Systems Mapping

Essence

“All models are wrong, but some are useful.” — George E.P. Box

Systems mapping is the central piece of systems analysis. While the term can mean many different things, at TCI, we understand it as a process of constructing visual models of a system (or a system/challenge constellation) that generates useful insights, such as making visible the nodes of a system and the causal relationship among those nodes.

There are a number of reasons why systems mapping is such an important step in designing a systemic investment program:

It is a form of analysis that fosters a systemic mindset, by illuminating the relationships and connections between the different elements in a given system.
It often leads to new knowledge and insights about your system of interest, which you will rely on in the subsequent phases of your systemic investment journey.
It is a great tool when utilized in a collaborative process. By helping to bring together multiple perspectives, it can support you in building relationships with other stakeholders in the system, and aligning on key actions needed for system transformation.

But what kind of tool is a systems map really?

In essence, a system map is a simplified representation of certain aspects of reality, designed to support informed action.

System maps are highly flexible, with no strict rules on the type or robustness of information used in the mapping process. In our systemic investing practice, for example, the lived experience and expertise of stakeholders in a particular system are just as valid as mapping inputs as evidence from academic literature.

However, this flexibility also introduces risks, particularly the potential for bias. Subjectivity is inevitable, as intuition is shaped by one's education, cultural background, expertise, personal traits, and life experiences. To enhance validity and minimize bias, it is crucial that you involve a broad and diverse group of stakeholders in the development and testing of any systems map.

Finally, systems maps have inherent limitations. While they can help anticipate outcomes, they are not suited for precise predictive analysis. Nor can they provide clear, definitive answers about how a complex adaptive system functions or how best to intervene in it. Instead, systems maps serve as a powerful tool for building intuition, forming hypotheses, and guiding action. Given the unpredictability and complexity of real-world systems, using systems maps effectively requires humility, patience, and a willingness to embrace uncertainty.

The essence of all this is that systems mapping presents an analysis tool that doesn’t and cannot produce a definitive representation of reality. Rather, it will help you and your partners pull together a wide range of perspectives into a coherent whole that can serve as basis for collaboration and investing.

Overview of Systems Mapping Frameworks

There is no shortage of free online resources and guiding materials on systems mapping—just do a simple web search with the term “system mapping” to get an impression.

Here are three prominent systems mapping methods, though they are by no means the only ones:

Causal Loop Diagrams (CLDs): CLDs help map cause-and-effect relationships in complex systems. By identifying reinforcing and balancing feedback loops—circular relationships where nodes in the system affect themselves—they reveal patterns of system behavior over time. For example, in the food system, a CLD might illustrate how conventional, yield-maximizing agriculture based on mono-cropping and heavy fertilizer use leads to soil degradation and declining biodiversity; how that results in reduced agricultural productivity; and how that, in turn, prompts farmers to further increase fertilizer usage, thereby creating a reinforcing feedback loop which ultimately leads to complete soil depletion.

Stock and Flow Diagrams: Rooted in system dynamics, this framework distinguishes between "stocks" (accumulated resources like capital, soil fertility, or workforce skills) and "flows" (changes over time, such as investment inflows or carbon emissions). This approach will help you understand how interventions affect resource accumulation or depletion.

Value Network Mapping: This approach visualizes how value (monetary, social, or environmental) moves through a system. It is particularly useful in identifying financial bottlenecks and opportunities to realign incentives for systemic impact. A food system transformation project might use this to trace how investment capital flows from investors to regenerative farmers.

Each of these maps provides a unique lens on system complexity and can be combined for deeper insight into your system/challenge constellation.

Transition Mapping

At the TCI, we have created a new type of map that we believe is particularly useful in systemic investing: the “Transition Map”.

In some sense, a Transition Map is a combination of multiple map archetypes, specifically tailored to the practice of systemic investing. It provides the kind of information and insight critical to investors seeking to create transformative change. You can find a detailed description of the mapping methodology here. To study an example (on regenerative agriculture in the American Midwest), check out the map proper here or read an intro report here.

