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Systems thinking

75 Systems Thinking Tools Proven To Give Deeper Insights

If you want to become a systems thinker and achieve deeper insights about our complex world, you’re going to need a range of systems thinking tools.

But you might be overwhelmed by the sheer number and range of tools out there. You’ve probably seen a number of systems thinking toolboxes online. Some focus only on engaging stakeholders while others just cover mapping tools or systems thinking concepts.

This blog provides a comprehensive list of 75 essential systems thinking tools helpfully sorted into 10 categories.

For each tool I’ve created a short description. This will help you choose the right tool for the right purpose at the right time.

Linking to the best systems thinking tools and resources

When relevant, I link to the best place on the web to either get the tool itself or read a more in-depth description.

If you’re interested in learning how to use these tools, check out my online course all about how to solve complex social problems.

75 systems thinking tools in 10 categories

Instead of sorting through all the systems thinking tools, you can just jump to the category that best fits what you’re trying to do by clicking one of the sections below.

  1. Types of system problems (4)
  2. Types of system dynamics and system archetypes (16)
  3. Mental models and metaphors about systems (4)
  4. Concepts about systems (13)
  5. Mapping and visualizing systems (16)
  6. Brainstorming and engaging with stakeholders (7)
  7. Decision-making tools (6)
  8. Evaluation tools (6)
  9. Simulation and learning experiences (2)
  10. The ultimate multipurpose tool (1)

Why use systems thinking tools?

In a word, sense-making. Systems thinking tools help make sense of situations that are complex and hard to understand.

Sometimes the tool is a helpful mental model or concept that succinctly captures a dynamic otherwise hard to describe. On other occasions, for example, a tool may create a visual that better describes a system than words alone. Finally, tools may be about helping others see the systems they are operating within.

But just like any other tool, systems thinking tools extend your ability to modify the surrounding environment. By helping you better understand your environment, you’ll be better prepared to influence it in the way you want.

If you’re operating in a system – and it’s likely you are – becoming familiar with the tools below can enhance your complex problem-solving ability. That means you’re more likely to solve system problems and create the long-term impact you want.

Chapter 1: Types of system problems

It is important to recognize the type of systems problem you face because it has implications for how you should approach it.

#1. Mess: Coined by Ackoff in 1974, a mess is not just one problem, but a system of interrelated problems. The related insight, according to Ackoff, is that “the sum of the optimal solutions to each component problem taken separately is no an optimal solution to the mess.”

systems thinking tools

#2. Wicked problems: Introduced by C. West Churchman in 1967, a wicked problem is one where the information required to solve it depends on problem understanding. Rittel and Webber have aptly described the impossible implication: “in order to describe a wicked-problem in sufficient detail, one has to develop an exhaustive inventory of all conceivable solutions ahead of time.”

#3. Swamps: Developed by Schon, a swamp implies leaving the “high ground” of using research-based theory and techniques, and the necessity of using less rigorous techniques for swampy “low land” problems that don’t have a technical solution.

#4. Problematique (meta-problem): Introduced by Ozbekhan in 1970, a problematique is a system of messes. This is particularly apt for social problems in our society that are so interrelated as to be impossible to discuss in isolation.

Chapter 2: Types of dynamics and archetypes within systems

Recognizing basic variable relationships, as well as how they add up to common dynamics can help you more quickly see a system.

Dynamics

There are two basic types of causal loops: reinforcing (#4) and balancing (#5).

#5. Reinforcing feedback loops: Two variables change in the same direction, resulting in a compounding or exponential effect over time. An increase in X increases Y, which in turn increase X. Or, a decrease in X decreases Y, which in turn decreases X.

#6. Balancing feedback loops: Two variables change in opposite directions, often resulting in a plateau. An increase in X decreases Y, which decreases X.

systems thinking tools

#7. Time delays: The time between an action and the change(s) it manifests. Time delays often result in oscillations or inertia in system dynamics. In causal loop diagrams, delays are represented by two hash marks on any relational arrow.

#8. Unintended consequences: Outcomes of purposeful action not intended or foreseen. The law of unintended consequences is an adage used to describe the likely unintended (and undesirable) consequences of any kind of intervention in complex systems.

