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 EdmondsCentre for Policy Modelling

Manchester Metropolitan University

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/

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• The qualitative and quantitative do not sit well

together• 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• When mixed-methods models 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

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

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 5

Staging Abstraction

Data-Integration Simulation Model

Micro-Evidence Macro-Data

Abstract Simulation Model 1

Abstract Simulation Model 2

SNA Model Analytic Model

Qualitative Quantitative

Increasing Abstraction

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 6

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 Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 7

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 Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 8

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 Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 9

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 were confirmed with a small qualitative survey• These provided the skeleton for the “menu” of behaviours

that were programed into the agents

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 10

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

Inpu

t

Out

put

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 11

Discuss-politics-with person-23 blue expert=false neighbour-network year=10 month=3Lots-family-discussions year=10 month=2Etc.

Memory

Level-of-Political-Interest

Age

Ethnicity

Class Activities

A Household

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

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 12

Example Output: why do people vote (if they do)

Intervention: voter mobilisation

Effect: on civic duty norms Effect: on habit-

based behaviour

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 13

Example Output: Simulated Social Network at 1950

Established immigrants: Irish, WWII Polish etc.

Majority: longstanding ethnicities

Newer immigrants

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 14

Example Output: Simulated Social Network at 2010

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 15

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 home21: (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 party29: (person 578)(aged 30) voted for the blue party

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 16

Retaining Maximally Clear Reference

Data-Integration Simulation Model

Micro-Evidence Macro-Data

Abstract Simulation Model 1

Abstract Simulation Model 2

SNA Model Analytic Model

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 17

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!

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 18

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 clearHowever, all of these are at least partially fixable as an abstraction of a well-founded simulation model

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 19

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”

Using Agent-Based Simulation to integrate micro/qualitative evidence, macro-quantitative data and network analysis, Bruce Edmonds, London, May 2014. 20

The End!

Bruce Edmonds:http://bruce.edmonds.nameCentre for Policy Modelling:

http://cfpm.orgThe SCID Project:

http://www.scid-project.orgSlides uploaded to: http://slideshare.net/BruceEdmonds