Artificial Science
– a simulation test-bed for studying the social processes of science

By: Bruce Edmonds
Date: 24th Spetember 2004
CPM Report No.: CPM-04-138

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.

Presentation accessible as:

Paper accessible as: