Fractals and Epicycles

There is no bilaterally-symmetrical, nor eccentrically-periodic curve used in any branch of astrophysics or observational astronomy which could not be smoothly plotted as the resultant motion of a point turning within a constellation of epicycles, finite in number, revolving around a fixed deferent.

Norwood Russell Hanson, “The Mathematical Power of Epicyclical Astronomy”


A friend recently showed me this image…


…and thus I was drawn into the world of epicycles and fractals.

Epicycles were first used by the Greeks to reconcile observational data of the motions of the planets with the theory that all bodies orbit the Earth in perfect circles. It was found that epicycles allowed astronomers to retain their belief in perfectly circular orbits, as well as the centrality of Earth. The cost of this, however, was a system with many adjustable parameters (as many parameters as there were epicycles).

There’s a somewhat common trope about adding on endless epicycles to a theory, the idea being that by being overly flexible and accommodating of data you lose epistemic credibility. This happens to fit perfectly with my most recent posts on model selection and overfitting! The epicycle view of the solar system is one that is able to explain virtually any observational data. (There’s a pretty cool reason for this that has to do with the properties of Fourier series, but I won’t go into it.) The cost of this is a massive model with many parameters. The heliocentric model of the solar system, coupled with the Newtonian theory of gravity, turns out to be able to match all the same data with far fewer adjustable parameters. So by all of the model selection criteria we went over, it makes sense to switch over from one to the other.

Of course, it is not the case that we should have been able to tell a priori that an epicycle model of the planets’ motions was a bad idea. “Every planet orbits Earth on at most one epicycle”, for instance, is a perfectly reasonable scientific hypothesis… it just so happened that it didn’t fit the data. And adding epicycles to improve the fit to data is also not bad scientific practice, so long as you aren’t ignoring other equally good models with fewer parameters.)

Okay, enough blabbing. On to the pretty pictures! I was fascinated by the Hilbert curve drawn above, so I decided to write up a program of my own that generates custom fractal images from epicycles. Here are some gifs I created for your enjoyment:

Negative doubling of angular velocity

(Each arm rotates in the opposite direction of the previous arm, and at twice its angular velocity. The length of each arm is half that of the previous.)


Trebling of angular velocity


Negative trebling


Here’s a still frame of the final product for N = 20 epicycles:

Screen Shot 2018-11-27 at 7.23.55 AM



ωn ~ (n+1) 2n

(or, the Fractal Frog)


ωn ~ n, rn ~ 1/n

radius ~ 1:n, frequency ~ n.gif

ωn ~ n, constant rn


ωn ~ 2n, rn ~ 1/n2


And here’s a still frame of N = 20:

high res pincers

(All animations were built using, which I highly recommend for quick and easy construction of visualizations.)

Taxonomy of infinity catastrophes for expected utility theory

Basics of expected utility theory

I’ve talked quite a bit in past posts about the problems that infinities raise for expected utility theory. In this post, I want to systematically go through and discuss the different categories of problems.

First of all, let’s define expected utility theory.

Given an action A, we have a utility function U over the possible consequences
U = { U1, U2, U3, … UN }
and a credence distribution P over the consequences
P = { P1, P2, P3, … PN }.
We define the expected utility of A to be EU(A) = P1U1 + P2U2 + … + PNUN

Expected Utility Theory:
The rational action is that which maximizes expected utility.

Just to give an example of how this works out, suppose that we can choose between two actions A1 and A2, defined as follows:

Action A1
U1 = { 20, -10 }
P1 = { 50%, 50% }

Action A2
U2 = { 10, -20 }
P2 = { 80%, 20% }

We can compare the expected utilities of these two actions by using the above formula.

EU(A1) = 20∙50% + -10∙50% = 5
EU(A2) = 10∙80% + -20∙20% = 4

Since EU(A1) is greater than EU(A2), expected utility theory mandates that A1 is the rational act for us to take.

Expected utility theory seems to work out fine in the case of finite payouts, but becomes strange when we begin to introduce infinities. Before even talking about the different problems that arise, though, you might be tempted to brush off this issue, thinking that infinite payouts don’t really exist in the real world.

While this is a tenable position to hold, it is certainly not obviously correct. We can easily construct games that are actually do-able that have an infinite expected payout. For instance, a friend of mine runs the following procedure whenever it is getting late and he is trying to decide whether or not he should head home: First, he flips a coin. If it lands heads, he heads home. If tails, he waits one minute and then re-flips the coin. If it lands heads this time, he heads home. If tails, then he waits two minutes and re-flips the coin. On the next flip, if it lands tails, he waits four minutes. Then eight. And so on. The danger of this procedure is that on overage, he ends up staying out for an infinitely long period of time.

This is a more dangerous real-world application of the St. Petersburg Paradox (although you’ll be glad to know that he hasn’t yet been stuck hanging out with me for an infinite amount of time). We might object: Yes, in theory this has an infinite expected time. But we know that in practice, there will be some cap on the total possible time. Perhaps this cap corresponds to the limit of tolerance that my friend has before he gives up on the game. Or, more conclusively, there is certainly an upper limit in terms of his life span.

