Here is a sampling of some tools and methods that practitioners and researchers might use when using a complex systems thinking (CST) lens*.
Tools and Methods
Causal loop diagrams (CLDs)
Qualitative illustrations of mental models, highlighting causality and feedback loops. Feedback loops can be either reinforcing or balancing, and CLDs can help to explain the role of such loops within a given system. The drawings can be further developed by quantifying the relationships between variables to form a stock and flow diagram.
Quantitative tools used to illustrate causal loop diagrams. They are an illustration of a system that can be used for model-based policy analysis in a simulated, dynamic environment. Stock and flow diagrams explicitly incorporate feedback to understand complex system behavior and capture nonlinear dynamics.
Also known as change management history. It aims to generate knowledge about an actual system by compiling a systematic history of key events, intended and unintended outcomes, and measures taken to address emergent issues. It involves in-depth interviews with key stakeholders to build an understanding of the performance of the system from a number of different points of view.
A workshop-based approach that combines impact pathway logic models and network mapping through a process involving stakeholder engagement. PIPA workshops aim to help participants make explicit their mental models about how projects run, and to reach consensus on how to achieve impact.
A set of tools, such as flow charts, to provide a pictorial representation of a sequence of actions and responses. Their use can be quite flexible, such as to make clear current processes, as a basis for identifying bottlenecks or inefficient steps, or to produce an ideal map of how they would like them to be.
A number of generic patterns that are commonly found in behaviors between the parts of a system. They provide templates to demonstrate different types of balancing and reinforcing feedback loops, which can be used by teams to come to a consensus about how a system is working, and particularly about how performance changes over time.
Agent based modeling (ABM)
Computer models that create a virtual representation of a complex system, modeling individual agents (people, institutions, bacteria, etc.) who interact with each other and the environment. These interactions are based on simple, pre-defined rules; however, in a complex system, these simulated interactions identify complex patterns of emergence and self-organization.
Involves the application of network theory—which uses graphs to identify the relationships between units—to social entities (e.g., people, groups, organizations). SNA uses nodes (individual actors within a network), and ties (the type of relationships) between the actors, and displays the networks and analyzes the nature of the relationships.
A strategic planning method that identifies possible future events, thus producing an array of all future possible outcomes. These can involve quantitative projections and/or qualitative judgments about alternatives. The value lies more in learning from the planning process than the actual plans or scenarios.
Not a single method, but an approach that uses a set of tools (primarily causal loop diagrams and stock and flows) to understand the behavior of complex systems over time. By focusing on the role of feedback loops, they are designed to solve the problem of simultaneity (mutual causation) by being able to change variables over small periods of time, while allowing for feedback and various interactions and delays.