It is probably commonplace to know that the crux of the economic process (and even just the scientific process in general) is through formulating “models”[1] as a way to try to illustrate some phenomena and perhaps, extrapolate some conclusion/explanation for the larger reality at hand. For of course the most ideal situation to understanding phenomena would be for one to observe every single event and replay it back with certain modifications and iterate the process however many times as needed. Not to mention, be able to identify every single “variable.” Sadly, until science finds a way to reverse the laws of the time and space, that will not be an option. As such, the next best approaches include experimentation; in particular, natural experiments or so-called “twin studies.” Of course, this approach, is already a significantly simplified/reduced form of reality. While strategies such as random sampling and random assignment of treatment/control are employed to simulate closer reality, they come at costs including financial and timely ones. Thus, we arrive at the use of symbols and mathematical equations what we know as “models” as a basis for research. Specifically, as suggested throughout the late Robert Lucas Jr’s research, the use of “analogue systems” to bridge the gap between fiction and reality.

In the memo “What Economists Do…”, Lucas (1988) uses an example of from the Kennywood Amusement Park in Pittsburgh, PA where he had spent a significant portion of his career as researcher at the Graduate School of Industrial Administration, now the Tepper School of Business at Carnegie Mellon University. He compares the amusement park as a closed, independent monetary system, and suggests that by applying a one-time shock to the ticket “money” supply by varying the prices, he could induce a mini depression to the closed economy. The broader point of his analogy, though, was to illustrate the effect of monetary policy on the macro-economy but done through what he referred to as storytelling rather than normative economics.

And perhaps this is an important point, or analogy, to be made more broadly on economic modeling. While models are used to assess a particular reality of interest, so should the model selection and model review reflect the model of interest. Take benchmark models for instance. Benchmark/challenger models are often built to provide a way to compare different methodologies to a similar problem. Hence, the goal is to build a representative enough model for the given problem, of which predictive success (predictability) may very well just be one aspect in the assessment of a model. Whereas the ability to parse out useful and useless information, potentially identifying unintended results, may be valuable as well. As summarized by Galbács (2020),

Empirical success thus did not take priority over other requirements. On the contrary, Lucas expected his approach to lead to successful models due to his success in seizing the real causal processes in highly abstract ways. The results above thus contribute to the clarification of Lucas’s methodological principles and emphasize his ambition to latch onto the way reality works.

And perhaps this is something us economists and scientists shall heed from the late Nobel laureate.

[1] Or referred to as “useful imitations of reality by subjecting them to shocks for which we are fairly certain how actual economies, or parts of economies, would react” (Lucas (1980)).