Are there any real infinities out there that could translate into infinite utilities? Once again, plausibly no. But it doesn’t seem impossible that such infinities could arise. For instance, even if we wanted to map utilities onto positive-valence experiences and believed that there was a theoretical upper limit on the amount of positivity you could possible experience in a finite amount of time, we could still appeal to the possibility of an eternity of happiness. If God appeared before you and offered you an eternity of existence in a Heaven, then you would presumably be considering an offer with a net utility of positive infinity. Maybe you think this is implausible (I certainly do), but it is at least a possibility that we could be confronted with real infinities in expected utility calculations.

Reassured that infinite utilities are probably not a priori ruled out, we can now ask: How does expected utility theory handle these scenarios?

The answer is: not well.

There are three general classes of failures:

  1. Failure of dominance arguments
  2. Undefined expected utilities
  3. Nonsensical expected utilities

Failure of dominance arguments

A dominance argument is an argument that says that if the expected utility of one action is greater than the expected utility of another, no matter what is the case.

Here’s an example. Consider two lotteries: Lottery 1 and Lottery 2. Each one decides on whether a player wins or not by looking at some fixed random event (say, whether or not a radioactive atom decays within a fixed amount of time T), but the reward for winning differs. If the radioactive atom does decay within time T, then you would get $100,000 from Lottery 1 and $200,000 from Lottery 2. If it does not, then you lose $200 dollars from Lottery 1 and lose $100 dollars from Lottery 2. Now imagine that you can choose only one of these two lotteries.

To summarize: If the atom decays, then Lottery 1 gives you $100,000 less than Lottery 2. And if the atom doesn’t decay, then Lottery 1 charges you $100 more than Lottery 2.

In other words, no matter what ends up happing, you are better off choosing Lottery 2 than Lottery 1. This means that Lottery 2 dominates Lottery 1 as a strategy. There is no possible configuration of the world in which you would have been better off by choosing Lottery 1 than you were by Lottery 2, so this choice is essentially risk-free.

So we have the following general principle, which seems to follow nicely from a simple application of expected utility theory:

Dominance: If action A1 dominates action A2, then it is irrational to choose A2 over A1.

Amazingly, this straightforward and apparently obvious rule ends up failing us when we start to talk about infinite payoffs.

Consider the following setup:

Action 1
U = { ∞, 0 }
P = { .5, .5 }

Action 2
U = { ∞, 10 }
P = { .5, .5 }

Action 2 weakly dominates Action 1. This means that no matter what consequence ends up obtaining, we always end up either better off or equally well off if we take Action 2 than Action 1. But when we calculate the expected utilities…

EU(Action 1) = .5 ∙ ∞ + .5 ∙ 0 = ∞
EU(Action 2) = .5 ∙ ∞ + .5 ∙ 10 = ∞

… we find that the two actions are apparently equal in utility, so we should have no preference between them.

This is pretty bizarre. Imagine the following scenario: God is about to appear in front of you and ship you off to Heaven for an eternity of happiness. In the few minutes before he arrives, you are able to enjoy a wonderfully delicious-looking Klondike bar if you so choose. Obviously the rational thing to do is to eat the Klondike bar, right? Apparently not, according to expected utility theory. The additional little burst of pleasure you get fades into irrelevance as soon as the infinities enter the calculation.

Not only do infinities make us indifferent between two actions, one of which dominates the other, but they can even make us end up choosing actions that are clearly dominated! My favorite example of this is one that I’ve talked about earlier, featuring a recently deceased Donald Trump sitting in Limbo negotiating with God.

To briefly rehash this thought experiment, every day Donald Trump is given an offer by God that he spend one day in Hell and in reward get two days in Heaven afterwards. Each day, the rational choice is for Trump to take the offer, spending one more day in Hell before being able to receive his reward. But since he accepts the offer every day, he ends up always delaying his payout in Heaven, and therefore spends all of eternity in Hell, thinking that he’s making a great deal.

We can think of Trump’s reason for accepting each day as a simple expected utility calculation: U(2 days in Heaven) + U(1 day in Hell) > 0. But iterating this decision an infinity of times ends up leaving Trump in the worst possible scenario – eternal torture.

Undefined expected utilities

Now suppose that you get the following deal from God: Either (Option 1) you die and stop existing (suppose this has utility 0 to you), or (Option 2) you die and continue existing in the afterlife forever. If you choose the afterlife, then your schedule will be arranged as follows: 1,000 days of pure bliss in heaven, then one day of misery in hell. Suppose that each day of bliss has finite positive value to you, and each day of misery has finite negative value to you, and that these two values perfectly cancel each other out (a day in Hell is as bad as a day in Heaven is good).

Which option should you take? It seems reasonable that Option 2 is preferable, as you get a thousand to one ratio of happiness to unhappiness for all of eternity.

