Using a Data-Integration Model to Stage Abstraction in Voter Turnout

Using a Data-Integration Model to Stage Abstraction in Voter Turnout

Bruce Edmonds, et. alCentre for Policy Modelling

Manchester Metropolitan University

Social Complexity of Immigration and Diversity

• A 5 year EPSRC-funded project between:• University of Manchester

– Institute for Social Change• Ed Fieldhouse, Nick Shryane, Nick Crossely, Yaojun Li,

Laurence Lessard-Phillips, Huw Vasey

– Theoretical Physics Group• Alan McKane, Tim Rogers

• Manchester Metropolitan University– Centre for Policy Modelling

• Bruce Edmonds, Ruth Meyer, Stefano Picassa

• Aim is to apply complexity methods to social issues with policy relevance

Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 2

The Underlying Problem

• The Anti-Anthropocentric Principle: the world we study is not arranged for our convenience (as academics)

• Corollary: there is no reason to suppose that the social world is such that a model adequate to its representation will be simple enough for us to understand

• There are reasons to suppose that it is not (social embeddedness, SIH, Machiavellian Intelligence, cognitive competition etc.)


The Resulting Dilemma

• 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)


the Modelling Approach

Data-Integration Simulation Model

Micro-Evidence Macro-Data

Abstract Simulation Model 1

Abstract Simulation Model 2

SNA Model Analytic Model


Data-Integration Models

• To develop a simulation that integrates as much of the relevant available evidence as possible, both qualitative and statistical 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

• Central idea is to stage abstraction and provide a fixed target for more rigorous models


DIM Development Method• A relatively tight interactive “loop” between the social

scientists who are experts in the subject matter and data and the simulation developers...

• ...trying to give as much ownership and control to social scientists as possible.

• First target: What makes people vote within a diverse community?

• Started with developing a fairly complete list of “causal stories” concerning the various processes that might contribute from

• Then initial model iteratively developed in NetLogo to enable maximum responsiveness and transparency

• To be reimplemented in Java/Repast when the target becomes more “settled” for more extensive simulation exploration and analysis


Some examples of Causal Stories

Someone may vote or abstain because....• of habit – they are used to doing so and

don’t really consider alternatives• of self/group interest – they calculate that it

is in their own benefit to do so• of social norms – they have internalised a

form of “civic duty” which obligates them• of mobilisation – because somebody

(perhaps from a party) asked them to vote


Model Overview


Inp

ut Individual

characteristics• Demographic,

psychological

Individual behaviours

Memory of events

External shocksM

od

el la

yers

Generic population dynamics• Birth, death, movement

Social networks• Households, spatial,

political discussion networks, etc.

Influence• Vertical and horizontal

socialisation and mobilisation

Voting decision• Intention vs. action

Ou

tpu

t Characteristics of system

Aggregate outcomes (fed back to model layers)

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


Population Model I

• 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


Population Model II

• As well as households there are activities: schools, places of work, and (currently 2) kinds of activity (e.g. church, sports 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• 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


Influence

• Social norms transmitted in household (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 who to vote for may evolve due to coherence / incoherence in the messages about politics

• Interest & bias in politics may convert to voting probabilistically


Voting Decision

4 stage model of whether to vote:1. Habit – when an agent has voted in 3 out of 4 of the last

elections they tend to continue to do so2. Factors – politically involvement, civic duty norms, habit,

friends’ conversations, education, level of interest, past “success” at voting/abstaining etc.

3. Intention – above come together to an intention to vote (or otherwise)

4. Modifying factors – recent young child, recent move, householder going to vote, canvassed by party etc. then may alter this intention

Then compared with historical result of election which affects the satisfaction of the individual with the result (election results are exogenous)


Example Output


Early, “Proof of Concept” Version of the Model

• Simulation model still being developed, validation stage yet to begin in earnest

• Demonstrated here with 2 different scenarios:– “Rural”: 85% density, 95% maj., 1%

Immigration rate– “Urban”: 30% denisity, 65% maj., 5 %

Immigration rate• 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

17

Low immigration, High majority population, low density

High immigration, Low majority population, high density


“Rural”Case

“Urban”Case

Turnout − “Urban” Case


Underlying Factors − “Urban” Case


Interest in Politics

Habit VotingPolitical Involvement

Civic Duty Norm

Time

Ind

ex

Log − “Urban” Case1945: (person 712) did not vote

1946: (person 712) started at (workplace 31)

1947: (person 712)(aged 29) moved from (patch 4 2) to (patch 5 3) due to moving to an empty home

1947: (person 712) partners with (person 698) at (patch 5 3)

1950: (person 712) did not vote

1951: (person 712) seperates from (person 698) at (patch 5 3)

1951: (person 712)(aged 33) moved from (patch 5 3) to (patch 4 2) due to moving back to last household after separation



1954: (person 712)(aged 36) moved from (patch 4 2) to (patch 23 15) due to moving to an empty home


1964: (person 712) started at (activity2-place 71)

1964: (person 712) voted for the red party






1983: (person 712) died at (patch 23 15)


Turnout − “Rural” Case


Underlying − “Rural” Case


Interest in Politics

Habit VotingPolitical Involvement

Civic Duty Norm

Time

Ind

ex

Log − “Rural” Case


1950: (person 561) stops going to (school 2)






















2009: (person 561) died at (patch 11 16)


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


Further Work/Unresolved Issues

• Development of qualagent rule process needs more research so it is more systematic, replicable and transparent

• Shows need for a library of ‘mundane’, underlying agent-based models of, say, population, household structure change

• Descriptive/diagramming techniques to make simulation design more accessible


The End

The SCID Project

http://scid-project.org

Bruce Edmonds

http://bruce.edmonds.name

Centre for Policy Modelling

http://cfpm.org

Using a Data-Integration Model to Stage Abstraction in Voter Turnout

Education

social scientists

integration

voter turnout

red party1979

red party1970

bruce edmonds

stage abstraction

simple