A talk at the workshio on "Microsimulationof chronoc disease: current methods & future directions", 27th Feb 2014, LSHTM, Tavistock place, London

1. Integrating Microsimulation, Mathematics,
and Network Models Using ABM
– prospects and issues
Bruce Edmonds
Centre for Policy Modelling
Manchester Metropolitan University
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 1

2. The Modelling Background
We use many kinds of model in the development and
expression of knowledge, including:
• data, equations, logic/rules, networks, NL
descriptions, pictures and computer programs
These capture what we observe, our ideas and how our
ideas and observations relate at different levels of:
• abstraction, granularity and generality
They also vary according to their intended purpose or
use to which we attempt to put them, including:
• prediction, explanation, illustration, storage,
description, communication, detecting patterns,
understanding ideas, simplifying
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 2

3. The “No Free Lunch” Theorems
• These are a set of theorems from the field of Machine
Learning (e.g. Wolpert 1996) that say:
– There is no technique that will automatically succeed in
prediction, search, pattern detection across all kinds of
problem and kinds of data
• That is, you have to choose the technique that works
best for your goals, the nature of the data and the
nature of what is being investigated
• In other words, for good prediction etc. one has to
apply knowledge about the situation to get better
results out from any technique
• Thus I will start with a review of pros and cons of the
various techniques I am discussing
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 3

4. Mathematical Equations
• Represents complex relationships between a set of
variables in a formal way
• Is global to the “system” it is applied to (but that
system can be at many degrees of granularity)
• Holds out the possibility of general form solutions, but
only if the equations are simple enough, otherwise
using (numerical) simulation
• Is good at representing dynamics over time
• Is poor at distributed systems requiring hundreds of
separate but linked equations (since this effectively
reduces one to simulation anyway)
• Tends to be theory-driven and global
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 4

5. MicroSimulation Models (MSM)
In the most abstract terms:
– Divides the data into chunks (e.g. geographically)
– Then applies a model to each chunk (maybe fuzzily)
– Aggregates or displays the results from all chunks
Thus, in practice, tends to:
– Have a great many implicit free parameters, hence can
flexibly fit a broad variety of patterns
– Be more data-driven than theory-driven
– Fits patterns local to the chunks, thus can be contextsensitive (relative to the way the data has been divided)
Can be seen as a kind of data-mining technique that
uses knowledge in terms of how to segment the data
and what models are applied to each segment
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 5

6. Social Network Models (SNM)
These use a particular kind of abstraction step:
– The representation of interactions between agents as a link
between them
•
•
•
•
•
•
This can be data-driven, but always given the
assumptions implicit in the abstraction to links and
the assumptions in their analysis
Capture elements of structure well
Links are essentially static (each link representing a
series of interaction over a period)
Lots of mathematical results, but these difficult to
know if these are applicable to any particular
network
Are very hard to validate, but are suggestive
Tend to be explanatory rather than predictive
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 6

7. Agent-Based Models
Divides the system up into parts, then represents the interactions
between these parts in terms of messages between parts of a
computer simulation
• Bridges the micro- and macro-levels
• Good at revealing complex dynamics in systems
• Is very flexible in terms of structure and rules, in particular in
terms of heterogeneity and context-specificity
• Can be very abstract and divorced from data…
• …but can also be very complex and specific to particular sets of
evidence
• Needs a lot of data to validate well
• Are always somewhat theory driven, but the “theory” can be
mundane and informed by evidence
• Tend not to be predictive in any narrow sense, but can be
useful for an informative but possibilistic “risk analysis”
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 7

8. What happens in ABSS
• Entities in simulation are decided on
• Behavioural Rules for each agent specified (e.g. sets of
rules like: if this has happened locally then do this)
• Repeatedly evaluated in parallel to see what happens
• Outcomes are aggregated, inspected, graphed, pictured,
measured and interpreted in different ways
Specification (incl. rules)
Representations of Outcomes
Simulation
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 8

9. Some modelling trade-offs
Use of existing knowledge
Network
Models
MSM
ABM
Contextspecificity
Global
Statistical
Models
Macro
predictive
goal
Abstract
Mathematics
Capturing complex dynamics
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 9

