DEPARTMENT OF SOCIOLOGY

Give Me A Place To Stand On and I Will Move The Earth: The Potential

for Agent-Based Modelling in Understanding Social Networks

Edmund Chattoe-Brown (ecb18@le.ac.uk)

Introduction

• Clarification only please.

• “Give me a place to stand on, and I will move the Earth.” (Archimedes)

• “To the man who only has a hammer, everything he encounters begins to look like a nail.” (Abraham Maslow)

• Two Agent-Based Models (ABM) and what they signify.

• Caution: Novelty ahead!

Background

• We are used to quantitative approaches: Class origin is correlated with educational attainment.

• We are used to qualitative approaches: This is how class advantage actually works in Class 3B of Anytown Comprehensive. (Restricted code users are perceived as stupid by middle class teachers.)

• Theories as equations and theories as narratives?

• The “argument” between these approaches does not seem terribly fruitful to date. Nor is their failure to argue however!

The Schelling Model

• I DO NOT “BELIEVE” THIS MODEL! THAT IS NOT WHY I AM SHOWING IT TO YOU!

• To explain urban residential segregation of ethnic groups.

• Squares on a grid: Green, red or empty. Initialisation: Randomly distributed. 8 “neighbour” positions exist for each “agent”.

• Each green (and red) has a PP (a fraction of own type it wants around it). So PP=0.5 mean at least half occupied cells round a red occupied by reds. Assume PP same for all.

• If “happy” then stay. If “not” then move randomly to empty.

• What happens with different PP?

Initialisation

NetLogo

What happens if PP=0.6?

PP=0.6

What happens if PP=0.3?

PP=0.3

What happens if PP=1?

PP=1

Why?

Computational experiments

Individual Desires and Collective Outcomes

-20

0

20

40

60

80

100

120

0 50 100 150

% Similar Wanted (Individual)

% Similar Achieved (Social)

% similar% unhappy

A non linear

system and an

(unusual?) “lower bound

argument”

WDTM 1: Quals “versus” quants

• Clusters do not (always) “drill down” to majority preference.

• Majority preference does not (always) “gross up” to clusters.

• “Common sense” inference from one level to the other just doesn’t work well even in very simple systems (let alone plausible ones).

• Doesn’t quant “drill down” and qual “gross up” exactly like this? In SNA too? Uh-oh?

WDTM 2: Data collection

• How people see their environment and decide to move seems pretty qualitative (but maybe also experimental: Bruch and Mare).

• The patterns in which people live seem pretty quantitative (as do “characterisations” of clusters.)

• So maybe not quals or quants but quals and quants? Gosh!

• PS: No good just bolting realism onto Schelling. (How do I get to Dublin?)

“Footnote”

WDTM 3: Methodology

• Data free models: No thanks!• “Curve fitting”: What about equifinality? What

about non-linearity?• If the micro behaviours are our “best guess”

and they give rise to patterns that are “like” (buffer zones versus soft clusters) the macro aggregates then perhaps we have actually explained rather than simply fitted the data.

• It is like this because xx …• Plenty more to do: How do we create “best

guesses?” What is “like?”

WDTM 4: Distinctive Models

• Explicit agency (SIENA?): Agents “create” segregation through process.

• Except (arguably) PP, no “theoretical constructs” and no “dice roll events”.

• However empirically silly, this is a “complete process specification”.

• Trust me: ABM can get a lot more complicated than this. I have never seen an ABM “fail” because it can’t implement an arbitrary social process.

Summing up

• Understanding theories not as equations or narratives but as computer instantiated processes with a distinctive methodology and orientation to existing forms of social science data.

Networks please!To construct a

classroom sociogram, ask

each pupil to list (confidentially) two students to work with on an

activity.

How did it “get that

way?”

Network processes (Anecdata alert!)

• On several occasions I had seen a particular man in the queue at UL Starbucks. After a while he either recognised me too or was smiling anyway. Then, during a very crowded period, I asked if I could share his table (because we both read articles at lunch). He was welcoming. Finally, as a “joke” I said that we must both get thirsty at about the same time each day and we had a brief chat. Now we are acquaintances.

Now get ready to shout at me please …

• Apart from “it works” (but watch out for non-linearity, equifinality and “thin” data - does network sampling occur at anything like the speed that networks evolve), do we have any “independent” reason to suppose that networks can be represented as evolving by probabilities?

• Or, could simulation show us how reported “qualitative practices” add up to the networks we observe? (Other examples: Dinner parties and pub trips not triads, temporal co-location effects, criminal networks.)

Hummon (2000) model

• Early ABM (kind of) to implement the Jackson-Wolinsky (JET) “rational network” model (published in SN).

• Quite problematic with hindsight (but hindsight is easy.)

• Why implement an analytic model at all? Results arguably “obvious” (star, null, complete).

• Only gives divergence from JW at “boundaries”: Is this a “result” at all? (Or an assumption artefact?)

• Very small networks for historical computational reasons: What else could these small numbers of nodes do?

• Are “real” star networks a “factoid” anyway?

Real workgroup data

c

d

a b

h

e

g

f[7 7 5 7 5 5 7 7]

[4 4 0 4 0 0 3 4][7 1 1 1 1 1 1 1]

Hummon replicated• Another advantage of “modern” ABM (with Simone Gabbriellini).

• Add node capacity limits. (But many more nodes.)

• Add less “punishment” for long routes: Borgatti point well taken.

• Hummon “result” collapses.

• Important to draw the right lesson: Not to fiddle about endlessly with models according to arcane criteria of “plausibility” (or “ad hocery”) to get “appealing” result.

