In statistics, pattern recognition is the application of mathematical tools to enhance decision-making support. It involves classifying and clustering based on the patterns identified in data. In psychology, pattern recognition is a cognitive process that matches information from a stimulus with information retrieved from memory. This pattern recognition usually requires repetition of experience. Semantic memory is the main type of memory involved in pattern recognition. Semantic memory is the long-term storage of general knowledge about the world, including facts, concepts, meanings, and definitions. It is what we know.

Decision-making processes in individuals are also related to the identification of patterns. For example, the next best move in a chess game is based on the pattern presented on the board. In this case, we are pattern “spotting” – the act of recognizing repeated sequences or recurring themes in data, events, or information. It is a fundamental cognitive ability that allows people to understand the world, make predictions, and solve problems.

Pattern spotting uses the brain’s natural ability for pattern recognition, which is how we compare new information from our environment with experiences stored in our long-term memory. This cognitive ability allows us to process large amounts of information effectively, understand what is happening, and predict what might happen next. It is a fundamental human skill used for everything from understanding language to enjoying music. According to Laura Winn of the School of System Change, the practice of pattern spotting in systems change work invites a more structured approach.

The most straightforward form of pattern spotting is two-dimensional, akin to seeing a repeating motif on a flat surface like wallpaper. In practice, this involves clustering similar ideas or organizing components along a spectrum. Examples include identifying sequences in numbers or geometric shapes or grouping similar concepts together during a workshop. This is considered the starting point for recognizing patterns in organizational systems.

Three-dimensional pattern spotting adds the ability to see patterns that repeat across different scales and contexts, similar to how fractals in nature (like a fern leaf or a cauliflower) show the same shapes at both small and large scales. It involves asking how a dynamic at a small scale, such as within a team, might mirror a problematic characteristic of the wider organization. This can be challenging because it requires the ability to “zoom out” to see the broader complexity while holding onto a pattern identified at a “zoomed in” level.

A brilliant modern approach to three-dimensional pattern spotting is HexiChange, a visual, tactile card-deck system from @justin of @IdeaLeap. It is intended for change leaders, facilitators, and teams who are wrestling with complex change challenges. The cards are hexagon-shaped, which allows them to be placed next to or around each other in ways that highlight connections or patterns, supporting the visual mapping of relationships. The cards represent insights into critical change dynamics: observable behaviors, underlying systemic tensions, beliefs/narratives, etc. These cards allow you to zoom-out and zoom-in to quickly and efficiently identify meaningful change strategies and interventions.

The most complex level, four-dimensional pattern spotting, incorporates the dimension of time. Unlike a static wallpaper pattern, patterns in living systems are dynamic, relational, and constantly changing. This level of pattern spotting seeks out the dynamic patterns of behavior that unfold over time, such as growth, decline, or oscillation. According to Meadows, this long-term behavior provides crucial clues to a system’s underlying structure. Understanding this structure is key not just to seeing what is happening, but why it is happening. The goal is not just to see how things repeat, but to understand the pattern of change itself and the relational elements affecting it.

Winn goes on to describe several methods for identifying four-dimensional patterns:

Method	Description
Interacting patterns of structure and process	The fundamental principle is to recognize the evolution of patterns by examining the dynamic and cyclical relationship between a system’s structure and the processes that occur within it. Understanding the dynamic relationship between a system’s structure (like a tree’s trunk and branches) and the processes that maintain it (like photosynthesis).
A dance between patterns and events	The central idea is that the future and the evolution of a system are shaped by the dynamic interaction, or “dance,” between two distinct elements: patterns and events. Looking at the interplay between established patterns and specific, disruptive events that can create non-linear change.
Sensing into essence patterns	The central goal is to understand the “essence patterns” that influence how complex living entities evolve and change in a dynamic world, while still staying true to what makes them unique. These core patterns are the source of a system’s recognizable character and nature, its essence.

Cultivating and developing pattern spotting skill is vital during complex systems change. It is a skill that can be cultivated and developed through frequent practice. And it is a practice that can generate “fierce engagement” with the complex work of systems change. Practitioners must look beyond just the repeating patterns and pay attention to the “non-patterns” –the specific, contextual signs that something different is happening. Pattern spotting enables change practitioners to better engage with a world that is constantly “becoming.”

Ultimately, pattern spotting turns complexity into clarity. By cultivating this skill, leaders and teams can see beyond noise, act with precision, and shape change that lasts. Patterns don’t just reveal what is happening, they show us what is possible. When we learn to see them, we unlock new ways to understand, influence, and transform systems.