A Computational Model of Immigration and Diversity

Computational Modelling of Immigration and Diversity, Bruce Edmonds, CrimeEx Kickoff Meeting, Rome, Jan 2012, slide 1

A Computational Model of Immigration and Diversity

Bruce EdmondsCentre for Policy Modelling,

Manchester Metropolitan University


A €3M, 5-year UK project funded by the

Under their “Complexity in the Real World” Initiative

Institute for Social Change &Theoretical Physics Group,University of ManchesterCentre for Policy Modelling,

Manchester Metropolitan University


SCID Researchers

• UoM, Institute for Social Change:Ed FieldhouseNick ShryaneNick CrossleyYaojun LiLaurence Lessard-PhillipsHuw Vasey

• MMU, Centre for Policy ModellingBruce EdmondsRuth MeyerStefano Picascia

• UoM, Dept. for Theoretical PhysicsAlan McKaneTim Rogers


Where this fits in FuturICT

• An example of Complexity Science, Social Sciences and ICT combining to model social processes

• Specifically to make Complexity Science useful to the other

• Also, to road-test ways of increasing innovation within the Social Sciences

• And (when further developed) ideal for exploiting Big Data sources from mobile devices etc.

• A demonstration of the kind of approach that might be used for simulating Crime etc.


Interfacing Complexity and Social Science Approaches • Physics and Social Science have very different

languages, cultures and approaches• We would like the power of approaches and tools of

complexity physics but appropriately applied and not in “brave leaps” of abstraction which lose relevance to the observed

• (In particular the way that much work in economics involves unrealistic assumptions and a lack of relevance to what is observed)

• Thus in SCID simulations, albeit complex ones, will be the common interface and provided a common reference


In Vitro vs In Vivo

• In biology there is a well established distinction between what happens in the test tube (in vitro) and what happens in the cell (in vivo)

• In vitro is an artificially constrained situation where some of the complex interactions can be worked out…

• ..but that does not mean that what happens in vitro will occur in vivo, since processes not present in vitro can overwhelm or simply change those worked out in vitro

• One can (weakly) detect clues to what factors might be influencing others in vivo but the processes are too complex to be distinguished without in vitro experiments


Possibilistic vs Probibilistic

• The idea is to map out some of the possible social processes that may happen

• Including ones one would not have thought of or ones that have already happened

• The global coupling of context-dependent behaviours in society make projecting probabilities problematic

• Increases understanding of why processes (such as the spread of a new racket) might happen and the conditions that foster them

• Good for analysing risk – how a prediction might go wrong• Can be used for designing early-warning indicators of newly

emergent trends• Complementary to statistical models


Unravelling the Micro-Macro Link

Micro/ Individual data Qualitative, behavioural, social psychological data

Theory, narrative accounts

Social, economic surveys; Census Macro/ Social data

Simulation

Upw

ard

caus

atio

n –

emer

genc

e

Dow

nward causation –

imm

ergence


KISS vs. KIDS

• KISS: Models that are simple enough to understand and check (rigour) are difficult to directly relate to both macro data and micro evidence (lack of relevance)

• KIDS: Models that capture the critical aspects of social interaction (relevance) will be too complex and slow to understand and thoroughly check (lack of rigour)

• But we need both rigour and relevance• Mature science connects empirical fit and explanation

from micro-level (explanatory and phenomenological models)


KISS vs. KIDS as a search strategy

Simplest Possible

More Complex in Aspect 2

etc.

More Complex in Aspect 1

KISS

KIDS


The Modelling Approach

Data-Integration Simulation Model

Micro-Evidence Macro-Data

Abstract Simulation Model 1

Abstract Simulation Model 2

SNA Model Analytic Model


Aims and Objectives of a DIM

• To develop a simulation that integrates as much as possible of the relevant available evidence, both qualitative and statistical (a Data-Integration Model – a DIM)

• Regardless of how complex this makes it• A description of a specified kind of situation (not

a general theory) that represents the evidence in a single, consistent and dynamic simulation

• This simulation is then a fixed and formal target for later analysis and abstraction


But why not just jump straight to simple models?• There are many possible models and you don’t know

why to choose one rather than another, this method provides the underlying reasons

• Much social behaviour is context-specific, and this approach allows one to check whether a particular simple model holds when background features/assumptions change

• The chain of reference to the evidence is explicit, allowing one to trace their effect and possibly better criticise/improve the model

• This approach facilitates the mapping onto qualitative stories/evidence


An overview of model structureUnderlying Data from Surveys about Population Composition etc.

Demographics of people in households (both native and immigrant)

Homophily effects the social network and membership of organisations etc.

Social network effects how individuals influence each other, reinforcing and/or changing existing norms/opinions

This effect the behaviours of individuals, which can then be extracted from the simulation as model results and compared with evidence etc.


