Decoherence is not wave function collapse

In the double slit experiment, particles travelling through a pair of thin slits exhibit wave-like behavior, forming an interference pattern where they land that indicates that the particles in some sense travelled through both slits.


Now, suppose that you place a single spin bit at the top slit, which starts off in the state |↑⟩ and flips to |↓⟩ iff a particle travels through the top slit. We fire off a single particle at a time, and then each time swap out that spin bit for a new spin bit that also starts off in the state |↑⟩. This serves as an extremely simple measuring device which encodes the information about which slit each particle went through.

Now what will you observe on the screen? It turns out that you’ll observe the classically expected distribution, which is a simple average over the two individual possibilities without any interference.


Okay, so what happened? Remember that the first pattern we observed was the result of the particles being in a superposition over the two possible paths, and then interfering with each other on the way to the detector screen. So it looks like simply having one bit of information recording the path of the particle was sufficient to collapse the superposition! But wait! Doesn’t this mean that the “consciousness causes collapse” theory is wrong? The spin bit was apparently able to cause collapse all by itself, so assuming that it isn’t a conscious system, it looks like consciousness isn’t necessary for collapse! Theory disproved!

No. As you might be expecting, things are not this simple. For one thing, notice that this ALSO would prove as false any other theory of wave function collapse that doesn’t allow single bits to cause collapse (including anything about complex systems or macroscopic systems or complex information processing). We should be suspicious of any simple argument that claims to conclusively prove a significant proportion of experts wrong.

To see what’s going on here, let’s look at what happens if we don’t assume that the spin bit causes the wave function to collapse. Instead, we’ll just model it as becoming fully entangled with the path of the particle, so that the state evolution over time looks like the following:


Now if we observe the particle’s position on the screen, the probability distribution we’ll observe is given by the Born rule. Assuming that we don’t observe the states of the spin bits, there are now two qualitatively indistinguishable branches of the wave function for each possible position on the screen. This means that the total probability for any given landing position will be given by the sum of the probabilities of each branch:


But hold on! Our final result is identical to the classically expected result! We just get the probability of the particle getting to |j⟩ from |A⟩, multiplied by the probability of being at |A⟩ in the first place (50%), plus the probability of the particle going from |B⟩ to |j⟩ times the same 50% for the particle getting to |B⟩.


In other words, our prediction is that we’d observe the classical pattern of a bunch of individual particles, each going through exactly one slit, with 50% going through the top slit and 50% through the bottom. The interference has vanished, even though we never assumed that the wave function collapsed!

What this shows is that wave function collapse is not required to get particle-like behavior. All that’s necessary is that the different branches of the superposition end up not interfering with each other. And all that’s necessary for that is environmental decoherence, which is exactly what we had with the single spin bit!

In other words, environmental decoherence is sufficient to produce the same type of behavior that we’d expect from wave function collapse. This is because interference will only occur between non-orthogonal branches of the wave function, and the branches become orthogonal upon decoherence (by definition). A particle can be in a superposition of multiple states but still act as if it has collapsed!

Now, maybe we want to say that the particle’s wave function is collapsed when its position is measured by the screen. But this isn’t necessary either! You could just say that the detector enters into a superposition and quickly decoheres, such that the different branches of the wave function (one for each possible detector state) very suddenly become orthogonal and can no longer interact. And then you could say that the collapse only really happens once a conscious being observes the detector! Or you could be a Many-Worlder and say that the collapse never happens (although then you’d have to figure out where the probabilities are coming from in the first place).

You might be tempted to say at this point: “Well, then all the different theories of wave function collapse are empirically equivalent! At least, the set of theories that say ‘wave function collapse = total decoherence + other necessary conditions possibly’. Since total decoherence removes all interference effects, the results of all experiments will be indistinguishable from the results predicted by saying that the wave function collapsed at some point!”

But hold on! This is forgetting a crucial fact: decoherence is reversible, while wave function collapse is not!!! 

Screen Shot 2019-03-17 at 8.21.51 PM
Pretty picture from doi: 10.1038/srep15330

Let’s say that you run the same setup before with the spin bit recording the information about which slit the particle went through, but then we destroy that information before it interacts with the environment in any way, therefore removing any traces of the measurement. Now the two branches of the wave function have “recohered,” meaning that what we’ll observe is back to the interference pattern! (There’s a VERY IMPORTANT caveat, which is that the time period during which we’re destroying the information stored in the spin bit must be before the particle hits the detector screen and the state of the screen couples to its environment, thus decohering with the record of which slit the particle went through).

If you’re a collapse purist that says that wave function collapse = total decoherence (i.e. orthogonality of the relevant branches of the wave function), then you’ll end up making the wrong prediction! Why? Well, because according to you, the wave function collapsed as soon as the information was recorded, so there was no “other branch of the wave function” to recohere with once the information was destroyed!

This has some pretty fantastic implications. Since IN PRINCIPLE even the type of decoherence that occurs when your brain registers an observation is reversible (after all, the Schrodinger equation is reversible), you could IN PRINCIPLE recohere after an observation, allowing the branches of the wave function to interfere with each other again. These are big “in principle”s, which is why I wrote them big. But if you could somehow do this, then the “Consciousness Causes Collapse” theory would give different predictions from Many-Worlds! If your final observation shows evidence of interference, then “consciousness causes collapse” is wrong, since apparently conscious observation is not sufficient to cause the other branches of the wave function to vanish. Otherwise, if you observe the classical pattern, then Many Worlds is wrong, since the observation indicates that the other branches of the wave function were gone for good and couldn’t come back to recohere.

This suggests a general way to IN PRINCIPLE test any theory of wave function collapse: Look at processes right beyond the threshold where the theory says wave functions collapse. Then implement whatever is required to reverse the physical process that you say causes collapse, thus recohering the branches of the wave function (if they still exist). Now look to see if any evidence of interference exists. If it does, then the theory is proven wrong. If it doesn’t, then it might be correct, and any theory of wave function collapse that demands a more stringent standard for collapse (including Many-Worlds, the most stringent of them all) is proven wrong.

Wave function entropy

Entropy is a feature of probability distributions, and can be taken to be a quantification of uncertainty.

Standard quantum mechanics takes its fundamental object to be the wave function – an amplitude distribution. And from an amplitude distribution Ψ you can obtain a probability distribution Ψ*Ψ.

So it is very natural to think about the entropy of a given quantum state. For some reason, it looks like this concept of wave function entropy is not used much in physics. The quantum-mechanical version of entropy that is typically referred to is the Von-Neumann entropy, which involves uncertainty over which quantum state a system is in (rather than uncertainty intrinsic to a quantum state).

I’ve been looking into some of the implications of the concept of wave function entropy, and found a few interesting things.

Firstly, let’s just go over what precisely wave function entropy is.

