Ethnic diversity, density and their
consequences on political
participation:
An agent-based simulation
Laurence Lessard-Phillips, Institute for Social Change, University of Manchester
Nick Crossley, Department of Sociology, University of Manchester
Bruce Edmonds, Centre for Policy Modelling, Manchester Metropolitan University
Ed Fieldhouse, Institute for Social Change, University of Manchester
Yaojun Li, Institute for Social Change, University of Manchester
Ruth Meyer, Centre for Policy Modelling, Manchester Metropolitan University
Nick Shryane, Institute for Social Change, University of Manchester
Background
• Ethnic minorities are (slowly) becoming a bigger part of
the UK’s national population
• ~5.6% (1991) – ~7.9% (2001) – ~ 14-18% (2051)
• Engagement of ethnic minorities in ‘conventional
politics’, and its main determinants, is an interesting
topic of enquiry
– Ethnic minorities becoming increasingly important
segment of the electorate
• Especially given their location, density and diversity in the UK
– Link to socio-political integration/incorporation and other
related issues (representation, etc.)
– UK case peculiar given voting right of Commonwealth
citizens
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Theorising the role of ethnic diversity
and density on turnout
• Ethnic diversity (based on Fieldhouse and Cutts, 2008)
– Group conflict theory: diversity leading to higher levels of
conflict and hence mobilisation of the population, leading
to higher levels of turnout
• Can also have depressing effect on turnout
– Economic resources theory: highly diverse communities
have weaker mobilising effects and higher barriers to
participation due to lack of resources
– Racial diversity thesis: high levels of diversity display more
inequalities and hence lower participation
– Social capital theory (?): link between diversity and levels
of interpersonal/generalised trust
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Theorising the role of ethnic diversity
and density on turnout
• Ethnic density (based on Fieldhouse and Cutts, 2008)
– Social capital theory: group concentration leading to
higher levels of bonding capital, connectedness and
networks, which generate higher levels of political
mobilisation and hence turnout
– Ethnic community model: higher levels of group
consciousness/awareness leading to higher levels of
turnout
• May also cause alienation
– Relative deprivation theory: higher levels of deprivation
may lead to increased levels of alienation and, in turn, to
decreased turnout
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Political participation of ethnic
minorities in the UK: Existing evidence
• Research-based evidence has found divergence in the
turnout rates of various ethnic minority groups, with
(some) stabilisation of turnout rates over time
– Asian turnout > than turnout for non-Asians
• Differentiation within Asian groups
– Black Caribbean and African groups: lower levels amongst ethnic
minority groups
– Yet more recent evidence seems to contradict these claims
• Somehow difficult to disentangle ethnic group effects from
other effects such as age, socio-economic status, etc.
• No clear agreement as to the impact of density on ethnic
minority turnout
– Data/methods issues
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But…
• We are still a long way from understanding this
issue without integrating the varied accounts that
exist into a unified model that captures the
complexity of processes that might be at play
– Linking the micro to the macro
• One way in which you can try to do this is via
agent-based simulation
– Underutilised method informed by
data/evidence/theory in the social sciences that can
link multiple and multi-faceted influential processes
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Agent-based simulation
What is it?
What happens?
• Computational description of a
given process
• Entities in simulation are
decided up
• Behavioural rules for each
agent specified
– Not usually analytically tractable
• More context-dependent…
– … but assumptions are much less
drastic
• Detail of unfolding processes
accessible
– more criticisable (including by
non-experts)
• Used to explore inherent
possibilities
• Validatable by data, opinion,
narrative ...
– Often very complex
– e.g. sets of rules like: if this has
happened then do this
• Repeatedly evaluated in
parallel to see what happens
• Outcomes are inspected,
graphed, pictured, measured
and interpreted in different
ways
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Dilemmas using this approach
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)
• Strong inferences possible
about within-model
processes
• Weak mapping to the thing
being modelled
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)
• Weak inferences about
within-model processes
• Clear mapping to the thing
being modelled
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• Demographic, psychological
Individual behaviours
Memory of events
External shocks
Generic population
dynamics
• Birth, death, movement
Social networks
• Households, spatial,
political discussion
networks, etc.
Output
Individual
characteristics
Model layers
Input
Agent-based simulation model of
voting behaviour: ‘the’ model
Characteristics of
system
Aggregate outcomes
(fed back to model
layers)
Influence
• Vertical and horizontal
socialisation and
mobilisation
Voting decision
• Intention vs. action
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Encapsulating narrative stories of
voting in the simulation
• Based on collected evidence, we set out stories
according to which our agents act
– E.g.
• 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?
• These narratives also take into account the
characteristics of the agents, their dynamics, and
other influences
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Using the Simulation: the context
• Run simulations that correspond to different settings
or behavioural hypotheses
• See how it affects outcomes, e.g.:
– Ethnic minority turnout
– Ethnic majority turnout
• Results do not predict, but reveal possible emergent
outcomes
• More importantly
– Raises new questions and gaps in knowledge
– An “in vitro” exploration of some of the complex
relationships between factors that can occur
– Suggests new hypotheses (or refinements on old hypotheses)
in an explicit and demonstrated form
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RESULTS FROM PRELIMINARY
“PROOF OF CONCEPT” VERSION
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Early, “Proof of Concept” Version of
the Model
• Simulation model still being
developed
• Validation stage yet to begin in
earnest
• Demonstrated with 4 different
scenarios
• Only difference in minorities are
(a) those inherent in the data we
used to initialise the model and
(b) the homophily effect of agents
tending to make social links with
similar age/ethnicity/politics
• Model was run 25 times
• Average turnout in minority and
majority is then measured
Low
immigration,
High majority
population
High
immigration,
High majority
population
Low
immigration,
Low majority
population
High
immigration,
Low majority
population
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70%
Turnout by majority, minority (1%IR)
Prop.
Maj.
0.65 - Average of turnout-maj
65%
0.65 - Average of turnout-min
0.95 - Average of turnout-maj
0.95 - Average of turnout-min
Turnout (proportion)
60%
55%
50%
45%
40%
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28
30
32
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Simulation Tick
40
42
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70%
Turnout by majority, minority (5%IR)
Prop.
Maj.
0.65 - Average of turnout-maj
65%
0.65 - Average of turnout-min
0.95 - Average of turnout-maj
0.95 - Average of turnout-min
Turnout (proportion)
60%
55%
50%
45%
40%
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14
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20
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24
26
28
30
32
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Simulation Tick
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58
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62
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Conclusion
• ABS links micro- and macro-level processes in an
explicit manner, enabling the exploration of the effects
of how individuals behave and relate to each other at
the aggregate level
• Still in the process of developing the model
– Gathering evidence
– Making linkages
– Updating narratives
• Special focus on extensive exploration of dynamic
social networks (e.g., household-level influences)
• Future development of narrative  model
“translations”
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http://www.scid-project.org
THANK YOU!
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What it looks like…
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What it looks like…
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