Networks are an abstraction of complex social processes. Albeit themselves formal, the social processes on which they are based can be researched using both quantitative and qualitative methods. The problem in combining these approaches comes from the very different natures and levels on which they are based. Here we describe an approach which uses agent-based modelling (ABM) as a stepping stone towards the more abstract network models. These ABMs are more in the nature of complex and dynamic descriptions than general theories, and are ideally suited for integrating a variety of kinds of evidence into a coherent fashion - including quatitative evidence to inform the micro-level behaviours of agents, and quantitative evidence about the macro, aggregate levels. The assumptions behind these kinds of ABM are relatively transparent, and the ABMs used to generate networks in a precise manner. Thus this "staging" of the abstraction process allows a well-founded mixed-methods approach to social network research. A worked example of this on voting behaviour is presented.

1. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 1
Using Agent-based Simulation to Integrate
Micro/Qualitative Evidence, Macro-
Quantitative Data and Network Analysis
Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University
Slides available at: http://slideshare.net/BruceEdmonds

2. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 2
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 and will last until August 2015. Staff
involved are: Nick Crossley, Louise Dyson, Bruce Edmonds, Ed
Fieldhouse, Alan McKane, Ruth Meyer, Luis Fernandez Lafuerza, Laurence
Lessard-Phillips, Yaojun Li, Nick Shryane, Gennaro Di Tosto, and Huw
Vasey.
The project is applying the techniques and tools of complexity science to
real world issues: (1) why people bother to vote and how social influence
within/across communities affects this (2) how the impoverished networks of
immigrants may limit effective job search and (3) inter-community trust.
Project Website:
http://scid-project.org/

3. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 3
Example problems in mixed-methods
(including some SNA) research
• It is often quite ad hoc, and hence hard to repeat
• It can be difficult to tell if qualitative and quantitative
elements are consistent with each other
• Models in mixed-methods research can have
elements whose meaning is not completely clear
• If models from mixed-methods research do not work it
can be difficult to tell what part of it might be wrong
• Validation can be very weak – it can sometimes not
be clear if the model was, in fact, successful/useful
• It is not always clear when it is helpful to use one
method/tool on the results from another method/tool

4. Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 4
Some Guiding Principles
Unlike some areas of qualitative and quantitative
science, mixed methods has not been formalised.
So here are some principles I use to guide my practice:
• In science one should not ignore evidence without a
very, very, very good reason.
– including available qualitative and quantitative evidence
• As far as possible, in any model the reference of its
elements should be as clear as possible
– what parts of a model mean should not be fudged/vague
• The more drastic/heroic the abstraction, the more the
resulting model needs validating
• Modelling choices/steps should be as transparent and
replicable as possible – including reasons for choices

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Staging Abstraction
Data-Integration Simulation Model
Micro-Evidence Macro-Data
Abstract Simulation
Model 1
Abstract Simulation
Model 2
SNA Model Analytic Model
IncreasingAbstraction

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Data Integration Models
• Are a particular style of agent-based simulation
• You may be aware of some simple, abstract
simulation models that purport to be a theory…
• …this is at the opposite end of the spectrum.
• Intended more as a computational description of a
particular case than a (generalistic) theory
• Aims 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)
• Thus it separates representation and abstraction

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Agent-Based Simulation
• Is a kind of computer simulation…
• …where individual social actors and their interactions are
separately represented (agents)
• The heterogeneity of actors is represented, different:
characteristics, behaviours and contexts
• What happens is not centrally determined, but rather
emerges from the interactions of the agents
• Both “top-down” constraint and “bottom-up” emergence
can occur simultaneously in models
Representations of OutcomesSpecification (incl. rules)

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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

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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

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The “54” Causal Stories
• Reviewing the literature we extracted different “causal
stories” impacting on whether people vote
• Examples:
– Out of a feeling of civic duty
– Due to sheer habit, “its what I have always done”
– Interest in politics due to discussions within household,
partner and friends
– Due to participation in higher education
– Evaluation of past efficacy of voting
– Member of household taking them with them to vote
• Some of these confirmed via a small qualitative
survey
• These provided the skeleton for the “menu” of
behaviours that were programed into the agents

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Overall Structure of Model
Underlying data about
population composition
Demographics of people in
households
Social network formation and
maintenance (homophily)
Influence via social networks
• Political discussions
Voting Behaviour
Input
Output

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Discuss-politics-with person-23 blue expert=false
neighbour-network year=10 month=3
Lots-family-discussions year=10 month=2
Etc.
Memory
Level-of-Political-Interest
Age
Ethnicity
Class
Activities
AHousehold
An Agent’s Memory of Events
Etc.
Changing personal
networks over which
social influence occurs
Composed of households of
individuals initialised from
detailed survey data
Each agent has a rich variety of
individual (heterogeneous)
characteristics
Including a (fallible) memory of
events and influences

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Example Output: why do people vote (if
they do)
Intervention: voter
mobilisation
Effect: on civic
duty norms Effect: on habit-
based behaviour

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Example Output: Simulated Social Network
at 1950
Established
immigrants: Irish,
WWII Polish etc.
Majority: longstanding
ethnicities
Newer
immigrants

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Example Output: Simulated Social Network
at 2010

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Example Output: 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

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Retaining Maximally Clear Reference
Data-Integration Simulation Model
Micro-Evidence Macro-Data
Abstract Simulation
Model 1
Abstract Simulation
Model 2
SNA Model Analytic Model

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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)
• The DIM allowed the formal representation of context-
dependent 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!

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Fixing “Weaknesses” of SN Models
In much social network research:
• The definition of links is often unclear and/or
inconsistent
• The machinery of social network models do not
explain changing networks
• Validation of social network models is often weak
• Network measures are often used as if it is known
that they give reliable indicators (e.g. centrality)
• How to apply narrative data is not clear
However, all of these are at least partially fixable as
an abstraction of a well-founded simulation model

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Conclusions
• Complex agent-based models are good vehicles for
integrating different kinds of data
• In particular qualitative data can very usefully inform
the “menu” of micro-level behaviours, importing some
of the “mess” of social reality
• Data Integration Models can provide consistent
pictures including dynamics, albeit complicated
• Staging abstraction into more gentle steps can help
retain meaning reference in the modelling
• Network models are useful, but with other very
abstract models, higher up the abstraction “chain”
with the qual/quat integration occuring “lower down”
• Sometimes macro-level phenomena needs to be
explained from micro-level detail and embedding

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The End!
Bruce Edmonds:
http://bruce.edmonds.name
Centre for Policy Modelling:
http://cfpm.org
The SCID Project:
http://www.scid-project.org
Slides available at: http://slideshare.net/BruceEdmonds