This is a framing practicum post. We’ll talk about what timescale separation is, how to recognize timescale separation 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: pick 3 examples of equilibria from the previous three practica, and for each of them, give one use-case on a fast enough (or slow enough) timescale that we can treat the system as constant (or in equilibrium). 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 minutes at most.

What’s Timescale Separation?

If I put a piece of iron in the ocean, its equilibrium state is rusted through. However, it takes a long time to reach that equilibrium. If I swim past and look at the piece of iron, I probably won’t even see it gradually becoming rustier.

At the timescale of a person looking at the piece of iron on the way by, its state is roughly constant, even though it’s out-of-equilibrium. The changes are so slow that we can ignore them.

On the other hand, consider pushing a wheelbarrow. The force isn’t transmitted to the wheelbarrow instantaneously - for a brief fraction of a second after I push on the handle, the handle actually compresses a bit, and the compressed handle presses on the wheelbarrow frame, which compresses the frame a bit, which then presses on the load… the compression propagates as a wave, transmitting the force through the whole wheelbarrow. And the wave also bounces back, exerting pressure from the load back on my hands. But from my point of view, this whole process happens extremely quickly. Within a fraction of a second, the waves have settled down to an equilibrium force (and matching acceleration) between my hands and the wheelbarrow.

At the timescale of a person pushing the wheelbarrow, the forces are always roughly in equilibrium. The force-propagation process is so fast that we can ignore it.

This is timescale separation:

  • If a system equilibrates on a timescale much slower than whatever-we’re-interest-in, then we can approximate it as being in a constant non-equilibrium state.
  • If a system equilibrates on a timescale much faster than whatever-we’re-interested-in, then we can approximate it as always being in equilibrium (even if the equilibrium changes slowly over time).

Note that a system may involve multiple processes which equilibrate on different timescales. For instance, in the wheelbarrow example, there’s a very fast equilibrium of forces between my hands and the wheelbarrow, but also a slower equilibrium in which I set a steady walking pace.

One common pattern to watch for: often a system doesn’t return exactly to equilibrium, but exponentially decays toward equilibrium. (In general, this happens whenever the rate-at-which the system moves toward equilibrium is proportional to its “distance” from the equilibrium state.) In this case, the half-life is a good “equilibration timescale” for purposes of Fermi estimates and timescale separation.

What To Look For

Timescale separation should come to mind whenever we have a stable equilibrium. For any equilibrium it’s worth asking:

  • Fermi estimate: how fast does the system equilibrate? If it decays to equilibrium exponentially, what’s its half-life? (Note that there may be multiple processes in the same system which equilibrate on different timescales.)
  • What things am I interested in which happen much faster than the equilibrium?
  • What things am I interested in which happen much slower than the equilibrium?

The Challenge

(Rules adapted from the Babble Challenges)

Pick 3 examples of equilibria from the previous three practica, and for each of them, give one use-case on a fast enough (or slow enough) timescale that we can treat the system as constant (or in equilibrium).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:

  • First, estimate how long it takes the system to equilibrate if it’s “poked” somehow.
  • Next, think about ways you might interact with the system much faster/slower than that.


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 post is meant to practice the “action” step: once we recognize an equilibrium, what questions should we ask or what approximations should we test?

Hopefully, this will make it easier to notice when a timescale separation frame can be applied to a new problem you don’t understand in the wild, and to actually use it.


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4 Answers sorted by

  • (From the Stable Equilibrium post) Profound distaste for a specific food: whenever I've seen such a change, it has been after years of avoiding the stuff, not a couple of days after last trying it. So the change is too slow compared to the daily decision of choosing what to eat.
  • (From the Bistability post) Market with much competition: on a daily basis, the winner of the market will tend to be the winner of the previous day, because the changes are slower than this timeframe.
  • (From the Dynamic Equilibrium post) Research: At the scale at which I look at my plans (weeks or months), the changes are fast enough to correct each other and reach some sort of equilibrium. 

