Biology Reference
In-Depth Information
Working with agent-based models can engage students at all levels of Bloom's
Taxonomy [
70
]. Experiments using pre-programmed ABM simulations offer the
opportunity to inform students in a classroom setting, helping them to understand
and remember basic mechanisms that contribute to the global dynamics [
71
]. For
example, students could adjust parameters in pre-created epidemic models, running
repeated simulations to understand the impact of various disease properties or health
policies on the spread of disease. Students could explore a simulation of wound heal-
ing to gain an intuitive understanding of the factors that positively and negatively
influence healing. They could explore a model that explains how ants might queue
themselves into our kitchens. Through a careful evaluation of pre-existing models,
students have an opportunity to develop a deep and memorable understanding of
underlying behaviors. In addition, students are able to observe that a single simula-
tion may have dramatically different results from what is most commonly observed
in other simulations, thus reinforcing valuable lessons about stochasticity and the
importance of repetition.
Students who take the time to develop and code individual simulations have the
most opportunity to learn. As we mentioned earlier, through the creation of an ABM
and its subsequent analysis, students have a platform which allows them to postulate
and experiment with hypotheses. The process of model creation requires students to
consider all factors that are contributing to the agent-environment interactions and
evolution. Students are forced to explicitly describe all relevant assumptions, and the
creation process will most likely alert students to gaps in their understanding, encour-
aging a search for new sources of information or perhaps a re-reading of existing
research papers with a new mind set. Complex models are best approached with a
divide-and-conquer technique, and teamwork becomes rather meaningful and inspir-
ing as individual group members contribute code for the behaviors they choose to
describe. Now group work, rather than feeling burdensome, allows the team to come
together and see a whole that is far better than the sum of its parts, and students
begin to appreciate the collaborative nature of science. Through the culmination of
the model creation and analysis process, students often gain a first experience as true
scientific researchers.
Agent-based models provide a powerful tool for research and exploration in many
subjects including biology. The intuitive nature of the ABM framework makes the
exploration of existingABMs an inviting and informative tool for biologists regardless
of their prior computational experiences. Software platforms such as NetLogo make
model development a realistic goal for scientists from all backgrounds.
Acknowledgments
HDG would like to thank Daniel Drake Tillinghast, David Gauthier, Dan Sonenshine,
and Wayne Hynes at Old Dominion University and Colleen Burgess for her help in an
initial version of TICKSIM. HDG also thanks the ODUHonors College for support of
Research for Undergraduates in Math and Science. ES and DG thank the DISCOVER
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