Arnold Kling  

Russ Roberts and Dan Pink

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They discuss motivation and incentives. Very enjoyable conversation. If anything, it is a little too fast-paced. One point is that people seek autonomy, mastery, and purpose. I think that is very true in people's avocations, or hobbies. I think, though, that finding those in your job is something of a luxury.

I suspect that a big reason that mathematics took over economics is that it gives you a sense of mastery. Indeed, it may give you a false sense of mastery. As you learn mathematical economics, you realize that you are getting really good at doing something that only a small group of people is able to master. And you get the sense that because you completed a mathematical proof that you accomplished something. It is very seductive.



COMMENTS (10 to date)
Kling is Crazy writes:

"And you get the sense that because you completed a mathematical proof that you accomplished something. It is very seductive."

Equations are useful because you TRACK logical arguments much easier. In a subject where many things create many consequences, math is the best way to organize that. However, it is true, that to be a professional economist at a fine institution, you basically have to be a field medalist.

Regardless, what is the alternative Kling?

[edited for tone]

blink writes:

Yes, finding autonomy, mastery, and purpose at work remains rare. In his book, Pink argues that because people are more productive when then find such things in their work, employers do well to create conditions for them. Further, he claims that these can be found in reasonable measure in any job short of robotic assembly line work. Of course, if he is correct, there is a lot of money being left on the table.

Chris Koresko writes:

Kling is Crazy: Equations are useful because you TRACK logical arguments much easier.

Surely this is true. They also let you handle quantitative arguments far more easily than you could without them.

But I think Kling is right: Math can lead you astray by making your models look more rigorous than they are. A set of equations can be complicated and internally consistent without describing reality. All it takes is a mistake made when pruning the model down to a managable size by eliminating the effects you think are not important, or simply failing to consider one that is.

They might also tend to reinforce your prior assumption. If you think sticky wages are important during a recession, when you make a model you'll include them and probably tune their effect to make the output look sensible. If you think they don't matter, you'll put in some other phenomenon and tune that instead. In either case you'll probably be tempted to look at how nicely your tuned model fits your data and conclude that you made the right assumptions about how the economy works.

Does that sound reasonable?

Seth writes:
I think, though, that finding those in your job is something of a luxury.

I'm not sure I agree with this. I think it may just take time.

I've learned a great deal (and also realized just how little I know) from masters of mundane things. It seems, at some point in their career, folks can tap into the subtleties of their profession and if you ask the right questions, you can hear some surprisingly interesting things.

MernaMoose writes:

They might also tend to reinforce your prior assumption. If you think sticky wages are important during a recession, when you make a model you'll include them and probably tune their effect to make the output look sensible. If you think they don't matter, you'll put in some other phenomenon and tune that instead. In either case you'll probably be tempted to look at how nicely your tuned model fits your data and conclude that you made the right assumptions about how the economy works.

At last, we have the perfect explanation as to why I don't trust mathematical economists.

In physics, the governing equations (and the physical effects that are, or are not, significant in any given problem) are not negotiable.

In real world practice, negotiating is what economics is all about.

Tracy W writes:

Chris Koresko and MernaMoose - but aren't these problems even more true if you don't use mathematics?

Plenty of times I've run across people who don't realise that every buyer must have a seller, and consequently every dollar spent buying something means that someone else must have received a dollar (not necessarily the legal seller, there are sales taxes and commission fees and the like).

And, on more subtle points, I've read economics journals that have made questionable assumptions. For example a history of duelling that argued a signalling effect, that duelling signaled you were of high social status, without ever considering that low-social-status men could be equally as willing to otherwise-senselessly risk their lives (and indeed many of them did risk their lives as part of their daily work). Or a paper on the Indian caste system which asserted blithely that one could improve one's bargaining position by refusing to do work for yourself, a remarkable assertion.

Mathematics isn't a perfect way of avoiding those traps, but it strikes me as a lot better for that purpose than not using mathematics.

Chris Koresko writes:

Kling is Crazy: Equations are useful because you TRACK logical arguments much easier.

Surely this is true. They also let you handle quantitative arguments far more easily than you could without them.

But I think Kling is right: Math can lead you astray by making your models look more rigorous than they are. A set of equations can be complicated and internally consistent without describing reality. All it takes is a mistake made when pruning the model down to a managable size by eliminating the effects you think are not important, or simply failing to consider one that is.

They might also tend to reinforce your prior assumption. If you think sticky wages are important during a recession, when you make a model you'll include them and probably tune their effect to make the output look sensible. If you think they don't matter, you'll put in some other phenomenon and tune that instead. In either case you'll probably be tempted to look at how nicely your tuned model fits your data and conclude that you made the right assumptions about how the economy works.

Does that sound reasonable?

MernaMoose writes:

Tracy W,

But there are two problems

First, as others have said, too many people do not understand the true nature of mathematical models.

Mathematics, as a subject unto itself, is precise. Mathematical models are intended to represent "the real world" and as such, they are at best approximations.

As an engineer I use math and models every day. But I've also had the experience of developing models which we later found had connection to reality. Most people do not carry this kind of experience base around in their heads.


Second, in the physical universe, we can test our mathematical models against reality. Scientific phenomenon may reach scales (big or small) where you cannot practically run experiments, but most man-made things can be tested. We can calibrate our mathematical relations and assumptions.

This is not the case in economics. There are usually "soft" variables, like "how does the paint color in the kitchen impact the price the buyer will pay for a house?" which defy mathematical description. You're never really sure what your equations represent vis a vis reality.

And then, there's usually no way to run and experiment and test your model. Even the retrospective analysis economists do isn't a true experimental validation of their models, in general.


All of that given, I'd still say that economic models are of value -- among economists themselves. Not because they're so infallibly accurate but because they force economists to think about what all they're assuming, and what their models may be missing and/or leaving out.

not amused writes:

"However, it is true, that to be a professional economist at a fine institution, you basically have to be a field medalist."

This is a joke, right?

Tracy W writes:

MernaMoose, I don't understand your comment. You appear to be disagreeing with me, but everything you say is what I already agreed with. You don't present any argument that the problems you identify are any better without the mathematics.

For your point of physics, how many non-physicy types (to cover engineers and the like as well as actual physicsts) really understand Newtonian physics? Even though these can easily be tested in the day-to-day world? (Unlike relativity theory or macroeconomics).

As for your final comment, that "economic models are of value -- among economists themselves", I agree with that entirely, and I will go on and say that economic models are also of value to non-economists who have to form opinions on economic matters, ie everyone who votes.

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