artificial things are often discussed, particularly
when they are designed, in terms of imperatives as well as descriptives
Simulation
- artificiality connotes perceptual similarity
but essential differences, resemblances from without rather than
within
- the computer (based on its abstract character
and its symbol-manipulating generality) has greatly extended the
range of systems whose behavior can be imitated
- Questions: how can a simulation ever tell us
anything that we do not already know?
- a simulation is no better than the assumptions
built into it
- a computer can do only what it is programmed to
do
Simulation as a Source for new Knowledge
- even when we have correct premises, it may be
difficult to discover what they imply (e.g.: weather prediction;
predict how an assemblage of components will behave)
- simulation of poorly understood systems
we are seldom interested in explaining or predicting
phenomena in all their particularity; we are interested only in
a few properties abstracted from complex reality (e.g.: neurophysiological
level versus information processing level in understanding human
cognition)
- resemblance in behavior of systems without identity
of the inner systems ---> feasible for: emphasis on organization
of parts, not in the properties of the individual components
Computers and Thought
- the organization of components, and not theirphysical properties, largely determines behavior
- computers are organized somewhat in the image
of humans
- computer becomes an obvious device for exploring
the consequences of alternative organizational assumptions for
human behavior
- psychology can move forward without awaiting
the solutions by neurology of the problems of component design
- however interesting and significant these components turn out
to be
Computers as Empirical Objects
this highly abstractive quality of computers makes
it easy to introduce mathematics into the study of their theory
- and has led some to the erroneous conclusion that, as a computer
science emerges, it will necessarily be a mathematical rather
than an empirical science.
- see Turing Award Lecture from Newell and Simon,
"Computer Science as an Empirical Inquiry: Symbols and Search",
CACM, Vol. 19, No 3, 1976, pp. 113-136
- example: time-sharing systems ---> main route
to develop and improve them was: build them and see how they behave
- perhaps theory could have anticipated these experiments
and made them unnecessary ---> in fact: it did not
Physical Symbol Systems or Information-Processing
Systems
- examples: human mind
and brain, computer
- symbols and symbol structures
- processes that create, modify, copy and destroy
symbols
- serve as internal representations ("mental
images") of the environments to which they are seeking to
adapt
- communication with the environment
- Physical Symbol Systems Hypothesis:
"A Physical Symbol System has the necessary
and sufficient means for general intelligent action"
----> an empirical hypothesis