm]]. It is part of the game. It is within the rules. It is soluble, by sufficiently skilled application of the rules. Complication can be beaten by an algorithm. You ''can'' brute force it. | ||
[[Complexity]], you cannot. | [[Complexity]], you cannot. [[Complexity]] describes the ''limits'' of the [[narrative]]. [[Complexity]] is the wilderness ''beyond'' the [[rules of the game]]. [[Complexity]] inhabits the noise, and the data we do not yet have, not the signal. Where there is complexity, ''algorithmic rules do not work''. Here ''data'' is relegated to ''noise''.<ref>Provisional theory: “information” is [[data]] framed with a hypothesis.</ref> | ||
[[Complexity]] describes the ''limits'' of the [[narrative]]. [[Complexity]] is the wilderness ''beyond'' the [[rules of the game]]. [[Complexity]] inhabits the noise, not the signal. Where there is complexity, ''algorithmic rules do not work''. Here ''data'' is relegated to ''noise''.<ref>Provisional theory: “information” is [[data]] framed with a hypothesis.</ref> | |||
Behold, the difference between [[complication]] and [[complexity]]: complication is from the ''past''. It is known [[Known known - Risk Article|knowns]] and [[Known unknown|known unknowns]]: we can solve its problems with the information we already have, and derivations from it. But complexity is of the ''present'' and the ''future'': it problems and opportunities which are currently unfolding, or which haven’t yet presented themselves. | |||
This is why physical sciences apparently have a greater success than social sciences: they ask themselves easier questions: Physical sciences generally address behaviours of independent events — rolling balls, [[Coin flip|flipping coins]], waves [[and/or]] particles of light. But rolling balls are not autonomous agents. They act independently. The behaviour of one will not influence that of another. Each [[coin flip]] is, as a condition of probability theory — independent.<ref>The technical term: “platykurtic”.</ref> Independent events | This is why physical sciences apparently have a greater success than social sciences: they ask themselves easier questions: Physical sciences generally address behaviours of independent events — rolling balls, [[Coin flip|flipping coins]], waves [[and/or]] particles of light. But rolling balls are not autonomous agents. They act independently. The behaviour of one will not influence that of another. Each [[coin flip]] is, as a condition of [[probability]] theory — independent.<ref>The technical term: “platykurtic”.</ref> Independent events may be modelled. That is to say, they may be [[complicated]] but they remain predictable, at least in theory. When physical systems inexplicably go bang — Chernobyl, the Shuttle ''Challenger'', the ''Torrey Canyon'' — the [[root cause]] will not be a failure of the physical science underlying the engineering, but some supervening cause invalidating the underlying assumptions on which the physical science was based. Things go bang because of [[non-linear interaction|''non-linear'' interactions]]. | ||
[[Social science]]s don’t have that get-out-of-jail-free card: they address precisely that kind of supervening cause: behaviour that is, intrinsically, ''un''predictable. Psychology, sociology, anthropology, economics — these concern themselves with human agents, who ''are'' influenced by each other — which is why we don’t use physical science to predict their behaviour. Social sciences have to deal with the inherently complex, non-Gaussian interactions between human beings.<ref>physical sciences set up closed logical systems within which their rules will work, and often these systems are dramatically simplified as compared with anything you see in the real world: Newton, for example, assumes a frictionless, stationery, stable, neutral frame of reference: circumstances which, in any observed environment, do not and ''cannot'' not exist. {{author|Nancy Cartwright}} calls these structures “[[nomological machine]]s”. Because of this explicit caveat, we can put any variances between Newton’s prediction and the observed outcome down not to [[falsification]], but to the messy real world “contaminating” the idealised experimental conditions. Hence, the proverbial [[crisp packet blowing across St Mark’s Square]].</ref> | [[Social science]]s don’t have that get-out-of-jail-free card: they address precisely that kind of supervening cause: behaviour that is, intrinsically, ''un''predictable. Psychology, sociology, anthropology, economics — these concern themselves with human agents, who ''are'' influenced by each other — which is why we don’t use physical science to predict their behaviour. Social sciences have to deal with the inherently complex, non-Gaussian interactions between human beings.<ref>physical sciences set up closed logical systems within which their rules will work, and often these systems are dramatically simplified as compared with anything you see in the real world: Newton, for example, assumes a frictionless, stationery, stable, neutral frame of reference: circumstances which, in any observed environment, do not and ''cannot'' not exist. {{author|Nancy Cartwright}} calls these structures “[[nomological machine]]s”. Because of this explicit caveat, we can put any variances between Newton’s prediction and the observed outcome down not to [[falsification]], but to the messy real world “contaminating” the idealised experimental conditions. Hence, the proverbial [[crisp packet blowing across St Mark’s Square]].</ref> | ||