Option 1: 💀, 💀, 💀, 💀, 
Option 2:
😇 x 1000, 😟, 😇 x 1000, 😟, …

Since U(💀) = 0, we can calculate the expected utility of Option 1 fine. But what about Option 2? The answer we get depends on the order in which we add up the utilities of each day. If we take the days in chronological order, than we get a total infinite positive utility. If we alternate between Heaven days and Hell days, then we get a total expected utility of zero. And if we add up in the order (Hell, Hell, Heaven, Hell, Hell, Heaven, …), then we end up getting an infinite negative expected utility.

In other words, the expected utility of Option 2 is undefined, giving us no guidance as to which we should prefer. Intuitively, we would want a rational theory of preference would tell us that Option 2 is preferable.

A slightly different example of this: Consider the following three lotteries:

Lottery 1
U = { ∞, -∞ }
P = { .5, .5 }

Lottery 2
U = { ∞, -∞ }
P = { .01, .99 }

Lottery 3
U = { ∞, -∞ }
P = { .99, .01 }

Lottery 1 corresponds to flipping a fair coin to determine whether you go to Heaven forever or Hell forever. Lottery 2 corresponds to picking a number between 1 and 100 to decide. And Lottery 3 corresponds to getting to pick 99 numbers between 1 and 100 to decide. It should be obvious that if you were in this situation, then you should prefer Lottery 3 over Lottery 1, and Lottery 1 over Lottery 2. But here, again, expected utility theory fails us. None of these lotteries have defined expected utilities, because ∞ – ∞ is not well defined.

Nonsensical expected utilities

A stranger approaches you and demands twenty bucks, on pain of an eternity of torture. What should you do?

Expected utility theory tells us that as long as we have some non-zero credence in this person’s threat being credible, then we should hand over the twenty bucks. After all, a small but nonzero probability multiplied by -∞ is still just -∞.

Should we have a non-zero credence in the threat being credible? Plausibly so. To have a zero credence in the threat’s credibility is to imagine that there is no possible evidence that could make it any more likely. It is true that no experience you could have would make the threat any more credible? What if he demonstrated incredible control over

In the end, we have an inconsistent triad.

  1. The rational thing to do is that which maximizes expected utility.
  2. There is a nonzero chance that the stranger threatening you with eternal torture is actually able to follow through on this threat.
  3. It is irrational to hand over the five dollars to the stranger.

This is a rephrasing of Pascal’s wager, but without the same problems as that thought experiment.

Hyperreal decision theory

I’ve recently been writing a lot about how infinities screw up decision theory and ethics. In his paper Infinite Ethics, Nick Bostrom talks about attempts to apply nonstandard analysis to try to address the problems of infinities. I’ll just briefly describe nonstandard analysis in this post, as well as the types of solutions that he sketches out.


So first of all, what is nonstandard analysis? It is a mathematical formalism that extends the ordinary number system to include infinitely large numbers and infinitely small numbers. It doesn’t do so just by adding the symbol ∞ to the set of real numbers ℝ. Instead, it adds an infinite amount of different infinitely large numbers, as well as an infinity of infinitesimal numbers, and proceeds to extend the ordinary operations of addition and multiplication to these numbers. The new number system is called the hyperreals.

So what actually are hyperreals? How does one do calculations with them?

A hyperreal number is an infinite sequence of real numbers. Here are some examples of them:

(3, 3, 3, 3, 3, …)
(1, 2, 3, 4, 5, …)
(1, ½, ⅓, ¼, ⅕, …)

It turns out that the first of these examples is just the hyperreal version of the ordinary number 3, the second is an infinitely larger hyperreal, and the third is an infinitesimal hyperreal. Weirded out yet? Don’t worry, we’ll explain how to make sense of this in a moment.

So every ordinary real number is associated with a hyperreal in the following way:

N = (N, N, N, N, N, …)

What if we just switch the first number in this sequence? For instance:

N’ = (1, N, N, N, N, …)

It turns out that this change doesn’t change the value of the hyperreal. In other words:

N = N’
(N, N, N, N, N, …) = (1, N, N, N, N, …)

In general, if you take any hyperreal number and change a finite amount of the numbers in its sequence, you end up with the same number you started with. So, for example,

3 = (3, 3, 3, 3, 3, …)
= (1, 5, 99, 3, 3, 3, …)
= (3, 3, 0, 0, 0, 0, 3, 3, …)

The general rule for when two hyper-reals are equal relies on the concept of a free ultrafilter, which is a little above the level that I want this post to be at. Intuitively, however, the idea is that for two hyperreals to be equal, the number of ways in which their sequences differ must be either finite or certain special kinds of infinities (I’ll leave this “special kinds” vague for exposition purposes).

Adding and multiplying hyperreals is super simple:

(a1, a2, a3, …) + (b1, b2, b3, …) = (a1 + b1, a2 + b2, a3 + b3, …)
(a1, a2, a3, …) · (b1, b2, b3, …) = (a1 · b1, a2 · b2, a3 · b3, …)

Here’s something that should be puzzling:

A = (0, 1, 0, 1, 0, 1, …)
B = (1, 0, 1, 0, 1, 0, …)
A · B = ?

