Jorge Luis Borges once wrote a short story about the greatest cartographer who ever lived:
…In that empire, the art of cartography attained such perfection that the map of a single province occupied the entirety of a city, and the map of the empire, the entirety of a province. In time, those unconscionable maps no longer satisfied, and the cartographers guilds struck a map of the empire whose size was that of the empire, and which coincided point for point with it. The following generations, who were not so fond of the study of cartography as their forebears had been, saw that that vast map was useless, and not without some pitilessness was it, that they delivered it up to the inclemencies of sun and winters…
Given that the title of this work is On Exactitude in Science, it’s probably safe to assume Borges had more here in mind than just Cartography – map making and scientific modeling have a lot in common. In this regard, I think Milton Friedman would have agreed with the later generations in Borges’ story: our economic models, like our maps, will only be useful if we simplify and scale down. Only then can we understand where we are and how to get where we want to go.
In his 1953 paper The Methodology of Positive Economics, Friedman argues that simplicity in our models actually leads to better predictions. This is of great importance for him because he states that “the ultimate goal of a positive science is the development of a ‘theory’ or ‘hypothesis’ that yields valid and meaningful (i.e., not truistic) predictions about phenomena not yet observed” (Friedman p.148). In other words, Friedman views scientists like cartographers whose goal is to simplify reality into a more usable form. But is this an accurate description of the goal of science?
An alternative notion would be that the goal of science is not merely to make theories that pass tests, more generally, the ultimate goal of science is to better know reality. In his 2006 book The Origin of Wealth (where I first learned of Borges’ cartographers), economist Eric Beinhocker claims just that: “the hallmark of a science is not its ability to forecast the future, but its ability to explain things – to increase our understanding of the workings of the universe” (Beinhocker p.58). If he is right, then Friedman is confusing one possible means of achieving scientific ends with the end itself. This confusion in Friedman’s reasoning has left room in his argument for some potentially troubling implications, such as the accuracy of premises in an economic argument not being important so long as the conclusion survives empirical testing.
Validity and Economic Argument
Anyone who has taken a few math classes has probably learned the hard way that you can diligently work through a problem and yet one small false assumption at the beginning will render all your subsequent hard work in vain. No matter how faithfully you follow the procedure, that misplaced decimal point will inevitably lead to a wrong answer. Computer scientists have coined this phenomenon as “garbage in, garbage out”. This always holds in programming because computers, like mathematics, can only follow logical sequences of operations. In order for any argument to be considered logically valid, the conclusion must follow from the premises – i.e. if the premises are all true, then it must follow that the conclusion is true, but if even one premise is false the conclusion will be false. If the conclusion doesn’t follow directly from the premises than, true or not, the argument is invalid.
But what this all means is that truth of the conclusion is not necessarily dependent on the validity of the argument itself – invalid arguments can still have true conclusions. Friedman then is essentially making the claim that validity of an economic argument is not important so long as the conclusion is true. This view could be summed up as saying that so long as there is not garbage coming out, then it doesn’t matter what is going in (Beinhocker p.49).
Ten years after Friedman’s paper, economist Herbert Simon in his paper Testability and Approximation, took issue with Friedman’s lack of regard for validity in economic arguments. For this, Simon coined Friedman’s methodology the “principle of unreality” (Simon p.179). He felt that the Friedman argument wrongfully glorified simplicity and false premises as the best ways to get usable conclusions and to advance economics as a science. Simon wanted to make clear the point that the “unreality of premises is not a virtue in scientiﬁc theory; it is a necessary evil – a concession to the ﬁnite computing capacity of the scientist” (ibid p.181). The less our assumptions match reality, the less our conclusions will work to explain the world we live in.
In the next post, I’ll explain why I agree with Simon, why the “principle of unreality” is not an ideal principle to adhere to, and what Simon proposes as an alternative – “the principle of continuity of approximation”.
Beinhocker, Eric D. The Origin of Wealth: The Radical Remaking of Economics and What It Means for Business and Society. Boston, MA: Harvard Business School, 2006.
Borges, Jorge Luis. “On Exactitude in Science.” Trans. Andrew Hurley. Collected Fictions. KWARC: Knowledge Adaptation and Reasoning for Content.
Friedman, Milton. “The Methodology of Positive Economics.” 1953. The Philosophy of Economics: An Anthology. Ed. Daniel M. Hausman. 3rd ed. New York: Cambridge UP, 200. 145-78.
Simon, Herbert. Testability and Approximation. 1963. The Philosophy of Economics: An Anthology. Ed. Daniel M. Hausman. 3rd ed. New York: Cambridge UP, 2007. 179-82.