As Simple as Possible But No Simpler: Agent-Based Modelling Meets Sociology and Physics

http://www.simian.ac.uk
Edmund Chattoe-Brown (ecb18@le.ac.uk)
Department of Sociology, University of Leicester
As Simple as Possible But No Simpler:
Agent-Based Modelling Meets
Sociology and Physics

2 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 regarding Nigel
Gilbert (co-PI SIMIAN, Sociology, Surrey).

Three thoughts
• “Make everything as simple as possible, but not
simpler.” (Albert Einstein, physicist)
• “To the man who only has a hammer,
everything he encounters begins to look like a
nail.” (Abraham Maslow, psychologist)
• “Scientists tend not to ask themselves
questions until they can see the rudiments of an
answer in their minds. Embarrassing questions
tend to remain unasked or, if asked, to be
asked rudely.” (Sir Peter Medawar, zoologist
and physiologist)

Two big (connected) questions
• How do we represent our theories?
• How do we decide when we have made
them simpler than possible? (Einstein)
• Different disciplines (and the physical and
social sciences) have different answers to
the first question and are more or less
aware of the challenges raised by the
second.

A worry
• We all know in an everyday sense that
humans are not like atoms (or other
physical entities) in important ways but how
do we show (how and how much) this
matters to the way we should build theory?
(Maslow)
• For example: Deliberation and decision, a
“model” of the world, self-awareness and
ability to recognise and react to patterns
that they are themselves part of.
(Evacuation example.)

An important distinction
• If there weren’t regularities to be found in
social science, all social scientists should
give up and go home (or become novelists
and biographers): Boxes and shelves
example.
• There plainly are such regularities but they
don’t arise in the same way as they do in
the physical sciences: Socialisation, social
reproduction, evolution (social or
biological), conventions (norms) and so on.

A social regularity
All cause death rate
per 100,000
population, Scotland
2001 (men and
Women)

Lots of causes
• Poverty (bad food).
• Economics (dirty towns, poor housing, dirty or
dangerous jobs).
• Cultural differences (“chips with everything”,
“Who wants to live forever?”)
• Postcode lottery in healthcare and education?
• Genetic effects: Political correctness alert!
• All happening together in different combinations
at different times for different people. Really, on
reflection, the mere existence of social
regularities is quite impressive.

Representing theories
• Physics and the “extraordinary coincidence”
with “every day” mathematics.
• Social science tries statistics too but does not
have “laws” to work with (and has relatively little
good quality data too).
• Because of self-awareness we almost certainly
have to have methods for capturing subjective
self-report in social science. This is pretty alien
to physics I guess. Sociology is quite good at it.
• So basically, it comes down to “mathematics” or
“narratives”. (Experiments? Other?)

Numbers and narratives
• Echoing a big “row” in sociology (among
others), equations are rigorous but “restrictive”
(limit applicability?) while narratives are rich but
sloppy.
• A third way: Computer programmes are rich but
rigorous. (Medawar.)
• A particular kind of computer programme
(“agent-based”) for theories of social behaviour.
• I’m about to take a “left turn” into simplicity but
trust me, I’m not retreating from what you were
promised.

The Schelling segregation model
• I’m not presenting this because it represents social reality but because it
is easy to explain and gives a good point of access to important issues.
• “Agents” live on a square grid so each has maximum 8 neighbours.
• There are two “types” of agents (pink and white) and some grid spaces
are vacant. Initially agents/vacancies are distributed randomly.
• All agents decide what to do in the same very simple way.
• Each agent has a preferred proportion (PP) of neighbours of its own kind
(0.5 PP means you want at least half your neighbours to be your own kind
- but you would accept all of them i. e. PP is minimum.) Vacant grid
spaces “don’t count” which is why the PP is a fraction not a number.
• If an agent is in a position that satisfies its PP then it does nothing
otherwise it moves to a vacancy chosen at random.

The software
NetLogo: Free,
cross platform,
comes with a
models library
that will “click
and go”.

Initial random state

Clustering
Schelling (early
seventies) was
interested in urban
residential
segregation of
ethnic groups in
the US.

Some real data for reference
What it looks like depends on how we measure
it/what data we can get hold of.

A comment and two questions
• This is an “agent based” model because each
“social actor” has its “sensing” of the
environment, decision process and actions
represented explicitly (and “privately”) in the
programme code. We do not need homogeneity
assumptions or any kind of implausible “global
knowledge” to make the model “go”.
• What is the smallest PP (i. e. a number between
0 and 1) that will produce clusters?
• What happens when the PP is 1?

Answers
• About 0.3.
• No clusters form.
• Challenge: Had you “seen” the cluster data
generated by PP=0.3, might you (if you were of a
particular political or sociological persuasion)
have attributed xenophobia or racism to the
system? (Or preference?)
• We can explicitly compare more or less
“sophisticated” agents including “random within
structure”: Very useful for complex null
hypotheses. What is a “null” social network?

Why?
• Because PP is a minimum, people are always happy
“inside” a cluster of their own kind.
• If a cluster is “full” (no internal vacancies) then it cannot
be “disrupted” except at the edges.
• Whether clusters form/persist thus depends on whether
their shape is compatible with the PP for “edge agents”.
(No “sharp corners”: Defines minimum cluster size?)
• When PP is 1, no shape of the cluster edge is
compatible with the satisfaction of edge agents so the
cluster cannot maintain itself. (PP heterogeneity?)
• An aggregate entity (the cluster) thus becomes a
organising principle for individual behaviour: This is a
very concrete example of what sociologists call (often
very verbosely) “structuration”.