Apparently, the answer is that A · B = 0. This means that at least one of A or B must also be 0. But both of them differ from 0 in an infinity of places! Subtleties like this are why we need to introduce the idea of a free ultrafilter, to allow certain types of equivalencies between infinitely differing sequences.

Anyway, let’s go on to the last property of hyperreals I’ll discuss:

(a1, a2, a3, …) < (b1, b2, b3, …)
an ≥ bn for only a finite amount of values of n

(This again has the same weird infinite exceptions as before, which we’ll ignore for now.)

Now at last we can see why (1, 2, 3, 4, …) is an infinite number:

Choose any real number N
N = (N, N, N, N, …)
ω = (1, 2, 3, 4, …)
So ωn ≤ Nn for n = 1, 2, 3, …, floor(N)
and ωn > Nn for all other n

This means that ω is larger than N, because there are only a finite amount of members of the sequence for which ωn is greater than ωn. And since this is true for any real number N, then ω must be larger than every real number! In other words, you can now give an answer to somebody who asks you to name a number that is bigger than every real number!

ε = (1, ½, ⅓, ¼, ⅕, …) is an infinitesimal hyperreal for a similar reason:

Choose any real number N > 0
N = (N, N, N, N, …)
ε = (1, ½, ⅓, ¼, ⅕, …)
So εn ≥ Nn for n = 1, 2, …, ceiling(1/N)
and εn < Nn for all other n

Once again, ε is only larger than N in a finite number of places, and is smaller in the other infinity. So ε is smaller than every real number greater than 0.

In addition, the sequence (0, 0, 0, …) is smaller than ω for every value of its sequence, so ε is larger than 0. A number that is smaller than every positive real and greater than 0 is an infinitesimal.

Okay, done introducing hyperreals! Let’s now see how this extended number system can help us with our decision theory problems.

Saint Petersburg Paradox

One standard example of weird infinities in decision theory is the St Petersburg Paradox, which I haven’t talked about yet on this blog. I’ll use this thought experiment as a template for the discussion. Briefly, then, imagine a game that works as follows:

Game 1

Round 1: Flip a coin.
If it lands H, then you get $2 and the game ends.
If it lands T, then you move on to Round 2.

Round 2: Flip the coin again.
If it lands H, then you get $4 and the game ends.
If T, then move on to Round 3.

Round 3: Flip a coin.
If it lands H, then you get $8 and the game ends.
If it lands T, then you move on to Round 4.

(et cetera to infinity)

This game looks pretty nice! You are guaranteed at least $2, and your payout doubles every time the coin lands H. The question is, how nice really is the game? What’s the maximum amount that you should be willing to pay in to play?

Here we run into a problem. To calculate this, we want to know what the expected value of the game is – how much you make on average. We do this by adding up the product of each outcome and the probability of that outcome:

EV = ½ · $2 + ¼ · $4 + ⅛ · $8 + …
= $1 + $1 + $1 + …
= ∞

Apparently, the expected payout of this game is infinite! This means that in order to make a profit, you should be willing to give literally all of your money in order to play just a single round of the game! This should seem wrong… If you pay $1,000,000 to play the game, then the only way that you make a profit is if the coin lands heads twenty times in a row. Does it really make sense to risk all of this money on such a tiny chance?

The response to this is that while the chance that this happens is of course tiny, the payout if it does happen is enormous – you stand to double, quadruple, octuple, (et cetera) your money. In this case, the paradox seems to really be a result of the failure of our brains to intuitively comprehend exponential growth.

There’s an even stronger reason to be unhappy with the St Petersburg Paradox. Say that instead of starting with a payout of $2 and doubling each time from there, you had started with a payout of $2000 and doubled from there.

Game 2

Round 1: Flip a coin.
If it lands H, then you get $2000 and the game ends.
If it lands T, then you move on to Round 2.

Round 2: Flip the coin again.
If it lands H, then you get $4000 and the game ends.
If T, then move on to Round 3.

Round 3: Flip a coin.
If it lands H, then you get $8000 and the game ends.
If it lands T, then you move on to Round 4.

(et cetera to infinity)

This alternative game must be better than the initial game – after all, no matter how many times the coin lands T before finally landing H, your payout is 1000 better than it would have been previously. So if you’re playing the first of the two games, then you should always wish that you were playing the second, no matter how many times the coin ends up landing T.

But the expected value comparison doesn’t grant you this! Both games have an infinite expected value, and infinity is infinity. We can’t have one infinity being larger than another infinity, right?

