This document discusses using narrative explanations to summarize agent-based models (ABMs). It notes that ABMs are well-suited for systems with interactions between heterogeneous agents that produce emergent, complex patterns. Narratives can explain how a sequence of events at the micro-level lead to observed macro patterns by telling a coherent story. An example model of breeding synchrony in bird colonies is described to show how narratives can move between low-level agent interactions and system-level patterns to build understanding.

1. Narrative explanation
in agent-based modelling
James D.A. Millington*
David O’Sullivan
George L.W. Perry
http://landscapemodelling.net

2. When ABM are worth it
 ABM most appropriate for systems with:
 Interactions (between agents)
 Heterogeneity (in agents’ context)
 Organized Complexity (i.e., middle-numbered)
Argument
these properties mean a narratives can
explain how ABM structure produces
emergent system-level patterns
http://landscapemodelling.net

3. ‘Generative’ Simulation
 Specification of micro-level properties, or
rules of element interactions, used to
generate observed macro-level patterns
“If you didn’t grow it,
you didn’t explain its emergence”
Epstein (1999, p.43)
http://landscapemodelling.net

4. Statistical Portraits
Artificial Anasazi
Axtell et al. (2002)
Statistical Portraits
of Pattern
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5. Elaborate Black-Boxes?
“There are some warning signs here in the ABM
enterprise insofar as that greatest criterion of
‘success’ – and the claim to novelty itself – is that
patterns are produced as outcomes whereas the
intermediate process (i.e. interactions between
simple rules) which leads to structure is shrouded.”
(Clifford 2008, p. 682).
http://landscapemodelling.net

6. ABM are Event-Driven
 Event: any interaction between modelled
entities that results in a change in state of
at least one entity attribute
Direction
Stress Level
of travel
Location
Wealth Any other
attribute
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7. ABM are Event-Driven
 Event: any interaction between modelled
entities that results in a change in state
 Events are consequences of code
executed in context
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8. ABM are Event-Driven
 Event: any interaction between modelled
entities that results in a change in state
 Events are consequences of code
executed in context
 Event-driven: sequences of low-level
events produce system-level patterns
http://landscapemodelling.net

9. Narrative Explanation
 Explain causes of events from numerous,
and potentially distal sources through a
coherent sequence of prior events (Cleland 2011)
 Historical Natural Science distinguished
from ‘Classical’ Experimental Science
 Narrative shows how a focal event or state
came to occur by fitting it into a coherent
account of a sequence of preceding events
http://landscapemodelling.net

10. What is a narrative?
Narrative
Understanding
Events
http://landscapemodelling.net

11. 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
http://landscapemodelling.net

12. An example
 Breeding synchrony in bird colonies
 Jovanni and Grimm (2008) Proc. R. Soc. B
http://landscapemodelling.net

13. An example
 Breeding synchrony in bird colonies
 Jovanni and Grimm (2008) Proc. R. Soc. B
Hypothesis:
interactions between neighbouring
birds’ stress levels drives synchrony
http://landscapemodelling.net

14. Breeding Synchrony Model
SLt+1 = [(1-NR) × SLt] + (NR × NSLt) – SD
 SL: stress level [initially 100-300]
 NSL: neighbour(s) stress level
 NR: neighbour relevance [0,1]
 SD: stress decay [1,100]
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15. Stress Level Change
NR = 0.0 NR = 0.2
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16. Synchrony for different NR
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17. Heterogeneity in context
 All parameters apply to all birds identically
 Only difference is initial stress level
 Narratives more useful with heterogeneity
 Heterogeneity varies context of interactions
 Consequently, events are more important
 Modify model
 so birds arrive at colony at different times
 different neighbours influence stress level
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18. Longest time to lay
http://landscapemodelling.net

19. Influencing Neighbours (IN)
IN = 8 IN = 1
http://landscapemodelling.net

20. Influencing Neighbours (IN)
IN = 8 IN = 1
http://landscapemodelling.net

21. Reciprocal Influences
http://landscapemodelling.net

22. Communication
http://landscapemodelling.net

23. Potential Issues
 (Re)Introducing uncertainty?
 From formal model to informal language
 Which narrative do we choose?
 How do we know if our narrative is ‘good’
(enough)?
 Loss of objectivity?
 Highlights subjectivities of modelling
 But maybe this is a good thing…
http://landscapemodelling.net

24. Summary
 Why simulate individuals and then report
aggregated patterns alone?
 Spatially-explicit model without maps
 Explaining ABM events via narrative can
reveal process
Millington et al. (2012) Geoforum
james.millington@kcl.ac.uk
http://landscapemodelling.net