Quantum mechanics is primarily concerned with calculating the wave function Ψ(x), which distributes complex amplitudes over phase space. The physical meaning of these amplitudes is interpreted by taking their absolute square Ψ*Ψ, which is a probability distribution.

Thus, the entropy of the wave function is given by:

S = – ∫ Ψ*Ψ ln(Ψ*Ψ) dx

As an example, I’ll write out some of the wave functions for the basic hydrogen atom:

*Ψ)1s = e-2r / π
*Ψ)2s = (2 – r)2 e-r / 32π

*Ψ)2p = r2 e-r cos(θ) / 32π
*Ψ)3s = (2r2 – 18r + 27)2 e-⅔r / 19683π

With these wave functions in hand, we can go ahead and calculate the entropies! Some of the integrals are intractable, so using numerical integration, we get:

S1s ≈ 70
S2s ≈ 470
S2p ≈ 326
S3s ≈ 1320

The increasing values for (1s, 2s, 3s) make sense – higher energy wave functions are more dispersed, meaning that there is greater uncertainty in the electron’s spatial distribution.

Let’s go into something a bit more theoretically interesting.

We’ll be interested in a generalization of entropy – relative entropy. This will quantify, rather than pure uncertainty, changes in uncertainty from a prior probability distribution ρ to our new distribution Ψ*Ψ. This will be the quantity we’ll denote S from now on.

S = – ∫ Ψ*Ψ ln(Ψ*Ψ/ρ) dx

Now, suppose we’re interested in calculating the wave functions Ψ that are local maxima of entropy. This means we want to find the Ψ for which δS = 0. Of course, we also want to ensure that a few basic constraints are satisfied. Namely,

∫ Ψ*Ψ dx = 1
∫ Ψ*HΨ = E

These constraints are chosen by analogy with the constraints in ordinary statistical mechanics – normalization and average energy. H is the Hamiltonian operator, which corresponds to the energy observable.

We can find the critical points of entropy that satisfy the constraint by using the method of Lagrange multipliers. Our two Lagrange multipliers will be α (for normalization) and β (for energy). This gives us the following equation for Ψ:

Ψ ln(Ψ*Ψ/ρ) + (α + 1)Ψ + βHΨ = 0

We can rewrite this as an operator equation, which gives us

ln(Ψ*Ψ/ρ) + (α + 1) + βH = 0
Ψ*Ψ = ρ/Z e-βH

Here we’ve renamed our constants so that Z =  eα+1 is a normalization constant.

So we’ve solved the wave function equation… but what does this tell us? If you’re familiar with some basic quantum mechanics, our expression should look somewhat familiar to you. Let’s backtrack a few steps to see where this familiarity leads us.

Ψ ln(Ψ*Ψ/ρ) + (α + 1)Ψ + βHΨ = 0
HΨ + 1/β ln(Ψ*Ψ/ρ) Ψ = – (α + 1)/β Ψ

Let’s rename – (α + 1)/β to a new constant λ. And we’ll take a hint from statistical mechanics and call 1/β the temperature T of the state. Now our equation looks like

HΨ + T ln(Ψ*Ψ/ρ) Ψ = λΨ

This equation is almost the Schrodinger equation. In particular, the Schrodinger equation pops out as the zero-temperature limit of this equation:

As T → 0,
our equation becomes…
HΨ = λΨ

The obvious interpretation of the constant λ in the zero temperature limit is E, the energy of the state. 

What about in the infinite-temperature limit?

As T → ∞,
our equation becomes…
Ψ*Ψ = ρ

Why is this? Because the only solution to the equation in this limit is for ln(Ψ*Ψ/ρ) → 0, or in other words Ψ*Ψ/ρ → 1

And what this means is that in the infinite temperature limit, the critical entropy wave function is just that which gives the prior distribution.

We can interpret this result as a generalization of the Schrodinger equation. Rather than a linear equation, we now have an additional logarithmic nonlinearity. I’d be interested to see how the general solutions to this equation differ from the standard equations, but that’s for another post.

HΨ + T ln(Ψ*Ψ/ρ) Ψ = λΨ

Bayesian experimental design

We can use the concepts in information theory that I’ve been discussing recently to discuss the idea of optimal experimental design. The main idea is that when deciding which experiment to run out of some set of possible experiments, you should choose the one that will generate the maximum information. Said another way, you want to choose experiments that are surprising as possible, since these provide the strongest evidence.

An example!

Suppose that you have a bunch of face-down cups in front of you. You know that there is a ping pong ball underneath one of the cups, and want to discover which one it is. You have some prior distribution of probabilities over the cups, and are allowed to check under exactly one cup. Which cup should you choose in order to get the highest expected information gain?

The answer to this question isn’t extremely intuitively obvious. You might think that you want to choose the cup that you think is most likely to hold the ball, because then you’ll be most likely to find the ball there and thus learn exactly where the ball is. But at the same time, the most likely choice of ball location is also the one that gives you the least information if the ball is actually there. If you were already fairly sure that the ball was under that cup, then you don’t learn much by discovering that it was.

Maybe instead the better strategy is to go for a cup that you think is fairly unlikely to be hiding the ball. Then you’ll have a small chance of finding the ball, but in that case will gain a huge amount of evidence. Or perhaps the maximum expected information gain is somewhere in the middle.

The best way to answer this question is to actually do the calculation. So let’s do it!

First, we’ll label the different theories about the cup containing the ball:

{C1, C2, C3, … CN}

Ck corresponds to the theory that the ball is under the kth cup. Next, we’ll label the possible observations you could make:

{X1, X2, X3, … XN}

Xk corresponds to the observation that the ball is under the kth cup.

Now, our prior over the cups will contain all of our past information about the ball and the cups. Perhaps we thought we heard a rattle when somebody bumped one of the cups earlier, or we notice that the person who put the ball under one of the cups was closer to the cups on the right hand side. All of this information will be contained in the distribution P:

(P1, P2, P3, … PN)

Pk is shorthand for P(Ck) – the probability of Ck being true.

Good! Now we are ready to calculate the expected information gain from any particular observation. Let’s say that we decide to observe X3. There are two scenarios: either we find the ball there, or we don’t.

Scenario 1: You find the ball under cup 3. In this case, you previously had a credence of P3 in X3 being true, so you gain -log(P3) bits of information.

Scenario 2: You don’t find the ball under cup 3. In this case, you gain –log(1 – P3) bits of information.

With probability P3, you gain –log(P3) bits of information, and with probability (1 – P3) you gain –log(1 – P3) bits of information. So your expected information gain is just –P3 logP3 – (1 – P3) logP3.

In general, we see that if you have a prior credence of P in the cup containing the ball, then your expected information gain is:

-P logP – (1 – P) logP

What does this function look like?

Experimental design

We see that it has a peak value at 50%. This means that you expect to gain the most information by looking at a cup that you are 50% sure contains the ball. If you are any more or less confident than this, then evidently you learn less than you would have if you were exactly agnostic about the cup.

