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).

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Simulated Social Network at 2010
Using Data Integration Models for understanding complex social systems, Bruce Edmonds, MMUBS, 5 th Feb 201. slide 23

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. 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. 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. 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. 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. 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