Using NetLogo to Build an Agent-Based Model for Teaching Purposes at the Graduate Student Level

Scholars and educators in agricultural economics face changing paradigms moving toward system-wide studies. Complex issues often involve quantitative and qualitative approaches, and it is difficult to access or acquire user-friendly tools that integrate both approaches. Agent-based modeling (ABM) offers a unique supplement to more conventional system-wide modeling frameworks, such as supply chain models, circular economy models, or coupled human and natural system models. The purpose of this paper is to show educators and graduate students about how agent-based models can be used in a graduate program curriculum. The paper shares some insight about the concept and sample applications of ABM, a popular analytic tool to study system-behavior-decision consequences through the interactions of entities. We use an example of simulating a buyer-grower market interaction for poultry products to demonstrate step-by-step strategies of using the NetLogo program to create an agent-based model. The benefits of using agent-based models include flexibilities of generating micro-level assumptions to approximate macro-level activities and outcomes, and the comprehensive integration between quantitative and qualitative analyses. The challenges are at the beginning phase to comprehend the scope and scale of analysis, define proper agents and behavioral characteristics, and generate meaningful interactions among agents in a logical manner.