Intuitively speaking, this means that we stand to learn the most by doing an experiment on a quantity that we are perfectly agnostic about. Practically speaking, however, the mandate that we run the experiment that maximizes information gain ends up telling us to always test the cup that we are most confident contains the ball. This is because if you split your credences among N cups, they will be mostly under 50%, so the closest you can get to 50% will be the largest credence.

Even if you are 99% confident that the fifteenth cup out of one hundred contains the ball, you will have just about .01% credence in each of the others containing the ball. Since 99% is closer to 50% than .01%, you will stand to gain the most information by testing the fifteenth ball (although you stand to gain very little information in a more absolute sense).

This generalizes nicely. Suppose that instead of trying to guess whether or not there is a ball under a cup, you are trying to guess whether there is a ball, a cube, or nothing. Now your expected information gain in testing a cup is a function of your prior over the cup containing a ball Pball, your prior over it containing a cube Pcube, and your prior over it containing nothing Pempty.

-Pball logPball – Pcube logPcube – Pempty logPempty

Subject to the constraint that these three priors must add up to 1, what set of (Pball, Pcube, Pempy) maximizes the information gain? It is just (⅓, ⅓, ⅓).

Optimal (Pball, Pcube, Pempy) = (⅓, ⅓, ⅓)

Imagine that you know that exactly one cup is empty, exactly one contains a cube, and exactly one contains a ball, and have the following distribution over the cups:

Cup 1: (⅓, ⅓, ⅓)
Cup 2: (⅔, ⅙, ⅙)
Cup 3: (0, ½, ½)

If you can only peek under a single cup, which one should you choose in order to learn the most possible? I take it that the answer to this question is not immediately obvious. But using these methods in information theory, we can answer this question unambiguously: Cup 1 is the best choice – the optimal experiment.

We can even numerically quantify how much more information you get by checking under Cup 1 than by checking under Cup 2:

Information gain(check cup 1) ≈ 1.58 bits
Information gain(check cup 2) ≈ 1.25 bits
Information gain(check cup 3) = 1 bits

Checking cup 1 is thus 0.33 bits better than checking cup 2, and 0.58 bits better than checking cup 3. Since receiving N bits of information corresponds to ruling out all but 1/2N possibilities, we rule out 20.33 ≈ 1.26 times more possibilities by checking cup 1 than cup 2, and 20.58 ≈ 1.5 times more possibilities than cup 3.

Even more generally, we see that when we can test N mutually exclusive characteristics of an object at once, the test is most informative when our credences in the characteristics are smeared out evenly; P(k) = 1/N.

This makes a lot of sense. We learn the most by testing things about which we are very uncertain. The more smeared out our probabilities are over the possibilities, the less confident we are, and thus the more effective a test will be. Here we see a case in which information theory vindicates common sense!

Why relative entropy

Background for this post: Entropy is expected surprise, A survey of entropy and entropy variants, and Maximum Entropy and Bayes

Suppose you have some old distribution Pold, and you want to update it to a new distribution Pnew given some information.

You want to do this in such a way as to be as uncertain as possible, given your evidence. One strategy for achieving this is to maximize the difference in entropy between your new distribution and your old one.

Max (Snew – Sold) = ∑ -Pnew logPnew – ∑ -Pold logPold

Entropy is expected surprise. So this quantity is the new expected surprise minus the old expected surprise. Maximizing this corresponds to trying to be as much more surprised on average as possible than you expected to be previously.

But this is not quite right. We are comparing the degree of surprise you expect to have now to the degree of surprise you expected to have previously, based on your old distribution. But in general, your new distribution may contain important information as to how surprised you should have expected to be.

Think about it this way.

One minute ago, you had some set of beliefs about the world. This set of beliefs carried with it some degree of expected surprise. This expected surprise is not the same as the true average surprise, because you could be very wrong in your beliefs. That is, you might be very confident in your beliefs (i.e. have very low EXPECTED surprise), but turn out to be very wrong (i.e. have very high ACTUAL average surprise).

What we care about is not how surprised somebody with the distribution Pold would have expected to be, but how surprised you now expect somebody with the distribution Pold to be. That is, you care about the average value of surprise, given your new distribution, your new best estimate of the actual distribution

That is to say, instead of using the simple difference in entropies S(Pnew) – S(Pold), you should be using the relative entropy Srel(Pnew, Pold).

Max Srel = ∑ -Pnew logPnew – ∑ -Pnew logPold

Here’s a diagram describing the three species of entropy: entropy, cross entropy, and relative entropy.

Types of Entropy.png

As one more example of why this makes sense: imagine that one minute ago you were totally ignorant and knew absolutely nothing about the world, but were for some reason very irrationally confident about your beliefs. Now you are suddenly intervened upon by an omniscient Oracle that tells you with perfect accuracy exactly what is truly going on.

If your new beliefs are designed by maximizing the absolute gain in entropy, then you will be misled by your old irrational confidence; your old expected surprise will be much lower than it should have been. If you use relative entropy, then you will be using your best measure of the actual average surprise for your old beliefs, which might have been very large. So in this scenario, relative entropy is a much better measure of your actual change in average surprise than the absolute entropy difference, as it avoids being misled by previous irrationality.

A good way to put this is that relative entropy is better because it uses your current best information to estimate the difference in average surprise. While maximizing absolute entropy differences will give you the biggest change in expected surprise, maximizing relative entropy differences will do a better job at giving you the biggest difference in *actual* surprise. Relative entropy, in other words, allows you to correct for previous bad estimates of your average surprise, and substitute in the best estimate you currently have.

These two approaches, maximizing absolute entropy difference and maximizing relative entropy, can give very different answers for what you should believe. It so happens that the answers you get by maximizing relative entropy line up nicely with the answers you get from just ordinary Bayesian updating, while the answers you get by maximizing absolute entropy differences, which is why this difference is important.

A survey of entropy and entropy variants

This post is for anybody that is confused about the numerous different types of entropy concepts out there, and how they relate to one another. The concepts covered are:

  • Surprise
  • Information
  • Entropy
  • Cross entropy
  • KL divergence
  • Relative entropy
  • Log loss
  • Akaike Information Criterion
  • Cross validation

Let’s dive in!

Surprise and information

Previously, I talked about the relationship between surprise and information. It is expressed by the following equation:

Surprise = Information = – log(P)

I won’t rehash the justification for this equation, but highly recommend you check out the previous post if this seems unusual to you.

In addition, we introduced the ideas of expected surprise and total expected surprise, which were expressed by the following equations:

Expected surprise = – P log(P)
Total expected surprise = – ∑ P log(P)

As we saw previously, the total expected surprise for a distribution is synonymous with the entropy of somebody with that distribution.

Which leads us straight into the topic of this post!


