Paper presented at Seminar Series, Methodology Institute, London School of Economics, 27 February 2006.

1. What is simulation and what
use is it?
Edmund Chattoe
Department of Sociology
University of Oxford
Manor Road, Oxford, OX1 3UQ
edmund.chattoe@sociology.ox.ac.uk
http://www.sociology.ox.ac.uk/people/chattoe.html

2. Plan of the talk
• Clearing the ground
• Types of theorising
• A simple example with methodological
implications
• A more realistic example and contribution
to “live” sociological debates
• Styles of simulation: A brief comparison
• Conclusions

3. Simulation: A confusing term
• Gaming or role playing: “Simulated” United Nations
for schools
• Instrumental and descriptive simulation: Dealing with
messy calculus (Buffon)
• A realist/empiricist approach to social theory:
Nothing to do with Baudrillard, PoMo and simulacra
• A third type of representation for social processes:
neither a “mathematical” model, nor a narrative but a
computer programme
• Simulation types: agent based, system dynamics

4. Mathematical theory: Lotka-Volterra
• Let us assume that the prey in our model are rabbits and
that the predators are foxes. If we let R(t) and F(t)
represent the number of rabbits and foxes, respectively,
that are alive at time t, then the Lotka-Volterra model is:
• dR/dt = a*R - b*R*F
• dF/dt = e*b*R*F - c*F
• a is the natural growth rate of rabbits absent predation
• c is the natural death rate of foxes absent food (rabbits)
• b is the death rate per encounter of rabbits due to
predation
• e is the efficiency of turning predated rabbits into foxes

5. Narrative theory: Marx
• But with the development of industry, the proletariat not only increases
in number; it becomes concentrated in greater masses, its strength grows,
and it feels that strength more. The various interests and conditions of
life within the ranks of the proletariat are more and more equalised, in
proportion as machinery obliterates all distinctions of labour, and nearly
everywhere reduces wages to the same low level. The growing
competition among the bourgeois, and the resulting commercial crises,
make the wages of the workers ever more fluctuating. The increasing
improvement of machinery, ever more rapidly developing, makes their
livelihood more and more precarious; the collisions between individual
workmen and individual bourgeois take more and more the character of
collisions between two classes. Thereupon, the workers begin to form
combinations (trade unions) against the bourgeois; they club together in
order to keep up the rate of wages; they found permanent associations in
order to make provision beforehand for these occasional revolts. Here
and there, the contest breaks out into riots. (Communist Manifesto)

6. Simulated theory: Schelling example
• Three state regular grid (red agent, green agent or
vacant site)
• Red and green agents have two psychological
states (“satisfied” and “dis-satisfied”) based on an
innate and fixed “preference” for sharing the type
of their immediate neighbours
• If agent is satisfied, it stays still. If dis-satisfied,
it moves to a randomly selected vacant site
• Randomly ordered updating for whole agent
population determines each simulated “period”

7. Sample Initialisation
50% similarity
1500
agents and
1000
vacant
sites

8. Two questions
• How xenophobic do agents have to be to
produce segregation? (A percentage for the
same neighbour requirement at or above
which recognisable clustering results.)
• How does the type of clustering change for
total xenophobia? (100% same neighbour
requirement.)
• DON’T SPOIL IT IF YOU ALREADY
KNOW THE ANSWERS!

9. Type A "Error": Non Xenophobic Clusters
80% similarity

10. Type B "Error": Xenophobic Non Clusters
50.4% similar:
stopped after 50
periods

11. Simulated theory: Schelling again• to find-new-spot
• rt random 360
• fd random 10
• if any other-turtles-here
• [ find-new-spot ] ;; keep going until we find an unoccupied patch
• end
• to update-patches
• ask patches [
• ;; in next two lines, we use "neighbors" to test the eight patches surrounding the current patch
• set reds-nearby count neighbors with [any turtles-here with [color = red]]
• set greens-nearby count neighbors with [any turtles-here with [color = green]]
• set total-nearby reds-nearby + greens-nearby ]
• end
• to update-turtles
• ask turtles [
• if color = red
• [ set happy? reds-nearby >= ( %-similar-wanted * total-nearby / 100 ) ]
• if color = green
• [ set happy? greens-nearby >= ( %-similar-wanted * total-nearby / 100 ) ] ]
• end

