Ignacio J. Martinez Moyano

In many areas of animal science, the questions are dominated by dynamic problems: outcomes evolve over time, adapt to interventions, and emerge from interacting biological, managerial, and institutional processes operating across temporal and spatial scales. These systems exhibit accumulation, feedback, nonlinearities, and time delays that can make intuitive reasoning unreliable, and that can cause well-intended policies to underperform or generate unanticipated consequences. Dynamic modeling and simulation provide a rigorous way to make such mechanisms explicit and to test whether observed patterns of behavior can arise endogenously from system structure. This paper positions system dynamics modeling as a foundational approach for feedback-driven explanation and policy design in animal systems. It also highlights agent-based modeling as a natural approach to capture heterogeneity, networks, and localized interactions in system behavior. The paper describes how artificial intelligence can strengthen dynamic modeling workflows by scaling evidence synthesis, supporting transparent and auditable structure development, and expanding feasible calibration and updating methods under uncertainty. Finally, the paper describes how high-performance computing enables ensemble-based inference and robust policy evaluation by making large-scale sensitivity analysis, uncertainty quantification, calibration, and scenario development and testing routine. Across these themes, the paper emphasizes methodological discipline: artificial intelligence and high-performance computing should augment—not replace—critical thinking, causal reasoning, rigorous structure development, thoughtful analysis, and transparent reporting. The combined use of dynamic modeling, artificial intelligence, and high-performance computing can help animal science build interpretable, testable, and updateable models that support learning and timely decision making in complex systems.

Public health experts studying infectious disease spread often seek granular insights into population health outcomes. Metapopulation models offer an effective framework for analyzing disease transmission through subpopulation mixing. These models strike a balance between traditional, homogeneous mixing compartmental models and granular but computationally intensive agent-based models. In collaboration with the Chicago Department of Public Health (CDPH), we developed MetaRVM , an open-source R package for modeling the spread of infectious diseases in subpop-ulations, which can be flexibly defined by geography, demographics, or other stratifications. MetaRVM is designed to support real-time public health decision-making and through its co-creation with CDPH, we have ensured that it is re-sponsive to real-world needs. We demonstrate its flexible capabilities by tracking influenza dynamics within different age groups in Chicago, by integrating an efficient Bayesian optimization-based calibration approach.

This article describes the history of the Beer Game. By triangulating information from literature, archival analysis, and interviews with experts in the field, the main changes in the game over its almost 70‐year history are identified. The article discusses three aspects of the game: 1) its structure (phases of its history, stocks and flows, parameters, etc.); 2) the process for playing the game; and 3) the game debrief. The structure of the Beer Game, and the process for running it, have stabilized over the years into what is now a de facto standard approach. Additional work is needed in the game debrief, specifically in the clarification of key insights and messages (depending on the context of the use of the game), in how to communicate such messages to different audiences, and in the development of support materials for its delivery. © 2024 UChicago Argonne, LLC. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Decisions are made, actions are taken, and the world is changing constantly as a result. The consequences of past decisions create the information that affects future decisions in an iterative, continuous set of feedback processes. To better comprehend real-world systems, change, and complex interactions, identifying and understanding feedback is paramount. The system dynamics (SD) approach, part of the operations research field and identified by systems philosophers as part of the first three waves of systems thinking, emerged out of servomechanisms engineering and control theory. It has been applied to understand and manage a wide range of complex problems in natural, engineered, and social systems. The SD approach combines the theory of information feedback, human decision making, and computer simulation to create a computer-aided approach to policy analysis and design. The SD approach favors a point of view that originates from within—an endogenous approach—to explain and propose alternatives for change in complex systems. Feedback and endogeneity, core concepts in system dynamics, enable closed-loop thinking to understand complex systems which go beyond open-loop thinking and reveal, in a holistic way, not only how, but also why things change over time. Additionally, SD uses a broad system boundary to provide such endogenous structural explanations of system behavior, and embracesmethodological pluralism in order to evolve, adapt, and take advantage of advances in computer processing speed and storage, data availability and analysis, and social science research and analytical methods.

This research explores opinions about best practices in system dynamics modeling elicited from a distinguished group of experts in the field. We address three questions: What do practitioners believe is the best way to undertake system dynamics modeling? What specific core activities are essential for exemplary action during the different stages of the modeling process? What do experts believe are the most important practices during the different stages of the modeling process? The researchers used a multi‐method approach involving interviews, virtual meetings using the Internet, statistical analysis of the generated data and, finally, a facilitated face‐to‐face meeting in which experts discussed the results of the study and their implications. The results of this research include 72 best practices grouped into six categories that reflect the stages of the system dynamics modeling process. Copyright © 2013 System Dynamics Society.

In the spirit of closing the “research-practice gap,” the authors extend evidencebased principles to statistics instruction in social science graduate education.¹ The authors employ a Delphi method to survey experienced statistics instructors to identify teaching techniques to overcome the challenges inherent in teaching statistics to students enrolled in practitioner-oriented master’s degree programs. Among the teaching techniques identified as essential are using real-life examples, requiring data collection exercises, and emphasizing interpretation rather than results. Building on existing research, preliminary interviews, and the findings from the study, the authors develop a model describing antecedents to the strength of the link between research and practice.