Transcript What`s right about social simulation?

What’s right about social simulation?
Edmund Chattoe-Brown ([email protected])
Department of Sociology, University of Leicester
http://www.simian.ac.uk
Thanks
• This research funded by the Economic and Social
Research Council as part of the National Centre for
Research Methods (http://www.ncrm.ac.uk).
• The usual disclaimer applies particularly regarding Nigel
Gilbert (co-PI SIMIAN, Sociology, Surrey).
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Some context
• Research methods are spread unevenly across the social
sciences in different combinations. They are a “small N”
phenomenon and even smaller in specific social sciences.
• While pursuing “novelty” in research methods (as in other
areas of social science) we seem to be hazy about its
potential implications.
• This is particularly true of major innovations (as opposed to
“more of the same” developments like focus groups from
qualitative interviewing). This is no disparagement. Most
innovation is necessarily MOTS.
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Some novel questions
• I think these questions have been “off the radar” because N
is too small to generalise in each specific social science.
• What is a research method?
• On this basis, “how many” are there?
• Have we found them all, taking the social sciences as a
whole?
• If not, how would we go about looking for new ones? What
kind of thing(s) might be missing?
• What are the ramifications of a “new” research method? How
important are they to find?
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Two cases
• The simpler case is “import” of methods across disciplines.
• We tend to forget that the “main” methods of sociology
(ethnography and statistical models) were both novel (and one
an import) in the twenties and thirties.
• What would happen if qualitative methods were imported into
economics tomorrow? Whatever it was, it would be dramatic!
• The more interesting case is the discovery of a really new
research method: What implications would that have for the
social sciences?
• We seem to be in a state of “doublethink” as academics,
claiming as producers that most things are novel while actually
acting as consumers as if nothing were really new.
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First claim
• Social simulation/agent based modelling (SS/ABM) is a radical
innovation in research methods. (More humility here than might
appear.)
• It focuses on the theory building stage of the research process (as
opposed, for example, to the data collection or analysis phases).
• Thought: Could we define the space of research methods in terms of
their distinctive contributions to the research process?
• Corollary: If a novel method makes a distinctive contribution to the
research process, it should be “compatible” with existing methods
rather than claiming to replace them.
• SS/ABM requires both qualitative and quantitative data for validation
and calibration. This is part of its “formal” methodology.
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First important implication
• All research methods (including SS/ABM) shape the
questions we ask and the answers we offer to social
understanding.
• However, a novel method has the opportunity (despite its
own biases which will have to be revealed later) to cast light
on the biases, lacunae and theoretical preconceptions of
research until that point.
• This deep re-evaluation (and the process based nature of
SS/ABM) often helps with synthesis of competing views.
• This is a hugely important opportunity and one would expect
it to lead to an active search for novel methods.
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Quick case study
• Social capital is a hugely important idea which even its advocates
worry may be being “blurred into uselessness”.
• Key “dimensions” of SC contend (networks, resource access, trust,
repeated interaction …) to produce disjointed research agendas
which are all “plausible”. (This is a very general phenomenon in social
science reflecting our inability to deal with large complex systems.)
• It is possible to show, using really quite a simple simulation, how all
these processes are linked aspects of the same phenomenon.
• This leads to important insights. For example, the “trust” dimension of
SC sits awkwardly with the repeated interaction dimension because
most research represents RI in game theoretic terms as
simultaneous rather than sequential games.
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Second important implication
• Because of its methodology, SS/ABM can cast light on the “how
complex is it” problem that besets existing methods.
• For a real social domain, methods presume (but can’t actually
demonstrate) that they are of appropriate complexity.
• This leads to a futile debate between quals and quants. Quals says
quants is “too simple” but quants says it “fits”. Quals cannot show
that the “descriptive complexity” it finds actually bears on model fit.
Quants can’t be sure that associations between variables really
reflect causal process.
• SS/ABM can “show” (for example) that a simplified simulation
explains “no less” than a more complex one in terms of tracking a
variety of real data with simulated data.
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Second claim
• Because of its relationship to qualitative and quantitative
data, SS/ABM has a distinctive methodology.
• Rather than “fitting” data to a model (which tells us how well
we are doing but only on the presumption that the method
was applicable in the first place - an idea to be revisited),
SS/ABM directly compares real and simulated output based
on “separately established” micro foundations.
• The former is the quantitative component and the latter the
qualitative but both are essential and allow, at least in
principle, for falsification.
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Third claim
• SS/ABM does not mix up “technical” assumptions with
empirical ones.
• YHTTMOT but I have never seen a social process specified
that can’t be turned into a programme. (Compare noncomputable decision processes in economics.)
• A lot of older simulation methods “bundled” assumptions to
make the method “go” with claims about behaviour (i. e
“pools” of identical actors in system dynamics).
• This problem is very real in methods we take for granted i. e.
various kinds of normality and independence assumptions in
statistics. These are often invisible to non-specialists.
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Fourth claim
• With the appropriate infrastructure, SS/ABM is now no harder
to learn than basic statistics.
• As another example of “novelty doublethink” we don’t
compare like with like. The comparison is not between
learning simulation and statistics “now” but learning
simulation a few years back and statistics in 1920 when there
was no SPSS and all the sums had to be done by hand.
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Fifth claim
• SS/ABM may form a genuine basis for interdisciplinarity.
• Discussion can focus on process and does not necessarily
have to be couched in terms of the theoretical categories of a
particular discipline (i. e. rational choice, “class”, attitudes).
• By direct observation, simulators seem to have a lot less
trouble talking to each other across disciplines than some
people within disciplines have talking to each other!
• Again, with some amendments (that should not be too
unpalatable) different disciplines can continue to focus on
what they are “good at”. This is not an approach that intends
to turn simulation into the “queen of social sciences”.
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Sixth claim
• A novel approach always looks “messier” than an established one
but that doesn’t mean it won’t get sorted out. Applied statistics
looked pretty clunky in 1920 too but we tend to forget that now.
• YHTTMOT but simulation knows what its problems are and they are
being addressed. I haven’t yet spotted anything that scares me
enough to want to go and do something else before the “bubble
bursts”.
• There is some element of the dialogue between methods that
doesn’t deal with technicalities but with implicit beliefs, the
establishment of fair bases of comparison and so on. Discussions
about how we establish the novelty of a method are not “mere”
propaganda.
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Tentative conclusions
• Some of the most distinctive opportunities of SS/ABM arise
not from details of the method but from its novelty.
• A novel method also has “meta implications” for thinking
creatively about the whole scope of research methods.
• It is important for people to recognise just how novel it is
(avoiding various kinds of “novelty doublethink”) and not
assume that existing intuitions will serve them in
understanding it. In particular, it is not now (though it may
have been in the past) just a “flavour” of statistics.
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Further resources
• Simulation Innovation, A Node (Part of NCRM: research, training
and advice): <http://www.simian.ac.uk>.
• NetLogo (software used here, free, works on Mac/PC/Unix, with a
nice library of examples): <http://ccl.northwestern.edu/netlogo/>.
• Simulation for the Social Scientist, 2nd edition, 2005,
Gilbert/Troitzsch. [Don’t get first edition, not in NL!]
• Agent-Based Models, 2007, Gilbert.
• Journal of Artificial Societies and Social Simulation (JASSS):
<http://jasss.soc.surrey.ac.uk/JASSS.html>. [Free online and
peer reviewed.]
• simsoc (email discussion group for the social simulation
community): <https://www.jiscmail.ac.uk/cgibin/webadmin?A0=SIMSOC>.
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