12. First two uses of simulation
• Simulation as “complexoscope”: Just as a microscope
allows us to see things too small for the naked eye, a
simulation allow us to understand things too complex for
the “bare” brain. As the Schelling model shows, even quite
simple systems are complex.
• Simulation as theory building tool: Even the simple
Schelling model captures and solidifies the potentially
abstruse notion of structuration. (A simulation is worth a
thousand words?) In choosing, agents determine the
“environment” which then influences their choice: “white
flight”, tipping points.

13. Simulation and data: A distinctive relationship
• What would we need to do to make the
Schelling model more realistic?
• First: How do people classify neighbours
and make consequent relocation decisions?
• Second: How “similar” are the clusters
produced by the simulation model and those
observed in real urban settings?
• A combination of “traditional” qualitative
and quantitative data (plus novel methods?)

14. Two types: No love lost
60% same
type
preference

15. Three types: No love lost
60% same
type
preference

16. Three types: The colonel’s lady ...
reds and
greens both
consider
each other as
acceptable
“company”
ISSUE OF
“EQUIVALENCE
CLASSES”

17. The GT Box
QUALITATIVE
QUANTITATIVE
FALSIFICATION
RESEARCH
DESIGN

18. Revisiting types of theory
• Statistical models (found in quantitative research) make
the comparison between model and real system at the
“aggregate” level but seldom specify an explicit micro
mechanism generating the observed pattern. To my
knowledge, no such mechanism has been independently
tested even where proposed.
• Narrative theories (found in ethnography and “pure” social
theory) describe individual states and interactions but
ethnography seldom even attempts to generalise nowadays
and simulations of social theories often don’t generate the
outcomes hypothesised (Friedman example) because of
complexity. Formalising theories is another interesting (if
minority) use for simulation.

19. Case study: The strength of strict churches
• Begins with Kelley and a potentially
counter-intuitive claim: The way to
maintain a church is to ask more from
adherents not less
• Statistical debate about whether this is
true.
• Problems with causality, contributions that
are hard to measure (differential
association) and explanation
• Iannacconne RCT model of strict churches

20. The Iannaccone explanation
• Worshippers face a time/money allocation
problem between secular and religious activities
• Religion is a club good
• This creates a free-rider problem
• One solution is prohibiting secular activities
• This often creates an enforcement problem
• A solution is to effectively raise costs of
prohibited activities using apparently “irrational”
practices (dietary restrictions, dress codes)

21. Unpacking this argument
• Although intended as a RCT account of
worshippers, this is also an interesting
functionalist account of church dynamics
• Churches that demand, prohibit and enforce
simultaneously will thrive, others will not
(based on income/membership constraints)
• Iannaccone proves an equilibrium result
assuming unbounded rationality and perfect
information

22. Building a simulation
• Objection 1: Agents cannot choose over whole
space of allocations so have them compare only
pairs of allocations at any instant.
• Objection 2: There is social structure not global
knowledge. Comparators come (differentially)
from self (choice), deliberate recruitment to new
churches, own church members (social imitation)
or other church members (social learning)
• Objection 3: The population of churches is
dynamic with new creeds being born and
churches with no members or income “dying”.

23. Figure 1. The Effect of Strictness on Church Survival
( N=12 2 aggregat ed from 4 simulat ion runs)
0
1
2
3
4
5
0 100 200 300 400 500 600
Church Lifet ime ( Simulat ed Periods)

24. Figure 2. The Trend Effect on Church Survival of Religious
Pract ices Raising t he Effect ive Cost s of Proscribed
Secular Act ivit ies
0
50
100
150
200
250
300
0 1 2 3 4 5 6
Fact or by which Pract ices Raised Effect ive Cost s
0 Proscript ions
1 Proscript ions
2 Proscript ions
3 Proscript ions
4 Proscript ions

25. Figure 3. Membership Hist ory ( 5 Randomly Selected
Simulat ed Churches)
0
10
20
30
40
50
60
70
80
90
100
Church Age ( Simulat ed Periods)
Church 1
Church 2
Church 3
Church 4
Church 5

26. Interesting implications
• Under “more realistic” assumptions the Iannaccone
result breaks down.
• What kind of data do we need to build better
simulations? Meta-analysis of existing
ethnography, theory driven comparative studies of
successful and unsuccessful churches, different
styles of “quantitative” data (contact diaries?)
• Can we use falsification based on more than one
“dimension” of data: longitudinal church
membership as well as cross sectional?
• Is functionalism coherent after all?

