Biology Reference
In-Depth Information
CHAPTER
4
Global Dynamics Emerging
from Local Interactions:
Agent-Based Modeling for
the Life Sciences
David Gammack
∗
, Elsa Schaefer
∗
and Holly Gaff
†
∗
Department of Mathematics, Marymount University, Arlington, VA, USA
†
Department of Biological Sciences, Old Dominion University, Norfolk, VA, USA
4.1
INTRODUCTION
4.1.1
Agent-Based Modeling and the Biology Mind Set
Agent-based (or individual-based) models (ABMs) are stochastic simulations built on
interactions between individuals in a population (agents) or a set of populations and
their environment. The modeler defines an ABM by identifying a set of autonomous
agents, each defined by individual characteristics, who interact locally using adaptive
behavior that is based on their characteristics, their neighbors' characteristics, and
the local environment. The culmination of the model design ultimately leads to
emergence: local interactions leading to global phenomena. Bonabeau writes that
agent-based modeling is “a mind set more than a technology” [
1
]. Indeed, ABMs are
particularly helpful because of the intuitive nature of their structure and approach.
Biological research seeks to identify truths about biological systems at the most
fundamental level. This often involves a great amount of detail that is not easily
incorporated into population-level models. Biologists appreciate the ability of the
ABM to include intricate details at multiple scales. The rules applied to agents within
a model are intuitive analogies to biological interactions. Additionally, ABMs can
be built with language that is easily understood, in contrast to modeling efforts that
abstract populations and interactions to mathematical constructs.
As we will explore in more detail in Section
4.3
, ABM frameworks also encourage
repeated simulations mimicking the replication of a typical experimental setup. This
parallel helps scientists understand how a model is simulating the experiment and
how one can interpret the results. ABMs allow for agents to adapt and grow, which
also mimics most biological systems. While error propagation is a concern with many
calculations, careful model development and sensitivity analysis can help reduce the
likelihood of spurious results from accumulating errors [
2
]. Overall, ABMs provide
a natural translation of biological experiments into computer-simulated models that
allow the exploration of research questions and hypotheses beyond what can be done
in a lab or field setting because of cost, time, biological or ethical constraints.
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