Identifying Narrative Descriptions in Agent-Based Models Representing Past Human-Environment Interactions
Authored by David O'Sullivan, George L W Perry
Date Published: 2018
DOI: 10.1007/s10816-017-9355-x
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Platforms:
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Model Documentation:
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Abstract
There is a growing use of bottom-up simulation models to reconstruct
past human-environment interactions. Such detailed representations pose
difficult questions not only in their design (the generality-realism
trade-off) but also about the inferences that are made from them. The
historical sciences are faced with seeking to make robust inferences
from limited, potentially biased and/or incomplete samples from
uncontrolled systems, and as a result have sometimes employed narrative
explanation. By contrast, simulation models can be used experimentally
and can generate large amounts of data. Here, using an agent-based model
of hunter-gatherer foraging in a previously unexplored ecosystem, we
consider how narratives might be identified from the trajectories
produced by simulations. We show how machine learning methods can
isolate qualitatively similar types of model behaviour based on
summaries of model outcomes and time series. We stand to learn from this
approach because it enables us to answer two questions: (i) under what
conditions (representations and/or parameterisations) do we observe in
the model what is recorded in the archaeological and/or
palaeoenvironmental record? and (ii) does the model yield unobserved
dynamics? If so, are they plausible? Using models to develop narratives
is a logical extension of the bottom-up approach inherent in agent-based
modelling and has the potential, alongside conventional methods of model
evaluation, to aid in learning from the rich dynamics of such
simulations.
Tags
Simulation
Agent-based models
Complexity
Machine learning
Validation
ecology
Settlement
explanation
systems
human-environment interactions
Science
Collapse
History
Pacific
Island
Long
Narrative
Historical sociology
Random forests