10. ABM as a tool for integration
ABM can relate to a broad range of evidence, e.g.:
• Macro-level quantitative statistics
• Distributions and tendencies in dynamics
• Qualitative evidence or expert knowledge to inform
micro-level rules
• Aggregate behavior and stats at all levels of
aggregation including local and meso-levels
• Network data either as an input or as an abstraction
of the interactions coming out of it
The disadvantage is that it is so flexible, there are many
ways to simulate any system, a lot of choice
The advantage of this is that this can all be explicit
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 10

11. Staging Abstraction (in SCID)
SNA Model
Analytic Model
Abstract Simulation
Model 1
Abstract Simulation
Model 2
Data-Integration Simulation Model
Micro-Evidence
Macro-Data
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 11

12. Chains/Clusters of Model
“Chains” or “Clusters” of model allow one to combine the
need for different goals, e.g.:
–
–
–
–
relevance and rigour
prediction and explanation
connection to data and what-if analyses
context-specificity and global outcomes
However this is at a cost of a plurality of models, which
involves more input in terms of: development,
maintenance and checking…
…especially in the relationship between models
But can help:
–
–
stage abstraction more carefully
maintain meaningful reference of model components
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 12

13. Examples of “Causal Stories”
Initial party preference inherited
– party preference can be linked to learning from parents.
People vote out of habit
– going to the polls in one election will lead to a greater likelihood of returning to the polls in a subsequent
election.
People vote because they care about who wins
- voters are more likely to turnout if they have a stronger preference for one party or another.
Voting is a social norm
– civic duty is an important rationale for individual-level turnout.
People share the political views of their greater networks
– probability of agreement within a network depends on the distribution of political opinion within one‟s
network (autoregressive networks).
Electors can be mobilised to vote by family, friends and political parties
– household members, friends and political parties will ask people to vote on election day.
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 13

14. Overall Structure of SCID Voter 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
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 14

15. 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
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 15

16. Example Quantitative Output
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 16

17. Simulated Social Network at 1950
Majority: longstanding
ethnicities
Newer
immigrants
Established
immigrants: Irish,
WWII Polish etc.
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 17

18. Simulated Social Network at 2010
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 18

19. How to integrate MicroSimulation I
To condition the context-specific rules of an ABM,
i.e. an input to it, staging the abstraction from data
• One could cluster/segment the data according to
the different strategies that actors use
• Then use MSM to estimate the context-specific
strengths of interactions/behaviours, e.g.:
– In different communities/localities
– In different classes or economic circumstances
• This would allow the ABM to be better grounded
in the data, not only in terms of local initialisation
but also in the varying strategies of agents
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 19

20. How to integrate MicroSimulation II
To use a MSM along side an ABM, both models
simulating the same phenomena, using the same basic
segmentation of the system.
• The MSM:
– Being more data driven
– Providing „surprise free‟ but numerical predictions
• The ABM:
– Adding in more interaction
– Applying other features and constraints based on domain
knowledge
– Providing possibilistic, „what if‟ risk analyses covering some
of the possible structural changes
Both models could be validated against each other as
well as separately against their data and outcomes
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 20

21. How to integrate MicroSimulation III
Interlace ABM and MSM techniques together in the
same model.
• This is a little hard, due to the fundamentally different
natures of the two approaches (interactive vs.
independent, data-driven vs. theory driven, predictive
vs. explanatory etc.)
But is possible in some cases, e.g.:
– Some aspects of the environment of agents being
determined by Microsimulation
– „Fitting‟ an ABM to each data segment, allowing a weaker
interaction between segments
– Movement (or other action) of agents, changing the basis of
the MicroSimulation analysis
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 21

22. Conclusions
• Integrating a variety of techniques is possible, and
ABM often provide a flexible way of doing this
• A shift to „packages‟ of models where the
properties of each model is understood and with a
clear purpose
• Rather than trying to use a single model for many
different purposes
• I argue this is inevitable to make progress with
complex phenomena (Edmonds 2013)
• MSM, ABM and SNM allow for an inclusion of
context-specific/local behaviours compared to
analytic mathematical models (in practice)
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 22

23. Thanks!
Bruce Edmonds
http://bruce.edmonds.name
Centre for Policy Modelling
http://cfpm.org
I will (soon after) make these slides available at:
http://www.slideshare.net/BruceEdmonds
Integrating Microsimulation, Mathematics, Network Models Using ABM, Bruce Edmonds, Microsimulation of chronic disease, London, 27th Feb 201. slide 23