A clear definition of system dynamics modeling can provide shared understanding and clarify the impact of the field. We introduce a set of characteristics that define quantitative system dynamics, selected to capture core philosophy, describe theoretical and practical principles, and apply to historical work but be flexible enough to remain relevant as the field progresses. The defining characteristics are: (1) models are based on causal feedback structure, (2) accumulations and delays are foundational, (3) models are equation‐based, (4) concept of time is continuous, and (5) analysis focuses on feedback dynamics. We discuss the implications of these principles and use them to identify research opportunities in which the system dynamics field can advance. These research opportunities include causality, disaggregation, data science and AI, and contributing to scientific advancement. Progress in these areas has the potential to improve both the science and practice of system dynamics. © 2024 The Authors. System Dynamics Review published by John Wiley & Sons Ltd on behalf of System Dynamics Society.

Exploring the dynamic mechanisms of coupled sociohydrologic systems is necessary to solve future water sustainability issues. This paper employs system dynamics modeling to determine hydrologic and economic implications of an irrigation efficiency (IE) policy (increased conveyance efficiency and field efficiency) in a coupled sociohydrologic system with three climate scenarios. Simulations are conducted within the lower Rio Grande region (LRG) of New Mexico for the years 1969 to 2099, including water, land, capital, and population modules. Quadrant analysis is utilized to compare the IE policy outcomes with the base case and to categorize results of simulations according to hydrologic and economic sustainability. The four categories are beneficial, unacceptable, unsustainable agricultural development, and unsustainable hydrology. Simulation results for the IE policy analyzed here fall into the categories of unsustainable agricultural development or unacceptable, suggesting there are long-term negative effects to regional economies in all scenarios with mixed results for hydrologic variables. IE policy can yield water for redistribution as increased unit water supply in the field produces more deep percolation; however, IE policy sacrifices regional connectivity. Specifically, simulation results show that the policy increases abundance by 4.7–74.5% and return flow by −3.0–9.9%. These positive results, however, come at the cost of decreased hydrologic connectivity (−31.5 to −25.1%) and negative economic impacts (−32.7 to −5.7%). Long-term net depletions in groundwater are also observed from loss of hydrologic connectivity and increased agricultural water demand from projections of increased consumptive use of crops. Adaptive water management that limits water use in drought years and replenishes groundwater in abundant years as well as economic incentives to offset the costs of infrastructure improvements will be necessary for the IE policy to result in sustainable agriculture and water resources.

This paper explores the possibility and plausibility of developing a hybrid simulation method combining agent-based (AB) and system dynamics (SD) modeling to address the case study of produced water management (PWM). In southeastern New Mexico, the oil and gas industry generates large volumes of produced water, while at the same time, freshwater resources are scarce. Single-method models are unable to capture the dynamic impacts of PWM on the water budget at both the local and regional levels, hence the need for a more complex hybrid approach. We used the literature, information characterizing produced water in New Mexico, and our preliminary interviews with subject matter experts to develop this framework. We then conducted a systematic literature review to summarize state-of-the-art of hybrid modeling methodologies and techniques. Our research revealed that there is a small but growing volume of hybrid modeling research that could provide some foundational support for modelers interested in hybrid modeling approaches for complex natural resource management issues. We categorized these efforts into four classes based on their approaches to hybrid modeling. It appears that, among these classes, PWM requires the most sophisticated approach, indicating that PWM modelers will need to face serious challenges and break new ground in this realm.

The utility perceived by individuals is believed to be different from the utility experienced by that individual. System dynamicists implicitly categorize this phenomenon as a form of bounded rationality, and traditionally employ an exponential smoothing function to capture it. We challenge this generalization by testing it against an alternative formulation of utility perception that is suggested by modern theories of behavioral sciences. In particular, the traditional smoothing formulation is compared with the peak–end rule in a simple theoretical model as well as in a medium-size model of electronic health records implementation. Experimentation with the models reveals that the way in which utility perception is formulated is important, and is likely to affect behavior and policy implications of system dynamics models.