I'm torqued at the economists, but from what I saw in the OP I should be torqued at that, also. The article seemed to say that econometrics or some such was no

I'm torqued at the economists, but from what I saw in the OP I should be torqued at that, also. The article seemed to say that econometrics or some such was not within their criticism and that, instead, they were criticizing the -- pure, theoretical, mathematical Nobel-prize seeking, etc.? -- economists or some such. Uh, it was not too clear just which brand, type, style, category, etc. of economists they were criticizing. Okay, I'll defend some of the economists! The field of economics is a train wreck, a theoretical, empirical, practical, intellectual, scientific, academic train wreck. E.g., they still have no F = ma (Newton's second law), and their predictions are as bad as those for the weather. Why? Sure, likely like the weather, the economy is darned complicated. Next, it's tough to get good data about the economy, e.g., the usual US Department of Labor, etc. statistics are crude approximations to reality. E.g., for the crash of 2008, it was mostly a fairly closely held secret just how bad so many of the mortgages were, and those bad mortgages were the key to the crash. So, for the 2008 crash, which was over 8 years ago and that we are still pulling out of, that is, has been 2/3rds as long as the Great Depression, we were missing clear views of just the basic data. Outrageous. E.g., at http://www.pbs.org/wgbh/pages/frontline/oral-history/financial-crisis/richard-kovacevich/ http://www.pbs.org/wgbh/pages/frontline/oral-history/financi... see the Frontline interview of Well Fargo CEO Richark Kovacevich with in part: "... when they came to me, I would say: 'This is toxic waste. We're building a bubble. We're not going to like the outcome. I'm very concerned.'" Not many people had enough data to see the problem. Outrageous. Next, emotions, fear, mob behavior, the news looking for headlines all can affect what people do and the economy. So, to do economics as a science is tough. Well people in high end academics are supposed to do research. In the case of economics, they are supposed to try to make a good science out of it. So, they try. In particular, the most respected work in science mathematizes the field. Recently on the news was a remark about something else, but we can use it here: "Ask a Navy Seal how to eat an elephant, and he will say 'One bite at a time'". Well, in trying to make economics a science, in particular, to mathematize the field, about all the high end research academic economists can do is try one bite at a time. So, they do. So, the work has not yet eaten all the elephant, moved all of the mountain, cleaned up all the mess, finished building the castle, etc. So, it's a work in progress. In particular, for the castle, they are still working on the foundation, and so far there is nothing like a roof. So, when it rains, there's no roof and anyone in the castle gets wet. Or, the kitchen just isn't ready to serve good food yet; the land is not cleared, and we are not ready to grow a crop yet. Some of the mathematics I studied for my Ph.D. and some of the math research I did and published is close to some of the math some of the economists have tried to use. So, that math gives me a view of math in economics. For that math, some of it really is okay for some questions about economic things. E.g., optimization, mathematical programming, e.g., linear programming, linear integer programming, actually can and sometimes do save money in some economic, business situations. The academic economists usually call such situations micro economics. So, some decades ago, as such math was developed for operations research or whatever, the math got used by academic research economists. IIRC linear programming was the core math of several Nobel prizes in economics. My view is that linear programming, and optimization more generally, has next to nothing to do anytime soon with predicting, say, the growth in GDP over five years, but one could call that use of math in the research one bite of the elephant, one brick for the castle, one tree stump on the way to clearing the land ready for row crops, etc. E.g., there was a famous paper in mathematical economics by Arrow, Hurwicz, and Uzawa. Arrow won his Nobel prize long ago, and Hurwicz won a few years ago. The paper makes use of optimization, in particular, the Kuhn-Tucker conditions (KTC) of non-linear optimization. So, in grad school, when I was studying the KTC (not much like KFC), I saw a tricky problem, didn't see a solution in the library, did some research, and got a solution. Later when I went to publish I saw that my work also answered a question stated but not answered in the Arrow, et al. paper. Okay. So, alright, in some sense I knew the KTC as well as or better than Arrow, etc. My opinion of the KTC is that they have next to nothing to do with predicting, say, the growth in GDP over five years, but maybe that use of the KTC is a little progress. Maybe that work got some Nobel prizes not because it was especially good work, say, did for economics what Newton did for physics, but because it was the best work in economics the Nobel committee could find that year! So, net, economics is not yet a science; some researchers are trying to make it a science, in particular, to mathematize the field. There is a description of how this research goes: The researcher looks at some math and the economy, makes some simplifying assumptions about the economy, so, has a model of the economy, sees where they can apply that math to that model, makes the application, reads off the consequences for that model economy, publishes the stuff, and hopes for a Nobel prize, at least for tenure, and hopes that their students will do more along such lines. Yes, it's like in freshman physics were we have a frictionless ball bearing and no air resistance and, then, calculate how fast the ball would roll except, e.g., the KTC, much worse. Gee, if the research is really bad, then there should be a big opportunity to jump into the field, get a relatively good salary, maybe get a Nobel, get famous, get high consulting fees, maybe get some adoring coeds, etc. For me? I've got a startup. I've already done the original applied math research and written the software and am eager to go live. If the startup works, I'll be nicely wealthy. So, I'll stay with my startup! Besides, when I tried to study economics, I thought that the subject was so badly done I should just close the book and f'get about it. But once it was suggested that I take a course in economics. So, I did. I showed for the class, took notes, and said nothing. That was the first day with lots of freehand supply and demand curves. After the class, with just the prof and I, I asked him, nicely I thought, just what he was assuming about his curves, continuous, differentiable, continuously differentiable, infinitely differentiable, continuous and differentiable almost everywhere with respect to Lebesgue measure, convex, pseudo-convex, quasi-convex, etc. (or some such). Soon I got a call and was told "You are out of the economics class.". Gee, I was just asking! Gee, those questions were not so bad! For that thing I did for the KTC and the Arrow, et al. paper, part of the work was to show that for the real numbers R, a positive integer n, a set C a subset of R^n and closed in R^n with the usual topology, there exists an infinitely differentiable function f: R^n --> R so that f(x) = 0 for all x in C and f(x) > 0 otherwise. E.g., the Mandelbrot set is closed. So, there's an infinitely differentiable f 0 on the Mandelbrot set and strictly positive otherwise. Instead of the Mandelbrot set, use a sample path of Brownian motion, a Cantor set of positive measure, etc. -- amazing. So, gee, for the Arrow, et al., paper I considered infinitely differentiable!