Value of ABM for integrating geographical understanding - Millington Auckland 2013

http://www.landscapemodelling.net
James Millington
Dept. of Geography, King’s College London
Through Thick and Thin:
The value of agent-based modelling for
integrating geographical understanding

Use of ABM in geography
Established
 Land use/cover change
 Deadman et al. 2004; An et al. 2005; Evans and Kelley 2008
 Urban phenomena and change
 Haklay et al. 2001; Hochmair 2005; Jayaprakash et al. 2009
More recently
 Crime distribution (Malleson et al. 2012; Malleson 2012)
 School catchments (Harland & Heppenstall 2012; Millington et al. in review)
 Crowd dynamics (Torrens 2012; Johansson and Kretz 2012)

Early adopters in geography
 Contexts consistent with prior skills and
understanding (GIS, Computer Science)
Little evidence of ABM use to ask questions
arising from social or cultural theory
Movement & Locations Physical Change

Social and cultural aversion
Three proposed reasons
1. Misconceptions about this ‘modelling’
 ABM associated with previously rejected
quantitative methods?
2. Failure of ABM to exploit their potential
 Quantitative generalization etc. often still used
3. Models are too ‘thin’ to understand the world
 Alone maybe, but could be used more ‘thickly’

Quantitative revolution
Statistical approaches result in:
"sad degeneration and routinization of the
modelling exercise into mere data crunching,
numerical analysis and statistical inference
instead of careful theory-building"
Harvey (1989 p.213)
Prime position of mathematical modelling can
mean “conceptualization becomes the slave of
quantification”
Sayer (1982, p.75)

Millington et al. (2007)
Land use regression model

Modelling differences
Statistical modelling demands:
 Generalization and Aggregation
 Quantitative descriptions of relations
 Homogenization of measured objects

Land use agent-based model

Modelling differences
Statistical modelling demands:
 Generalization and Aggregation
 Quantitative descriptions of relations
 Homogenization of measured objects
Agent-based modelling allows:
 Abstraction and Disaggregation
 Autonomous heterogeneous objects
 Explicit representation of causal powers

Agent-based representation
Agents:
 Interactive (with other agents and env.)
 Autonomous behaviour
 Multiple attributes
 Adaptive (behaviour or attributes)
 Represent multiple levels of organization
Environment:
 Influences and influenced by agents

Galan et al. (2009)

 Quantitative generalization and aggregation is
not required
 Abstractions can be based on
 Interviews
 Survey results
 Participant observation
 Natural language (mental) models encoded
using logical symbolization

Unfulfilled potential
Quantitative
Generalization
Utility
Maximization
Perfect
Rationality
Prediction Forecasting

Thin description
 Models are too simple and uncoupled from
reality to be relevant for understanding it?
Yeah,
 Models are ‘thinner’ than ethnographers’ thick
descriptions
Nah
 Abstractions are needed for understanding
 ABM need not be so epistemologically thin

Epistemic Roles
Heuristic
Structure vs. Agency
Necessary vs. Contingent
Dialogic
Mensatic
Narrative

Recursion

Structuration
Structure Agency

Social Psychology Theory
 People hold multiple self-concepts within their
self-identity in a hierarchy (Stryker and Burke 2000)
 Farmer: Producer, Agri-business person,
Conservationist, Diversifier
 People attempt to express their identity
through their behaviour
 Identity changes slowly to match social
network if behaviour cannot match identity
http://landscapemodelling.net

Farmer Social Psychology

School Choice and Admissions
 Distance-based admissions policies
 hierarchies of school popularity
 lead to the reproduction of social inequality

Empirical Patterns

Empirical Patterns
Barking Abbey
Warren

Necessary or Contingent?
 What relationships necessary for patterns?
 School value-added? Family location constraints?
 What relationships are contingent?
 Examine combinations of rules
 No value-added (nVA), no location constraints (nLC)
 Value-added (VA), no location constraints (nLC)
 No value-added (nVA), location constraints (LC)
 Value-added (VA), movement constraints (LC)

nLC nVA nLC VA
LC nVA LC VA

20 40 60 80
GCSE score
0
2
4
6
8
10
A:Pratio
20 40 60 80
GCSE score
0
2
4
6
8
10
A:Pratio

A:P ratio
2 4 6 8 100
0
20
40
60
80
100
Max.Distance
A:P ratio
2 4 6 8 100
0
20
40
60
80
100
Max.Distance

Put your model where your mouth is
Present your mental model as a formal model
ABM

Participatory Modelling
D’Aquino et al. (2003)

Boundary Crossing
Demeritt (2009)
ABM

Narrative: Beyond Statistics
 Breeding synchrony in bird colonies
 Jovanni and Grimm (2008) Proc. R. Soc. B

Statistical Summaries
Jovani & Grimm (2008)

ABM are event-driven
Narrative
Understanding
Events

What is a narrative?
Narrative
…may move back and forth between
accounts of low-level events and
system level (statistical) summaries to
show how they are linked
… is not simply a chronicle of events

Influencing Neighbours (IN)
IN = 8 IN = 1

Influencing Neighbours in Space

Influencing Neighbours in Time

Narratives of contingencies

Thicker Approaches
Heuristic
Structure vs. Agency
Necessary vs. Contingent
Dialogic
Mensatic
Narrative

Outstanding Challenges
 Improving model representations
 Identifying and demonstrating causality
Clifford (2008)
Shared challenges?
“appropriate abstraction to understand how
structures underlying mechanisms produce
empirical events and the identification and
explanation of how necessity and
contingency combine to produce history”

Through thick and thin
Mixed methods
 Corroborating findings
 Alternative interpretations
 Development of theory
 Expansion of inquiry
Quantitative Qualitative+

Mixed methods
 Examining consequences of theories
 Identifying patterns to seek empirically
Simulation Qualitative+

Mixed methods
 Examining consequences of theories
 Identifying patterns to seek empirically
Simulation Qualitative

Theory
Observation
Simulation
Iterative Process

Challenges for ‘thick and thin’
Scepticism

Scepticism
Skills
 Teaching computational concepts
 ‘Interaction’ expertise rather than ‘contributory’

Scepticism
Skills
 Teaching computational concepts
 ‘Interaction’ expertise rather than ‘contributory’
Resources
 Time and Energy!

Acknowledgements
james.millington@kcl.ac.uk
@jamesmillington

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