System archetypes

Systems dynamics was an approach developed in the 1950s by Jay Wright Forrester. Problems and systems are represented by causal loop diagrams (#43), which is a simple map of the system, component parts and relationships. Systems archetypes, which built on the work of Forrrester and John Sterman, were first catalogued as described below by Peter Senge in The Fifth Discipline (and added to by Donella Meadows and David Peter Stroh).

Below are the twelve most common system structures or patterns. In this case, I’ve found that a dozen causal loop diagrams can be overwhelming for most people. As such, I’ve only used short descriptions and common examples because they seem to be easier for most people to quickly digest.

The best resources for more in-depth descriptions are from William Braun (10 archetypes), Jorge Taborga (8 archetypes), and Wikipedia (10 archetypes).

#9. Balancing with delay: Response to action is delayed. If the delay is not understood by participants, action may under- or over-shoot the necessary amount to reach goals. Common example is supply chains where delays result in large inventory fluctuations.

#10. Limits to growth (or unanticipated constraints): A reinforcing feedback loop that leads to exponential growth until a system limit is reached. Continued action that had previously resulted in growth will not work and perhaps cause the opposite effect. Common example is learning a new skill.

#11. Shifting the burden (or unintended dependency): The symptom is addressed with short-term solutions to some effect, but the long-term cause of the problem isn’t remedied and likely gets worse. Common example is paying debts with more loans.

#12. Shifting the burden to the intervenor: Similar to shifting the burden, this version uses outside intervenors to successfully address symptoms, preventing system participant from feeling agency to address causes themselves. Common example is hiring outside consultants rather than learning in-house.

#13. Success to the successful (or winner takes all): A process by which a winner’s gains lead to more gains, and a loser’s losses leads to more losses. Common example is two products competing in the same company, where initial success of one product leads to more resources, which propels further growth over the other product.

#14. Accidental adversaries: Two parties who may be working for the same goal, but inadvertently become enemies, hurting their individual and collective chance at success. Common example is two non-profits with the same goals undercutting each other’s work in the pursuit of additional funding.

#15. Drifting/eroding goals (or unintended poor performance): Current pressures lead system participants to lower standards or goals, leading to deteriorating performance over time. Common example is federal budget deficit.

#16. Competing goals: Conflicting goals that are not possible to simultaneously reach, or too many goals that result in not achieving any of them. Common example is a strategic plan with over a dozen goals that no one can remember.

#17. Escalation: Two parties acting in a perceived zero-sum game where they see their welfare as dependent on their advantage over the other. Results in cycles of ever-increasing threat and aggression. Common example is gang warfare.

#18. Tragedy of the commons: A common resource is depleted by individuals seeking their own gain. A decreasing resource supply or diminishing returns leads to intensified efforts, perhaps fully using up the resource. Common example is mining a natural resource.

#19. Fixes that fail (or unintended consequences): An effective short-term fix creates unforeseen, longer-term consequences that require even more of the same fix. Common example is delaying maintenance, creating more expensive repairs in the future.

#20. Growth and underinvestment (or self-created limits): Continued growth requires significant investment in longer-term capacity that may hurt current performance. To maintain current performance, underinvestment is justified, but growth nonetheless slows due to lack of capacity. Common example is over-investing in advertising while underinvesting in human capital, resulting in a sub-par product and eventually lower sales.

Chapter 3: Mental models and metaphors about systems

These are the four most common metaphors about systems, and they are often used as systems thinking tools in systems change initiatives (learn more in my No-Bullshit Systems Change Guide).

#21. Iceberg: The iceberg metaphor is meant to show that we can’t see most of a situation’s causes. So, while we focus on real-time events, which is the tip of the iceberg, more foundational causes lie beneath the surface of our consciousness. The model encourages people to look at things “below water” like patterns, beliefs, power dynamics and mindsets.

systems thinking tools

#22. Root causes: The root causes metaphor is all about finding a condition’s initiating cause. Similar to the iceberg metaphor, what is above ground (the leaves) are obvious, whereas the less obvious cause is unseen below (the roots). The purpose of this metaphor is to get people to distinguish between symptoms and causes.

