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Using Data Integration Models
for Understanding Complex Social Systems

Bruce Edmonds
Centre for Policy Modelling
Manchest...
Talk Outline

1.
2.
3.
4.

Some of the thinking behind this work
About Agent-Based Simulation
Data Integration Models
An e...
Underlying Principles
• Being “scientific” means not ignoring evidence
(at least without a very VERY good reason)!
• This ...
However…
…social phenomena are so complex that
• There is no reason to suppose we happen to have
brains with the capabilit...
Agent-Based Simulation
• Is a computer program
• Much like a multi-character game, where each
social actor is represented ...
Why Computer Simulations?
• Agent-based simulations are more expressive
than analytic mathematics ones, that is, they do
n...
System Dynamics or Statistical
modelling

Real World

Equation-based Model

Actual Outcomes
Aggregated
Actual Outcomes

Ag...
Individual- or Agent-based simulation

Real World

AgentIndividual-based Model

Actual Outcomes

Model Outcomes

Aggregate...
What happens in ABS
• Entities in simulation are decided on
• Behavioural Rules for each agent specified (e.g. sets of
rul...
E.G: Schelling’s Segregation Model
Schelling, Thomas C.
1971. Dynamic Models of
Segregation. Journal of
Mathematical Socio...
Micro-Macro Relationships
Macro/
Social data

Social, economic surveys; Census

Theory,
narrative
accounts

Micro/
Individ...
The Micro-Macro Link
• How do the tendencies, abilities and observed
behaviour of individuals…
• …relate to the measured a...
In Vitro vs In Vivo Analogy
• In biology there is a well established distinction
between what happens in the test tube (in...
Data Integration Models
• Are a particular style of agent-based simulation
• Intended more as a computational description ...
Aims and Objectives of DIM
• To develop a simulation that integrates as much
as possible of the relevant available evidenc...
So what kind of explanation does
this approach facilitate?
• At the micro-level causation is explicitly specified:
what ha...
The “SCID”
Project
The Social Complexity of Immigration and Diversity is a 5-year project with
the Institute for Social Ch...
the SCID Modelling Approach
SNA Model

Analytic Model

Abstract Simulation
Model 1

Abstract Simulation
Model 2

Data-Inte...
Overall Structure of Model

Demographics of people in
households

Social network formation and
maintenance (homophily)
Inf...
Changing personal
networks over which
social influence occurs

A Household

Class

Activities

Age
Etc.

Ethnicity
Level-o...
Example Output: why do people vote
(if they do)
Effect: on civic
duty norms

Effect: on habitbased behaviour

Intervention...
Simulated Social Network at 1950
Majority: longstanding
ethnicities

Newer
immigrants
Established
immigrants: Irish,
WWII ...
Simulated Social Network at 2010

Using Data Integration Models for understanding complex social systems, Bruce Edmonds, M...
Psuedo-Narrative Output
Following a single, randomly chosen agent…
4: (person 578)(aged 5) started at (school 1)
17: (pers...
Possibilistic vs Probibilistic
• The idea is to map out some of the possible social
processes that may happen
• Including ...
Using Qualitative Behaviour to
Inform the Agent Specification
• Narrative data (from semi-structured interviews,
observati...
Context-Dependency
• In the simulation (as in our social life) decisions,
adaption, communication, learning all take place...
Conclusion
• Complex simulations are a different way of
representing social phenomena than
mathematics, data and natural l...
Thanks!

Centre for Policy Modelling
http://cfpm.org
I will make these slides available at:
http://www.slideshare.net/Bruc...
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Using Data Integration Models for Understanding Complex Social Systems

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Describing the use of complex, descriptive simulations to integrate the maximum amount of evidence in a staged manner. With an example from the SCID project (http://www.scid-project.org).

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  • The link between micro and macro is complexRole of intermediate concepts, e.g. social capital
  • Transcript of "Using Data Integration Models for Understanding Complex Social Systems"

