Mathematical Modeling Used To Track and Label 83
Anti-Globalism writes to tell us that in a new book titled The Numerati, author Stephen Baker introduces us to some of the math wizardry that is used to label or track our movements through purchases, phone calls, internet usage and other habits. "One of the most promising laboratories for the Numerati is the workplace, where every keystroke, click, and e-mail can be studied. In a chapter called "The Worker," Baker travels to IBM, where mathematicians are building predictive models of their own colleagues. An excerpt: 'Samer Takriti, a Syrian-born mathematician. He heads up a team that's piecing together mathematical models of 50,000 of IBM's tech consultants. The idea is to pile up inventories of all of their skills and then to calculate, mathematically, how best to deploy them. I'm here to find out how Takriti and his colleagues go about turning IBM's workers into numbers. If this works, his team plans to apply these models to other companies and to automate much of what we now call management.'"
End of nepotism, at last! (Score:3, Insightful)
Good thing: the manager doesn't have a stupid son or nephew to promote instead of you.
Bad thing: no more open position available for promotion. (and so the death of the compagnies caused by the unability to apply Dilbert's rule).
Mathematics Is A Harsh Mistress (Score:5, Insightful)
You need to be careful about applying mathematics. Typically a mathematical model will have assumptions from which predictions can be made. The trouble is the instant you make your assumptions, the predictions become locked in. They are predetermined, even though you may not have discovered them yet. And of course if your assumptions are wrong, or inaccurate, your predictions are not going to match the real world very well. You can't massage the data or your results to get around this. Once your assumptions are made, mathematics leave no room for debate. Ever.
What this means of course is that it is often complete folly to apply mathematics to complex human interactions. Any assumptions you make will be totally inadequate to fully encompass any large organization and its members, and as a result, your predictions will probably be erroneous. Proceeding to apply your derived results to people will lead to unsatisfactory results and unexpected effects.
The Adam Curtis documentary The Trap [wikipedia.org], discusses the problems in reducing industries, outputs and people to numbers. Basically the numbers, which are more or less wrong, force people to conform to them, and you end up breaking existing systems completely or else converting them into an inefficient version of themselves, all the while thinking (and being told by the numbers) that your systems are improving.
The power of a mathematical model and its caveats, are in fact best described by Douglas Adams' fictional supercomputer Deep Thought [wikipedia.org]. Deep Thought could indeed provide the answer to Life the Universe and Everything(42), and it was perfectly correct. But it wasn't any good because no one knew the correct Question to ask in the first place.
Mathematics will give you answers, and they will be right. But you had better be sure that you asked the right questions before you act on them.
Qualitative vs Quantitative (Score:4, Insightful)
The problem is that the evaluation reports are precisely the most likely source to tell a good programmer from a just regular one, which is exactly what they don't take into account. A junior programmer may be excepional, but he will have less experience and a much lower salary than a "senior" programmer that could just market itself very well, and therefore be marked as a commodity
Categorizing programmers and treating them like commodities could be very dangerous if your model is flawed or considers the wrong parameters, especially if it provides a excuse for bad managers not to think or evaluate their employees individually (hey, if the model says it can't be wrong, right?)
By coincidence... (Score:3, Insightful)
If the facts don't fit the theory, change the facts. -- Albert Einstein
?
Re:Mathematics Is A Harsh Mistress (Score:3, Insightful)
What about stochastic methods?