#23. Swimming in water: Just like the fish who doesn’t know what water is, in this analogy the the philanthropic sector is “swimming” in a system of non-explicit factors that nonetheless drive social change. It is posited that by focusing on non-obvious factors, practitioners can increase their odds of success. Check out The Water of Systems Change for a whole report based on this metaphor.

#24. Bathtub: The bathtub analogy is a simple way of describing any stock and flow system. The water is the stock, and the flows are controlled by the faucet (in-flow) and drain (out-flow). The purpose of this system map is to help people go beyond quantity at any given moment, and expand to consider rates of change.

Chapter 4: Concepts about systems

#25. Emergence: Most simply, the whole is more than the sum of its parts. Specifically, denotes properties of the whole (its structure or activities) not reducible to components.

systems thinking tools emergence

#26. Self-organization: The principle that order emerges of out initially independent and uncoordinated components. Implies that trying to manage or control systems is difficult due to its own dynamics.

#27. Interconnectedness: The general everything is connected to everything else, like an ecosystem.

#28. Hierarchy: Parts are treated as wholes, which are themselves made up of smaller parts, which are themselves wholes. Higher “levels” are more complex and have emergent behavior not present at lower levels.

hierarchy systems thinking tools

#29. Darkness: The idea that no complex system can be completely known, because each human has limited perceptions. Implies the need for multiple perspectives.

#30. Complimentarity: Coined by Neils Bohr, this term refers to the idea that no single perspective of a system can provide complete knowledge about the system. Similar to darkness (#29), implies the need for multiple perspectives.

#31. Equifinality: No matter the starting point, all actions lead to the same end point. Implies that some actions will have the same effect regardless of initial state.

#32. Multifinality: The same starting points will have dissimilar end points. Implies that some actions may result in different effects even given the same initial state.

#33. Requisite saliency: The idea that the parts of a system don’t have equal importance or relevance, but that these factors can only be determined through a process of examination and comparison.

#34. Requisite parsimony: Humans have a limit to the number of things they can process simultaneously, likely between 5 and 9 observations. From Miller’s famous The Magical Number Seven, Plus of Minus Two.

#35. Suboptimization: The optimal solution to a mess (a collection of interrelated problems), is not the sum of optimal solutions to each component problem. Attributed to a World War II observation by Charles Hitch.

#36. Synthesis: The opposite of reductive analysis, synthesis brings two or more ideas together to understand the whole in a different way than treating the parts separately.

#37. Satisficing: Because humans have incomplete information (a far cry from the “perfect information” presumed by traditional economics), they make the best possible choice at any given time based on what satifies their goals to a suffient degree. Coined by Herbert Simon, the word satisfice is a  portmanteau  of satisfy and suffice.

Chapter 5: Mapping or visualizing systems

This section categorizes mapping tools under brainstorming, hypothesizing, and simulating.

Mapping as brainstorming

#38. Rich pictures: Originally developed as part of Soft Systems Methodology by Peter Checkland, rich pictures is an individual or group method of creating a visual representation of an ill-defined or complex situation. The purpose is not to define the problem structure or logic model (unlike some of the mapping tools below), but to actively reflect on various perspectives about the situation.

#39. Cognitive mapping: Cognitive mapping is a method of creating a visual representation of a concept or process. Concepts are connected with arrows and linking phrases like “increases”, “causes”, or “is a part of”. Cognitive mapping has been used in Strategic Options Development and Analysis (SODA), where operational research (OR) interviews mappers and facilitates group processes to learn from the map. Mind mapping can be similar but instead of a free-form map it typically uses a tree structure. There are many concept- and mind-mapping software options.

#40. Connection circles: A brainstorming exercise to help people get started thinking about how variables are related. A circle is drawn and variables are listed on the outside. Participants then begin connecting variables with lines that go through the circle. This method can help identify closed loops that can be converted into causal loop diagrams.

Mapping as hypothesizing and representing complexity

#41. Behavior over time graph: A simple line graph that shows a variable’s change over time. Plotting multiple variables at the same time can help participants theorize about causal relationships between variables.

#42. Graphical function diagram: Similar to behavior over time graphs (see #41) in that two variables are being graphed, but in this version time is removed and the relationship between variables is charted. One variable is on the X axis and the other on the Y. The resulting graph hypothesizes the causal relationship between variables.

