www.scid-project.org the social complexity of immigration and diversity 1
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www.scid-project.org
The Social Complexity of Immigration and Diversity
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Institutional partners
Theoretical Physics Group
Centre for Policy Modelling
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Researchers
• Nick Crossley• Bruce Edmonds• Edward Fieldhouse • Laurence Lessard-
Phillips • Yaojun Li
• Alan McKane• Ruth Meyer• Tim Rogers• Nick Shryane• Huw Vasey
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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
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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
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Modelling
• SCID will take modelling approaches from complexity science and apply them to social science issues
• “Modelling” means different things to different people
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Modelling
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Modelling
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Modelling
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• “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
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Modelling
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• 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
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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
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Schelling model
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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
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Schelling model of segregation
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Turn = 0d = 3
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Schelling model of segregation
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Turn = 10d = 3
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Schelling model of segregation
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Turn = 100d = 3
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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
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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
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Boids
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“flock of birds with Blender 2.5 Boid physics”. http://www.youtube.com/watch?v=pIvCgUnEb8A
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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
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ELECTORAL PARTICIPATIONSCID theme 1
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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
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230-4.9% 5.9.9% 10-19.9% 20. 29.9% > 40%
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Percentage turnout (weighted) by religion and % South Asian in street
HinduMuslimSikhNon-Asians
Percent South Asians (street)
Turn
out
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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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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
ous
ehol
ds
Source: Cutts & Fieldhouse 2009, Am J Pol Sci
Actual 2001 UK election data
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Household effect on voting
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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
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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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Agent-Based Model (ABM) of Voting
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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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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
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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)
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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
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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
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