The entropy of a distribution is how surprised we expect to be if we suddenly learn the truth about the distribution. It is also the amount of information we expect to gain upon learning the truth.

A small degree of entropy means that we expect to learn very little when we hear the truth. A large degree of entropy means that we expect to gain a lot of information upon hearing the truth. Therefore a large degree of entropy represents a large degree of uncertainty. Entropy is our distance from certainty.

Entropy = Total expected surprise = – ∑ P log(P)

Notice that this is not the distance from truth. We can be very certain, and very wrong. In this case, our entropy will be high, because it is our expected surprise. That is, we calculate entropy by looking at the average surprise over our probability distribution, not the true distribution. If we want to evaluate the distance from truth, we need to evaluate the average over the true distribution.

We can do this by using cross-entropy.

Cross Entropy

In general, the cross entropy is a function of two distributions P and Q. The cross entropy of P and Q is the surprise you expect somebody with the distribution Q to have, if you have distribution P.

Cross Entropy = Surprise P expects of Q = – ∑ P log(Q)

The actual average surprise of your distribution P is therefore the cross-entropy between P and the true distribution. It is how surprised somebody would expect you to be, if they had perfect knowledge of the true distribution.

Actual average surprise = – ∑ Ptrue log(P)

Notice that the smallest possible value that the cross entropy could take on is the entropy of the true distribution. This makes sense – if your distribution is as close to the truth as possible, but the truth itself contains some amount of uncertainty (for example, a fundamentally stochastic process), then the best possible state of belief you could have would be exactly as uncertain as the true distribution is. Maximum cross entropy between your distribution and the true distribution corresponds to maximum distance from the truth.

Kullback-Leibler divergence

If we want a quantity that is zero when your distribution is equal to the true distribution, then you can shift the cross entropy H(Ptrue, P) over by the value of the true entropy S(Ptrue). This new quantity H(Ptrue, P) – S(Ptrue) is known as the Kullback-Leibler divergence.

Shifted actual average surprise = Kullback-Leibler divergence
= – ∑ Ptrue log(P) + ∑ Ptrue log(Ptrue)
= ∑ Ptrue log(Ptrue/P)

It represents the information gap, or the actual average difference in difference between your distribution and the true distribution. The smallest possible value of the Kullback-Leibler divergence is zero, when your beliefs are completely aligned with reality.

Since KL divergence is just a constant shift away from cross entropy, minimizing one is the same as minimizing the other. This makes sense; the only real difference between the two is whether we want our measure of “perfect accuracy” to start at zero (KL divergence) or to start at the entropy of the true distribution (cross entropy).

Relative Entropy

The negative KL divergence is just a special case of what’s called relative entropy. The relative entropy of P and Q is just the negative cross entropy of P and Q, shifted so that it is zero when P = Q.

Relative entropy = shifted cross entropy
= – ∑ P log(P/Q)

Since the cross entropy between P and Q measures how surprised P expects Q to be, the relative entropy measures P’s expected gap in average surprisal between themselves and Q.

KL divergence is what you get if you substitute in Ptrue for P. Thus it is the expected gap in average surprisal between a distribution and the true distribution.


Maximum KL divergence corresponds to maximum distance from the truth, while maximum entropy corresponds to maximum from certainty. This is why we maximize entropy, but minimize KL divergence. The first is about humility – being as uncertain as possible given the information that you possess. The second is about closeness to truth.

Since KL divergence is just a constant shift away from cross entropy, minimizing one is the same as minimizing the other. This makes sense, the only real difference between the two is whether we want our “perfectly accurate” measure to start at zero (KL divergence) or at the entropy of the true distribution (cross entropy).

Since we don’t start off with access to Ptrue, we can’t directly calculate the cross entropy H(Ptrue, P). But lucky for us, a bunch of useful approximations are available!

Log loss

Log loss uses the fact that if we have a set of data D generated by the true distribution, the expected value of F(x) taken over the true distribution will be approximately just the average value of F(x), for x in D.

Cross Entropy = – ∑ Ptrue log(P)
(Data set D, N data points)
Cross Entropy ~ Log loss = – ∑x in D log(P(x)) / N

This approximation should get better as our data set gets larger. Log loss is thus just a large-numbers approximation of the actual expected surprise.

Akaike information criterion

Often we want to use our data set D to optimize our distribution P with respect to some set of parameters. If we do this, then the log loss estimate is biased. Why? Because we use the data in two places: first to optimize our distribution P, and second to evaluate the information distance between P and the true distribution.

This allows problems of overfitting to creep in. A distribution can appear to have a fantastically low information distance to the truth, but actually just be “cheating” by ensuring success on the existing data points.

The Akaike information criterion provides a tweak to the log loss formula to try to fix this. It notes that the difference between the cross entropy and the log loss is approximately proportional to the number of parameters you tweaked divided by the total size of the data set: k/N.

Thus instead of log loss, we can do better at minimizing cross entropy by minimizing the following equation:

AIC = Log loss + k/N

(The exact form of the AIC differs by multiplicative constants in different presentations, which ultimately is unimportant if we are just using it to choose an optimal distribution)

The explicit inclusion of k, the number of parameters in your model, represents an explicit optimization for simplicity.

Cross Validation

The derivation of AIC relies on a complicated set of assumptions about the underlying distribution. These assumptions limit the validity of AIC as an approximation to cross entropy / KL divergence.

But there exists a different set of techniques that rely on no assumptions besides those used in the log loss approximation (the law of large numbers and the assumption that your data is an unbiased sampling of the true distribution). Enter the holy grail of model selection!

The problem, recall, was that we used the same data twice, allowing us to “cheat” by overfitting. First we used it to tweak our model, and second we used it to evaluate our model’s cross entropy.

Cross validation solves this problem by just separating the data into two sets, the training set and the testing set. The training set is used for tweaking your model, and the testing set is used for evaluating the cross entropy. Different procedures for breaking up the data result in different flavors of cross-validation.

There we go! These are some of the most important concepts built off of entropy and variants of entropy.

Entropy is expected surprise

Today we’re going to talk about a topic that’s very close to my heart: entropy. We’ll start somewhere that might seem unrelated: surprise.

Suppose that we wanted to quantify the intuitive notion of surprise. How should we do that?

We’ll start by analyzing a few base cases.

First! If something happens and you already were completely certain that it would happen, then you should completely unsurprised.

That is, if event E happens, and you had a credence P(E) = 100% in it happening, then your surprise S should be zero.

S(1) = 0

Second! If something happens that you were totally sure was impossible, with 100% credence, then you should be infinitely surprised.

That is, if E happens and P(E) = 0, then S = ∞.

S(0) = ∞

So far, it looks like your surprise S should be a function of your credence P in the event you are surprised at. That is, S = S(P). We also have the constraints that S(1) = 0 and S(0) = ∞.

