“Simulating the Social Processes of Science”
Motivation
Science is the result of complex interactions between
institutions and individuals self-organizing their research. There has been a
tendency to depict/sketch science as an individual, cognitive endeavour marked
by new ideas, breakthroughs and problem solving activities. However, there is
no doubt that science is also substantially a social process. That is, science relies on many
inter-personal processes, including: selection and communication of research
findings, discussion of method, checking and judgement of others’ research,
development of norms of scientific behaviour, organisation of the application
of specialist skills/tools, and the organisation of each field (e.g. allocation
of funding). Furthermore, science is
full of the social phenomena that are observed elsewhere: fashions, concern
with status and reputation, group-identification, collective judgements, social
norms, competitive and defensive actions, to name a few.
Although we sometimes can directly observe scientists
interacting, usually our theorising about this is indirect, via the traces of
their behaviour (individual and social) in terms of the documents they produce,
such as journal articles, patents or grant applications. Empirical observations
of science – in the form of quantitative studies – for a long time remained
focuses primarily on traces of formal communication. The simple reason is, those
are captured in a structured way in bibliographic databases, and available for
analysis. However there are other sources of data about the behaviour of
researchers. These include, for example
altmetrics, but also the more qualitative evidence that can be harvested from
narratives, interviews, and questionnaires or science history accounts. Agent-based
models are particularly suitable for building upon hypotheses derived from such
micro-level data as they encapsulate hypotheses about behaviour and produce
outputs that can be validated against macro-level observable traces and data
(Moss and Edmonds 2005).
This special issue calls for models that explicitly
represent the interactions between individual researchers, capturing aspects of
the social processes of science. They may build upon hypotheses about
individual behaviour from a variety of sources, including: the sociology of
science, cognitive science, or qualitative observations as long as they also
are comparable with some kind of validation data. Their heuristic power unfolds
when the system under study involves substantial interaction between
heterogeneous actors or over within complex networks. Such models hold out the potential as a complementary
tool to the existing range of quantitative and metric approaches. New insights
about science can be gained by comparing and relating different kinds of model,
including data, statistical, observational, psychological and computational.
This special issue aims to present a state of the art in terms such computational
models.
The Topic
We are looking for original articles that use simulation
methods to understand and explore the social (i.e. inter-researcher) processes
that contribute to the phenomena we call science. These could be simple or complex, agent-based
or otherwise, but need to go beyond data analyses and data description approaches
that only deal with the relation of macro-level variables. All models should link individual
interactions between researchers to outcomes that are possible to measure (at
least in principle). All models should
list their conceptual, epistemic or empirical points of departure – being as
clear as possible as to the assumptions on which they are built. Given the
focus of the journal, the models should be presented and assessed in formal or
numerical ways. They should be validated with empirical data where possible. We
are also open to the submission of models that lead to new ideas about
measuring and observations and might accept one survey paper covering the field
up to the present. However, overall we will favour submissions that take some
effort in aligning assumptions and theories with observations and data.
Specific topics could include, but are not limited
to, the following:
- How individual and social behaviour of researchers
result in citation networks, such as those we observe?
- When and how researchers choose to co-author
papers or write grant applications together?
- How is current production of scientific
knowledge by individuals and groups influenced by project-funded science and
evaluation schemes?
- How does the individual career path unfolds
under the condition of globalization and team science? How the structure and
social practices of science impact upon the career of early stage researchers?
- What are the properties of peer review,
including the effectiveness and robustness of alternative systems and what are
their influences on scholarship?
- How academic topics emerge, are maintained and
finally fall out of fashion?
- What are the social and reputational factors behind
the success or frustration of interdisciplinary research?
- How can we describe, analyse and model the
impact of different social aspects upon the development of scientific
knowledge: trust, reference group identification, honesty, methods of measuring
reputation, team working, etc.?
History
Broadly this approach goes back to (Gilbert 1997), but
interest in this has recently intensified. There was a workshop, then book on
“Models of Science Dynamics” (Scharnhorst et al. 2012), which did not focus on
models of the social processes of science, but included a review of agent-based
models up to that point (Payette, 2012). Then their followed special issue of position papers on this topic in the
Journal of Artificial Societies and Social Simulation, in 2011 (Edmonds et al.
2011), followed by some papers appearing at simulation conferences (ESSA, ECMS,
MABS), leading up to the week-long Lorentz Workshop on this topic in April
2014. http://knowescape.org/simulating-the-social-processes-of-science-a-summary.
Contributors to this special issue are by no means limited to those involved in
the above.
Submission
Although participants of these previous events are
encouraged to submit revised and mature versions of their papers, this is an
open call that will be freshly peer-reviewed according to the normal standards
of
Scientometrics, without any prior
preference.
However, the editors reserve
the right to reject papers that clearly fall out of the scope of this special
issue, without review. Therefore, potential contributors are asked to submit
their papers prior to the regular reviewing process by email to Bruce Edmonds (
bruce@edmonds.name)
for possible selection by the 30
th April 2015. Authors will then
receive instructions for submission along with some technical notes.
Special Issue Editors
- Bruce Edmonds,
Professor of Social Simulation, Centre for Policy Modelling, Manchester
Metropolitan University, UK.
- Andrea Scharnhorst,
Royal Netherlands Academy of Arts and Sciences, Data Archiving and
Networked Services, Amsterdam, Netherlands
References
Edmonds, B., Gilbert, N., Ahrweiler, P. & Scharnhorst,
A. (2011) Special Issue of the Journal of
Artificial Societies and Social Simulation
on 'Simulating the Social Processes of Science' 14,(4) (Introduction to
special issue is at: http://jasss.soc.surrey.ac.uk/14/4/14.html).
Gilbert, N. (1997). A simulation of the structure of
academic science. Sociological
Research Online, 2(2), http://www.socresonline.org.uk/2/2/3.html.
Moss, S. and Edmonds, B. (2005) Sociology and Simulation:
- Statistical and Qualitative Cross-Validation, American Journal of
Sociology, 110(4) 1095-1131.
Payette N.
(2012) Agent-based models of science. In Scharnhorst, Börner, and Scharnhorst A., Börner K. & Van
den Besselaar, P. (eds.) (2012). Models
of Science Dynamics. Springer. Ch 4, pp. 127-157
