Template:M intro design System redundancy: Difference between revisions

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 The modernist programme is as simple to state as it is self-serving: a distributed organisation is best controlled centrally, and from the place with the best view of the big picture: the top. All relevant information can be articulated as data — you know: “[[Signal-to-noise ratio|In God we trust, all others must bring data]]” — and, with enough data everything about the organisation’s present can be known and its future extrapolated.
-Even though, inevitably, one has less than perfect information, extrapolations, mathematical derivations and [[Large language model|algorithmic pattern matches]] from a large but finite data set will have better predictive value than the gut feel of “[[ineffable]] expertise”: the status we have historically assigned to experienced experts is grounded in folk psychology, lacks analytical rigour and, when compared with sufficient granular data, cannot be borne out: this is the lesson of {{br|Moneyball: The Art of Winning an Unfair Game}}. In the same way that Wall Street data crunchers who know little about baseball could outperform veteran talent scouts, so can data models and analytics outperform humans when optimising business systems. Thus, from a network of programmed but uncomprehending rule-followers, a smooth, steady and stable business revenue stream [[emerge]]s.
+Even though, inevitably, one has less than perfect information, extrapolations, mathematical derivations and [[Large language model|algorithmic pattern matches]] from a large but finite data set will have better predictive value than the gut feel of “[[ineffable]] expertise”: the status we have historically assigned to experienced experts is grounded in folk psychology, lacks analytical rigour and, when compared with sufficient granular data, cannot be borne out: this is the lesson of {{br|Moneyball: The Art of Winning an Unfair Game}}. Just as Wall Street data crunchers can have no clue about baseball and still outperform veteran talent scouts, so can data models and analytics who know nothing about the technical details of, say, the law outperform humans who do when optimising business systems. Thus, from a network of programmed but uncomprehending rule-followers, a smooth, steady and stable business revenue stream [[emerge]]s.
 Since the world overflows with data, we can programmatise business. Optimisation is a mathematical problem to be solved. It is a [[knowable unknown]]. To the extent we fail, we can put it down to not enough data or computing power.