Though immediate effects of model flattening are already pretty dramatic, its largest contributions to jank are more long term. While the model flattening is a temporary phenomenon, our experiences of it are not. We remember them. Put in the terms of our little framework we’ve been developing, the model of our environment is updated with these weird wibble-wobble outcomes. They are at times awesomely awesome and at times awesomely horrifying, and the bluntness of model flattening leaves deep marks.
Each of these remembered experiences skews our sense of the expectation gradients. When we encounter a similar situation in the future, these deep marks influence how we evaluate it. I’ve been thinking about how to express this process visually, and this morning, the framing finally clicked into place. Yes, it’s terrible math magic time!
Imagine that there’s some baseline expectation gradient evaluation that we would do in a situation that we’re not familiar with. Now, we can visualize a relationship between this baseline and our actual evaluation. If this is indeed the entirely new situation, the relationship line will be a simple diagonal in a graph with baseline and actual gradient as axes.
The long-term effect of model flattening will manifest itself as the diagonal bending upward or downward. After a traumatic experience, we will tend to overestimate the expectation gradient in similar situations. Our model will inform us that we can’t actually cope with that situation. This will feel like an aversion: a pull away from the experience. I once was introduced to a team lead. Before the meeting, their colleague said: “Oh, and please don’t mention [seemingly innocuous project], it will sour the mood.” Back then, I just went “okay, sure” – but it stuck with me. What is this crater of aversion that is so deep that necessitated a special warning?
Bending in the other direction, there are cravings. If model flattening resulted in a miraculous breakthrough, our evaluation of the expectation gradient will skew to underestimate it in similar situations. We’ll be pushed toward these kinds of experiences, tending to seek them out, because our model will suggest that these situations are a piece of cake. And yes, a piece of cake is an example of a craving. A familiar process or tool that saved the team’s collective butt from some figurative tiger long ago are some other examples of cravings.
To capture this bending in one variable, I am going to reach for an exponent. Let’s call it the gradient skew. Then, the clean diagonal line is the skew exponent that equals to one. The skew that is larger than one will express an aversion, and skew between one and zero will express a craving.
Now, it is fairly easy to see how cravings and aversions mess with our required energy output estimates. An aversion will overestimate the output, triggering model flattening early and forming a vicious cycle: more model flattening will lead to more deep marks, compounding into more aversions. A craving will grossly underestimate the effort, resulting in prediction errors that accelerate the model clock and trigger macro jank. Since macro jank itself is an unpleasant experience, this feeds back into model flattening and more aversion-forming.
Over a long-enough period of time, the sheer number of cravings and aversions, collected within the model, is staggering. The model stops being the model of the environment per se, and instead becomes the map of cravings and aversions. Like relativistic gravity, this map will tug and pull a team or an individual along their journey. This journey will no longer be about the original or stated intention, but rather about making it to the next gravity well of a craving, tiptoeing around aversions. Within an organization that’s been around for a while, unless we regularly reflect on our cravings and aversions, chances are we’re in the midst of that particular kind of trip.