There are many candidates for a function like this, for example: S(P) = 1/P – 1, S(P) = -log(P), S(P) = cot(πx/2). So we need more constraints.

Third! If an event E1 happens that is surprising to degree S1, and then another event E2 happens with surprisingness S2, then your surprise at the combination of these events should be S1 + S2.

I.e., we want surprise to be additive. If S(P(E1)) = S1 and S(P(E2 | E1)) = S2, then S(P(E1 & E2) = S1 + S2.

This entails a new constraint on our surprise function, namely:

S(PQ) = S(P) + S(Q)

Fourth, and finally! We want our surprise function to be continuous – free from discontinuous jumps. If your credence that the event will happen changes by an arbitrarily small amount, then your surprise if it does happen should also change by an arbitrarily small amount.

S(P) is continuous.

These four constraints now fully specify the form of our surprise function, up to a multiplicative constant. What we find is that the only function satisfying these constraints is the logarithm:

S(P) = k logP, where k is some negative number

Taking the simplest choice of k, we end up with a unique formalization of the intuitive notion of surprise:

S(P) = – logP

To summarize what we have so far: Four basic desideratum for our formalization of the intuitive notion of surprise have led us to a single simple equation.

This equation that we’ve arrived at turns out to be extremely important in information theory. It is, in fact, just the definition of the amount of information you gain by observing E. This reveals to us a deep connection between surprise and information. They are in an important sense expressing the same basic idea: more surprising events give you more information, and unsurprising events give you little information.

Let’s get a little better numerical sense of this formalization of surprise/information. What does a single unit of surprise or information mean? With some quick calculation, we see that a single unit of surprise, or bit of information corresponds to the observation of an event that you had a 50% expectation of. This also corresponds to a ruling out of 50% of the weight of possible other events you thought you might have observed. In essence, each bit of information you receive / surprise you experience corresponds to the total amount of possibilities being cut in half.

Two bits of information narrow the possibilities to one-fourth. Three cut out all but one-eighth. And so on. For a rational agent, the process of receiving more information or of being continuously surprised is the process of whittling down your models of reality to a smaller and better set!

The next great step forward is to use our formalization of surprise to talk not just about how surprised you are once an event happens, but how surprised you expect to be. If you have a credence of P in an event happening, then you expect a degree of surprise S(P) with credence P. In other words, the expected surprise you have with respect to that particular event is:

Expected surprise = – P logP

When summed over the totality of all possible events that occurred we get the following expression:

Total expected surprise = – ∑i Pi logPi

This expression should look very very familiar to you. It’s one of the most important quantities humans have discovered…


Now you understand the title of this post. Quite literally, entropy is total expected surprise!

Entropy = Total expected surprise

By the way, you might be wondering if this is the same entropy as you hear mentioned in the context of physics (that thing that always increases). Yes, it is identical! This means that we can describe the Second Law of Thermodynamics as a conspiracy by the universe to always be as surprising as possible to us! There are a bunch of ways to explore the exact implications of this, but that’s a subject for another post.

Getting back to the subject of this post, we can now make another connection. Surprise is information. Total expected surprise is entropy. And entropy is a measure of uncertainty.

If you think about this for a moment, this should start to make sense. If your model of reality is one in which you expect to be very surprised in the next moment, then you are very uncertain about what is going to happen in the next moment. If, on the other hand, your model of reality is one in which you expect zero surprise in the next moment, then you are completely certain!

Thus we see the beautiful and deep connection between surprise, information, entropy, and uncertainty. The overlap of these four concepts is rich with potential for exploration. We could go the route of model selection and discuss notions like mutual informationinformation divergence, and relative entropy, and how they relate to the virtues of predictive accuracy and model simplicity. We could also go the route of epistemology and discuss the notion of epistemic humility, choosing your beliefs to maximize your uncertainty, and the connection to Bayesian epistemology. Or, most tantalizingly, we could go the route of physics and explore the connection between this highly subjective sense of entropy as surprise/ uncertainty, and the very concrete notion of entropy as a physical quantity that characterizes the thermal properties of systems.

Instead of doing any of these, I’ll do none, and end here in hope that I’ve conveyed some of the coolness of this intersection of philosophy, statistics, and information theory.

Inference as a balance of accommodation, prediction, and simplicity

(This post is a soft intro to some of the many interesting aspects of model selection. I will inevitably skim over many nuances and leave out important details, but hopefully the final product is worth reading as a dive into the topic. A lot of the general framing I present here is picked up from Malcolm Forster’s writings.)

What is the optimal algorithm for discovering the truth? There are many different candidates out there, and it’s not totally clear how to adjudicate between them. One issue is that it is not obvious exactly how to measure correspondence to truth. There are several different criterion that we can use, and in this post, I want to talk about three big ones: accommodation, prediction, and simplicity.
The basic idea of accommodation is that we want our theories to do a good job at explaining the data that we have observed. Prediction is about doing well at predicting future data. Simplicity is, well, just exactly what it sounds like. Its value has been recognized in the form of Occam’s razor, or the law of parsimony, although it is famously difficult to formalize.
Let’s say that we want to model the relationship between the number of times we toss a fair coin and the number of times that it lands H. We might get a data set that looks something like this:

Now, our goal is to fit a curve to this data. How best to do this?

Consider the following two potential curves:

Curve fitting

Curve 1 is generated by Procedure 1: Find the lowest-order polynomial that perfectly matches the data.

Curve 2 is generated by Procedure 2: Find the straight line that best fits the data.

If we only cared about accommodation, then we’ll prefer Curve 1 over Curve 2. After all, Curve 1 matches our data perfectly! Curve 2, on the other hand, is always close but never exactly right.

On the other hand, regardless of how well Curve 1 fits the data, it entirely misses the underlying pattern in the data captured by Curve 2! This demonstrates one of the failure modes of a single-minded focus on accommodation: the problem of overfitting.

We might want to solve in this problem by noting that while Curve 1 matches the data better, it does so in virtue of its enormous complexity. Curve 2, on the other hand, matches the data pretty well, but does so simply. A combined focus on accommodation + simplicity might, therefore, favor Curve 2. Of course, this requires us to precisely specify what we mean by ‘simplicity’, which has been the subject of a lot of debate. For instance, some have argued that an individual curve cannot be said to be more or less simple than a different curve, as just rephrasing the data in a new coordinate system can flip the apparent simplicity relationship. This is a general version of the grue-bleen problem, which is a fantastic problem that deserves talking about in a separate post.

Another way to solve this problem is by optimizing for accommodation + prediction. The over-fitted curve is likely to be very off if you ask for predictions about future data, while the straight line is likely going to do better. This makes sense – a straight line makes better forecasts about future data because it has gotten to the true nature of the underlying relationship.

What if we want to ensure that our model does a good job at predicting future data, but are unable to gather future data? For example, suppose that we lost the coin that we were using to generate the data, but still want to know what model would have done best at predicting future flips? Cross-validation is a wonderful technique that can be used to deal with exactly this problem.

