Much of what happens in our brains is chemical, why would depression not be? If by “imbalance” we happen to mean “a state of brain chemicals that the patient doesn’t want”, as opposed to, say, some specific theory that is now discredited like “not enough serotonin” (i.e., the low serotonin myth), then depression can reasonably be thought of as a “chemical imbalance”.

Disagreement about whether depression is a chemical imbalance may stem from confusion about what it means for something to be a “model” for something else. For instance, “chemical imbalance” is a model for depression. But it’s just one model, and it is often not the most useful one. Here are all the models I’ve seen people use for depression:

(1) Neurology – caused by a chemical imbalance of neurotransmitters in your brain; that’s why anti-depressants are effective for many people.

(2) Beliefs – caused by unhelpful perceptions of yourself, your future, or the world; that’s why cognitive therapy is helpful for many people.

(3) Circadian rhythm/sleep – that’s why some people may find chronotherapy (involving sleep and light) or sleep apnea treatment effective.

(4) Relationships – that’s why socially isolated people often feel depressed and why Interpersonal Therapy may be effective.

(5) Nutrition – that’s why people with depression are sometimes found to have vitamin deficiencies or harmful diets and may feel better if these are corrected.

(6) Society – that’s why groups of people who are oppressed, shunned, or in poverty are more likely to be depressed, and improving these social problems may resolve the depression.

(7) Behavior – that’s why depressed people are sometimes found stuck in harmful behavioral feedback loops and why Behavioral Activation for depression is helpful.

(8) Trauma – that’s why people who were unloved or abused as children may be more likely to be depressed, and why people often show depressive symptoms for a while after a loved one dies.

(9) Meaning – that’s why depressed people sometimes feel that nothing matters, and why techniques from the ACT approach that get you to take action towards what you value may be useful.

(10) Genetics, that’s partly why depression tends to run in families and helps explain why one person gets depressed in a particular circumstance that another person doesn’t get depressed in.

These can’t all be true, can they? I believe they can, in the sense that each of these is partially correct, or to be more precise, each of these is a model for depression that is better than useless. What’s nice about this set of models is that they are complementary: they each capture some distinct aspect of depression, and each will be especially useful in certain circumstances. In particular, a number of these refer to different things that can cause or trigger depression. Not all of these causes will be in play in any particular case, so sometimes one model will apply more than other.

There are many ways to model any system. A good model typically is not one that fully explains every detail of a system but rather one that explains important aspects of the system in important cases of interest. It is not a contradiction to find that there are multiple good models for the same system that are very different from each other. They may each capture different important attributes of the system, or may each be accurate in different situations, or they may just represent similar information in very different seeming ways. Even if one model is much better than the others on average, there may be specific cases where the alternative models make better predictions. And realizing that may allow us to combine models to create an even better one.

Consider the game of Pac-Man as an example. One way to model the game is to think of the ghosts as sentient beings that are trying to touch you, which will cause you to die. This is the intuitive model that players often have of the game, and despite being completely untrue on one level (the ghosts are not sentient), it is quite a useful model that enables you to play the game effectively.

However, if you train a reinforcement learning algorithm to play Pac-Man, it will produce a very different model of the system (one that may well contain no direct notion of ghosts that are out to get you, and likely the model it produces will be one that you’ll have a hard time understanding). Yet, using this model, the software may play Pac-Man as well as you do or perhaps even better. So whose model is right, the intuitive human one or that of the reinforcement learning algorithm? Neither is “right”, and neither is “wrong”; they both capture important elements of the game in different ways.

A more complete model of Pac-Man than either the intuitive human one from a reinforcement learning algorithm’s is the computer code of Pac-Man, which determines the gameplay. From the point of view of you playing the game effectively, it MIGHT help to some extent to study the code in advance to learn potential quirks of the ghosts’ behavior, but mostly the code would not be useful while you play, and you’ll find it a lot easier to think of ghosts being out to get than to mentally reference the underlying algorithms. So your “ghosts are out to get me” model may be more helpful to you than the “source code of the game” model, despite the latter being a much more accurate description.
But even the source code is not the ultimate model of your Pac-Man experience, as the ULTIMATE model would include the hardware that code runs on (maybe the behavior of that code varies slightly on different hardware), and even the people around who might distract you during the game, and ultimately the laws of physics on which the behavior of the hardware and everything else depends.

