Global Dynamics Emerging from Local Interactions: Agent-Based Modeling for the Life Sciences - Mathematical Concepts and Methods in Modern Biology - page 110

Another significant contributor to the development of ABMs is Craig Reynolds,
who, while he has not yet earned a Nobel prize, does have a 1998 Academy Award for
“pioneering contributions to the development of three dimensional computer anima-
tion for motion picture production” [ 13 ]. In 1986, Reynolds created a computer ani-
mation that simulated flocking by having each of his animated birds make its choices
for movement based only on the positions and velocities of its neighboring birds,
providing a strong illustration of how global properties emerge from local choices.
An interesting and unexpected property of his work is the unpredictability of a flock's
motion past short time scales [ 14 ]. Reynolds maintains a nice movie and description
of the flocking project on his personal webpage [ 14 ], where he also provides an exten-
sive list of his and others work in ABM creation and other computational models of
group motion.

4.1.3.2 ABMModelingExercises:Flocking
An example inspired by Reynold's work with flocking is also available as a sample
model within NetLogo under the Biology models [ 15 ]. The model gives each bird
the same set of instructions — there are no leaders with unique rules — and yet we
observe an emergent flocking behavior as the model runs. There are three sets of rules:
alignment, separation, and cohesion.
Exercise 4.19. Open the model and click the setup button. Do you see any pattern
in the alignment of the triangles?

Exercise 4.20. In what way(s) do you observe a uniform flocking throughout the
grid over time, and in what ways do you see individual variation affecting the global
patterns?

Exercise 4.21. What changes can you make to the sliders that increases the global
organization of the birds?

Exercise 4.22. Explore the “Procedures” 1 tab and give detailed, intuitive descrip-
tions of what is meant by alignment, separation, and cohesion.

Exercise 4.23. What are the agents in this model? What characteristic(s) does each
agent have? What are the rule(s) governing the system?

Since the work of the pioneers, agent-based models have been used extensively
to explore patterns in biology at various system granularities. The models allow us
to consider how the nature of individual variation, including variability in space,
demographic variability or life cycle details, phenotypic variation or behavior, expe-
rience and learning, and/or genetics and evolution combine to create population-level
“emergent” patterns or characteristics. A great resource for many specific examples
is SwarmWiki [ 16 ]. ABMs are used at the cellular level to understand intracellu-
lar signaling and metabolic pathways [ 17 - 20 ], cancer [ 21 , 22 ] or infectious diseases
[ 23 , 24 ]. At the cellular level, ABMs may be used to model vascular biology [ 25 , 26 ]

1 Instructor's note: While the code under “Procedure” is short, a careful answer to this exercise may
take an hour or more to complete.