An Active Learning Exercise for Introducing Agent‐Based Modeling

Abstract

ABSTRACT Recent developments in agent‐based modeling as a method of systems analysis and optimization indicate that students in business analytics need an introduction to the terminology, concepts, and framework of agent‐based modeling. This article presents an active learning exercise for MBA students in business analytics that demonstrates agent‐based modeling by solving a knapsack optimization problem. For the activity, students act as naïve agents by using dice to randomly selecting items for a finite capacity knapsack to maximize the value of the knapsack. Students then design a greedy heuristic to skew the probability of selection item. These pencil‐and‐paper models are then implemented in a spreadsheet model to demonstrate the effects of altering the agents’ behavior. Finally, a binary integer programming model is examined to contrast agent‐based modeling with traditional mathematical programming formulations. This exercise is innovative because it combines student engagement via active learning with an innovative, individual‐based, modeling methodology.

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