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

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  • 1. Using a Data-Integration Model to Stage Abstraction in Voter Turnout
    Bruce Edmonds, et. alCentre for Policy ModellingManchester Metropolitan University
  • 2. 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, HuwVasey
    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
  • 3. 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.)
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 3
  • 4. 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)
    Butwe need bothrigour and relevance
    Mature science connects empirical fit and explanation from micro-level (explanatory and phenomenological models)
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 4
  • 5. the Modelling Approach
    SNA Model
    Analytic Model
    Abstract Simulation Model 1
    Abstract Simulation Model 2
    Data-Integration Simulation Model
    Micro-Evidence
    Macro-Data
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 5
  • 6. 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 dynamicsimulation
    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
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 6
  • 7. 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
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 7
  • 8. 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
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 8
  • 9. Model Overview
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 9
  • 10. 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
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 10
  • 11. 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
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 11
  • 12. 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)
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 12
  • 13. 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
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 13
  • 14. 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
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 14
  • 15. Voting Decision
    4 stage model of whether to vote:
    Habit – when an agent has voted in 3 out of 4 of the last elections they tend to continue to do so
    Factors – politically involvement, civic duty norms, habit, friends’ conversations, education, level of interest, past “success” at voting/abstaining etc.
    Intention – above come together to an intention to vote (or otherwise)
    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)
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 15
  • 16. Example Output
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 16
  • 17. Early, “Proof of Concept” Version of the Model
    “Rural”Case
    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
    “Urban”Case
    17
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 17
  • 18. Turnout − “Urban” Case
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 18
  • 19. Underlying Factors − “Urban” Case
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 19
    Interest in Politics
    Index
    Political Involvement
    Habit Voting
    Civic Duty Norm
    Time
  • 20. Log − “Urban” Case
    1945: (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
    1951: (person 712) did not vote
    1952: (person 712) partners with (person 189) at (patch 4 2)
    1954: (person 712)(aged 36) moved from (patch 4 2) to (patch 23 15) due to moving to an empty home
    1955: (person 712) did not vote
    1964: (person 712) started at (activity2-place 71)
    1964: (person 712) voted for the red party
    1966: (person 712) voted for the red party
    1970: (person 712) voted for the red party
    1971: (person 712) started at (workplace 9)
    1974: (person 712) voted for the red party
    1979: (person 712) voted for the red party
    1983: (person 712) died at (patch 23 15)
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 20
  • 21. Turnout − “Rural” Case
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 21
  • 22. Underlying − “Rural” Case
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 22
    Interest in Politics
    Index
    Political Involvement
    Habit Voting
    Civic Duty Norm
    Time
  • 23. Log − “Rural” Case
    1949: (person 561) partners with (person 413) at (patch 11 16)
    1950: (person 561) stops going to (school 2)
    1950: (person 561) did not vote
    1951: (person 561) started at (activity2-place 23)
    1951: (person 561) did not vote
    1955: (person 561) did not vote
    1956: (person 561) started at (workplace 9)
    1964: (person 561) voted for the red party
    1965: (person 561) started at (activity2-place 25)
    1966: (person 561) voted for the red party
    1970: (person 561) voted for the red party
    1974: (person 561) voted for the red party
    1979: (person 561) voted for the red party
    1981: (person 561) started at (workplace 15)
    1983: (person 561) voted for the red party
    1987: (person 561) voted for the red party
    1992: (person 561) voted for the red party
    1995: (person 561) started at (workplace 11)
    1997: (person 561) started at (activity1-place 18)
    1997: (person 561) voted for the red party
    2000: (person 561) started at (workplace 13)
    2001: (person 561) voted for the red party
    2005: (person 561) voted for the red party
    2009: (person 561) died at (patch 11 16)
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 23
  • 24. But why not just jump straight to simple models?
    There are many possible models and you don’t know whyto 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
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 24
  • 25. Further Work/Unresolved Issues
    Development of qualagent 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
    Using a Data-Integration Model to Stage Abstraction in Voter Turnout, Bruce Edmonds, ECCS, Vienna, September 2011, slide 25
  • 26. The End
    The SCID Project
    http://scid-project.org
    Bruce Edmonds
    http://bruce.edmonds.name
    Centre for Policy Modelling
    http://cfpm.org

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