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1. Exploring the Viability of Agent-Based Modeling to Extend Qualitative Research: Comparison of Computational Platforms

   Splendido, S.; Berdanier, C.G.P. (January 2023, ASEE annual conference exposition)

   The purpose of this methods paper is to identify the opportunities and applications of agent-based modeling (ABM) methods to interpretative qualitative and educational research domains. The context we explore in this paper considers graduate engineering attrition, which has been a funded research focus of our group for ten years. In attrition research, as with all human research, it is impossible and unethical to imperil real graduate students by subjecting them to acute stressors that are known to contribute to attrition in order to “test” different combinations of factors on persistence and attrition. However, agent-based modeling (ABM) methods have been applied in other human decision-making contexts in which a computer applies researcher programmed logic to digital actors, invoking them to make digital decisions that mimic human decision making. From our research team’s ten years of research studying graduate socialization and attrition and informed from a host of theories that have been used in literature to investigate doctoral attrition, this paper compares the utility of two programming languages, Python and NetLogo, in conducting agent-based modeling to model graduate attrition as a platform. In this work we show that both platforms can be used to simulate attrition and persistence scenarios for thousands of digital agent-students simultaneously to produce results that agree with both with previous qualitative data and that agree with aggregate attrition and persistence statistics from literature. The two languages differ in their integrated development environments (IDE) with the methods of producing the models customizable to fit the needs of the study. Additionally, the size of the intended agent pool impacted the efficiency of the data collection. As computational methods can transform educational research, this work provides both a proof-of-concept and recommendations for other researchers considering employing these methods with these and similar platforms. Ultimately, while there are many programming languages that can perform agent-based modeling tasks, researchers are responsible for translating high quality, theory-driven, interpretive research into a computational model that can model human decision-making processes. 

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2. Assisted Parameter and Behavior Calibration in Agent-based Models with DistributedOptimization

   A'Auria, Matteo; Scott, Eric O.; Lather, Rajdeep Singh; Hilty, Javier (December 2020, International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS))

   Agent-based modeling (ABM) has many applications in the social sciences, biology, computer science, and robotics. One of the most important and challenging phases in agent-based model development is the calibration of model parameters and agent behaviors. Unfortunately, for many models this step is done by hand in an ad-hoc manner or is ignored entirely, due to the complexity inherent in ABM dynamics. In this paper we present a general-purpose, automated optimization system to assist the model developer in the calibration of ABM parameters and agent behaviors. This system combines two popular tools: the MASON agent-based modeling toolkit and the ECJ evolutionary optimization library. Our system distributes the model calibration task over very many processors and provides a wide range of stochastic optimization algorithms well suited to the calibration needs of agent-based models. 

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3. Agent-based model construction using inverse reinforcement learning

   https://doi.org/http://doi.org/10.1109/WSC.2017.8247872  

   Lee, K.; Rucker, M.; Scherer, W.; Beling, P.; Gerber, M.; Kang, H. (January 2017, 2017 Winter Simulation Conference (WSC))

   Agent-based modeling (ABM) assumes that behavioral rules affecting an agent's states and actions are known. However, discovering these rules is often challenging and requires deep insight about an agent's behaviors. Inverse reinforcement learning (IRL) can complement ABM by providing a systematic way to find behavioral rules from data. IRL frames learning behavioral rules as a problem of recovering motivations from observed behavior and generating rules consistent with these motivations. In this paper, we propose a method to construct an agent-based model directly from data using IRL. We explain each step of the proposed method and describe challenges that may occur during implementation. Our experimental results show that the proposed method can extract rules and construct an agent-based model with rich but concise behavioral rules for agents while still maintaining aggregate-level properties. 

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4. Distributed, Automated Calibration of Agent-based Model Parameters and Agent Behaviors

   D'Auria, Matteo; Scott, Eric O.; Lather, Rajdeep Singh; Hilty, Javier; Luke, Sean (July 2020, Autonomous Agents and Multiagent Systems (AAMAS))

   Agent-based models can present special challenges to model calibration due in part to their high parameter count, tunable agent behaviors, complex emergent macrophenomena, and potentially long runtimes. However, due to this difficulty, these models are most often calibrated by hand, or with hand-coded optimization tools customized per-problem if at all. As simulations increase in complexity, we will require general-purpose, distributed model calibration tools tailored for the needs of agent-based models. In this paper, we present the results of a system we have developed which combines two popular tools, the MASON agent-based modeling toolkit, and the ECJ evolutionary optimization library. This system distributes the model calibration task over many processors, provides many stochastic optimization algorithms well suited to the calibration needs of agent-based models, and offers the ability to optimize not just model parameters but agent behaviors. 

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5. Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus

   https://doi.org/10.3390/land9120519  

   Magliocca, Nicholas R. (December 2020, Land)

   null (Ed.)

   The nexus of food, energy, and water systems (FEWS) has become a salient research topic, as well as a pressing societal and policy challenge. Computational modeling is a key tool in addressing these challenges, and FEWS modeling as a subfield is now established. However, social dimensions of FEWS nexus issues, such as individual or social learning, technology adoption decisions, and adaptive behaviors, remain relatively underdeveloped in FEWS modeling and research. Agent-based models (ABMs) have received increasing usage recently in efforts to better represent and integrate human behavior into FEWS research. A systematic review identified 29 articles in which at least two food, energy, or water sectors were explicitly considered with an ABM and/or ABM-coupled modeling approach. Agent decision-making and behavior ranged from reactive to active, motivated by primarily economic objectives to multi-criteria in nature, and implemented with individual-based to highly aggregated entities. However, a significant proportion of models did not contain agent interactions, or did not base agent decision-making on existing behavioral theories. Model design choices imposed by data limitations, structural requirements for coupling with other simulation models, or spatial and/or temporal scales of application resulted in agent representations lacking explicit decision-making processes or social interactions. In contrast, several methodological innovations were also noted, which were catalyzed by the challenges associated with developing multi-scale, cross-sector models. Several avenues for future research with ABMs in FEWS research are suggested based on these findings. The reviewed ABM applications represent progress, yet many opportunities for more behaviorally rich agent-based modeling in the FEWS context remain. 

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