As statistician George Box once wrote, “The most that can be expected from any model is that it can supply a useful approximation to reality: All models are wrong; some models are useful.”

So let’s stop asking if depression “is” a chemical imbalance. Instead, let’s ask: is it useful to sometimes model depression as a chemical imbalance? I think the answer there is yes; this is one model that should be used among other models in certain specific instances. It may sometimes be a useful model, but other times other models of depression will be much more useful. And, of course, much of what happens in our brains can be modeled as chemical changes. So the question is: when is this a useful way to think about what’s happening chemically in the brain? When does such a model give us insight?

If a depressed person responds well to anti-depressants, we can use our Neurology model of depression to try to understand this (though that still may be very tricky to do). On the other hand, if a person’s depression lifts after they stop believing that they are inherently worthless, we can use our Beliefs model to try to understand this. And if a person’s depression improves when they adjust their sleep patterns, we can use our Circadian rhythm model to try to explain this. And so on.

Ideally, we’d try to figure out the most effective model for a given person’s depression so that we can accurately model their specific depression, not merely depression in general.

If it is obvious to most investors that there is a bubble, presumably, that bubble would be likely to burst quickly. So, we shouldn’t expect it to be particularly obvious from the inside of a bubble that one is currently happening.

That being said, here are some signs that I think tend to be predictive of bubbles (though they don’t all occur in all bubbles, nor are all trends that have all these traits a bubble).

Possible signs that a bubble may be occurring:

(1) Prices skyrocket

Explanation: This is simply part of the definition of a bubble (we wouldn’t say it was a bubble without this)

(2) The current level of prices (e.g., why prices are now at X rather than 2X or 0.5X) is hard for experts and investors to explain convincingly. They may be able to explain why a change in price occurred (“prices rose 20% in response to XYZ”), but they struggle to explain or can’t reach any consensus on why the raw price is X (as opposed to 2X or 0.5X).

Possible explanation: because price increases are, to a significant extent, being driven by high excitement, hopes of getting rich quickly, or fear of missing out, and those kinds of forces don’t tend to lend themselves to easily explainable or predictable price levels.

(3) Even while skyrocketing on average, prices tend to swing wildly up and down over short time horizons.

Possible explanation: a combination of reasons, I think. For instance, uncertainty in how to price the assets means that even those making lots of money tend to have high uncertainty about when to sell out positions vs. buy more. You get sudden pockets of increased buying when investors who had previously been on the sidelines jump into the fray, driven by fear of missing out or starry-eyed hopes of huge reward, but you also have pockets of increased selling as those who’ve made a ton start to cash out.

Sudden drops in prices may also spook other holders, who then might also sell, causing steeper declines. Some people may start trading “momentum” strategies, buying as prices appear to be rising and selling as they appear to be falling, which also exacerbates fluctuations.

(4) As prices continue to rise, some vocal, long-term holders start to claim that a new paradigm is occurring (where things are now fundamentally different than they were in the past or where previous risks people once feared no longer apply) and hence that past ways of thinking about valuation or risk or bubbles are irrelevant in this case.

Possible explanation: I suspect for some, it’s because they want to convince themselves they will continue making huge returns (and they have trouble explaining how that can happen without pointing to a paradigm shift), or in part because they want others to continue investing in large quantities, and it’s an exciting story. But also probably because those who become convinced of a paradigm shift are more likely to go on to become long-term investors in that asset class and then tell their theories excitedly to others.

John Templeton: “The four most dangerous words in investing are: ‘this time it’s different.”

(5) Lots of people who know little about investing and who know little about the asset in question start to flock, trying to find ways to put their money into that asset, too. More generally, the asset tends to grow massively in popularity, with higher and higher trading volumes occurring.

