Lenses, framings, and models

At a meeting this week, I realized that I use the terms “lens,” “framing,” and “model” in ways that hold deep meaning to me, but I am not certain that this meaning is clear to others. So here’s my attempt to capture the distinctions between them.

The way I see these, the lens is the most elemental of the three. A lens is a resonant,  easy to remember (or familiar) depiction of some phenomenon that offers a particular way of looking at various situations. Kind of like TV commercial jingles, effective lenses are catchy and brief. They usually have a moniker that makes them easy to recall, like “Goodhart’s Law” or “Tuckman’s stages of group development” or “Cynefin.” Just like their optical namesakes, lenses offer a particular focus, which means that they also necessarily distort. As such, lenses are subject to misuse if used too ardently. With lenses, the more the merrier, and the more lightly held, the better. Nearly everything can be turned into a lens. A prompt “How can this be a lens?” tends to yield highly generative conversations. For fun, think of a fairy tail or a news story and see how it might be used as a lens to highlight some dynamic within your team. Usually, while names and settings change, the movements remain surprisingly consistent.

Framings are a bit more specialized. They are an application of one or more lenses to a specific problem space. For example, when I am devising a strategy for a new team, I might employ Tuckman’s stages to describe the challenges the team will face in the first year of its existence. Then, I would invoke Cynefin to outline the kind of problems the team will need to be equipped to solve, rounding up with Goodhart’s Law to reflect on how the team will measure its success. When applied effectively, framings turn a vague, messy problem space into a solvable problem. To take me there, framings depend on the richness of the collection of lenses that are available to me. If these are the only three lenses I know, I will quickly find myself out of depth in my framing efforts: everything I come up with will limp with a particular Tuckman-Cynefin-Goodhart gait.

Finally, models are networks of causal relationships that form within my framings. The problem, revealed by my framing exercise, might yet be untenable. While I can start forming hypotheses, I still have little sense in how many miracles each will take. This is where models help. Models allow me to reason about the amount of effort each of my hypotheses will require. Because each of the hypotheses is a causal chain of events, models help uncover links of these chains that are superlinear.

Getting back to our team planning example, the first four Tuckman’s stages are a neat causal sequence and might lead us to conclude that the process we’re dealing with is linear and thus easily scheduled. However, if we study the network of causal relationships closer, we might be able to see that they aren’t. The team’s storming phase can tip the team’s environment into complex Cynefin space and thus extend the duration of the storming phase. Or, the arrival to the norming stage might make the team susceptible to over-relying on its metrics to steer, triggering Goodhart’s law, eventually leading to the slide into chaotic Cynefin space, setting the stages all the way back to forming.

The nonlinearity does not need to be surprising. Once we see it in our models, the conversation elevates from just looking at possible solutions to evaluating their effectiveness. Framings give us a way to see solvable problems. Models provide us with insight on how to realistically solve them.

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