This paper should be mandatory reading before anyone tries to decode an economic model. For engineers or physical scientists, the mathematical formalism of economics seems familiar but that’s about it. In particular the assumptions used seem woefully inadequate to act as a foundation for any meaningful representation of the real world. Rational consumer and firm behaviour, stylised economies with only one product (made with no capital) and the like may help make the math work out, but good luck finding homo economicus out in the field, carefully evaluating the marginal utility of just one more orange in the grocery store.
The authors of this paper however (economists themselves) are careful to point out that the function of economic models is slightly different. The confusion arises when we think of economic models as “rule-based knowledge”, that is representative pieces of knowledge that summarize the results of multiple individual cases. For example, if we watched the arc of thousands of balls fly through the air at different velocities and angles, we could state a general “rule” of Newton mechanics to explain this path. Economic models, on the other hand, are examples of “case-based knowledge” and should be treated like a piece of experimental data, a paper from the literature, an anecdote, or any other piece of information that helps the analyst think about a particular problem. In other words, theoretical models allow economists to play around with situations which may be intentionally unrealistic, but can nevertheless provide valuable insight to a real problem.
This seems like such a fundamental bit of epistemology that I find it hard to believe it’s not more widely known, among engineers, the general public, but also economists themselves. Indeed the wealth of quotes about economic models needing “beauty before truth” suggests that perhaps the economics profession itself has forgotten the difference between case-based and rule-based knowledge.
In any event, it’s an excellent paper with a nice summary of the standard critiques of economic modelling, the difference between rule and case-based knowledge and, ironically, an economic model explaining the argument. The key conclusion is that, whatever form of knowledge one uses, one should be clear about the limits of relevance and validity inherent in any given tool. The abstract:
People often wonder why economists analyze models whose assumptions are known to be false, while economists feel that they learn a great deal from such exercises. We suggest that part of the knowledge generated by academic economists is case-based rather than rule-based. That is, instead of offering general rules or theories that should be contrasted with data, economists often analyze models that are “theoretical cases”, which help understand economic problems by drawing analogies between the model and the problem. According to this view, economic models, empirical data, experimental results and other sources of knowledge are all on equal footing, that is, they all provide cases to which a given problem can be compared. We offer some complexity arguments that explain why case-based reasoning may sometimes be the method of choice; why economists prefer simple examples; and why a paradigm may be useful even if it does not produce theories.