Cryptography Reference
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aspects of the situations modeled. Models were resources, not (in any simple sense)
representations: ways of understanding and reasoning about economic processes,
not putative descriptions of reality. If the latter is the criterion of truth, all of the
financial economists discussed here would agree with their colleague Eugene Fama
that any model is “surely false.”
67
MacKenzie tracks how the Black-Scholes-Merton model of option pricing
became a material resource in the “scrum of human bodies on trading
floors,” through the simple technology of printed paper: “Away from the
hubbub, computers were used to generate Black-Scholes prices. Those
prices were reproduced on sets of paper sheets which floor traders could
carry around, often tightly wound cylindrically with only immediately
relevant rows visible so that a quick squint would reveal the relevant
prices.”
68
Such immediate access to the numerical models created a form
of “feedback loop from the theory.” By becoming part of the distributed
cognitive processes of traders, models—instead of merely representing the
world—became an essential element of its performance: “The availability
of convenient material implementations of the Black-Scholes-Merton
model . . . most likely had the effect of reducing discrepancies between
empirical prices and the model . . . there was thus an homology between
the way the model was tested econometrically and options market prac-
tices based on the model. Use of the model does indeed seem to have
helped to create patterns of prices consistent with the model.”
69
MacKenzie argues that the success of the models depended in no small
part on their simplicity. Floor traders were in no particular awe of academ-
ics, but found the “cognitive simplicity” of Black-Scholes-Merton's attrac-
tive, as its “one free parameter (volatility) was easily grasped, discussed,
and reasoned about.”
70
This was a marked advantage over competing
models: “When numbers of free parameters are larger, or parameters do
not have intuitive interpretations—as is often the case with more complex
models—communication and negotiation become much harder.”
71
Clearly, not all disciplines relate to their models in the same way. If, as
MacKenzie reports, mathematical finance embraced the feedback loops
between its equations and actual markets as a form of creative tension,
pure mathematics has followed an opposite trajectory. Historian of math-
ematics Herbert Mehrtens tracks this process in a paper on to three-dimen-
sional models, physical objects commonly used at the end of the nineteenth
century to represent geometrical entities and today mostly confined to the
