by From Lars Syll Paul Krugman has a piece up on his blog arguing that the ‘discipline of modeling’ is a sine qua non for tackling politically and emotionally charged economic issues: You might say that the way to go about research is to approach issues with a pure heart and mind: seek the truth, and derive any policy conclusions afterwards. But that, I suspect, is rarely how things work. After all, the reason you study an issue at all is usually that you care about it, that there’s something you want to achieve or see happen. Motivation is always there; the trick is to do all you can to avoid motivated reasoning that validates what you want to hear. In my experience, modeling is a helpful tool (among others) in avoiding that trap, in being self-aware when you’re starting to let your
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from Lars Syll
Paul Krugman has a piece up on his blog arguing that the ‘discipline of modeling’ is a sine qua non for tackling politically and emotionally charged economic issues:
You might say that the way to go about research is to approach issues with a pure heart and mind: seek the truth, and derive any policy conclusions afterwards. But that, I suspect, is rarely how things work. After all, the reason you study an issue at all is usually that you care about it, that there’s something you want to achieve or see happen. Motivation is always there; the trick is to do all you can to avoid motivated reasoning that validates what you want to hear.
In my experience, modeling is a helpful tool (among others) in avoiding that trap, in being self-aware when you’re starting to let your desired conclusions dictate your analysis. Why? Because when you try to write down a model, it often seems to lead some place you weren’t expecting or wanting to go. And if you catch yourself fiddling with the model to get something else out of it, that should set off a little alarm in your brain.
Hmm …
So when Krugman and other ‘modern’ mainstream economists use their models — standardly assuming rational expectations, Walrasian market clearing, unique equilibria, time invariance, linear separability and homogeneity of both inputs/outputs and technology, infinitely lived intertemporally optimizing representative agents with homothetic and identical preferences, etc. — and standardly ignoring complexity, diversity, uncertainty, coordination problems, non-market clearing prices, real aggregation problems, emergence, expectations formation, etc. — we are supposed to believe that this somehow helps them ‘to avoid motivated reasoning that validates what you want to hear.’
Yours truly is, to say the least,
far from convinced. The alarm that sets off in mybrain is that this, rather than being helpful for understanding real-world economic issues, sounds more like an ill-advised plaidoyer for voluntarily taking on a methodological straight-jacket of unsubstantiated and known to be false assumptions.
Some people have trouble with the fact that from allowing false assumptions mainstream economists can generate whatever conclusions they want in their models.
But that’s really nothing very deep or controversial. What I’m referring to is the well-known ‘principle of explosion,’ according to which if both a statement and its negation are considered true, any statement whatsoever can be inferred.
Using false assumptions, mainstream modellers can derive whatever conclusions they want. Wanting to show that ‘all economists consider austerity to be the right policy,’ just e.g. assume ‘all economists are from Chicago’ and ‘all economists from Chicago consider austerity to be the right policy.’ The conclusions follow by deduction — but is of course factually totally wrong. Models and theories building on that kind of reasoning is nothing but a pointless waste of time.