Week of 2021-12-13
This issue is a continuation of a larger “Jank in Teams” series. If you want to catch up on the whole story, check out Jank in Teams, which starts at the beginning.
Model flattening
Before we move on from our discovery of the inner OODA loop, I want to talk about a phenomenon that plays a significant role in our lives and in the amount of jank we produce. I struggled to capture it succinctly, and here’s my current best effort. I call this phenomenon model flattening.
If we look into the strategies that the inner OODA loop applies in its Decide step, we can loosely identify three, each of non-linearly increasing severity, neatly following that expectation gradient tangent curve. At the lower end of the curve, the inner OODA loop yields all of the resources to whatever else might need them.
As the gradient approaches the kink in the curve, the belt begins to tighten. Sensing the approach of the asymptote, the strategy shifts to mobilization. Cutting down anything that might consume resources, our body acts as a ruthless bureaucrat. using a set of powerful tools to make that happen. When this strategy is employed, it almost feels like we are taken over by something else. We know this sensation as the amygdala hijack. “Yeah, buddy. I saw you drive, and that was cool, but it’s time for the pros to intervene. Moooove!”
Further beyond, the body recognizes that the asymptote territory was reached and shifts into the “freeze” mode, flopping onto the ground and basically waiting for danger to pass. There’s no way to create infinite output to overcome impossible challenges, so we cleverly evolved a shutdown function.
If you know me, you were probably expecting me to inevitably stir Adult Development Theory (ADT) concepts into this stew. You were right.
Very briefly, ADT postulates that through our lives, we all traverse a stair-step like progression of stages. With each stage including and transcending the previous one, we become capable of seeing more and creating and holding more subtle models of the environment. In the context of this narrative, fallback is the short-term reversal of this process, where we rapidly lose access to the full complexity of our models.
Fallback might be a great way to express how our inner OODA loop achieves resource mobilization. Like that thermal control system for microprocessors, it has first dibs on throttling resources. However, while the microprocessor is just getting its clock speed reduced, the human system does something a little more interesting: it flattens our model of the environment.
With each progressive strategy, the bureaucrat in charge closes more doors in the metaphorical house of our mind, smashing the delicate filigree of our models into a flatland. As we experience it, this flattening feels like a simplification of our environment. Our surroundings become more cartoon-like, having fewer details and moving parts. Only things that the inner OODA loop judged to have our immediate survival at stake are left within the model. Those connections are strengthened and drawn with thicker lines, and the others are ignored. As a result, the number of imaginable alternatives shrinks. Our OODA loops collapse into OO or DA. You already know what happens next.
The effect on jank is somewhat different from the one we’ve seen in overheating phones. Sometimes, this flattening will result in Action that we need. Sometimes, it will do the opposite. The flattening can save my butt in a tiger encounter, and it can also ruin a delicate conversation. Model flattening is a blunt instrument and in fluid, ambiguous environments, it is probably the most significant source of prediction error rate, and subsequently – jank. Unless your job involves evading actual tigers, model flattening is likely working against you.
🔗 https://glazkov.com/2021/12/16/model-flattening/
Cravings and Aversions
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> to <lead>, 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.
🔗 https://glazkov.com/2021/12/17/cravings-and-aversions/
It’s almost the end of the year, and our story is approaching its end. Will it be an epic climax or a cliffhanger? The only way to find out is to wait for the next episode of Jank in Teams.