Www.scid-project.orgInstitute for Advanced Studies, April 2011 The Social Complexity of Immigration and Diversity (SCID) Nick Shryane ([email protected])

www.scid-project.org

Institute for Advanced Studies, April 2011

The Social Complexity of Immigration and Diversity

(SCID)

Nick Shryane ([email protected])

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Funding

• Five-year programme grant from the EPSRC, part of their Complexity Science for the Real World initiative• “...to develop and apply the tools and

techniques of complexity science for tackling major societal research challenges.”


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Institutional partners

Theoretical Physics Group

Centre for Policy Modelling


Research team

• Complexity Science– The Centre for Policy Modelling (MMU)

• Researching complexity science and social simulation since 1992

– Bruce Edmonds– Ruth Meyer

– Theoretical Physics Group (UoM)• Part of the School of Physics and Astronomy

– Alan McKane– Tim Rogers

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Research team

• Social Science– The Institute for Social Change (UoM)

• Interdisciplinary social science research centre– Nick Crossley (Sociology DA)– Ed Fieldhouse (PI)– Laurence Lessard-Phillips– Yaojun Li– Nick Shryane– A. N. Other

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SCID aims

• New methods for complexity science– Integrating analytic and descriptive approaches

at different levels of abstraction• New social theory

– Linking micro- and macro-processes– Informing policy-makers


INTEGRATING MODELLING APPROACHES

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Example analytical model• ‘SIR’ epidemic model

(Kermack & McKendrick, 1927)

– Few parameters– Known causal structure– Clear interpretation– Simple, deterministic,

solvable dynamics– Where the simplifying

assumptions are reasonable, good fit to data, e.g. Measles

– Not so good for other diseases, e.g. HIV/AIDS

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Recovered

Infected

Susceptible

β

γ


Example analytical model

9Source: wikimedia commons

http://upload.wikimedia.org/wikipedia/commons/3/32/Sirsys-p9.png


Complex analytical model

• SIR model is non-linear, but does not show particularly complex dynamics

• Even very simple models, though, may exhibit seemingly random, chaotic dynamics

• Logistic map xn+1 = 4xn (1-xn)

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Complex analytical model

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Example descriptive model

• Alam, Meyer & Norling (2007) The impact of HIV/AIDS in the context of socioeconomic stressors: an evidence-driven approach. JASSS

• Agent-based model of spread and impact of HIV/AIDS in a South African region

• Dozens of parameters– Agents are born, age and die; have jobs, receive pensions and

grants; have friends, family, sexual partners; marry and divorce; have health statuses, food intake, may have HIV.

– Many rules govern the dynamics of all of the above.

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0

5

10

15

20

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0 30 60 90 120 150 180 210 240 270 300 330 360 390

AspirationEndorsementsHIV/AIDS prevalence

*** TICK 189 ***person-2 (female) DIED AT AGE 62 OF AIDSwas head of household-1person-2 had 10 friendsperson-2 had 29 relatives in 5 households (including own)person-7 (male, 36) becomes new head of household-1burialSociety-0 pays out 360.0 Rand to household-1 for the death of its member person-2household-0 contributes 20.0 to burial costs of related household-1household-2 contributes 20.0 to burial costs of related household-1household-3 contributes 80.0 to burial costs of related household-1household-23 contributes 879 to burial costs of related household-1household-19 contributes 10.0 to burial costs of neighbour household-1…*** TICK 190 ***household-1 pays 4217 for burial of person-2person-7 joins burialSociety-0

Death of a household head When Hilda died of AIDS, she was 62 years old. As a head of household, she was a member of a burial society, which paid out 360 Rand to her family.

This didn't cover the funeral costs of 4217 Rand at all. Relatives and neighbours attended the funeral and contributed to the costs in varying degrees, up to the sum of 1339 Rand. This left the household with costs of 2518 Rand to pay on their own.

Her 36-year-old son Honest followed her as the head of household. He decided to carry on membership in the burial society and joined it directly after the funeral.

