10 MAR 2017 by ideonexus

 Rise of the Useless Class

As algorithms push humans out of the job market, wealth and power might become concentrated in the hands of the tiny elite that owns the all-powerful algorithms, creating unprecedented social and political inequality. Alternatively, the algorithms might themselves become the owners. Human law already recognizes intersubjective entities like corporations and nations as “legal persons.” Though Toyota or Argentina has neither a body nor a mind, they are subject to international laws, they ca...
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19 JAN 2016 by ideonexus

 Chomsky on the Failure of Postmodernism to Simplify

Since no one has succeeded in showing me what I'm missing, we're left with the second option: I'm just incapable of understanding. I'm certainly willing to grant that it may be true, though I'm afraid I'll have to remain suspicious, for what seem good reasons. There are lots of things I don't understand -- say, the latest debates over whether neutrinos have mass or the way that Fermat's last theorem was (apparently) proven recently. But from 50 years in this game, I have learned two things: (...
Folksonomies: postmodernism
Folksonomies: postmodernism
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19 DEC 2014 by ideonexus

 How the Finance Industry Hurts the Economy

In perhaps the starkest illustration, economists from Harvard University and the University of Chicago wrote in a recent paper that every dollar a worker earns in a research field spills over to make the economy $5 better off. Every dollar a similar worker earns in finance comes with a drain, making the economy 60 cents worse off. [...] ...the growth of complex financial products has served primarily to boost income for the firms themselves, Philippon said. A new paper from researchers in t...
Folksonomies: economy finance
Folksonomies: economy finance
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27 DEC 2013 by ideonexus

 Why Economists Will Continue to be Wrong

Carter had initially used arbitrary parameters in his perfect model to generate perfect data, but now, in order to assess his model in a realistic way, he threw those parameters out and used standard calibration techniques to match his perfect model to his perfect data. It was supposed to be a formality--he assumed, reasonably, that the process would simply produce the same parameters that had been used to produce the data in the first place. But it didn't. It turned out that there were many ...
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They make models based on past data, and when they fail to predict the future, they adjust them to match the new past data. The problem is that so many models will match the past data, there could be no end to the number of models they throw out.