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What is economics good for?
http://opinionator.blogs.nytimes.com/2013/08/24/what-is-economics-good-for/?_r=0The fact that the discipline of economics hasn’t helped us improve our predictive abilities suggests it is still far from being a science, and may never be. Still, the misperceptions persist. A student who graduates with a degree in economics leaves college with a bachelor of science, but possesses nothing so firm as the student of the real world processes of chemistry or even agriculture.
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It’s easy to understand why economics might be mistaken for science. It uses quantitative expression in mathematics and the succinct statement of its theories in axioms and derived “theorems,” so economics looks a lot like the models of science we are familiar with from physics. Its approach to economic outcomes — determined from the choices of a large number of “atomic” individuals — recalls the way atomic theory explains chemical reactions. Economics employs partial differential equations like those in a Black-Scholes account of derivatives markets, equations that look remarkably like ones familiar from physics. The trouble with economics is that it lacks the most important of science’s characteristics — a record of improvement in predictive range and accuracy.
The irony is that for a long time economists announced a semiofficial allegiance to Karl Popper’s demand for falsifiability as the litmus test for science, and adopted Milton Friedman’s thesis that the only thing that mattered in science was predictive power. Mr. Friedman was reacting to a criticism made by Marxist economists and historical economists that mathematical economics was useless because it made so many idealized assumptions about economic processes: perfect rationality, infinite divisibility of commodities, constant returns to scale, complete information, no price setting.
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Moreover, many economists don’t seem troubled when they make predictions that go wrong. Readers of Paul Krugman and other like-minded commentators are familiar with their repeated complaints about the refusal of economists to revise their theories in the face of recalcitrant facts. Philosophers of science are puzzled by the same question. What is economics up to if it isn’t interested enough in predictive success to adjust its theories the way a science does when its predictions go wrong?
...
It’s easy to understand why economics might be mistaken for science. It uses quantitative expression in mathematics and the succinct statement of its theories in axioms and derived “theorems,” so economics looks a lot like the models of science we are familiar with from physics. Its approach to economic outcomes — determined from the choices of a large number of “atomic” individuals — recalls the way atomic theory explains chemical reactions. Economics employs partial differential equations like those in a Black-Scholes account of derivatives markets, equations that look remarkably like ones familiar from physics. The trouble with economics is that it lacks the most important of science’s characteristics — a record of improvement in predictive range and accuracy.
The irony is that for a long time economists announced a semiofficial allegiance to Karl Popper’s demand for falsifiability as the litmus test for science, and adopted Milton Friedman’s thesis that the only thing that mattered in science was predictive power. Mr. Friedman was reacting to a criticism made by Marxist economists and historical economists that mathematical economics was useless because it made so many idealized assumptions about economic processes: perfect rationality, infinite divisibility of commodities, constant returns to scale, complete information, no price setting.
...
Moreover, many economists don’t seem troubled when they make predictions that go wrong. Readers of Paul Krugman and other like-minded commentators are familiar with their repeated complaints about the refusal of economists to revise their theories in the face of recalcitrant facts. Philosophers of science are puzzled by the same question. What is economics up to if it isn’t interested enough in predictive success to adjust its theories the way a science does when its predictions go wrong?
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