I'd guess blood cells and neutrophils dominate turnover largely because there's so many of them; IIRC blood cells turn over on a timescale of months, which isn't especially fast. The stomach lining presumably turns over very quickly because it's exposed to extreme chemical stress (mitigated by a mucus layer, but that can only do so much), so I'd guess that's the dominant "gut cell" term.
That's an interesting thing to know because it tells us what processes are likely to eat up bodily resources, aside from obvious things like moving muscles or firing neurons.
Bonus exercise:
I'm assuming the point is that I've not seen the examples used as examples of dynamic equilibrium before, not that I've not seen the equilibrium before? Given that that's the case:
Great examples. The first one points to equilibria on multiple timescales - e.g. at one timescale people moving in are in equilibrium with people moving out or dying, and at another timescale the distribution of neighborhoods is in equilibrium.
This one was a lot of fun!
How the equilibrium gets restored in each direction:
I think my browser tab and social interaction examples on the post on stable equilibria fit in better here. They're much more dynamic than stable.
Lots of great economic examples here. #2 in particular makes some great points about incentives inducing an equilibrium, in ways that a lot of overly-simple economic models wouldn't capture very well.
This is a framing practicum post. We’ll talk about what dynamic equilibrium is, how to recognize dynamic equilibrium in the wild, and what questions to ask when you find it. Then, we’ll have a challenge to apply the idea.
Today’s challenge: come up with 3 examples of dynamic equilibrium which do not resemble any you’ve seen before. They don’t need to be good, they don’t need to be useful, they just need to be novel (to you).
Expected time: ~15-30 minutes at most, including the Bonus Exercise.
Our picture of stable equilibrium was a marble sitting at the bottom of a bowl. This is a static equilibrium: the system’s state doesn’t change.
Picture instead a box full of air molecules, all bouncing around all over the place. The system’s state constantly changes as the molecules move around. But if we look at the box at large scale, we don’t really care about the motions of individual molecules, we just care about the overall distribution of molecule positions and velocities - the number of molecules in the upper-left quadrant, for instance. And this distribution may be (approximately) constant, even though the individual molecules are bouncing around.
This is dynamic equilibrium: even though the states of the system’s components are constantly changing, the distribution of states of the system’s components has a stable equilibrium.
The box-of-air example involves a distribution of similar physical parts (i.e. molecules), but we can also have a dynamic equilibrium with a Bayesian belief-distribution. For instance, I can think about which of my dishes I expect to be dirty tomorrow or next week or next month. I don’t run the dishwasher every day, so in the short term I expect dirty dishes to pile up - a non-equilibrium expectation. But in the long run, I generally expect the distribution of dirty dishes to be roughly steady - I don’t know exactly which days I’ll wash them, but my expectations for 100 days from now are basically the same as my expectations for 101 days from now.
The dirty dishes themselves may pile up and then be cleaned and then pile up again, never reaching an equilibrium state. But my expectations or forecasts about the dishes do reach an equilibrium.
In general, dynamic equilibrium should spring to mind in two situations:
If the number of air molecules in the upper-left quadrant of a box is roughly constant, then the rate at which molecules enter that quadrant must roughly equal the rate at which they leave. If the number of molecules in the quadrant is lower than usual, then molecules will enter faster than they leave until the number returns to equilibrium.
In general, dynamic equilibrium involves a balance: the rate at which parts enter some state is roughly equal to the rate at which they leave that state. Three key questions are:
In order for the equilibrium to be stable, we also need parts to enter a state a bit faster/leave it a bit slower when there are fewer-than-equilibrium-number of parts in that state. So, besides the rates, we also want some idea of how the rates change if there are slightly more or less parts in a given state.
We can also ask the corresponding questions for a dynamic equilibrium of expectations/forecasts. Rather than parts moving between states, we have probability-mass moving between states. If there’s a chance that I wash the dishes in two days, then there’s a flow of probability-mass from the “n dirty dishes” state to the “all dishes clean state” between two and three days in the future. If my expectations reach an equilibrium, then that means the rate of probability-mass-flow into each state equals the rate of probability-mass-flow out. So, the three key questions are:
Once we know how the state-change rates work, we can also ask all the usual questions about stable equilibrium.
Come up with 3 examples of dynamic equilibrium which do not resemble any you’ve seen before. They don’t need to be good, they don’t need to be useful, they just need to be novel (to you).
Any answer must include at least 3 to count, and they must be novel to you. That’s the challenge. We’re here to challenge ourselves, not just review examples we already know.
However, they don’t have to be very good answers or even correct answers. Posting wrong things on the internet is scary, but a very fast way to learn, and I will enforce a high bar for kindness in response-comments. I will personally default to upvoting every complete answer, even if parts of it are wrong, and I encourage others to do the same.
Post your answers inside of spoiler tags. (How do I do that?)
Celebrate others’ answers. This is really important, especially for tougher questions. Sharing exercises in public is a scary experience. I don’t want people to leave this having back-chained the experience “If I go outside my comfort zone, people will look down on me”. So be generous with those upvotes. I certainly will be.
If you comment on someone else’s answers, focus on making exciting, novel ideas work — instead of tearing apart worse ideas. Yes, And is encouraged.
I will remove comments which I deem insufficiently kind, even if I believe they are valuable comments. I want people to feel encouraged to try and fail here, and that means enforcing nicer norms than usual.
If you get stuck, look for:
Bonus Exercise: for each of your three examples from the challenge, what are the relevant “parts”, part-states and state-change rates (or probability-mass-flows, for expectations)? Can you do a Fermi estimate of the relevant rates, or estimate a rough (i.e. big-O) functional relationship between the state-change rates and the number of parts in each state (or probability-mass in each state)? How do the state-change rates change when the number of parts (or probability mass) in some state is higher/lower than its equilibrium value?
This bonus exercise is somewhat more abstract and conceptually tricky than previous exercises, especially for the probability-mass questions. I recommend it especially if you want some extra challenge.
Much of the value I get from math is not from detailed calculations or elaborate models, but rather from framing tools: tools which suggest useful questions to ask, approximations to make, what to pay attention to and what to ignore.
Using a framing tool is sort of like using a trigger-action pattern: the hard part is to notice a pattern, a place where a particular tool can apply (the “trigger”). Once we notice the pattern, it suggests certain questions or approximations (the “action”). This challenge is meant to train the trigger-step: we look for novel examples to ingrain the abstract trigger pattern (separate from examples/contexts we already know).
The Bonus Exercise is meant to train the action-step: apply whatever questions/approximations the frame suggests, in order to build the reflex of applying them when we notice dynamic equilibrium.
Hopefully, this will make it easier to notice when a dynamic equilibrium frame can be applied to a new problem you don’t understand in the wild, and to actually use it.