Abstract
Agent-based simulation can model simple micro-level mechanisms capable of generating macro-level patterns, such as frequency distributions and network structures found in bibliometric data. Agent-based simulations of organisational learning have provided analogies for collective problem solving by boundedly rational agents employing heuristics. This paper brings these two areas together in one model of knowledge seeking through scientific publication. It describes a computer simulation in which academic papers are generated with authors, references, contents, and an extrinsic value, and must pass through peer review to become published. We demonstrate that the model can fit bibliometric data for a token journal, Research Policy. Different practices for generating authors and references produce different distributions of papers per author and citations per paper, including the scale-free distributions typical of cumulative advantage processes. We also demonstrate the model’s ability to simulate collective learning or problem solving, for which we use Kauffman’s NK fitness landscape. The model provides evidence that those practices leading to cumulative advantage in citations, that is, papers with many citations becoming even more cited, do not improve scientists’ ability to find good solutions to scientific problems, compared to those practices that ignore past citations. By contrast, what does make a difference is referring only to publications that have successfully passed peer review. Citation practice is one of many issues that a simulation model of science can address when the data-rich literature on scientometrics is connected to the analogy-rich literature on organisations and heuristic search.
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Notes
The simulation model, CitationAgents1, was developed initially in VBA within Excel 2003, and then, after a break of several months, reproduced using NetLogo 4.1. Replicating a simulation model in this way helps to verify that the program is working as intended. The extra work involved in replicating the model was worthwhile, as several minor errors in the original version were exposed. A version of it may be downloaded from OpenABM: http://www.openabm.org/model/2470.
The exact relation between computing time and model scale is difficult to state, and differs between the VBA and NetLogo versions, for reasons internal to those software environments.
The options for generating author lists also include those that produce cumulative advantage (in papers per author), but preliminary testing showed that these mechanisms have smaller effects than those for generating references, perhaps because papers have more references than authors on average.
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Acknowledgments
This research was supported by SIMIAN (Simulation Innovation: A Node), a part of the UK’s National Centre for Research Methods, funded by the Economic and Social Research Council.
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Watts, C., Gilbert, N. Does cumulative advantage affect collective learning in science? An agent-based simulation. Scientometrics 89, 437–463 (2011). https://doi.org/10.1007/s11192-011-0432-8
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DOI: https://doi.org/10.1007/s11192-011-0432-8