System Dynamics for Academia
Scholars carry out academic research in system dynamics in universities and national and corporate research labs around the world. In universities, system dynamics scholars are found in schools of business, engineering, and science, among others. System dynamics research includes theorizing about, empirically testing and quantifying the processes that underlie the dynamics of diverse social, technical, natural and biological systems. From organizational transformation, project management and process improvement to macroeconomics and economic cycles, climate change, epidemiology, body weight dynamics, and the evolution of ecosystems, system dynamics research has deepened our understanding of the origin of dynamics, tested hypotheses empirically, and informed policy analysis. Two additional goals are sometimes pursued in these studies. The process of system dynamics modeling with clients is leveraged to bring diverse stakeholders together, improve their mental models, and enhance policy. In addition, system dynamics models and tools are frequently used for pedagogical purposes, from the K-12 grades, to universities and graduate schools, to executive education, to creating learning environments with management flight simulators for business leaders and government policy makers. System dynamics researchers not only develop and use the tools in these contexts but carry out research on their effectiveness to help improve protocols for their use.
Another stream of SD research has focused on understanding human performance in dynamic environments. Experimental studies have explored how human subjects make decision and learn in dynamic tasks, where individual actions change the state of the system and the rewards and opportunities in future. These studies motivate the use of formal models to support improvements in human mental models and point to better designs for simulation based learning environments.
Expanding the analytical toolbox of system dynamics constitutes a third research stream. Methods for estimation of dynamic models (e.g. derivatives of maximum likelihood and indirect inference), optimization and control of these systems (e.g. approximate dynamic programming), formalizing the links between model structure and behavior (e.g. eigenvalue analysis), and decision analysis (e.g. decision trees) using these models are common research targets. This type of research can be heavily analytical and often closely interacts with literature in control theory, econometrics, operations research and decision analysis, among others.
While closely related to simulation research in management science and beyond, the system dynamics approach to modeling has a few distinctive features. It is characterized by a focus on endogenous explanations for dynamic phenomena. Dynamics are explained as arising primarily endogenously within the boundary of a model from the interactions among the elements and actors in the system, rather than from exogenous inputs. Every attempt is made to represent these causal processes realistically, consistent with available empirical evidence, and robust to extreme inputs outside of the historically observed range. System dynamics researchers strive to capture the causal processes at play and the representation of these should correspond to the real-world processes in the system under study, be consistent with available empirical evidence, and be robust to extreme inputs outside of the historically observed range. These considerations require SD modelers to draw on a wide range of qualitative and quantitative data. For example, system dynamics modelers not only use traditional econometric methods to estimate model parameters using quantitative data, but also routinely augment those methods with qualitative research methods including use of archival documents, interviews, and ethnographic methods and direct observation of decision making and organizational processes. Model testing involves quantitative assessment of the ability of the model to reproduce the behavior of the system of interest, and a wide range of additional tests including structure assessment, dimensional consistency, extreme condition, behavior reproduction, surprise behavior, sensitivity analysis, and system improvement tests, among others. Furthermore, the broad model boundary and first-hand understanding of complex, multi-stakeholder systems typically requires collaborative research involving domain experts, clients (e.g. policy makers), and system dynamics modelers. Additionally, model transparency and replicability are key, policymakers and experts are engaged throughout, and graphical model representation and intuitive variable names are utilized extensively.
Besides System Dynamics Review, the flagship journal for the field, system dynamics research has appeared in over 500 academic journals, from Administrative Science Quarterly to Zeitschrift fur Physik, including the top publications in disciplines as diverse as management, public policy, and healthcare. The results have also had significant real world impact on important management and policy problems, such as climate change negotiations, healthcare reform, and polio eradication, to name a few, winning many awards from different academic societies outside the field of system dynamics.