Enter the hyperreals! We’ll turn the expected value of the first game into a hyperreal as follows:

EV1 = ½ · $2 = $1
EV2 = ½ · $2 + ¼ · $4 = $1 + $1 = $2
EV3 = ½ · $2 + ¼ · $4 + ⅛ · $8 = $1 + $1 + $1 = $3

EV = (EV1, EV2, EV3, …)
= $(1, 2, 3, …)

Now we can compare it to the second game:

Game 1: $(1, 2, 3, …) = ω
Game 2: $(1000, 2000, 3000, …) = $1000 · ω

So hyperreals allow us to compare infinities, and justify why Game 2 has a 1000 times larger expected value than Game 1!

Let me give another nice result of this type of analysis. Imagine Game 1′, which is identical to Game 1 except for the first payout, which is $4 instead of $2. We can calculate the payouts:

Game 1: $(1, 2, 3, …) = ω
Game 1′: $(2, 3, 4, …) = $1 + ω

The result is that Game 1′ gives us an expected increase of just $1. And this makes perfect sense! After all, the only difference between the games is if they end in the first round, which happens with probability ½. And in this case, you get $4 instead of $2. The expected difference between the games should therefore be ½ · $2 = $1! Yay hyperreals!

Of course, this analysis still ends up concluding that the St Petersburg game does have an infinite expected payout. Personally, I’m (sorta) okay with biting this bullet and accepting that if your goal is to maximize money, then you should in principle give any arbitrary amount to play the game.

But what I really want to talk about are variants of the St Petersburg paradox where things get even crazier.

Getting freaky

For instance, suppose that instead of the initial game setup, we have the following setup:

Game 3

Round 1: Flip a coin.
If it lands H, then you get $2 and the game ends.
If it lands T, then you move on to Round 2.

Round 2: Flip the coin again.
If it lands H, then you pay $4 and the game ends.
If T, then move on to Round 3.

Round 3: Flip the coin again.
If it lands H, then you get $8 and the game ends.
If it lands T, then you move on to Round 4.

Round 4: Flip the coin again.
If it lands H, then you pay $16 and the game ends.
If it lands T, then you move on to Round 5.

(et cetera to infinity)

The only difference now is that if the coin lands H on any even round, then instead of getting money that round, you have to pay that money back to the dealer! Clearly this is a less fun game than the last one. How much less fun?

Here things get really weird. If we only looked at the odd rounds, then the expected value is ∞.

EV = ½ · $2 + ⅛ · $8 + …
= $1 + $1 + …
= ∞

But if we look at the odd rounds, then we get an expected value of -∞!

EV = ¼ · -$4 + 1/16 · -$16 + …
= -$1 + -$1 + …
= -∞

We find the total expected value by adding together these two. But can we add ∞ to -∞? Not with ordinary numbers! Let’s convert our numbers to hyperreals instead, and see what happens.

EV = $(1, -1, 1, -1, …)

This time, our result is a bit less intuitive than before. As a result of the ultrafilter business we’ve been avoiding talking about, we can use the following two equalities:

(1, -1, 1, -1, …) = 1
(-1, 1, -1, 1, …) = -1

This means that the expected value of Game 3 is $1. In addition, if Game 3 had started with you having to pay $2 for the first round rather than getting $2, then the expected value would be -$1.

So hyperreal decision theory recommends that you play the game, but only buy in if it costs you less than $1.

Now, the last thought experiment I’ll present is the weirdest of them.

Game 4

Round 1: Flip a coin.
If it lands H, then you pay $2 and the game ends.
If it lands T, then you move on to Round 2.

Round 2: Flip the coin again.
If it lands H, then you get $2 and the game ends.
If T, then move on to Round 3.

Round 3: Flip the coin again.
If it lands H, then you pay $2.67 and the game ends.
If it lands T, then you move on to Round 4.

Round 4: Flip the coin again.
If it lands H, then you get $4 and the game ends.
If it lands T, then you move on to Round 4.

Round 4: Flip the coin again.
If it lands H, then you pay $6.40 and the game ends.
If it lands T, then you move on to Round 4.

(et cetera to infinity)

The pattern is that the payoff on the nth round is (-2)n / n. From this, we see that the expected value of the nth round is 1/n. This sum converges as follows:

n=1 (-1)/ n = -ln(2) ≈ -.69

But by Cauchy’s rearrangement theorem, it turns out that by rearranging the terms of this sum, we can make it add up to any amount that we want! (this follows from the fact that the sum of the absolute values of the term is infinite)

This means that not only is the expected value for this game undefined, but it can be justified having every possible value. Not only do we not know the expected value of the game, but we don’t know whether it’s a positive game or a negative game. We can’t even figure out if it’s a finite game or an infinite game!

Let’s apply hyperreal numbers.

EV1 = -$1
EV2 = $(-1 + ½) = -$0.50
EV3 = $(-1 + ½ – ⅓) = -$0.83
EV4 = $(-1 + ½ – ⅓ + ¼) = -$0.58

So EV = $(-1.00, -0.50, -0.83, -0.58, …)

Since this series converges from above and below to -ln(2) ≈ -$0.69, the expected value is -$0.69 + ε, where ε is a particular infinitesimal number. So we get a precisely defined expectation value! One could imagine just empirically testing this value by running large numbers of simulations.