27. Types of simulation
• Instrumental: Numerical integration,
probability distributions for hard functions
• Microsimulation, system dynamics: Based
on assumptions of underlying stability in
“transition probabilities”
• Agent based simulation: Grounds out all
behaviour at the individual level. The only
“parameters” are those used by agents
themselves in their mental models.

28. Example: Trends in drug use (Caulkins)
LIGHT
USERS
HEAVY
USERS
NON
USERS
a g
b
L(t+1)=(1-a-b)L(t)+I(t), H(t+1)=(1-g)H(t)+bL(t)
Initiation

29. Issues
• Presumed constancy of a, b, g, category
boundaries of use.
• Non explanation of I(t).
• If model design criterion is curve fitting, is
this explanation or data mining? (Should
there be a distinctive box for “never used”
and if so, where do we stop with building
boxes based on something other than best
fit?)

30. DrugChat Model
• DTI Foresight Replication of DrugTalk
(Agar)
• Explicit (but simple) representation of agent
social networks
• Different types distinguished by behaviour
(“partying”) and evaluation (credibility of
reported drug attitudes) rather than use level
• No parameter constancy assumptions just
attributes and states at agent level

31. Figure 2 . Correlat ion bet ween Risk Attit ude and Final
Drug Use Stat us ( Whole Population)
0
0.5
1
1.5
2
2.5
0 20 40 60 80 100
Init ial Risk At t it ude ( 0 -1 0 0 )

32. Figure 3. Flow Rat es Between Drug User Statuses (Whole
Populat ion)
-0 .01 5
-0 .0 1
-0 .00 5
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
T im e
Non User t o User
User t o Addict

33. Simulation and data
• Having a new method calls attention to the
need for new data/theory (Maslow):
dynamic decision mechanisms,
unstructured choices, large scale network
structure, generic network properties
• It also shapes the collection and use of data
(comparative statistics and qualitative
similarity measures rather than model fit,
“theory building” ethnography, systematic
analysis of published research)

34. Institutional issues
• Protective expectations: “Substantiveness”,
“rigour” and other exclusionary codes.
• Size of simulator population: Quality as a
function of investment.
• Length of existence of field: How to tell
exuberance from sloppiness?
• Lack of infrastructure: professional training,
journals, conferences and so on
• Raising standards internally (replication, data
protocols, systematic literature review)
• The high frontier?

35. Conclusions
• Simulation as complexoscope: No social reality
needed
• Simulation as theory exploration tool: Need only
a stated theory and wise intuitions (Iannaccone)
• Simulation as both “generative” and “falsifiable”
social science: Need real micro and macro data
(Schelling)
• Simulation as a tool for “detheorising” theory
(removing parameters and implicit assumptions)
and developing interdisciplinary programmes of
progressive research: Watch this space, I hope!

36. Now read on
• Journal of Artificial Societies and Social Simulation
(JASSS): http://jasss.soc.surrey.ac.uk/JASSS.html
• NetLogo: http://ccl.northwestern.edu/netlogo/
• Gilbert, Nigel and Troitzsch, Klaus G. (2005) Simulation
for the Social Scientist (Open University Press).
• BJS: ‘Using Simulation to Develop and Test Functionalist
Explanations: A Case Study of Dynamic Church
Membership’, http://users.ox.ac.uk/~econec/bjs-1.doc
• DTI:
<http://www.foresight.gov.uk/Brain_Science_Addiction_a
nd_Drugs/Reports_and_Publications/DrugsFutures2025/In
dex.htm>