What is simulation and what use is it?

Edmund ChattoeDepartment of Sociology

University of OxfordManor Road, Oxford, OX1 3UQ

edmund.chattoe@sociology.ox.ac.ukhttp://www.sociology.ox.ac.uk/people/chattoe.html

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

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

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

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)

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”

Sample Initialisation

50% similarity

1500 agents and

1000 vacant sites

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!

Type A "Error": Non Xenophobic Clusters

80% similarity

Type B "Error": Xenophobic Non Clusters

50.4% similar: stopped after 50

periods

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

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.

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?)

Two types: No love lost

60% same type

preference

Three types: No love lost

60% same type

preference

Three types: The colonel’s lady ... reds and

greens both consider

each other as acceptable “company”

ISSUE OF “EQUIVALENCE

CLASSES”

The GT Box

QUALITATIVE

QUANTITATIVE

FALSIFICATION

RESEARCHDESIGN

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.

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

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)

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

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”.

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?

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.

Example: Trends in drug use (Caulkins)

LIGHTUSERS

HEAVYUSERS

NONUSERS

a g

b

L(t+1)=(1-a-b)L(t)+I(t), H(t+1)=(1-g)H(t)+bL(t)

Initiation

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?)

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

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)

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?

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!

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_and_Drugs/Reports_and_Publications/DrugsFutures2025/Index.htm>