A weirdness about all of this is that the order in which you count up your expected value is extremely important. This is a general property of infinite summation, and seems like a requirement for consistent reasoning about infinities.

We’ve seen that hyperreal numbers can be helpful in providing a way to compare different infinities. But hyperreal numbers are only the first step into the weird realm of the infinite. The surreal number system is a generalization of the hyperreals that is much more powerful. In a future post, I’ll talk about the highly surreal decision theory that results from application of these numbers.

Infinite ethics

There are a whole bunch of ways in which infinities make decision theory go screwy. I’ve written about some of those ways here. This post is about a thought experiment in which infinities make ethics go screwy.

WaitButWhy has a great description of the thought experiment, and I recommend you check out the post. I’ll briefly describe it here anyway:

Imagine two worlds, World A and World B. Each is an infinite two-dimensional checkerboard, and on each square sits a conscious being that can either be very happy or very sad. At the birth of time, World A is entirely populated by happy beings, and World B entirely by sad beings.

From that moment forwards, World A gradually becomes infected with sadness in a growing bubble, while World B gradually becomes infected with happiness in a growing bubble. Both universes exist forever, so the bubble continues to grow forever.

Picture from WaitButWhy

The decision theory question is: if you could choose to be placed in one of these two worlds in a random square, which should you choose?

The ethical question is: which of the universes is morally preferable? Said another way: if you had to bring one of the two worlds into existence, which would you choose?

On spatial dominance

At every moment of time, World A contains an infinity of happiness and a finite amount of sadness. On the other hand, World B always contains an infinity of sadness and a finite amount of happiness.

This suggests the answer to both the ethical question and the decision theory question: World A is better. Ethically, it seems obvious that infinite happiness minus finite sadness is infinitely better than infinite sadness minus finite happiness. And rationally, given that there are always infinitely more people outside the bubble than inside, at any given moment in time you can be sure that you are on the outside.

A plot of the bubble radius over time in each world would look like this:

Infinite Ethics Plots

In this image, we can see that no matter what moment of time you’re looking at, World A dominates World B as a choice.

On temporal dominance

But there’s another argument.

Let’s look at a person at any given square on the checkerboard. In World A, they start out happy and stay that way for some finite amount of time. But eventually, they are caught by the expanding sadness bubble, and then stay sad forever. In World B, they start out sad for a finite amount of time, but eventually are caught by the expanding happiness bubble and are happy forever.

Plotted, this looks like:

Infinite Ethics Plots 2.png

So which do you prefer? Well, clearly it’s better to be sad for a finite amount of time and happy for an infinite amount of time than vice versa. And ethically, choosing World A amounts to dooming every individual to a lifetime of finite happiness and then infinite sadness, while World B is the reverse.

So no matter which position on the checkerboard you’re looking at, World B dominates World A as a choice!

An impossible synthesis

Let’s summarize: if you look at the spatial distribution for any given moment of time, you see that World A is infinitely preferable to World B. And if you look at the temporal distribution for any given position in space, you find that B is infinitely preferable to A.

Interestingly, I find that the spatial argument seems more compelling when considering the ethical question, while the temporal argument seems more compelling when considering the decision theory question. But both of these arguments apply equally well to both questions. For instance, if you are wondering which world you should choose to be in, then you can think forward to any arbitrary moment of time, and consider your chances of being happy vs being sad in that moment. This will get you the conclusion that you should go with World A, as for any moment in the future, you have a 100% chance of being one of the happy people as opposed to the sad people.

I wonder if the difference is that when we are thinking about decision theory, we are imagining ourselves in the world at a fixed location with time flowing past us, and it is less intuitive to think of ourselves at a fixed time and ask where we likely are.

Regardless, what do we do in the face of these competing arguments? One reasonable thing is to try to combine the two approaches. Instead of just looking at a fixed position for all time, or a fixed time over all space, we look at all space and all time, summing up total happiness moments and subtracting total sadness moments.

But now we have a problem… how do we evaluate this? What we have in both worlds is essentially a +∞ and a -∞ added together, and no clear procedure for how to make sense of this addition.

In fact, it’s worse than this. By cleverly choosing a way of adding up the total amount of the quantity happiness – sadness, we can make the result turn out however we want! For instance, we can reach the conclusion that World A results in a net +33 happiness – sadness by first counting up 33 happy moments, and then ever afterwords switching between counting a happy moment and a sad moment. This summation will eventually end up counting all the happy and sad moments, and will conclude that the total is +33.

But of course, there’s nothing special about +33; we could have chosen to reach any conclusion we wanted by just changing our procedure accordingly. This is unusual. It seems that both the total expected value and moral value are undefined for this problem.

The undefinability of the total happiness – sadness of this universe is a special case of the general rule that you can’t subtract infinity from infinity. This seems fairly harmless… maybe it keeps us from giving a satisfactory answer to this one thought experiment, but surely nothing like this could matter to real-world ethical or decision theory dilemmas?