Almost as important, here are examples where I don't think the frame applies

  • (From the Bistability post) Chess move, which is a complex 3D object being manipulated in complex continuous ways, but result in a discrete state (the position). Because the equilibrium comes from neglecting some spatial noise instead of a transformation, the timescale separation frame doesn't add anything.
  • (From the Dynamic Equilibrium post) Jazz improvisation following chord changes or modes: here there is a change through time, but the equilibrium is neither coming from a process going much faster or much slower than what we care. It's instead an abstract quality that is maintained, and this abstract quality itself includes some movement.

I love the "examples where the frame doesn't apply" idea.

What is a "winner of the market"?

  • Stable equilibrium: fashion trends. Any given possible trend is by default not in fashion. That's what makes it a trend, when it does become fashionable. On a short timescale, though, the coalescing of a "trend" is a process of its own, producing a trend that feels like a short-term stable equilibrium before it ultimately breaks down. Ones' relationship with fashion might be defined by whether one is focused more on the long-term or the short-term equilibrium.
  • Bistable equilibrium: promising research. Many major discoveries depend on the accumulation of evidence and theory over an extended period of time. Sometimes, that accumulation ultimately leads instead to the failure of an idea (priming research), other times to a resounding confirmation of its merit (evolution). In the middle, though, there is a period of uncertainty, both in terms of the truth of the idea and interest in studying it further. An idea being established and central enough to gain the support of the scientific establishment, yet new enough to motivate gathering more evidence, is a temporary yet steady-feeling state. One way to describe the role of the scientist is that they help to clear out topics from this state of uncertainty more quickly, so that new topics can enter it.
  • Dynamic equilibrium: food in the pantry. Although over time, the amount of food has an average, for real-world cooking decisions we go off the fleeting "steady state" of what precise foods are present in the fridge. On a shorter time scale, you're dealing with temporary "steady states" like having plenty of X or being out of Y. When I check my fridge or pantry before going shopping, I make a judgment call about whether I have "plenty of butter" or "need to get more butter," rather than quantifying the precise amount of butter I have and buying enough to restore some optimal amount.

I love the idea of defining one's relation to fashion by focus on short-term vs long-term equilibrium.

  1. Returning to my number of muscle cells an adult human body example (from the initial stable equilibrium post), for the purposes of calculating lean vs. fat mass (or just weight), we don't care about the fact that the distribution shifts as the person ages and experiences sarcopenia.
  2. For predator-prey population size ratios, the ratio fluctuates slightly on a daily basis assuming the predators hunt at certain times of the day and potentially seasonally. Assuming both species live more than a year, neither matters for estimating the carrying capacity of the ecosystem for the predator species.
  3. For calculating the average body temperature of a species, we can mostly ignore real but small fluctuations that occur throughout the day due to circadian rhythms, digestion, etc.

One theme in these: they're all conclusions which seem pretty intuitive. One of the nice things about timescale separation is that it gives us a formal justification for a lot of intuitively-sensible conclusions.

  1. Stable equilibrium: The surface of a pond when I throw something in. I might be interested in the overall water level, which slowly changes over days and weeks. At these timescales the surface is always in equilibrium. (Equilibrates quickly re what I'm interested in)
  2. Bistability: The ruling party in a democracy. Mostly stable over a monthly to yearly timescale. I need to worry about who to vote for once every few years, not every day. (Equilibrates quickly re what I'm interested in)
  3. Dynamic equilibrium: The amount of train delays per day in a city. If I'm in the city right now, I care about the current situation and not that incentives might reduce the amount of delays in the long run. (Equilibrates slowly re what I'm interested in)

Good examples. One theme these highlight: we intuitively use timescale separation all the time in our day-to-day lives.

2 comments, sorted by Click to highlight new comments since: Today at 7:05 PM

You might want to recatalog this in your Framing Practicum sequence. It's not listed there currently.

Thanks, got it.

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