#43. Causal loop diagram: A diagram that visually shows how a set of variables are causally related, usually denoted by a increasing (positive) or decreasing (negative) relationships. This type of diagram focuses most on closed loops that are labeled as reinforcing or balancing. Causal loop diagrams are especially useful for identifying common system dynamics, or archetypes (see tools #9 through #20).

#44. Bayesian belief networks: A probabilistic visual representation of relationships that can be used in network analysis to determine probabilities. Often used in social network analysis.

#45. GIGA-maps: A mapping process that emerged from how designers apply systems thinking, GIGA-mapping attempts to create extensive visual maps that transcend traditional boundaries of boundaries and scale. Designers have used GIGA-mapping as a research tool to investigate design questions, as well as to create new knowledge.

#46. Viable systems model (VSM): Developed by operations researcher Stafford Beer, VSM models the organizational structure on any autonomous system capable of adapting itself to a changing environment. A key feature of all viable systems is that they are recursive.

#47. Social network analysis (SNA): A process of exploring and visualizing social structures using network and graph theory. Many different types of SNA software are able to analyze networks for key metrics like degree centrality, betweenness centrality, and closeness centrality. In addition to data analysis, visual representations provide avenues for qualitative analysis not otherwise available. There are many options of social analysis software.

#48. Policy structure diagram: A visual representation of steps in a decision-making processes. The map can be expanded by adding the factors that are weighed for each step.

Mapping as simulating

#49. Fuzzy cognitive mapping (FCM): FCM is a type of cognitive mapping that quantifies relationships between concepts with fuzzy logic, and allows the creation of simulation models to determine strength of impact. It is an excellent qualitative tool that combines the benefits of concept mapping with the empirical side of system dynamics.

#50. System dynamics simulation model: A computerized model that quantifies relationships and can measure stock and flows, analyze feedback loops, and run scenarios. This is a great option when there is an abundance of empirical data and all nodes can be quantified.

#51. Stock and flow diagram: Stock and flow is a way of representing quantities and rates in a more detailed way than in a causal loop diagram. Oftentimes people will start with a causal loop diagram and convert it into a stock and flow diagram by adding relevant quantities and rates.

#52. Discrete-event simulation: A way to model a system as a series of discrete events in time. Events occur at specific moments in time and result in state changes for the system. This type of simulation is particularly helpful in understanding complex behavior in processes, like in manufacturing and supply chains.

#53. Agent-based modeling: A computational model for understanding the behavior of independent agents and how their decisions result in aggregate, or system-wide behavior. This type of model is often used to re-create and/or predict how micro-scale behavior results in macro-scale behavior.

Chapter 6: Brainstorming and engaging with stakeholders

Brainstorming

#54. Double-Q (QQ) diagram: A modified “fishbone” diagram of cause and effect that separates “hard” (quantitative) variables from “soft” (qualitative) variables.

#55. Actor mapping: A visual way to brainstorm or represent stakeholders who are involved with or influence an issue. Distinct from stakeholder analysis, actor mapping helps to depict the many relationships between stakeholder to explore relational dynamics in a system.

Engaging stakeholders

FSG has the best step-by-by resources for engaging stakeholders with the following group mapping methods. These methods are good for brainstorming, exploring issues in groups, and finding common understanding. However, most mapping and visualizing methods listed above are far superior in terms of mapping actual system structures and dynamics.

#56. Appreciative inquiry: A method for helping a group of stakeholders find or create a common vision. Unlike other methods that may focus on problems, appreciative inquiry takes a positive, strengths-based approach that builds on successes that already exist within a system.

#57. Ecocycle mapping: A collaborative activity and mapping process that applies a 4-stage (development, conservation, destruction, and renewal) closed-loop system to a particular initiative or organization. The insight being explored is how an organization’s work may, like a natural ecosystem, go through life stages over time.

#58. Timeline mapping: A process for creating a visual and chronological map of events, actions, achievements over time. The purpose is to put an initiative’s or organization’s work into a broader context of how it is affected by external events.

#59. Trend mapping: A group process for brainstorming trends related to any given topic. Like timeline mapping, the purpose is to consider the broader context of system changes over time.

