Here’s a particularly interesting article at the intersection of philosophy of science and economic theory. Economic theorists have, for much of the twentieth century, linked high theory to observable data using the technique of axiomatization. Many axiomatizations operate by proving that if an agent has such-and-such behavioral properties, their observed actions will encompass certain other properties, and vice versa. For example, demand functions over convex budget sets satisfy the strong axiom of revealed preference if and only if they are generated by the usual restrictions on preference.

You may wonder, however: to what extent is the axiomatization interesting when you care about falsification (not that you *should* care, necessarily, but if you did)? Note first that we only observe partial data about the world. I can observe that you choose apples when apples and oranges are available (A>=B or B>=A, perhaps strictly if I offer you a bit of money as well) but not whether you prefer apples or bananas when those are the only two options. This shows that a theory may be falsifiable in principle (I may observe that you prefer strictly A to B, B to C and C to A, violating transitivity, falsifying rational preferences) yet still make nonfalsifiable statements (rational preferences also require completeness, yet with only partial data, I can’t observe that you either weakly prefer apples to bananas, or weakly prefer bananas to apples).

Note something interesting here, if you know your Popper. The theory of rational preferences (complete and transitive, with strict preferences defined as the strict part of the >= relation) is universal in Popper’s sense: these axioms can be written using the “for all” quantifier only. So universality under partial observation cannot be all we mean if we wish to consider only the empirical content of a theory. And partial observability is yet harsher on Popper. Consider the classic falsifiable statement, “All swans are white.” If I can in principle only observe a subset of all of the swans in the world, then that statement is not, in fact, falsifiable, since any of the unobserved swans may actually be black.

What Chambers et al do is show that you can take any theory (a set of data generating processes which can be examined with your empirical data) and reduce it to stricter and stricter theories, in the sense that any data which would reject the original theory still reject the restricted theory. The strongest restriction has the following property: every axiom is UNCAF, meaning it can be written using only “for all” operators which negate a conjunction of atomic formulas. So “for all swans s, the swan is white” is not UNCAF (since it lacks a negation). In economics, the strict preference transitivity axiom “for all x,y,z, not x>y and y>z and z>x” is UNCAF and the completeness axiom “for all x,y, x>=y or y>=x” is not, since it is an “or” statement and cannot be reduced to the negation of a conjunction. It is straightforward to extend this to checking for empirical content relative to a technical axiom like continuity.

Proving this result requires some technical complexity, but the result itself is very easy to use for consumers and creators of axiomatizations. Very nice. The authors also note that Samuelson, in his rejoinder to Friedman’s awful ’53 methodology paper, more or less got things right. Friedman claimed that the truth of axioms is not terribly important. Samuelson pointed out that either all of a theory can falsified, in which case since the axioms themselves are always implied by a theory Friedman’s arguments are in trouble, or the theory makes some non-falsifiable claims, in which case attempts to test the theory as a whole are uninformative. Either way, if you care about predictive theories, you ought choose those the weakest theory that generates some given empirical content. In Chambers et al’s result, this means you better be choosing theories whose axioms are UNCAF with respect to technical assumptions. (And of course, if you are writing a theory for explanation, or lucidity, or simplicity, or whatever non-predictive goal you have in mind, continue not to worry about any of this!)

Dec 2012 Working Paper (no IDEAS version).