Wrong! If in fact we live in an infinite universe, then we are faced with exactly this problem. If there are an infinite number of conscious experiencers out there, some suffering and some happy, then the total quantity of happiness – sadness in the universe is undefined! What’s more, a moral system that says that we ought to increase the total happiness of the universe will return an error if asked to evaluate what we ought to do in an in infinite universe!

If you think that you should do your part to make the universe a happier place, then you must have some notion of a total amount of happiness that can be increased. And if the total amount of happiness is unbounded, then there is no sensible way to increase it. This seems like a serious problem for most brands of consequentialism, albeit a very unusual one.

Paradoxes of infinite decision theory

(Two decision theory puzzles from this paper.)


Donald Trump has just arrived in Purgatory. God visits him and offers him the following deal. If he spends tomorrow in Hell, Donald will be allowed to spend the next two days in Heaven, before returning to Purgatory forever. Otherwise he will spend forever in Purgatory. Since Heaven is as pleasant as Hell is unpleasant, Donald accepts the deal. The next evening, as he runs out of Hell, God offers Donald another deal: if Donald spends another day in Hell, he’ll earn an additional two days in Heaven, for a total of four days in Heaven (the two days he already owed him, plus two new ones) before his return to Purgatory. Donald accepts for the same reason as before. In fact, every time he drags himself out of Hell, God offers him the same deal, and he accepts. Donald spends all of eternity writhing in agony in Hell. Where did he go wrong?

Satan’s Apple

Satan has cut a delicious apple into infinitely many pieces, labeled by the natural numbers. Eve may take whichever pieces she chooses. If she takes merely finitely many of the pieces, then she suffers no penalty. But if she takes infinitely many of the pieces, then she is expelled from the Garden for her greed. Either way, she gets to eat whatever pieces she has taken.

Eve’s first priority is to stay in the Garden. Her second priority is to eat as much apple as possible. She is deciding what to do when Satan speaks. “Eve, you should make your decision one piece at a time. Consider just piece #1. No matter what other pieces you end up taking and rejecting, you do better to take piece #1 than to reject it. For if you take only finitely many other pieces, then taking piece #1 will get you more apple without incurring the greed penalty. On the other hand, if you take infinitely many other pieces, then you will be ejected from the Garden whether or not you take piece #1. So in that case you might as well take it, so that you can console yourself with some additional apple as you are escorted out.”

Eve finds this reasonable, and decides provisionally to take piece #1. Satan continues, “By the way, the same reasoning holds for piece #2.” Eve agrees. “And piece #3, and piece #4 and…” Eve takes every piece, and is ejected from the Garden. Where did she go wrong?

The second thought experiment is sufficiently similar to the first one that I won’t say much about it – just included it in here because I like it.


Let’s assume that Trump is able to keep track of how many days he has been in hell, and can credibly pre-commit to strategies involving only accepting a fixed number of offers before rejecting. Now we can write out all possible strategies for sequences of responses that Trump could make:

Strategy 0 Accept none of the offers, and stay in Purgatory forever.

Strategy N Accept some finite number N of offers, after which you spend 2N days in Heaven and then infinity in Purgatory.

Strategy ∞ Accept all of the offers, and stay in Hell forever.

Assuming that a day in hell is exactly as bad as a day in heaven, Strategy 0 nets you 0 days in Heaven, Strategy N nets you N days in Heaven, and Strategy ∞ nets you ∞ days in Hell.

Obviously Strategy ∞ is the worst option (it is infinitely worse than all other strategies). And for every N, Strategy N is better than Strategy 0. So we have < 0 < N.

So we should choose Strategy N for some N. But which N? Obviously, for any choice of N, there will be arbitrarily better choices that you could have done. The problem is that there is no optimal choice of N. Any reasonable decision theory, when asked to optimize N for utility, is going to just return an error. It’s like asking somebody to tell you the largest integer. This is perhaps something that is difficult to come to terms with, but it is not paradoxical – there is no law of decision theory that every problem has a best solution.

But we still want to answer what we would do if we were in Trump’s shoes. If we actually have to pick an N, what should we do? I think the right answer is that there is no right answer for what we should do. We can say “x is better than y” for different strategies, but cannot say definitively the best answer… because there is no best answer.

One technique that I thought of, however, is the following (inspired by the Saint Petersburg Paradox):

On the first day, Trump should flip a coin. If it lands heads, then he chooses Strategy 1. If it lands tails, then he flips the coin again.

If on the next flip the coin lands heads, then he chooses Strategy 2. And if it lands tails, again he flips the coin.

If on this third flip the coin lands heads, then he chooses Strategy 4. And if not, then he flips again.

Et cetera to infinity.

With this decision strategy, we can calculate the expected number N that Trump will choose. This number is:

E[N] = ½・1 + ¼・2 + ⅛・4 + … = ∞

But at the same time, the coin will certainly eventually land heads, and the process will terminate. The probability that the coin lands tails an infinite number of times is zero! So by leveraging infinities in his favor, Trump gets an infinite positive expected value for days spent in heaven, and is guaranteed to not spend all eternity in Hell.