    1. 1. Using Data Integration Models for Understanding Complex Social Systems Bruce Edmonds Centre for Policy Modelling Manchester Metropolitan University Business School Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 1
    2. 2. Talk Outline 1. 2. 3. 4. Some of the thinking behind this work About Agent-Based Simulation Data Integration Models An example from the SCID project: Voter Turnout 5. Concluding Discussion Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 2
    3. 3. Underlying Principles • Being “scientific” means not ignoring evidence (at least without a very VERY good reason)! • This means not ignoring qualitative evidence and not ignoring quantitative evidence • Also that if theory and evidence clash then (almost always) one should go with the evidence • Using formal (i.e. ultimately precise) models means that the process of knowledge formation can be far more social • These models are open to a process of critique, communication without error, and collaboration over a long period of time and many researcher Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 3
    4. 4. However… …social phenomena are so complex that • There is no reason to suppose we happen to have brains with the capability of understanding it in a scientific manner • It is inevitable that we will have to make many compromises in obtaining any useful knowledge about it • So “heroic leaps” to simple and supposedly general theories will not work • Rather we will have to settle for complex and situation-specific formulations Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 4
    5. 5. Agent-Based Simulation • Is a computer program • Much like a multi-character game, where each social actor is represented by a different “agent” • These agents can each have very different behaviours and characteristics • Social phenomena (such as social networks) can emerge out of the decisions and interaction of these individual agents (upwards “emergence”) • But, at the same time, the behaviour of individuals can be constrained by “downwards” acting rules and social norms from society and peers Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 5
    6. 6. Why Computer Simulations? • Agent-based simulations are more expressive than analytic mathematics ones, that is, they do not require strong assumptions to work (unlike economics) • They are largely theory-free, that is they can implement a wide range of different kinds of accounts, hence allowing a more naturalistic style of representation • They can be very detailed, allowing representation and exploration of some of the meso-level complex mess that much social phenomena consists of (unlike system dynamic models) Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 6
    7. 7. System Dynamics or Statistical modelling Real World Equation-based Model Actual Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes Social influence and the domestic demand for water, Aberdeen 2002, http://cfpm.org/~bruce slide-7 systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 7 Using Data Integration Models for understanding complex social
    8. 8. Individual- or Agent-based simulation Real World AgentIndividual-based Model Actual Outcomes Model Outcomes Aggregated Actual Outcomes Aggregated Model Outcomes Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5th Feb 201. slide 8
    9. 9. What happens in ABS • Entities in simulation are decided on • Behavioural Rules for each agent specified (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 Specification (incl. rules) Representations of Outcomes Simulation Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 9
    10. 10. E.G: Schelling’s Segregation Model Schelling, Thomas C. 1971. Dynamic Models of Segregation. Journal of Mathematical Sociology 1:143-186. Rule: each iteration, each dot looks at its 8 neighbours and if less than 30% are the same colour as itself, it moves to a random empty square Segregation can result from wanting only a few neighbours of a like colour Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5th Feb 201. slide 10
    11. 11. Micro-Macro Relationships Macro/ Social data Social, economic surveys; Census Theory, narrative accounts Micro/ Individual data Simulation Qualitative, behavioural, social psychological data Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5th Feb 201. slide 11
    12. 12. The Micro-Macro Link • How do the tendencies, abilities and observed behaviour of individuals… • …relate to the measured aggregate properties of society? • Social Embedding etc. implies this link is complex • Averaging assumptions (a general tendency + random noise) do not capture non-linear interaction • This is often two-way, with society constraining and framing individual action as well as individual constituting society in an emergent fashion • Somewhat-persistent, complicated meso-level structures mediate these effects – these might be key to understanding this Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5th Feb 201. slide 12
    13. 13. In Vitro vs In Vivo Analogy • In biology there is a well established distinction between what happens in the test tube (in vitro) and what happens in the cell (in vivo) • In vitro is an artificially constrained situation where some of the complex interactions can be worked out… • ..but that does not mean that what happens in vitro will occur in vivo, since processes not present in vitro can overwhelm or simply change those worked out in vivo • One can (weakly) detect clues to what factors might be influencing others in vivo but the processes are too complex to be distinguished without in vitro experiments Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5th Feb 201. slide 13
    14. 14. Data Integration Models • Are a particular style of agent-based simulation • Intended more as a computational description of a particular case than a (generalistic) theory • Its aim is to represent as much of the relevant evidence as possible in one coherent and dynamic simulation • Provides a precise target for abstraction (which are then checkable against it) • Stages abstraction from data to theory • Separates representation and abstraction • Preserves chains of reference Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5th Feb 201. slide 14
    15. 15. Aims and Objectives of DIM • To develop a simulation that integrates as much as possible of the relevant available evidence, both qualitative and statistical (a Data-Integration Model – a DIM) • 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 Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5th Feb 201. slide 15
