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Economics is Too Focused on Math

Understanding the limits of mathematical models

Does economics rely too much on math?

A fellow blogging friend recently pointed me to a scholarly article discussing the increasing reliance of economics on mathematical models.

Dr. Gibson, an engineering and economics expert, takes a strong stance against the “allure” of mathematical models, arguing that they offer no significant contribution if the importance of common sense and human interaction is ignored. He argues,

“But while the mathematicians, some of them at least, are explicit about doing math for its own sake, engineers are hired to produce results and economists should be, too.”

According to Gibson, engineers realize that data and real-life experiments are the most important. A study reveals a different focus in economic papers.

“Perusing the contents of the American Economic Review, [Wassily Leontief (1982)] found that a slight majority of the [economic] articles presented mathematical models without any data, just 12% presented analysis without any math, while the rest were mainly empirical studies.”

I just completed an undergraduate economics course, Intermediate Macroeconomics. The course was almost entirely mathematical, relying on calculus and algebra techniques to find solutions for theoretical problems related to consumption, investment, and taxation. With almost every model we considered, however, there were notable flaws once applied to real-life situations. There’s a famous quote by mathematician George E.P. Box,

“All models are wrong, but some are useful.”

To be sure, we shouldn’t try to dissociate mathematics from economics. Economic theory and so-called “common sense” are no good if the facts tell a different story. However, there is a difference between data and mathematical models. The data, or numbers, of economics will always be undeniably vital to any conclusion. The problem arises when the underlying economic problem is lost amid the cacophony of additional formulas and derivatives.

As economists realize, mathematical models can explain trends with some degree of confidence, but there will always be outlying observations. Furthermore, almost every model relies on core assumptions in order to be useful. There are simply too many variables in the real world to completely explain any economic trend.

Essentially, applying mathematical formulas to economics can tell us if one event should happen, given a set of conditions. Sometimes these conditions, such as assuming consumers always optimize their money, do not translate well into the real world. This may lead to contradictory conclusions.

The human aspect of economic decisions must be first considered. If we primarily understand how economic problems affect real people, the math takes on a purely supporting role.

Gibson urges clarity for economists, hoping to make the conclusions more applicable to the everyday American.

“What if real answers to urgent problems could be delivered in plain English? Do economists have the courage to shun the romance of mathematics and produce such answers? Let us hope so.”

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