Basic Elements

• 2D grid of locations each of which has either a: household, work place, school, activity 1 centre, activity 2 centre, or empty

• People in household going through lifecycle according to the timescale: 1945-2010 (birth, death, migration, partnering, separation, moving out. etc.)

• Social network made of: intra-household links, shared activity membership (schools, work, religion, etc.), “friendship” links

• Influence occurs over the social network contingent on the state of those involved


Population Model

• Agents are in households: parents, children etc. of different ages in one location

• Initialised from a sample of 1992 BHPS• Agents are born, age, make partnerships

have children, move house, separate, die• UK-based moving in/out of region, as well

as international immigration/emigration• Rates of all the above estimated from

available statistics


Agent Characteristics

• Age, Ethnicity, location, children, parent, partner, political leaning, date last moved, etc.

• The activities it participates in• Its social connections• Plus a memory of facts, e.g.:

– “talked about politics with” agent324 blue 1993– “got desired result from voting” red 1997– “I am a voter” 2003– “pissed off with my own party” 2004


Immigration and Movement

• No special rules for different ethnicities or kinds of people (e.g. class)

• Rather composition (household size, income, class, education, civic involvement etc.) derived from survey data

• Class and ethnicity come into effect via homophily – people have a tendency to make friends with those similar to themselves (including age, ethnicity, education level, class, location etc.)

• This effects the social networks that develop• Which, in turn, effect mutual influence, communication

and the spread of social norms


Activities Model

• As well as households there are activities: schools, places of work, and (currently 2) kinds of activity (church and canoe clubs)

• Kids (4-18) attend one of 2 local schools• Those employed (from 16-65) attend a

place of work randomly• Activities are joined probabilistically, with

choice related to homophily (similarity to existing members)


Social Network Model

• A “connection” is a relationship where a conversation about politics might occur (but only if the participants are inclined/receptive)

• All members of a household are connected; when someone moves out there is a chance of these being dropped as connections

• There is a probability of people attending the same activity to be connected (chance varying according to similarity)

• There is a chance of spatial neighbours who are most similar being connected

• There is a chance of a “Friend of a Friend” becoming a connection

• Connections can be dropped


Communication and its Effects

• Social norms transmitted in pimarily within households (if not contradictory)

• Interest in politics transmitted via contact network by interested/involved agents with those who are receptive

• Some discussants may be more influential than others• Bias in terms of held beliefs and norms may evolve

due to coherence / incoherence in the messages from others

• Interest & biases might convert to action if the situation the agent is in is appropriate


Approach

• Learning process with social scientists, consisting of iterations of:– Rapid prototyping of simulations– Critique and response from social scientists

base on evidence• Until the social scientists start becoming (in

a small way) informal programmers• Thus prototype is in NetLogo for ease of

access and rapidity of adaption• “Production” version will be in Java/Repast


Demonstration Run

Parametersand

Controls

Pseudo-narrative log of eventshappening to a single agent

SimpleStatistics

concerningOutcomes

Pictureof World

IndicativeGraphs

andHistograms


Two Contrasting Sets of Runs

“Inner City” set, 20 runs• death-mult 1.2• immigration-rate 0.035• density 0.9• forget-mult 2.28• drop-friend-prob 0.3• prob-move-near 0.2• majority-prop 0.6• drop-activity-prob 0.15• int-immigration-rate 0.01• prob-partner 0.35• move-prob-mult 0.7• init-move-prob 2.5• emmigration-rate 0.055• birth-mult 1

“Country” set, 20 runs• death-mult 1.5• immigration-rate 0.005• density 0.32• forget-mult 0.56• drop-friend-prob 0.18• prob-move-near 0.2• majority-prop 0.95• drop-activity-prob 0.065• int-immigration-rate 0.015• prob-partner 0.17• move-prob-mult 0.2• init-move-prob 2.5• emmigration-rate 0.15• birth-mult 0.6


Population Makeup

“Inner City” set, 20 runs “Country” set, 20 runs

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Same Ethnicity over Links


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Example Development of Social

• Three “snapshots” of the social network from a single run of the “Inner City” version

• Darker links are within-household, lighter are other social links

• Each link indicates a relationship where if the agents are so minded they might discuss or otherwise influence each other concerning politics, voting etc.

• The issue about initialisation is clearly visible here


Social Network at 1950






Effect of Immigration Rate on Voting

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Conclusions

• Statistical models give little information about social causation within the context of individuals

• But crime cannot be properly understood without the social processes that facilitate or act to reduce it

• Crime is not treated as a special social phenomena, but just one kind of behaviour that might arise

• A data driven approach to these social process might enable us to understand the prevalence (or relative absence!) or crime

• Such simulations are data hungry, so are ideal for using detailed person-by-person data as input

• Context-dependent data-mining techniques could well be used in both input data as well as for understanding outputs

• This will involve a lot of work, and probably a multi-model approach stretching from cognitive models up to social trends in a chain of models…

• …but it is possible!


The End

Documents

A Computational Model of Immigration and Diversity