How does it work? The idea is that we randomly split up the data we have into two sets, the training set and the testing set. Then we train our models on the training set (see which curve each model ends up choosing as its best fit, given the training data), and test it on the testing set. For instance, if our training set is just the data from the early coin flips, we find the following:

Curve fitting cross validation
Cross validation

We can see that while the new Curve 2 does roughly as well as it did before, the new Curve 1 will do horribly on the testing set. We now do this for many different ways of splitting up our data set, and in the end accumulate a cross-validation “score”. This score represents the average success of the model at predicting points that it was not trained on.

We expect that in general, models that overfit will tend to do horribly badly when asked to predict the testing data, while models that actually get at the true relationship will tend to do much better. This is a beautiful method for avoiding overfitting by getting at the deep underlying relationships, and optimizing for the value of predictive accuracy.

It seems like predictive accuracy and simplicity often go hand-in-hand. In our coin example, the simpler model (the straight line) was also the more predictively accurate one. And models that overfit tend to be both bad at making accurate predictions and enormously complicated. What is the explanation for this relationship?

One classic explanation says that simpler models tend to be more predictive because the universe just actually is relatively simple. For whatever reason, the actual relationships between different variables in the universe happens to be best modeled by simple equations, not complicated ones. Why? One reason that you could point to is the underlying simplicity of the laws of nature.

The Standard Model of particle physics, which gives rise to basically all of the complex behavior we see in the world, can be expressed in an equation that can be written on a t-shirt. In general, physicists have found that reality seems to obey very mathematically simple laws at its most fundamental level.

I think that this is somewhat of a non-explanation. It predicts simplicity in the results of particle physics experiments, but does not at all predict simple results for higher-level phenomenon. In general, very complex phenomena can arise from very simple laws, and we get no guarantee that the world will obey simple laws when we’re talking about patterns involving 1020 particles.

An explanation that I haven’t heard before references possible selection biases. The basic idea is that most variables out there that we could analyze are likely not connected by any simple relationships. Think of any random two variables, like the number of seals mating at any moment and the distance between Obama and Trump at that moment. Are these likely to be related by a simple equation? Of course!

(Kidding. Of course not.)

The only times when we do end up searching for patterns in variables is when we have already noticed that some pattern does plausibly seem to exist. And since we’re more likely to notice simpler patterns, we should expect a selection bias among those patterns we’re looking at. In other words, given that we’re looking for a pattern between two variables, it is fairly likely that there is a pattern that is simple enough for us to notice in the first place.

Regardless, it looks like an important general feature of inference systems to provide a good balance between accommodation and either prediction or simplicity. So what do actual systems of inference do?

I’ve already talked about cross validation as a tool for inference. It optimizes for accommodation (in the training set) + prediction (in the testing set), but not explicitly for simplicity.

Updating of beliefs via Bayes’ rule is a purely accommodation procedure. When you take your prior credence P(T) and update it with evidence E, you are ultimately just doing your best to accommodate the new information.

Bayes’ Rule: P(T | E) = P(T) ∙ P(E | T) / P(T) 

The theory that receives the greatest credence bump is going to be the theory that maximizes P(E | T), or the likelihood of the evidence given the theory. This is all about accommodation, and entirely unrelated to the other virtues. Technically, the method of choosing the theory that maximizes the likelihood of your data is known as Maximum Likelihood Estimation (MLE).

On the other hand, the priors that you start with might be set in such a way as to favor simpler theories. Most frameworks for setting priors do this either explicitly or implicitly (principle of indifference, maximum entropy, minimum description length, Solomonoff induction).

Leaving Bayes, we can look to information theory as the foundation for another set of epistemological frameworks. These are focused mostly on minimizing the information gain from new evidence, which is equivalent to maximizing the relative entropy of your new distribution and your old distribution.

Two approximations of this procedure are the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), each focusing on subtly different goals. Both of these explicitly take into account simplicity in their form, and are designed to optimize for both accommodation and prediction.

Here’s a table of these different procedures, as well as others I haven’t mentioned yet, and what they optimize for:

Optimizes for…




Maximum Likelihood Estimation

Minimize Sum of Squares

Bayesian Updating

Principle of Indifference

Maximum Entropy Priors

Minimum Message Length

Solomonoff Induction


Minimize Mallow’s Cp

Maximize Relative Entropy

Minimize Log Loss

Cross Validation

Minimize Akaike Information Criterion (AIC)

Minimize Bayesian Information Criterion (AIC)

Some of the procedures I’ve included are closely related to others, and in some cases they are in fact approximations of others (e.g. minimize log loss ≈ maximize relative entropy, minimize AIC ≈ minimize log loss).

We can see in this table that Bayesianism (Bayesian updating + a prior-setting procedure) does not explicitly optimize for predictive value. It optimizes for simplicity through the prior-setting procedure, and in doing so also happens to pick up predictive value by association, but doesn’t get the benefits of procedures like cross-validation.

This is one reason why Bayesianism might be seen as suboptimal – prediction is the great goal of science, and it is entirely missing from the equations of Bayes’ rule.

On the other hand, procedures like cross validation and maximization of relative entropy look like good candidates for optimizing for accommodation and predictive value, and picking up simplicity along the way.

Entropy vs relative entropy

This post is about the relationship between entropy and relative entropy. This relationship is subtle but important – purely maximizing entropy (MaxEnt) is not equivalent to Bayesian conditionalization except in special cases, while maximizing relative entropy (ME) is. In addition, the justifications for MaxEnt are beautiful and grounded in fundamental principles of normative epistemology. Do these justifications carry over to maximizing relative entropy?

To a degree, yes. We’ll see that maximizing relative entropy is a more general procedure than maximizing entropy, and reduces to it in special cases. The cases where MaxEnt gives different results from ME can be interpreted through the lens of MaxEnt, and relate to an interesting distinction between commuting and non-commuting observations.

So let’s get started!

We’ll solve three problems: first, using MaxEnt to find an optimal distribution with a single constraint C1; second, using MaxEnt to find an optimal distribution with constraints C1 and C2; and third, using ME to find the optimal distribution with C2 given the starting distribution found in the first problem.

