Saturday, 16 November 2013

Models. Behaving. Badly.

One of the best aspects of being an academic is all of the cool and interesting research papers that I get to read. One of the worst aspects of being an academic is not having enough time to read all of the cool and interesting research papers that I want to. Which is why I really love getting away from the distractions of the office. This week and next I am in Bangkok, for the 11th International Congress on AIDS in Asia and the Pacific, where I'm co-presenting an e-poster on microbicide acceptability.

Being away from the office gives me the chance to catch up on some reading (and usually a few revise-and-resubmit journal articles). I have an enormous pile of articles I've gathered over the last several years (my "collection"), which I've always meant to read. This was also a side reason why I started this blog - I'll start blogging about some of them this week.

Anyway, I've finally finished reading Emanuel Derman's "Models. Behaving. Badly.". It's been extensively reviewed elsewhere (see here for the review on Forbes, or here for the review on WSJ) - after all, it's a 2011 title - so I won't bother with a long review of it. I will say that a good read on the problems of financial modelling in the real world, and why economists in general should not get too overconfident about our models. I haven't done any work in financial economics myself, and haven't really touched on any finance since my undergraduate study, so I liked the book because it gave me some added value. I especially liked this bit on p.144:
If you open up the prestigious Journal of Finance, one of the select number of journals in which finance professors must publish in order to get tenure, many of the papers resemble those in a mathematics journal. Replete with axioms, theorems, and lemmas, they have a degree of rigor that is inversely proportional to their minimal usefulness.
It made me laugh, anyway. But the problem is not limited to finance or financial economics. Most of economics is buried in mathematics. Deirdre McClosky gives a very thorough treatment of the problems of mathematics and statistics in economics here. Redstate covers a similar issue here, while Justin Campbell argues that economics teaching hasn't become too mathematical here. For my part, I think we as a discipline teach too much of economics using mathematics, at principles level. Using too much mathematics at first year disengages students who are not strong at mathematics, and removes the opportunity to show them the real-world relevance of the discipline.

In contrast, here at the University of Waikato Steven Lim and I teach our first-year business economics paper (ECON100) while barely using any numerical mathematics at all (to the extent that students cannot (and do not need to) use calculators in the tests or final examination). For the most part we've successfully decoupled mathematics from teaching the basic principles and insights that are useful for business decision making. As a result, our paper gets past a lot of student resistance to economics ("economics aversion" maybe?) and gets very good student evaluations. We're not alone in doing this of course, as this book demonstrates.

Greg Mankiw argues that aspiring economists need mathematics. I wouldn't necessarily disagree with that, but mathematics should not be (and is not) necessary in order to share insights with students majoring in other disciplines, or when sharing our research with the general public.

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