What, exactly, makes a/our Transition Map distinctive?

Where most mapping seeks to represent the current reality of a particular system, Transition Maps are deliberately oriented towards the transformational intent defined at the outset of the systemic investing process. They help you highlight drivers and blockers and identify intervention areas that need to be tackled to move the system towards a specified future state. So Transition Mapping is deliberately action-oriented by illuminating a strategic blueprint for system change.

The explicit orientation towards a future vision also helps you with one of the thorniest challenges of drawing systems maps: scoping and setting boundaries. First, information is only included in the map if it’s relevant to the transformation you’re pursuing. In our regenerative agriculture prototype in the American Midwest, the intent is focused on the transition to conservation agriculture methods in the region. This means that elements of the system that are related to other farming methods (such as organic farming) are out of scope, or only included insofar as they influence the adoption of regenerative practices.

And second, the intent guides your inquiry towards aspects of the system that are relevant to the future vision, even if they are not a major part of the current reality. For example, in our Net-Zero Mobility Switzerland prototype, the intent is focused on decarbonization and reduction in personal car use, so we also included slow-mobility modes of transportation and the related infrastructure (e.g. electric cargo bikes and cycling highways) in our analysis despite their minor presence in the current system.

Guidance

Now, how to build transition maps?

As with any mapping process, it’s crucial that you start out by defining the map’s taxonomy. The basic building blocks for this type of mapping are what we refer to as transition factors. These are variables that are either enabling or inhibiting a system’s transition, and they are identified and defined based on the information gathered throughout your research phase. There is no definitive typology for what can or cannot constitute such a transition factor. In fact, the identification of these factors should primarily be guided by the realities of the system of interest as uncovered through the research phase.

In our Regenerative Agriculture Midwest prototype, transition factors included financial and economic variables like federal subsidies and farming input costs, factors related to knowledge and skills (e.g., regenerative land management expertise), socio-cultural factors (e.g., short-termism and demand for convenience), and physical infrastructure (e.g., processing and distribution facilities).

These transition factors can be represented as nodes on the system map. And once identified, you can begin to explore interconnections and dependencies between these nodes. Essentially, this is about asking “which factor drives which other factor” and slowly building up a picture of patterns and feedback loops.

There are two types of feedback loops: reinforcing and balancing feedback loops. Neither of these is intrinsically good or bad, and both may lead to outcomes that may be considered desirable or undesirable. There might also be time lags between the different causal elements unfolding—a crucial piece of information that influences the wider dynamic of the system.

When you draw loops and connections, what you will get is a picture of interrelationships (both between elements within loops and between entire loops) that signify the dynamics that shape the behavior of systems. And where these dynamics combine to maintain the status-quo of the system—where the system is deeply “stuck”—we refer to them as systemic challenges. These are another key feature of transition maps and they will point you to the areas that need to be addressed in order to support system transformation.

In our regenerative agriculture prototype, we identified eight of these systemic challenges, such as the “Farmers’ Trap”, a reinforcing cycle driven by land prices, diminishing profitability, and broader socio-economic dynamics that binds farmers to the established and unsustainable farming practices.

Drawing up a map that depicts transition factors and the resulting systemic challenges will allow you to move to the final and crucial step of the systems analysis phase: identifying leverage points for transformation.

Tips

Kumu is a great online tool for building all different kinds of maps, and it’s the tool in which we created the Transition Map for regenerative agriculture in the Midwest.
You could spend a lifetime creating a single system map. Don’t do that. Instead, think of systems mapping as an ongoing process rather than something you do once. Go back to your maps periodically to study them again (you’ll glean new insights every time!), and update them according to new developments.
As the saying goes, the map is not the territory. System maps are useful, but they are also structurally flawed and necessarily incomplete. Never forget that.