Part 1

Problem: Maximize – ∫ P logP dx with constraints
∫ P dx = 1
∫ C1[P] dx = 0

P ( – P1 logP1 + (α + 1) P1 + βC1[P1] ) = 0
– logP1 + α + β C1’[P1] = 0

Part 2

Problem: Maximize – ∫ P logP dx with constraints
∫ P dx = 1
∫ C1[P] dx = 0
∫ C2[P] dx = 0

P ( – P2 logP2 + (α’ + 1) P2 + β’C1[P2] + λ C2[P2] ) = 0
– logP2 + α’ + β’ C1’[P2] + λ C2’[P2] = 0

Part 3

Problem: Maximize – ∫ P log(P / P1) dx with constraints
∫ P dx = 1
∫ C2[P] dx = 0

P ( – P3 logP3 + P3 logP1 + (α’’ + 1)P3 + λ’ C2[P3] ) = 0
– logP3 + α’’ + logP1 + λ’ C2’[P3] = 0
– logP3 + α’’ + α + β C1’[P1] + λ’ C2’[P3] = 0
– logP3 + α’’’ + β C1’[P1] + λ’ C2’[P3] = 0

We can now compare our answers for Part 2 to Part 3. These are the same problem, solved with MaxEnt and ME. While they are clearly different solutions, they have interesting similarities.

– logP2 + α’ + β’ C1’[P2] + λ C2’[P2] = 0
∫ P2 dx = 1
∫ C1[P2] dx = 0
∫ C2[P2] dx = 0

– logP3 + α’’’ + β C1’[P1] + λ’ C2’[P3] = 0
∫ P3 dx = 1
∫ C1[P1] dx = 0
∫ C2[P3] dx = 0

The equations are almost identical. The only difference is in how they treat the old constraint. In MaxEnt, the old constraint is treated just like the new constraint – a condition that must be satisfied for the final distribution.

But in ME, the old constraint is no longer required to be satisfied by the final distribution! Instead, the requirement is that the old constraint be satisfied by your initial distribution!

That is, MaxEnt takes all previous information, and treats it as current information that must constrain your current probability distribution.

On the other hand, ME treats your previous information as constraints only on your starting distribution, and only ensures that your new distribution satisfy the new constraint!

When might this be useful?

Well, say that the first piece of information you received, C1, was the expected value of some measurable quantity. Maybe it was that x̄ = 5.

But if the second piece of information C2 was an observation of the exact value of x, then we clearly no longer want our new distribution to still have an expected value of x̄. After all, it is common for the expected value of a variable to differ from the exact value of x.

E(x) vs x

Once we have found the exact value of x, all previous information relating to the value of x is screened off, and should no longer be taken as constraints on our distribution! And this is exactly what ME does, and MaxEnt fails to do.

What about a case where the old information stays relevant? For instance, an observation of the values of a certain variable is not ‘cancelled out’ by a later observation of another variable. Observations can’t be un-observed. Does ME respect these types of constraints?


Observations of variables are represented by constraints that set the distribution over those variables to delta-functions. And when your old distribution contains a delta function, that delta function will still stick around in your new distribution, ensuring that the old constraint is still satisfied.

Pold ~ δ(x – x’)
Pnew ~ δ(x – x’)

The class of observations that are made obsolete by new observations are called non-commuting observations. They are given this name because for such observations, the order in which you process the information is essential. Observations for which the order of processing doesn’t matter are called commuting observations.

In summation: maximizing relative entropy allows us to take into account subtle differences in the type of evidence we receive, such as whether or not old data is made obsolete by new data. And mathematically, maximizing relative entropy is equivalent to maximizing ordinary entropy with whatever new constraints were not included in your initial distribution, as well as an additional constraint relating to the value of your old distribution. While the old constraints are not guaranteed to be satisfied by your new distribution, the information about them is preserved in the form of the prior distribution that is a factor in the new distribution.

Fun with Akaike

The Akaike information criterion is a metric for model quality that naturally arises from the principle of maximum entropy. It balances predictive accuracy against model complexity, encoding a formal version of Occam’s razor and solving problems of overfitting. I’m just now learning about how to use this metric, and will present a simple example that shows off a lot of the features of this framework.

Suppose that we have two coins. For each coin, we can ask what the probability is of each coin landing heads. Call these probabilities p and q.


Two classes of models are (1) those that say that p = q and (2) those that say that p ≠ q. The first class of models are simpler in an important sense – they can be characterized by a single parameter p. The second class, on the other hand, require two separate parameters, one for each coin.

The number of parameters (k) used by our model is one way to measure model complexity. But of course, we can also test our models by getting experimental data. That is, we can flip each coin a bunch of times, record the results, and see how they favor one model over another.

One common way of quantifying the empirical success of a given model is by looking at the maximum value of its likelihood function L. This is the function that tells you how likely your data was, given a particular model. If Model 2 can at best do better at predicting the data than Model 1, then this should count in favor of Model 2.

So how do we combine these pieces of information – k (the number of parameters in a model) and L (the predictive success of the model)? Akaike’s criterion says to look at the following metric:

AIK = 2k – 2 lnL

The smaller the value of this parameter, the better your model is.

So let’s apply this on an arbitrary data set:

n1 = number of times coin 1 landed heads
n2 = number of times coin 1 landed tails
m1 =number of times coin 2 landed heads
m2 = number of times coin 2 landed tails

For convenience, we’ll also call the total flips of coin 1 N, and the total flips of coin 2 M.

First, let’s look at how Model 1 (the one that says that the two coins have an equal chance of landing heads) does on this data. This model predicts with probability p each heads, on either coin, and with probability 1 – p each tails on either coin.

L1 = C(N,n1) C(M,m1) pn1+m1 (1 – p)n2+m2

The two choose functions C(N, n1) and C(M, m1) are there to ensure that this function is nicely normalized. Intuitively, they arise from the fact that any given number of coin tosses that land heads could happen in many possible, ways, and all of these ways must be summed up.

This function finds its peak value at the following value of p:

p = (n1 + m1) / (N + M)
L1,max = C(N, n1) C(M, m1) (n1 + m1)n1+m1 (n2 + m2)n2+m2 / (N + M)N+M

By Stirling’s approximation, this becomes:

ln(L1,max) ~ -ln(F) – ½ ln(G)
where F = (N + M)N+M/NNMM · n1n1m1m1/(n1 + m1)n1+m1 · n2n2m2m2/(n2 + m2)n2+m2
and G = n1n2m1m/ NM

With this, our Akaike information criterion for Model 1 tells us:

AIC= 2 + 2ln(F) + ln(G)

Moving on to Model 2, we now have two different parameters p and q to vary. The likelihood of our data given these two parameters are given by:

L2 = C(N, n1) C(M, m1) pn1 (1 – p)n2 qm1 (1 – q)m2

The values of p and q that make this data most likely are:

p = n1 / N
q = m1 / M
So L2,max = C(N, n1) C(M, m1) n1n1m1m1n2n2m2m2 / NNMM

And again, using Stirling’s approximation, we get:

ln(L1,max) ~  – ½ ln(G)
So AIC= 4 + ln(G)

We now just need to compare these two AICs to see which model is preferable for a given set of data:

AIC= 4 + ln(G)
AIC= 2 + 2ln(F) + ln(G)

AIC– AIC= 2 – 2lnF

Let’s look at two cases that are telling. The first case will be that we find that both coin 1 and coin 2 end up landing heads an equal proportion of the time, and for simplicity, both coins are tossed the same number of times. Formally:

Case 1: N = M, n1 = m1, n2 = m2

In this case, F becomes 1, so lnF becomes 0.