    16. 16. So what kind of explanation does this approach facilitate? • At the micro-level causation is explicitly specified: what happens under what circumstances with what probability/mechanism? • At the macro-level, different factors/measures/variables/outcomes can be correlated with each other just as with in vivo macro studies, however you have the possibility of doing controlled experiments! • However the simulation itself provides an inspectable instantiation of the micro-level events and interaction – Social processes are still deeply entangled and not necessarily separable – Local situation is important in understanding meso-level causal explanations Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5th Feb 201. slide 16
    17. 17. The “SCID” Project The Social Complexity of Immigration and Diversity is a 5-year project with the Institute for Social Change and the Department of Theoretical Physics at University of Manchester. It is funded under the “Complexity Science for the Real World” initiative of the EPSRC to the tune of £2.7 million and will last until August 2015. The idea of the project is to apply the techniques and tools of complexity science to real world issues, in this case of immigration and diversity. The project will focus on: (1) why people bother to go out and vote and how social influence within/across different communities affects this (2) how people use social networks to find employment, e.g. how the impoverished networks of immigrants may limit this and (3) inter-community trust. Project Website: http://scid-project.org/ Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5th Feb 201. slide 17
    18. 18. the SCID Modelling Approach SNA Model Analytic Model Abstract Simulation Model 1 Abstract Simulation Model 2 Data-Integration Simulation Model Micro-Evidence Macro-Data Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5th Feb 201. slide 18
    19. 19. Overall Structure of Model Demographics of people in households Social network formation and maintenance (homophily) Influence via social networks • Political discussions Output Input Underlying data about population composition Voting Behaviour Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5th Feb 201. slide 19
    20. 20. Changing personal networks over which social influence occurs A Household Class Activities Age Etc. Ethnicity Level-of-Political-Interest Composed of households of individuals initialised from detailed survey data Each agent has a rich variety of individual (heterogeneous) characteristics Memory Discuss-politics-with person-23 blue expert=false neighbour-network year=10 month=3 Lots-family-discussions year=10 month=2 Etc. Including a (fallible) memory of events and influences An Agent’s Memory of Events Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 20
    21. 21. Example Output: why do people vote (if they do) Effect: on civic duty norms Effect: on habitbased behaviour Intervention: voter mobilisation Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 21
    22. 22. Simulated Social Network at 1950 Majority: longstanding ethnicities Newer immigrants Established immigrants: Irish, WWII Polish etc. Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 22
    23. 23. Simulated Social Network at 2010 Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 23
    24. 24. Psuedo-Narrative Output Following a single, randomly chosen agent… 4: (person 578)(aged 5) started at (school 1) 17: (person 578)(aged 18) stops going to (school 1) 21: (person 578)(aged 22) moved from (patch 11 3) to (patch 12 2) due to moving to an empty home 21: (person 578)(aged 22) partners with (person 326) at (patch 12 2) 24: (person 578)(aged 25) started at (workplace 8) 24: (person 578)(aged 25) voted for the blue party 29: (person 578)(aged 30) voted for the blue party Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 24
    25. 25. Possibilistic vs Probibilistic • The idea is to map out some of the possible social processes that may happen • Including ones one would not have thought of or ones that have already happened • The global coupling of context-dependent behaviours in society make projecting probabilities problematic • Increases understanding of why processes (such as the spread of a new racket) might happen and the conditions that foster them • Complementary to statistical models and natural language formulation and discourse Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 25
    26. 26. Using Qualitative Behaviour to Inform the Agent Specification • Narrative data (from semi-structured interviews, observations etc.) can be used to inform the behavioural rules of agents within these simulations • This can be done in an informal or semi-formal manner (e.g. using techniques extended from GT) • This can provide a broader “menu” of possible behaviours and strategies that are used and thus import some of the “messiness” of social reality instead of overly neat formulations (e.g. economic) • Meso-level outcomes can be fed back using participatory techniques to aid validation • Macro-level measures can also be extracted and compared to known quantitative data Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 26
    27. 27. Context-Dependency • In the simulation (as in our social life) decisions, adaption, communication, learning all take place within a local context • Both “upwards” (emergent) and “downwards” (social control) forces operate within local contexts allowing social embeddedness • Abstraction to aggregates (e.g. averages) only takes place post-hoc (just as in social statistics) • Thus ABS allows the formal representation of contextdependent behaviour, albeit within a more specific “descriptive” simulation, that can be itself hard to understand • Thus opening the way to the study of context itself! Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 27
    28. 28. Conclusion • Complex simulations are a different way of representing social phenomena than mathematics, data and natural language • It by-passes the need for overly simplistic assumptions and allows for a more “naturalistic” manner of respresentation, e.g.: heterogenity, and dynamic/emergent social structure/phenomena • It allows micro-behaviour to be context-dependent – not requiring this to be dealt with as “random” • The micro-level relates to qualitative evidence, the meso to social networks and the macro to quantitative statistics in a well-founded manner Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 28
    29. 29. Thanks! Centre for Policy Modelling http://cfpm.org I will make these slides available at: http://www.slideshare.net/BruceEdmonds Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 29
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