Simple individuals/complex system
I ndividual Desires and Collective Outcomes
-20
0
20
40
60
80
100
120
0 50 100 150
% Similar W anted ( I ndividual)
%SimilarAchieved(Social)
% similar
% unhappy
Counter-intuitive
macro (social)
results from
simple micro
interactions. A
non-linear (and
complex)
system. I am
doing what
biologists call
“rerunning the
tape” here.

Disclaimer
• Simulation is distinctive in representing social
theories as computer programmes rather than
narratives or equation systems.
• However I don’t need to explain to you exactly
how these programmes work or how one writes
them any more than a statistician has to know
how to derive the formulae for regression or
understand how a PC runs MATHEMATICA.
• But if you do want to know …

Value of “trivial” model
• ABM combine qualitative/quantitative. We need to know both how people
act individually and what patterns are generated collectively.
• Unless we start with “calibrated” micro behaviour we face the
“microfoundations” problem of statistical modelling. (How do we know
how many models could have produced what we see? Have we
established causation or merely association?)
• Unless we validate macro patterns, we face the “scaling” problem of
qualitative research. How do we know what the effect of all this “detail” is
and whether it “matters” in aggregate? Computational experiment.)
• Even this trivial example problematises a simple link between micro and
macro and encourages careful process thinking.
• Qualitative similarity: What do we mean when we say that clusters (or
other structures rather than numbers) are “alike?”
• Falsification: We can derive multiple kinds of data from a complex
simulation “object”. Would anybody like to speculate on the distribution of
numbers of “migrations” for the population? Compare physics looking for
“new effects” ex ante i. e. binary pulsars for relativity.

Now throw it away!
• The Schelling model is no good as “real” social science
for at least five reasons:
• Not systematically based on existing knowledge
(economics, politics, psychology).
• Not proven necessary and ABM not properly “chosen”
as a method: Occam’s Razor strongly indicates
regression to explain a simple trend.
• Too much at risk of equifinality: For “toy” version, really
only two distinguishable states (clusters and not).
• Not calibrated.
• Not validated.

Methodology 1 (G+T)

Start again!
• Choose a research domain “complicated enough” to justify
ABM.
• Use a systematic review of what is known in the domain to
create the agents and their environment.
• Almost certainly collect more data: Peril of novelty.
• If you can’t avoid it by filling gaps in knowledge, “tune” the
simulation to match some dimensions of data.
• Run the simulation and compare results with aggregate
data (not the calibration data obviously). NOTE: Not all
social science domains even have both good qualitative
and quantitative data. There didn’t use to be a need!
• If the match is good, you have learnt something. If it is
poor you can “repeat” but not too many times.

Uses of simulation: Incomplete list
• Formalising existing verbal theories to see the gaps and
ambiguities.
• Synthesising “competing” theories: Heaps of “not
implausible” theories a peril of social science. Example:
Social capital.
• Developing novel theory not constrained by analytical
limitations (Maslow).
• Doing “experiments” that might not otherwise be possible.
• Framework for “less contentious” interdisciplinary work:
Don’t have to “talk in theories”.
• Data collection tool in its own right: Some “agents” may be
people (participative simulation, artificial stock markets).

Case study 1: Strict churches
• Iannaccone: Rational choice argument that details of
religion can restrain adherents from “free riding” and losing
“club good” benefits of religion.
• Argument run analytically in a “partial equilibrium” context.
• ABM (“general non-equilibrium?”) implementation
overturns “strict churches are strong” hypothesis.
• Raises other interesting issues: Role of networks and
information in recruitment, robust “ecology” of large liberal
and small strict churches, long term statistical structure of
church “succession”, the possibility of endogenous
renaissance.
• Starts to be a framework for ethnographic comparisons of
“successful” churches and “unsuccessful” ones.

Church “ecology”
Chattoe, E. (2006)
‘Using Simulation to
Develop and Test
Functionalist
Explanations: A Case
Study of Dynamic
Church Membership’,
British Journal of
Sociology, 57(3),
September, pp. 379-
397.

Case study 2: Hegemony
• Rational Choice says find set of options, pick the “best”.
• What happens when some feasible choices (as well as payoffs)
aren’t known?
• Impossible to tackle analytically: Easy in ABM.
• Agents transmit “possibility” information as well as payoffs.
• Counter argument to economic “Pollyanna” position that
subjective and objective views will converge. Belief traps.
• Unifies “tradition” (single action “choice”) and choice. Works
properly for “new” options.
• Majority tastes and choices can “disadvantage” minority groups
without coercion: Finding a satisfying expression of sexuality
(data from “coming out stories”).
• Chattoe-Brown, E. (2009) ‘The Social Transmission of Choice: A
Simulation with Applications to Hegemonic Discourse’, Mind and
Society, 8(2), December, pp. 193-207.

Conclusions
• Means and ends: Can’t necessarily interest you
in the social dimension of behaviour but if you
are interested then this is very good method to
use (and teach - NL has physics models too).
• There is an emerging methodology that
integrates previously disjoint data collection and
analysis methods in social science and
regulates the limitations of ABM (“toy” models).
• Welcome to Terra Nova. You can still give a
good student a whole sub discipline to conquer.

Now what?
• 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/cgi-
bin/webadmin?A0=SIMSOC>.

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