Agent-Based Modeling and Simulation in Mathematics and Biology Education
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Agent-Based Modeling and Simulation in Mathematics and Biology Education Erin N. Bodine1 · Robert M. Panoff2 · Eberhard O. Voit3 · Anton E. Weisstein4 Received: 12 February 2020 / Accepted: 11 July 2020 © Society for Mathematical Biology 2020
Abstract With advances in computing, agent-based models (ABMs) have become a feasible and appealing tool to study biological systems. ABMs are seeing increased incorporation into both the biology and mathematics classrooms as powerful modeling tools to study processes involving substantial amounts of stochasticity, nonlinear interactions, and/or heterogeneous spatial structures. Here we present a brief synopsis of the agent-based modeling approach with an emphasis on its use to simulate biological systems, and provide a discussion of its role and limitations in both the biology and mathematics classrooms. Keywords Agent-based models (ABMs) · Simulation · Pedagogy · Education
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Erin N. Bodine [email protected] Robert M. Panoff [email protected] Eberhard O. Voit [email protected] Anton E. Weisstein [email protected]
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Department of Mathematics and Computer Science, Rhodes College, 2000 N. Parkway, Memphis, TN 38112, USA
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Shodor Education Foundation and Wofford College, 701 William Vickers Avenue, Durham, NC 27701, USA
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Department of Biomedical Engineering, Georgia Institute of Technology, 2115 EBB, 950 Atlantic Drive, Atlanta, GA 30332-2000, USA
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Department of Biology, Truman State University, 100 E. Normal Street, Kirksville, MO 63501, USA 0123456789().: V,-vol
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1 Introduction Agent-based models (ABMs) are computational structures in which system-level (macro) behavior is generated by the (micro) behavior of individual agents, which may be persons, cells, molecules or any other discrete quantities. Typical ABMs contain three elements: agents, an environment, and rules governing each agent’s behavior and its local interactions with other agents and with the environment. Decades of advancement in computer power has made agent-based modeling a feasible and appealing tool to study a variety of complex and dynamic systems, especially within the life sciences. As the use of ABMs in research has grown, so too has the inclusion of ABMs in life science and mathematical modeling courses as a means of exploring and predicting how individual-level behavior and interactions among individuals lead to system-level observable patterns. ABMs are now one of the many types of models students studying the life sciences or applied mathematics should encounter in their undergraduate education. Prior to the introduction of ABMs into biological and applied mathematics curricula, the clear model format of choice was the ordinary differential equation (ODE), or maybe a pair of them; occasionally, discrete difference equations and/or matrix equations would also be introduced. Exponential growth and decay were ready examples, paving the way for extensions of the exponential growth process toward a carrying capacity in the form of th
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