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

Edmonds, Bruce (2004) Artificial Science – a simulation test-bed for studying the social processes of science. [Conference Paper] (Unpublished)

Full text available as:

[img] HTML


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.

Item Type:Conference Paper
Keywords:simulation, science, publishing, philosophy of science, distributed artificial intelligence, theorem proving, forward chaining, agents
Subjects:Computer Science > Artificial Intelligence
Psychology > Social Psychology > Social simulation
Philosophy > Philosophy of Science
ID Code:4263
Deposited By: Edmonds, Dr Bruce
Deposited On:20 Apr 2005
Last Modified:11 Mar 2011 08:55

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

[1] Buchan, B. and Mitchell, T. (1978) Model-directed learning of production rules. In D. Waterman and F. Hayes-roth (eds.), Pattern-Directed Inference Systems. New York: Academic Press.

[2] Campbell, D. T. (1960) Blind variation and selective retention in creative thought as in other knowledge processes. Psychological Review 67:380-400.

[3] Carruthers, P., Stich, S. and Siegal, M. (eds.) (2002) The Cognitive Basis Of Science. Cambridge University Press.

[4] Copi (1982) Introduction to Logic. New York: Macmillan.

[5] Edmonds, B. (2000) The Purpose and Place of Formal Systems in the Development of Science, CPM Report 00-75, MMU, UK. (

[6] Edmonds, B. (2004) From KISS to KIDS – an ‘anti-simplistic’ modelling approach. Edmonds, B. and Moss, S. (2004) From KISS to KIDS – an ‘anti-simplistic’ modelling approach. MAMABS 2004, AAMAS, New York, July. (

[7] Feyerabend, P. (1965) Against Method. London: New Left Books.

[8] Fisher, M. and Wooldridge, M. (1977) Distributed Problem-Solving as Concurrent Theorem-Proving. In Boman and van de Velde (eds.) Multi-Agent Rationality, Springer.

[9] Giere, R. (2002) Scientific cognition as distributed cognition. In [3], pp. 285-299.

[10] Gilbert, N. (1997) A simulation of the structure of academic science. SociologicalResearch Online, 2(2),

[11] Hempel, C. G. (1966) Philosophy of Natural Science. Englewood Cliffs, N.J. :Prentice-Hall.

[12] Holland, J., Holyoak, K. Nisbett, R. and Thagard, P. (1986) Induction: processes of inference, learning and discovery. MIT Press.

[13] Knorr-Cetina, K. (1999) Epistemic Cultures: how the science make knowledge. Harvard University Press.

[14] Kuhn, T. S. (1962). The Structure of Scientific Revolutions. Chicago, University of Chicago Press.

[15] Lakatos, I. (1970) Falsification and the methodology of scientific research programs. In, [16], pp.91-195.

[16] Lakatos, I. And Musgrave, A. (eds.) Criticism and the Growth of Knowledge. Cambridge University Press.

[17] Langley, P., Simon, H.A., Bradshaw, G.L. and Zytkow, J.M.. (1987) Scientific Discovery: Computational Explorations of the Creative Processes. MIT Press.

[18] Longino, H. (1990) Science as Social Knowledge. Princeton: Princeton University Press.

[19] Merton, R. K. (1973) Sociology of Science: theoretical and empirical investigations. Chicago: University of Chicago Press.

[20] Moss, S., Edmonds, B. and Gaylard, H.(1996) Modeling R&D strategy as a network search problem. Workshop on The Multiple Linkages between Technological Change, Human Capital and the Economy, University "Tor Vergata" of Rome, March 1996. (

[21] Newell, A. and Simon, H. A. (1972) Human Problem Solving. Englewood Cliffs, NJ: Prentice-hall.

[22] Pearl, J. (2000) Causality. Oxford University Press.

[23] Popper, K. (1965) Conjectures and Refutations. New York: Basic Books.

[24] Thagard, P. (1988) Computational Philosophy of Science. MIT Press.

[25] Toulmin, S. (1972) Human Understanding, vol. 1: The Collective Use and Evolution of Concepts. Oxford: Clarendon Press.

[26] Wos, L. Overleek, R. Lusk, E. and Boyle, J. (1984) Automated Reasoning: introduction and applications. Englewood Cliffs, NJ: Prentice-Hall.


Repository Staff Only: item control page