In recent decades, systems dynamics have proven to be a valuable tool for mapping relationships and key dynamics within complex systems and organizations. System dynamics is a broad methodology to help understand and manage feedback systems. Systems dynamics include various toolsets from the qualitative to the quantitative perspective. Nevertheless, they do have a shared focus on feedback, so we will delve into what that means. We distinguish systems dynamics from many other tools by including quality data in the form of clear diagrams and the structure of cuts across the full range of stages of the feedback process. Systems dynamics include: (1) Qualitative and quantitative modeling approaches. That means the feedback loops can be two qualitatively different kinds (i.e., positive and negative), and quantitative means that values associated with the two nodes within the related change in the same direction. (2) A rich set of analysis tools includes a qualitative and quantitative model. (3) Builds atop a rigorous mathematical foundation. That is a rich set of analysis tools that reflects the fact that it has its quantitative side based precisely on some accepted areas of applied mathematics built on a well-understood rigorous mathematical background that we know applies everywhere in engineering and science. (4) A set of time-frame techniques for working with various interdisciplinary bodies. (5) Evolved software allows you to focus on what is being described – not how it is being done. We call evolving software something that has been around for a long time that allows us to focus on what is described and less on how it works in the background. Thus, for those with an experience in computer science, this refers to declarative techniques for describing models. So, rather than requiring a modeler to spend a significant amount of time running the simulation, as is common in agent-based modeling or micro-simulation modeling, we describe it graphically in various ways. We are putting in some formulas, and the software is outstanding in determining how it used to be run. (6) Techniques for interfacing closely with cognate areas (e.g., design sciences evidence, based practices, applied mathematics, other modeling approaches).
The models we build with systems dynamics from the qualitative to the quantitative approach help us reflect on how the system works and how it can be affected by our attempts to change. So, their ways of asking what-if questions help us deal with counterfactuals. We can look at ways to interact with the system or other practices for structuring the plan. Adding feedback to the system or interrupting input could make things more cost-effective and cost-effective. Furthermore, they could have a high impact on the system in a desirable way. The models we built using system dynamics can help us prioritize the data collected. Our knowledge of the outside world is often limited and distorted by various biases and data collection. We have little time to improve the available data. We only have so much time to gather new information and so many resources to allow us to do that. By prioritizing, we can focus efforts on areas likely to be treated with the best results. Furthermore, it can help us understand how different system components interact to give rise to the phenomena we observe.
System dynamics may not give us accurate predictions of what will happen. Still, it can provide an overview of the dynamics in the system and let us know where those points would drive a nonlinear exponential change. For example, our best-laid plans may fail because we failed to account for the possibility of an energy crisis, which could disrupt economic and technological patterns and cause transportation services to fail.
Georgios Ardavanis – 18/02/2023