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

http://www.simian.ac.uk

Edmund Chattoe-Brown ([email protected])Department of Sociology, University of Leicester

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

Sociology and Physics

http://www.simian.ac.uk/

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 systemI ndividual Desires and Collective Outcomes

-20

0

20

40

60

80

100

120

0 50 100 150

% S imilar W anted ( I ndividual)

% S

imil

ar

Ach

iev

ed

(S

ocia

l)

% 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>.

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

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