Main trends of research in the social and human ... - unesdoc - Unesco

548 Raymond Boudon
cally or develop deductively, and observe its behaviour with a computer (ob-
viously, simulation as here used means only ‘machine simulation’, and excludes
simulation games involving human actors, which implies a completely differ-
ent type of theory and is an experimental rather than an analytical technique).
Simulation methods raise all sorts of problems in logic. Thus when the
number of parameters built into a simulated model is too great, it is impossible
toproceed to a systematic evaluation of the parametric space, leaving the investi-
gator with a situation not much clearer or more advantageous than the natural
situations which he is claiming to analyse. Generally speaking, it is probably
still too early to make an appreciation of simulated methods: despite their
widespread use, simulated experiments are still too few in number for it to be
possible to attempt a definition of their methodology. Nevertheless, it can be
predicted that, by reason of their flexibility, they will make rewarding contribu-
tions to sciences, especially economy and sociology, in which experimentation
is impossible and where, in addition, the complexity of the hypotheses often
makes them very difficult to translate into mathematical terms.
In demography, simulation has been used with some success and has led to
appreciable results in the matter of forecasting. Particularly notable is the work
of Orcutt and his associates.15 Here the simulation consisted in using a certain
number of functions (such as probabilities of marriage, divorce, death, etc.,
according to year, month, sex and age, etc.), the parameters of which were
estimated from official American statistics. With these functions to work on, a
computer simulated the evolution of a population from an initial state: here
the r61e of the computer was confined to producing series of random numbers.
These numbers, interpreted with the aid of the basic functions, simulated deaths,
births, divorces, etc. The retrospective estimates which Orcutt and his team
worked out for past phenomena proved remarkably good in themselves and, in
any case, enormously better than any obtainable by extrapolation. It will be
noted incidentally that this achievement is congruent with what was said
earlier about stochastic processes : in effect, Orcutt’s simulated experiment can
be regarded as the physical equivalent of a stochastic process. To be more
precise, it can be shown to be the equivalent of a Markov process in which the
number of states is astronomically high.
Simulation has been used in economics as a substitute for mathematical
models, in cases when the hypotheses introduced were too complex. An exam-
ple here is a publication by Hogatt,Ig in which he takes certain classical hypoth-
eses on cost functions, price formation, etc., and studies them in the context of
an open market, that is a market where old firms can disappear when they
operate at a loss and new firms come into being when opportunities for profit
exist. The hypotheses of this model are naturally very complex and analysable
on deductive lines only with great difficulty; here again, the model can be regard-
ed as the physical realization of a particularly complex stochastic process.
In sociology, examples of applied simulated methods are numerous. Sim-
ulated models have been made of the phenomena of social diffusion whose
productive quality was outstanding, and these methods have also been applied
successfully to the problems of forecasting election results. Yet another example