• BUT: It matters a lot what you assume. (Also update rules.)

• AND: How do we really justify assuming one thing rather than another? Maybe “fit” isn’t enough in complex systems?

SIENA: Snijders, van de Bunt, Steglich• “it is further assumed, as an approximation, that the changing network can be

interpreted as the outcome of a Markov process, i.e., that for any point in time, the current state of the network determines probabilistically its further evolution, and there are no additional effects of the earlier past. All relevant information is therefore assumed to be included in the current state.”

• “The actors control their outgoing ties. This means not that actors can change their outgoing ties at will, but that changes in ties are made by the actors who send the tie, on the basis of their and others’ attributes, their position in the network, and their perceptions about the rest of the network.”

• “It is assumed formally that actors have full information about the network and the other actors.”

• “At a given moment one probabilistically selected actor – ‘ego’ – may get the opportunity to change one outgoing tie. No more than one tie can change at any moment … This implies that tie changes are not coordinated, and depend on each other only sequentially, via the changing configuration of the whole network. For example, two actors cannot decide jointly to form a reciprocal tie; if two actors are not tied at one observation and mutually tied at the next, then one of the must have taken the initiative and extended a one-sided tie, after which, at some later moment, the other actor reciprocated and formed a reciprocal tie.”

What to make of this?• Instrumental or descriptive? Do we judge by whether it

“works” or whether we believe it? If we are not careful do we end up dodging about and judging it by neither?

• If we are working so hard building what is effectively a (problematic?) theory of human behaviour, why don’t we just build a theory of human behaviour? (Dilemma.)

• But to say much about this we need a solid place to stand. (Ethnographic grumbles not good enough.)

• We also need to realise that calling SIENA “actor based” does not mean SNA has already taken ABM effectively on board.

Examples

• The KGS reunion and Christian Stillmark.

• The redesign of my WES module.

• Try to reconstruct non ego ties for your best 20 friends.

• Less clear case: My dear father? The new university of Leicester religion study group?

• Criminal network “conventions”.

• Why model with so many “hostages to fortune” at all?

• Revisiting concerns with non-linearity and assumption sensitivity in a network context.

Not quite anecdata 1

• “Met her when we were nine and in the same class. She had just transferred and was being bullied by the local bullies. I whooped their ass, saved her and she was my maid of honor last year when I got married. We are like sisters today, over twenty years after we met for the first time :)”

• “We were in Hbio class in ninth grade he said “Hey cool shirt man what other bands do you like?” and I was his gateway to metal and he was my gateway into profound awkwardness. lol.”

Not quite anecdata 2

• “I met my best friend for the first time in 6th grade German class. I was extremely quiet and preferred being alone. I was sitting there drawing pictures of cats, and she, noisy person that she is, plops down at my table (where I was alone) and starts talking my ear off. I actually didn't like her at first, but she grew on me like a very resilient fungus and we’ve been best friends for 15 years now. :D”

• “We met at a daycare. I was the new kid and pretty lonely. One morning her dad brings her in the room and tells her to come say hello to me. The rest is history. :P Corny, but true.”

Manifesto

• Qualitative research (almost certainly tweaked) to identify real network formation processes.

• ABM to explore the aggregate outcomes of these.

• Quantitative SNA (possibly tweaked) to compare real and simulated networks to assess “generative adequacy” of ABM.

Novelty again

• Qualitative research to build ABM is not quite what quals currently does. (PoMo turn.)

• SNA to “match” networks (real and simulated) is not quite what quants does.

• Example: If you had to choose 5 measures of network structure which have “maximum orthogonality” (greatest information content to distinguish “different” networks) which 5 would they be? Why?

Some possible caveats• You can’t tell a big story without big generalisations.

• SIENA is not the only approach to modelling dynamic networks (but it does seem to be a “significant” one.)

• Not all simulation is ABM: Acid tes is theory representation language.

• There is nothing wrong with “proper” instrumental uses of simulation (quite popular in SNA.)

• There are ABM published in SN journals but their problems are ABM problems (i. e. arbitrary network structures, lack of comparison with real data) and not really what I want to talk about.

• There might be arguments that non-linearity washes out in large systems (or can be designed out) as chaos is when determinism is relaxed but I’m not seeing them.

• Not gaming and not simulacra.

Kind of conclusions

• We have a (newish?) way to “add up” individual practices and interactions through ABM.

• Complex systems may “invisibly” undermine our typical ways of doing social science: Complex systems can be very simple!

• ABM has a distinctive methodology and relation to existing forms of data. IMPORTANT: It isn’t just programming.

• SNA has not “got” what makes ABM valuable yet IMO.

• There is plenty more to do to make ABM an “everyday tool” of social science. That’s my day job!

• If nothing else does this give us a new place to stand?

Now read on 1 …• 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 and 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>.

Now read on 2 …• Chattoe-Brown, E. (2014) ‘Using Agent Based Modelling to Integrate Data

on Attitude Change’, Sociological Research Online, 19(1), February, <http://www.socresonline.org.uk/19/1/16.html>.

• Chattoe-Brown, E. (2013) ‘Why Sociology Should Use Agent Based Modelling’, Sociological Research Online, 18(3), August, <http://www.socresonline.org.uk/18/3/3.html>.

• Chattoe-Brown, E. and Edmonds, B. (2013) ‘Using Agent-Based Simulation to Understand Social Networks’, Draft Paper.

• Chattoe, E. and Hamill, H. (2005) 'It's Not Who You Know - It's What You Know About People You Don't Know That Counts: Extending the Analysis of Crime Groups as Social Networks', British Journal of Criminology, 45(6), November, pp. 860-876.

• 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.

• 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.