Complex is not the same as complicated.
Complicated systems have predictable outcomes. With enough information and understanding, you can forecast the behavior of the system. If you can accurately describe what each part of the system does, you can probably predict what the entire system will do. The more complicated the system, the harder it is to make accurate predictions, but it is still reasonable to believe you can. Complicated systems are also controllable. Scripts or sets of instructions can be designed to govern a complex system. Jigsaw puzzles have predictable outcomes and enable the solver to design a process for achieving that outcome.
Characteristics like homogeneity (when things are generally the same) and linearity (when specific inputs generally lead to specific associated outcomes) enable simple or complicated systems to behave more uniformly and predictably. These systems tend to be deterministic: given a certain input, the outcome is usually the same with little variability. Feedback loops are limited in simple or complicated systems, making adaptation and self-organization harder. And there are limited connections between levels or subsystems, so local actions do not shift or scale to the entire system.
Complex systems are inherently unpredictable. They can adapt, learn, or evolve in response to changes, making their future states or behaviours difficult to predict. They are not controlled by a single entity. Instead they self-organize through the interactions of their parts. Their future states are emergent, displaying characteristics that arise unexpectedly. Although sandcastle makers try to predict the outcome they hope to achieve, many elements (e.g., wind, rain, sand consistency, impact of other builders) are difficult to control, making the final sculpture somewhat emergent.
Complex systems are often heterogeneous, made up of diverse elements with varying behaviors and properties. They are also nonlinear: small inputs can produce disproportionately large outcomes, and cause-effect relationships are often unpredictable. They are dynamic, continuously evolving, and change over time in response to internal and external influences. Elements in a complex system are interdependent: the behavior of one part affects and is affected by others, and the system’s past behavior influences its future behavior (Feedback Loops). Complex systems are adaptive and self-organizing, capable of learning, evolving, and restructuring themselves without external control. Critically, complex systems show emergence, where new patterns, behaviors, or properties arise from the interactions among parts—outcomes that could not be predicted by examining individual elements, alone.
Deeper Dive
- Finegood, DT, and C Yakimov. Why Systems Thinking Is Needed to Center Trust in Health Policy and Systems Comment on “Placing Trust at the Heart of Health Policy and Systems.” Int J Health Policy Manag 13: 8706, 2024.
- Finegood, DT, LM Johnston, M Steinberg, CL Matteson, and PB Deck. Complexity, systems thinking, and health behavior change. In S. Kahan, A. C. Gielen, P. J. Fagan, & L. W. Green (Eds.), Health behavior change in populations (pp. 435–458). Johns Hopkins University Press, 2014.
Related Frameworks
- Cake Rocket Child: examples of simple, complicated and complex
- Cynefin: uses relationship between cause and effect to make distinctions
- Feedback Loops: A primary why that past behaviours of complex systems impact future behaviours
- Order to Chaos: Extends the distinction and labels boundaries
- Stacey Matrix: stratified by relationship between certainty and agreement