AIC– AIC1 = 2 > 0
So Model 1 is preferable.

This makes sense! After all, if the two coins are equally likely to land heads, then our two models do equally well at predicting the data. But Model 1 is simpler, involving only a single parameter, so it is preferable. AIC gives us a precise numerical criterion by which to judge how preferable Model 1 is!

Okay, now let’s consider a case where coin 1 lands heads much more often than coin 2.

Case 2: N = M, n1 = 2m1, 2n2 = m2

Now if we go through the algebra, we find:

F = 4N (4/27)(m1+n2) ~ 1.12N
So lnF ~ N ln(1.12) ~ .11 N
So AIC– AIC1 = 2 – .22N

This quantity is larger than 0 when N is less than 10, but then becomes smaller than zero for all other values.

Which means that for Case 2, small enough data sets still allow us to go with Model 1, but as we get more data, the predictive accuracy of Model 2 eventually wins out!

It’s worth pointing out here that the Akaike information criterion is an approximation to the technique of maximizing relative entropy, and this approximation assumes large sets of data. Given this, it’s not clear how reliable our estimate of 10 is for the largest data set.

Let’s do one last thing with our simple models.

As our two coins become more and more similar in how often they land heads, we expect Model 1 to last longer before Model 2 ultimately wins out. Let’s calculate a general relationship between the similarity in the ratios of heads to tails in coin 1 and coin 2 and the amount of time it takes for Model 1 to lose out.

Case 3: N = M, n1 = r·m1, r·n2 = m2

r here is our ratio of p/q – the chance of heads in coin 1 over the chance of heads in coin 2. Skipping ahead through the algebra we find:

lnF = N ln( 2 rr/(r+1) / (r + 1) )

Model 2 becomes preferable to Model 1 when AICbecomes smaller than AIC1, so we can find the critical point by setting ∆AIC = 2 – 2 lnF = 0

lnF = N ln( 2 rr/(r + 1) / (r + 1) ) = 1
N = 1 / ln( 2 rr/(r + 1) / (r + 1) )

We can see that as r goes to 1, N goes to ∞. We can see how quickly this happens by doing some asymptotics:

r = 1 + ε
N ~ 1 / ln(1 + ε)
So ε ~ e1/N – 1

N goes to infinity at an exponential rate as r approaches 1 linearly. This gives us a very rough idea of how similar our coins must be for us to treat them as essentially identical. We can use our earlier result that at r = 2, N = 10 to construct a table:

r N
2 10
1.1 100
1.01 1000
1.001 10,000
1.0001 100,000

Why you should be a Bayesian

In this post, I take Bayesianism to be the following normative epistemological claim: “You should treat your beliefs like probabilities, and reason according to the axioms of probability theory.”

Here are a few reasons why I support this claim:

I. Logic is not enough

Reasoning deductively from premises to conclusion is a wonderfully powerful tool when it can be applied. If you have absolute certainty in some set of premises, and these premises entail a new conclusion, then you can extend your certainty to the new conclusion. Alternatively, you can state clearly the conditions under which you would be granted certain belief, in the form of a conditional argument (if you were to convince me that A and B are true, then I would believe that C is true).

This is great for mathematicians proving theorems about abstract logical entities. But in the real world, deductive inference is simply not enough to account for the types of problems we face. We are constantly reasoning in a condition of uncertainty, where we have multiple competing theories about what’s going on, and we seek evidence – partial evidence, not deductively complete evidence – as to which of these theories we should favor.

If you want to know how to form beliefs about the parts of reality that aren’t clear-cut and certain, then you need to go beyond pure logic.

II. Probability theory is a natural extension of logic

Cox’s theorem shows that any system of plausible reasoning – modifying and updating beliefs in the presence of uncertainty – that is consistent with logic and a few minimal assumptions about normative reasoning is necessarily isomorphic to probability theory.

The converse of this is that any system of reasoning under uncertainty that isn’t ultimately functionally equivalent to Bayesianism is either logically inconsistent or violates other common-sense axioms of reasoning.

In other words, probability theory is the best candidate that we have for extending logic into the domain of the uncertain. It is about what is likely, not certain, to be true, and the way that we should update these assessments when receiving new information. In turn, probability theory contains ordinary logic as a special case when you take the limit of absolutely certainty.

III. Non-Bayesian systems of plausible reasoning result in inconsistencies and irrational behavior

Dutch-book arguments prove that any agent that is violating the axioms of probability theory can be exploited by cleverly capitalizing on logical inconsistencies in their beliefs. This combines a pragmatic argument (non-Bayesians are worse off in the long run) with an epistemic argument (non-Bayesians are vulnerable to logical inconsistencies in their preferences).

IV. You should be honest about your uncertainty

The principle of maximizing entropy mandates a unique way to set beliefs given your evidence, such that you make no presumptions about knowledge that you don’t have. This principle is fully consistent with and equivalent to standard Bayesian conditionalization.

In other words, Bayesianism is about epistemic humility – it tells you to not pretend to know things that you don’t know.

V. Bayesianism provides the foundations for the scientific method

The scientific method, needless to say, is humanity’s crowning epistemic achievement. With it we have invented medicine, probed atoms, and gone to the stars. Its success can be attributed to the structure of its method of investigating truth claims: in short, science is about searching theories for testable consequences, and then running experiments to update our beliefs in these theories.

This is all contained in Bayes’ rule, the fundamental law of probabilistic inference:

Pr(theory | evidence) ~ Pr(evidence | theory) · Pr(theory)

This rule tells you precisely how you should update your beliefs given your evidence, no more and no less. It contains the wisdom of empiricism that has revolutionized the world we live in.

VI. Bayesianism is practically useful

So maybe you’re convinced that Bayesianism is right in principle. There’s a separate question of if Bayesianism is useful in practice. Maybe treating your beliefs like probabilities is like trying to do psychology starting from Schrödinger’s equation – possible in principle but practically infeasible, not to mention a waste of time.

But Bayesianism is practically useful.

Statistical mechanics, one of the most powerful branches of modern science, is built on a foundation of explicitly Bayesian principles. More generally, good statistical reasoning is incredibly useful across all domains of truth-seeking, and an essential skill for anybody that wants to understand the world.

And Bayesianism is not just useful for epistemic reasons. A fundamental ingredient of decision-making is the ability to produce accurate models of reality. If you want to effectively achieve your goals, whatever they are, you must be able to engage in careful probabilistic reasoning.

And finally, in my personal experience I have found Bayesian epistemology to be infinitely mineable for useful heuristics in thinking about philosophy, physics, altruism, psychology, politics, my personal life, and pretty much everything else. I recommend anybody whose interest has been sparked to check out the following links: