Cryptography Reference
In-Depth Information
systems, some models clearly fit better than others. The better fitting models may
represent more aspects of the real world, or fit some aspects more accurately, or
both. In any case, “fit” is not simply a relationship between a model and the world.
It requires a specification of which aspects of the world are important to represent
and, for those aspects, how close a fit is desirable.
63
This fitness should not be understood as an inherent feature of the
models themselves: “It is not the model that is doing the representing; it
is the scientist using the model who is doing the representing. One way
scientists do this is by picking out some specific features of the model that
are then claimed to be similar to features of the designated real system to
some (perhaps fairly loosely indicated) degree of fit.”64
64
Recent scholarship in science studies has produced insightful analyses
of two scientific communities whose practices are intimately bound with
modeling—climatology and mathematical finance. The empirical material
obtained through extended fieldwork engagement with both communities
suggests the complex relationships that obtain between scientists, their
models, and the real-world systems they investigate. In his historical analy-
sis of climatology and global warming, Paul Edwards demonstrates that
models and data can never be cleanly separated but should instead be
understood as mutually constitutive. Models are not “pure theories,
ungrounded in observation” and data are never model-independent. In
fact, “
everything we know about the world's climate—past, present, future—we
know through models.
”
65
In his investigation of the mathematization of financial markets that
began in the 1960s, Donald Mackenzie argues that the success of the new
and contested discipline of financial economics was dependent on a certain
epistemological stance with respect to the “fitness” of mathematical
models. For this new breed of economists, the goal of theory was to
perform as an “engine of inquiry,” not as “an (infeasible) camera faithfully
reproducing all empirical facts.”
66
Such an approach did not imply a “com-
mitment to the literal truth of economics' models”: even if they trusted
the model as “identifying an economic process of great importance,” they
also recognized its empirical limitations, as well as the “economically con-
sequential ways in which the model's assumptions were unrealistic.” Their
approach was, at heart, pragmatic:
For Black, Scholes, and Merton . . . a model had to be simple enough to be math-
ematically tractable, yet rich enough to capture the economically most important
