2. Philosophy - Stefano Franchi

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P HILOSOPHY, NON-PHILOSOPHY, AND SCIENCE
ing hardware. It is no different in software design: many decisions are forced by the “raw
material” the programmer has to deal with and many design decisions have to be taken in
order to keep the problem tractable, e.g. to make the simulation work. As a consequence, it
becomes difficult to determine what counts as an explanation, i.e. what and where is the
psychological theory. A simple example, to make the problem clear: to keep the complexity
of the problem within the possibilities of the hardware available at the time, some chess
playing programs were forced to play on a 6x6 board, with no bishops, no en passant cap-
tures and no castling allowed. 33 It might be imagined that a chess expert would have serious
objections to the plausibility of the resulting simulation, since the game played differs sub-
stantively, and perhaps radically, from the game of chess.
Newell is well aware of this difficulty and calls it the irrelevant specification problem.
His proposed solution, however, is almost paradoxical since he suggests a unified theory of
cognition where every specification is relevant because the “theory comes closer to speci-
fying all of the details of the simulation, which is to say, all that is needed to make the sim-
ulation run”. 34 This suggestion involves, it seems, that the problem will go away when the
engineering aspect will have taken over completely, that is, when the simulation will be in-
distinguishable from the original. How to determine, however, at which level the similarity
should stop? In particular, how would it be possible, on such a view, to claim that simula-
tions run on computers can exhibits psychological plausibility? Sooner or later, some deci-
sion or other will have to do with a “substratum” that is irrelevant to the subject matter of
psychology: the materiality of the computer will not go away.
This last remark brings me to the second point of tension between AI-engineering and
AI-psychology, a problem that can be seen as the reverse of the first problem. It may be
claimed that AI works in abstracto (e.g. it is not empirical enough) because it does not
study “actual people” in “actual situations” but rather relies on idealized models. As Daniel
Dennett has remarked 35 —and countless texts could be provided from AI’s “founding fa-
thers”—at least part of the work done in Artificial Intelligence must conducted ‘top-down,’
33. Allen Newell, J.C, Shaw, and Herbert Simon, “Chess Playing Programs....,” 47.
34. Allen Newell, Unified theories…, 23.