Artificial Science
– a simulation test-bed for studying the social processes of science
By: Bruce Edmonds
Date: 24th Spetember 2004
CPM Report No.: CPM-04-1389
Presented
at: The 2nd
International Conference of the European Social Simulation Association
(ESSSA 2004), Valadollid,
Spain. 16-19th September 2004.
Science
is important, not only in the knowledge (and hence power) it gives
humans, but also as an example of effective distributed problem solving
that results in complex and compound solutions. For even though
it is usual for one or two individuals to be credited for any
particular discovery in science, their efforts almost always use a host
of results and techniques developed by other scientists. In this
sense all scientists "stand on the shoulders of giants" in order to
"see a little further". Thus, contrary to the architypal image of
lone geniuses labouring to prove their ideas despite the opinions of
thier colleagues, science is fundermentally a social activity. A
collection of individuals, however gifted, that did not communicate
would not be able to develop science as we know it. Even the
earliest scientists such as Galileo were building on the ideas of
others (Copernicus) and using the technology produced by others (the
telescope). Indeed some philosophers (e.g. [18]) have claimed
that the only things that distinguishes the process of science from
other social processes are its social norms and processes.
These processes become the more remarkable, the more they are
examined. It turns out that science manages to implement many
features that have been difficult to achieve in distributed AI and
multi-agent systems. These include: the spontaneous
specialisation and distribution of skills accross the problem space;
the combination of advances and results from many different individuals
to form complex chains of inference and problem solving; the
reliability of estabished results in comparison to the uncertain
reliability of individuals' work; the spontaneous autopoesis and
self-organisation of fields and sub-fields, continually adapting to
problems and degrees of success; the ability of science (as a whole) to
combine "normal science", characterised by much cooperative work within
a common framework and "revolutionary science" characterised by sharp
competition between individuals and frameworks; and the ability of
science to produce coherent developments of knowledge whilst, at the
same time, maintaining variety and criticism so that it can quickly
adapt to the failure of any particular developments. All of this
is achieved with a relative lack of: explicit central coordination; use
of market mechanisms; global and explicit agreement about norms or
methods; or well-defined hierarchies.
Thus science is an important subject for study in its own right, and
thus also its critical social processes. The question is not so
much that it is worth modelling, but how one approach modelling it in a
useful way. It is the aim of this paper to suggest a framework
for a set of investigations that will advance such a project.
Thus the framework will be described as well as a first implemented
instantation of the framework. Although it is the framework which
I consider more important, the exhibited simulation exhibits results
that are interesting in their own right.
Traditionally there is the ‘building-block’ picture of science [11]
where knowledge is slowly built up, brick by brick, as a result of
reliable contributions to knowledge – each contribution standing upon
its predecessors. Here, as long as each contribution is checked
as completely reliable, the process can continue until an indefinitely
high edifice of interdependent knowledge has been constructed.
However other pictures have been proposed. Kuhn in [14] suggested
that often science progresses not gradually but in revolutions, where
past structures are torn down and completely new ones built.
Despite the importance of the social processes in science to society,
they are relatively little studied. The philosophy of science has
debated, at some length, the epistemological aspects of science – how
knowledge is created and checked ‘at the coal face of the
individual’. Social processes have been introduced mainly by
critics of science – to point out that because science progresses
through social processes it is ‘only’ a social construction, and
thus has no special status or unique reliability.
Here I take a neutral view – namely, that it is likely that there are
many different social processes occurring in different parts of science
and at different times, and that these processes will impact upon the
nature, quality and quantity of the knowledge that is produced in a
multitude of ways and to different extents. It seems clear to me
that sometimes the social processes act to increase the reliability of
knowledge (such as when there is a tradition of independently
reproducing experiments) but sometimes does the opposite (when a closed
clique act to perpetuate itself by reducing opportunity for criticism).
Simulation can perform a valuable role here by providing and refining
possible linkages between the kinds of social processes and its results
in terms of knowledge. Earlier simulations of this sort include
Gilbert et al. in [10]. The simulation described herein aims to
progress this work with a more structural and descriptive approach,
that relates what is done by individuals and journals and what
collectively results in terms of the overall process.
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