Sexual network


Example descriptive model

• A data integration model– Combines qualitative and quantitative data from

many different sources into one model– Includes many different causal structures,

feedback loops, stochastic elements– Difficult to fully understand let alone

mathematically describe and analyse

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KISS vs. KIDS

• KISS (Keep it Simple, Stupid)– Models should be simple enough to understand and

check (rigour) – May omit critical aspects of the system of interest (lack

of relevance)• KIDS (Keep it Descriptive, Stupid)

– Models should capture the critical aspects of social interaction (relevance)

– They may be too complex to understand and thoroughly check (lack of rigour)

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KISS vs. KIDS

• KISS (Keep it Simple, Stupid)– Strong inferences possible about within-model

processes– Weak mapping to the thing being modelled

• E.g. Ideal gas law

• KIDS (Keep it Descriptive, Stupid) – Weak inferences about within-model processes– Clear mapping to the thing being modelled

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Chains of models

• SCID will develop “chains” of explicitly related models at different levels of abstraction, bridging the gap from evidence up to theory in a series of smaller, less ambitious jumps...

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Data-Integration Simulation Model

Micro-Evidence Macro-Data

Abstract Simulation Model 1

Abstract Simulation Model 2

SNA Model Analytic Model

Modelling strategy


Cross-disciplinary working

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Social Science Group (SSG) provides knowledge

(theories & empirical data) about a specific

social issue

Modellers attempt to integrate the information

provided by SSG by building complex

simulations

Outcomes from simulations are analysed

and simplified by theoretical physics

group


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Survey and collation of evidence at micro-level

Survey and collation of evidence at macro-level

D Simulation Building and Checking

D Simulation Criticism and Validation

D Simulation Analysis

Active Data Collection

Abstract Model Building and Checking

Analytic Development

Validity with D Simulation? Social Theory

Development Application to Policy Issues

Cross Case-Study Comparison and Generalisation

Descriptive Simulation Adequacy?

Progress in Theory?

Crucial Gaps in Evidence?

ISC

TPG

CPM

ISC

ISC

ISC

ISC

ISC

ISC

TPG

TPG

CPM

CPM

CPM CPM

Survey and analysis of existing formal models TPG

TPG

Statistical Data Preparation

ISC

Project flowchart= 1/3rd done


SOCIETAL RESEARCH CHALLENGE

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Immigration and diversity• UK has never seen higher

levels of immigration and ethnic diversity

• Rise of racist political parties and organisations

• But also rise in ethnic inter-marriage, tolerance and acceptance of diversity

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Immigration and diversity• These issues seem to be

rooted in beliefs about ethnicity and national identity, but can’t be divorced from– Social class– Education– Economic competition– Geographical and social

segregation– Institutional structures and

penalties

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Mapping macro and micro

Micro/ Individual data

Qualitative, behavioural, social psychological data

Theory, narrative accounts

Simulation

Social, economic surveys; Census Macro/ Social data


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Revealing causality and emergence

Micro/ Individual data

Qualitative, behavioural, social psychological data

Theory, narrative accounts

Simulation

Social, economic surveys; Census Macro/ Social data


Social science themes

1. Diversity, Homophily & Trust– What factors underlie and influence perceptions

and feelings of ‘us’ and ‘them’– How does this affect :

• the construction and operation of social networks• Norms of trust and reciprocity

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2. Political participation– How do diversity and immigration influence the

behaviour of • Individuals • Political parties

– Key outcomes• Electoral turnout• Party / issue choice

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3. Socio-economic integration– How do processes resulting from immigration

and diversity affect finding and gaining employment?• Multiple, overlapping social networks

– Homophily and migration limit network size and heterogeneity

– Effects of diversity on trust and tolerance under conditions of economic adversity

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THEME 2: ELECTORAL TURNOUT

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300-4.9% 5.9.9% 10-19.9% 20. 29.9% > 40%

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45

50

55

60

65

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Percentage turnout (weighted) by religion and % South Asian in street

HinduMuslimSikhNon-Asians

Percent South Asians (street)

Turn

out


Household effect on voting

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0 1 2 3 4 5 60.00

0.10

0.20

0.30

0.40

0.50

0.60

1 person household2 person household3 person household4 person household5 person household6 person household