Possible explanation: as knowledge of the asset class increasingly trickles down from the firstcomers to the general public, people start to hear about huge fortunes being made and, eventually, about fortunes being made by non-experts, which makes winning big seem attainable. Media coverage tends to spread these exciting developments as well (and increasingly so, the more prices rise because the story becomes more and more exciting). Amateurs may then start flocking to become investors because they think that maybe they, too, can make a fortune (or at least increase their investment by 10x) as so many people they’ve heard about have done.

(6) The supply of the asset (or of lookalikes to it) explosively grows, with many trying to make money from the steeply increasing prices by creating similar assets to sell to exuberant investors.

Possible explanation: even while investors are making great returns, those creating the asset to sell are potentially making even greater returns (or at least equally great returns), which attracts a frenzy of new would-be sellers attempting to produce similar assets.

The thing about bubbles is that even if you believe you are inside one, and even if you happen to be completely correct, it’s not necessarily easy to profit from this. Shorting a bubble can be extremely risky because even if you are right, it might rise in price by 2x or 10x before it collapses, causing you extreme losses. And buying put options to bet that it’s a bubble can also be tricky, as volatile markets make for expensive options, and you have to time the collapse well (which is very hard to do) or else buy expensive long-dated options. And those who say there is no bubble and keep buying (even if they eventually are proven wrong) may continue making huge returns for a while (or even for a long time).

Isaac Newton: “I can calculate the motions of heavenly bodies, but not the madness of people.”

A legitimate bubble may burst (or experience a big price correction) in 3 days, or 30, or 300, or 3000. And while you’re waiting, people may still make 3x, 30x, or 300x returns (before many of them lose nearly everything).

It’s notoriously hard to predict when a bubble will burst (or have a big price correction) because it may be triggered by a sudden reduction in confidence that produces a negative feedback loop (i.e., the more that prices fall, the more that confidence disappears). And confidence tends to shift for all sorts of hard-to-preempt reasons (e.g., a bunch of investors happening to cash out at the same time, creating a sudden increase in supply and hence a dropping price, or people catching wind that insiders are starting to sell out, or an exponentially increasing creation of similar assets diluting values, etc.)

If you believe you are in a bubble, you could bet against it by shorting the market (i.e., borrowing the asset and immediately selling what you borrowed), but if you get your timing wrong, it could still go up 20x before the bubble bursts, and you could lose your shirt.

Unknown: “Markets can remain irrational a lot longer than you, and I can remain solvent.”

You could try a safer pessimistic bet using some types of options or futures (if they even exist for this asset class), but the high volatility and huge market uncertainty may offer unexciting prices. A skeptic may prefer to make a bet with a true believer like “I’ll pay you $U if in Y years the asset is worth at least P% more at that time than it is today, and you pay me $D dollars if it’s worth at least P% less at that time than it is today.” where Y is probably at least 5 years. But this sort of bet is likely a difficult one to set up in practice (plus, the true believer may be bankrupt if the skeptic proves right, if the true believer never cashes out).

Skeptics (even when correct) are often not market participants, so they are not putting much downward price pressure on the asset (hence, the market may disproportionally consist of those who are exuberant). Skeptics often end up just watching from the sidelines, rolling their eyes as they witness others continue to make a fortune for 3 or 30 or 300, 3000 days until it all comes crashing down.

What I think is most important to remember regarding possible bubbles is that in possible bubbles, as in casinos, don’t bet more than you’re able to comfortably lose. Losing X% of your savings is likely to negatively impact your happiness significantly more than growing your savings by X% will make you happier.

True bubbles have an unfortunate aspect that is similar to Ponzi schemes: some people really do get rich from them (especially if they cash out just in time), but this is at the expense of those who are late to the game, who lose most or all of their investment.

Sometimes, but certainly not always, what emerges eventually after a bubble has burst (or had a large price correction) is something of true (and potentially great) value. It may be this intrinsic value that helped get people so excited about that new asset in the first place. It just takes a longer time to fully bloom than the bubble allowed for. Even the stuff of real worth may be unfairly punished during the crash because it’s not clearly distinguished from what was merely hype. But eventually (one hopes), the actual value shines through.

Benjamin Graham: “In the short run, the market is a voting machine, but in the long run, it is a weighing machine.”

This piece was first written on December 8, 2017, and first appeared on my website on July 16, 2025.