#60. World cafe method: A methodology for facilitating collaborative conversation and hosting large group dialogue.

Chapter 7: Decision-making tools

#61. Fundamental objectives hierarchy: A way of decomposing and organizing objectives that clarifies which are most important and which are a simply a means to another objective. The hierarchy is created by asking series of questions to clarify what is valued most. Often used in conjunction with means-end network.

#62. Means-end network: Usually created from a Fundamental Objectives Hierarchy, a means-end network is a way of organizing how objectives can be achieved.

#63. Cynefin framework: A conceptual framework for analyzing general behavior of a system and a guide for decision-making. It provides “sense-making” for five different types of system environments: obvious, complicated, complex, chaotic, and disorder.

#64. Stacey matrix: A two by two matrix to help categorize actions or tasks based on level of complexity. On one axis is a scale of agreement from “close to” and “far from”. On the other axis is a scale of certainty from “close to” to “far from”. Within the matrix are five areas very similar to the Cyfin framework: simple, complicated (2), complex and chaos.

#65. Ladder of inference: A visual representation of steps one goes through, often without realizing it, to get from a fact to a decision. The original concept of each step depicted as a rung on a ladder was created by Chris Argyris. The tool is often used to help people think about how they think, and to share their thinking process with others.

#66. Force field diagram: Created by social psychologist Kurt Lewin, a force field diagram represents situations in which forces that drive change are in equilibrium with forces that resist change. For a change to occur, it is hypothesized, driving forces must be increased and resisting forces must be decreased. Like the ladder of inference, this tool helps people explain their thinking about why they made a decision.

Chapter 8: Evaluation tools

#67. 5Rs framework: Used by USAID to assessing local systems and the effectiveness of programs, the framework focuses on the key aspect of any system: Results, Roles, Relationships, Rules and Resources. The tool is used as a lens in evaluation to see the system “as is” and to envision desired changes (the system “to be”).

#68. Theory of change: A method and comprehensive description of how actions lead to desired results. Organizations and programs often use the tool to brainstorm desired long-term outcomes and work backwards to identify the conditions that must exist to create those outcomes. While an excellent logic tool, its linear progression can be limiting in complex situations in which change is less straightforward.

#69. Logic model: Often used in program planning, the tool hypothesizes a logical sequence of what an intervention’s outcomes will be. Like the theory of change, logic models are often used to focus on desired outcomes and move backward to actions.

#70. After action review: A structured process for analyzing what happened, why, and how it can be done better in the future. An excellent learning tool to explore intended results versus actual results achieved.

#71. Outcome harvesting: A highly participatory evaluation approach that looks at what has changed and works backward to determine if or how an intervention contributed to the result. The approach is used in complex situations in which in is not possible to prescribe in advance what a program’s interventions or outcomes will be. Learn more at Better Evaluation or in Ricardo Wilson-Grau and
Heather Britt’s book.

#72. Critical systems heuristics (CSH): A framework of twelve questions, developed by Werner Ulrich and later enhanced with Martin Reynolds, to surface judgements of boundaries in any system. CSH is an evaluation tool that questions perspectives about means and ends by asking “what is” and “what should be”.

Chapter 9: Simulation and learning experiences

#73. Management flight simulator: A learning environment that provides participants with experiential and conceptual lessons based on simulated real-world situations. Time is usually compressed so that participants can see the long-term consequences of their actions. Often used as training for corporate managers (or in high-school economics courses), the most classic example is the Beer Game. The best resource to play simulations online is MIT Sloan.

#74. Learning laboratories: Workshops that combine instruction in systems thinking with repeated use of games and/or management flight simulators. The intent is to encourage reflection about one’s own role in systems with double-loop learning.

Chapter 10: The ultimate multipurpose systems thinking tool

#75. Your brain: Yup, that’s right. Your brain is a complex problem-solving machine. Don’t forget that systems thinking tools themselves can’t provide you with the answers. And, tools can even serve to magnify your own biases if you’re not careful. No matter what tools you use, your job is to be a critical systems thinker in search of reasonable conclusions.

Learn how to use these systems thinking tools

Are you ready to get to work actually applying these systems thinking tools to real-world problems? Strap on a tool belt and join me in my online course all about how to solve complex social problems.