Voters per household

Pro

por

tion

of h

ouse

hold

s

Assuming independentchoices



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0 1 2 3 4 5 60.00

0.10

0.20

0.30

0.40

0.50

0.60

1 person household2 person household3 person household4 person household5 person household6 person household

Voters per household

Pro

por

tion

of H

ous

ehol

ds

Source: Cutts & Fieldhouse 2009, Am J Pol Sci

Actual 2001 UK election data



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1 2 3 4 5 60.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

individual vote probif independent

Household size

Ind

ivid

ual

vot

e pr

oba

bil

ity

Source: Cutts & Fieldhouse 2009, Am J Pol Sci


Data integration model of voting

• Agent based simulation model– Agents have characteristics, e.g. age, party affiliation,

ethnicity, memory.– Agents have behaviours, e.g. voting, discussing politics,

making friends.• Households

– Every agent belongs to a household.• Networks

– Agents are linked to a varying number of other agents.

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Agent-Based Model (ABM) of Voting

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ABM dynamics

• Household dynamics– Households form (‘marriage’, ‘immigration’), change

(‘birth’, ‘death’, ‘kids moving out’) and dissolve (‘divorce’, ‘emigration’, ‘death’).

• Network dynamics– Friendships/associations form and dissolve, influenced

by• Characteristics in common (‘homophily’).• Activities in common.• Friends in common.

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ABM dynamics

• Voting dynamics– Every Nth timestep, agents may vote or abstain,

influenced by:• The voting behaviour of their cohabiters and friends

(‘conformity’, ‘norms’, ‘reaction’).• Their past history of voting (‘habit’).• Their interest in the outcome (‘rational-choice’).• Their desire to communicate / influence others (‘expression’)

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Agent-Based Model of Voting

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Agent-Based Model of Voting

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Narrative voting “stories”

• I voted for party X because it will put limits on immigration.

• “I voted for minor party Y because I wanted to send a message to those lying, cheating, fiddlers in Westminster.”

• I always vote – it’s part of who I am.• I didn’t vote – what’s the point?• Was there an election on?

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Analysing and simplifying

• After verifying and validating descriptive ABM, we will seek to “model the model”– which elements of model are ‘important’?– which bits are sensitive to variations in

parameter choices?• Aim to produce more abstract, targeted

ABMs of specific dynamics, and then analytic models

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EXAMPLE OF ANALYTICAL APPROACH

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Schelling model of segregation

• Schelling, (1969) – simple agent-based model– An equal number of agents of types A and B are placed

randomly on a grid, leaving a small fraction of spaces empty.

– An agent is unhappy if fewer than d of the eight squares surrounding it contain agents of the same type.

– Unhappy agents take turns to move themselves to vacant squares in which they would be happy.

• Even with low values of d, agents segregate themselves into homogeneous groups

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Time = 0d = 3



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Time = 10d = 3



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Time = 100d = 3


Analysing the Schelling model

• Hundreds of variants studied over the years, with different:– Network structures– Satisfaction functions – Movement functions– Initial conditions

• In nearly all cases, analysis is by simulation because the maths is too hard

• Can the key emergent properties be produced by a simplification of the model, one more amenable to mathematical analysis?

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Analysing the Schelling model• Simplified network

– Each agent has one neighbour

– No vacancies

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• At each timestep one of two things can happen• Segregate, with probability a• Integrate, with probability b


Analysing the Schelling model• Comparing simplified analytical model with four

variants of the Schelling model

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Timestep (x) against Interface density (y).

Interface density = fraction of A-B edges.

Red lines show analytical model. Black dots show simulations.


We’d love to hear from you

• Project website– http://www.scid-project.org

• Manchester complexity discussion group– http://manchester-complexity.blogspot.com

• One-day workshop– Complexity of evolutionary processes in biology and the

behavioural sciences– University of Manchester, 13th June 2011– http://bit.ly/fCtzzs

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Documents

Www.scid-project.orgInstitute for Advanced Studies, April 2011 The Social Complexity of Immigration and Diversity (SCID) Nick Shryane ([email protected])