www.scid-project.org the social complexity of immigration and diversity 1

www.scid-project.org

The Social Complexity of Immigration and Diversity


1


2

Institutional partners

Theoretical Physics Group

Centre for Policy Modelling


Researchers

• Nick Crossley• Bruce Edmonds• Edward Fieldhouse • Laurence Lessard-

Phillips • Yaojun Li

• Alan McKane• Ruth Meyer• Tim Rogers• Nick Shryane• Huw Vasey

3


4

SCID aims

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

approaches at different levels of abstraction• New social theory

– Use the new modelling approaches to• Link micro and macro processes and theory• Inform policy


5

SCID domains

• Immigration and diversity in relation to– Fundamental social processes

• Homophily, trust– Socio-economic processes

• Education, employment– Socio-political processes

• Political participation, effects of parties


Modelling

• SCID will take modelling approaches from complexity science and apply them to social science issues

• “Modelling” means different things to different people

6


Modelling

7


Modelling

8


Modelling

9

• “Traditional” social science approach:– Observe data, then try to infer the causal process

that generated it• Statistical modelling

(this also applies to qualitative analysis)– Problematic – we’re actually modelling conditional

probabilities/statistical distributions; many causal models might ‘fit’ a given set of data

– Research design can help – experiments vs. observation


Modelling

10

• Process modelling approach– Generate data, using a hypothesised model of

the causal process• Agent-based models & differential equations

– Problematic – need to be able to describe in detail the micro-level social processes

– Trade-offs required; rigour vs. relevance, simple & abstract vs. complicated & realistic


Example of a simple agent-based model

• Schelling (1969) – residential segregation– Take a 2D grid and randomly populate each square with

either a red ‘agent’, a blue ‘agent’, or a vacant space

11


Schelling model

12


Schelling model

• Schelling (1969) – residential segregation– Take a 2D grid and randomly populate each square with

either a red ‘agent’, a blue ‘agent’, or a vacant space– Social process:

• Agents are ‘happy’ if at least d of their eight neighbours are of the same colour

• Each ‘turn’ an agent is picked at random, and, if unhappy, will ‘move’ to a vacant square

– Even with low values of d, the grid quickly becomes spatially segregated

13


Schelling model of segregation

14

Turn = 0d = 3



15

Turn = 10d = 3



16

Turn = 100d = 3


Schelling model

• This segregation model is a model of how a micro process can become amplified at the macro level– It’s a model of an ‘idea’, not a model of the real

world• Even this simple model, though, shows how

ABMs can allow things like agent-agent interactions, to model processes such as social influence

17


Another ABM

• ‘Boids’– Agents represent animals that flock/school– Each agent follows simple rules:

• Stay close to nearby agents but avoid collisions• Travel in the direction of nearby agents

– Without central control, macro ‘flocking’ emerges as a consequence of the micro rules

18


Boids

19

“flock of birds with Blender 2.5 Boid physics”. http://www.youtube.com/watch?v=pIvCgUnEb8A


Boids

• The micro rules won’t allow one to predict the exact behaviours of individual real-world birds

• But they do allow insight into the sort of processes that can produce recognisably realistic real-world macro patterns

20


ELECTORAL PARTICIPATIONSCID theme 1

21


Social processes in voting

• Influences based on social factors seem to be at work in the decision to vote or not

• SCID will try to specify and model the way these processes operate and influence the decision to vote

22


230-4.9% 5.9.9% 10-19.9% 20. 29.9% > 40%

40

45

50

55

60

65

70

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

HinduMuslimSikhNon-Asians

Percent South Asians (street)

Turn

out


Household effect on voting

24

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



25

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



26

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.

27


Agent-Based Model (ABM) of Voting

28


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.

29


Agent-Based Model (ABM) of Voting

30


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’)

31


Agent-Based Model of Voting

32


Agent-Based Model of Voting

33


34


Data hungry

• With so much in the model, we need lots of data to be able to– Micro level - make sensible choices for model

parameters, processes and interactions– Macro level – to evaluate whether the model is

producing recognisably realistic behaviour• Ideally, data not just on individuals, but on

networks – who interacts with whom?– Nick Crossley to talk more on this next

35


Analysing and simplifying

• The ABM of voting will be very complex• Hopefully it will be realistic (relevance) but

it will be hard to fully understand (lack of rigour)

• We will therefore seek to “model the model”• Produce a simpler (less relevant) but more

analytically tractable dynamical model (rigour)

36


37

Data-Integration Simulation Model

Micro-Evidence Macro-Data

Abstract Simulation Model 1

Abstract Simulation Model 2

SNA Model Analytic Model

Modelling strategy


Thanks for listening

• 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

38

www.scid-project.org the social complexity of immigration and diversity 1

Documents

policy slide

observation slide

policy modelling slide

model processes

segregation model

schelling model schelling

simple model

complicated realistic