A weird question now arises: Why should Trump have started at Strategy 1? Or why multiply by 2 each time? Consider the following alternative decision process for the value of N:

On the first day, Trump should flip a coin. If it lands heads, then he chooses Strategy 1,000,000. If it lands tails, then he flips the coin again.

If on the next flip the coin lands heads, then he chooses Strategy 10,000,000. And if it lands tails, again he flips the coin.

If on this third flip the coin lands heads, then he chooses Strategy 100,000,000. And if not, then he flips again.

Et cetera to infinity.

This decision process seems obviously better than the previous one – the minimum number of days in heaven Trump nets is 1 million, which would only have previously happened if the coin had landed tails 20 times in a row. And the growth in number of net days in heaven per tail flip is 5x better than it was originally.

But now we have an analogous problem to the one we started with in choosing N. Any choice of starting strategy or growth rate seems suboptimal – there are always an infinity of arbitrarily better strategies.

At least here we have a way out: All such strategies are equivalent in that they net an infinite number of days. And none of these infinities are any larger than any others. So even if it intuitively seems like one decision process is better than another, on average both strategies will do equally well.

This is weird, and I’m not totally satisfied with it. But as far as I can tell, there isn’t a better alternative response.

Schelling fence

How could a strategy like Strategy N actually be instantiated? One potential way would be for Trump to set up a Schelling fence at a particular value of N. For example, Trump could pre-commit from the first day to only allowing himself to say yes 500 times, and after that saying no.

But there’s a problem with this – if Trump has any doubts about his ability to stick with his plan, and puts any credence in his breezing past Strategy N and staying in hell forever, then this will result in an infinite negative expected value of using a Schelling fence. In other words, use of a Schelling fence seems only advisable if you are 100% sure of your ability to credibly pre-commit.

Here’s an alternative strategy for instantiating Strategy N that smooths out this wrinkle: Each time Trump is given another offer by God, he accepts it with probability N/(N+1), and rejects it with probability 1/(N+1). By doing this, he will on average do Strategy N, but will sometimes do a different strategy M for an M that is close to N.

A harder variant would be if Trump’s memory is wiped clean after every day he spends in Hell, so that each day when he receives God’s offer, it is as if it is the first time. Even if Trump knows that his memory will be wiped clean on subsequent days, he now has a problem: he has no way to remember his Schelling fence, or to even know if he has reached it yet. And if he tries the probabilistic acceptance approach, he has no way to remember the value of N that he decided on.

But there’s still a way for him to get the infinite positive expected utility! He can do so by running a Saint Petersburg Paradox like above not just the first day, but every day! Every day he chooses a value of N using a process with an infinite expected value but a guaranteed finite actual value, and then probabilistically accepts/rejects the offer using this N.

Quick proof that this still ensures finitude: Suppose that he stays in Hell forever, never rejecting the offer. Since there is a finite chance that he selects N = 1, this means that he will select N = 1 an infinite number of times. For each of these times, he has a ½ chance of rejecting and ½ chance of accepting. Since this happens an infinite number of times, he is guaranteed to eventually reject an offer.

Question: what’s the expected number of days in Heaven for this new process? Infinite, just as before! But guaranteed finite. (There should be a name for these types of guaranteed-finite-but-infinite-expected-value quantities.)

Anyway, the conclusion of all of this? Infinite decision theory is really weird.

Advanced two-envelopes paradox

Yesterday I described the two-envelopes paradox and laid out its solution. Yay! Problem solved.

Except that it’s not. Because I said that the root of the problem was an improper prior, and when we instead use a proper prior, any proper prior, we get the right result. But we can propose a variant of the two envelopes problem that gives a proper prior, and still mandates infinite switching.

Here it is:

In front of you are two envelopes, each containing some unknown amount of money. You know that one of the envelopes has twice the amount of money of the other, but you’re not sure which one that is and can only take one of the two.

In addition, you know that the envelopes were stocked by a mad genius according to the following procedure: He randomly selects an integer n ≥ 0 with probability ⅓ (⅔)n, then stocked the smaller envelope with $2n and the larger with double this amount.

You have picked up one of the envelopes and are now considering if you should switch your choice.

Let’s verify quickly that the mad genius’s procedure for selecting the amount of money makes sense:

Total probability = ∑n ⅓ (⅔)n = ⅓ · 3 = 1

Okay, good. Now we can calculate the expected value.

You know that the envelope that you’re holding contains one of the following amounts of money: ($1, $2, $4, $8, …).

First let’s consider the case in which it contains $1. If so, then you know that your envelope must be the smaller of the two, since there is no $0.50 envelope. So if your envelope contains $1, then you are sure to gain $1 by switching.

Now let’s consider every other case. If the amount you’re holding is $2n, then you know that there is a probability of ⅓ (⅔)n that it is the smaller envelope and ⅓ (⅔)n+1 that it’s the larger one. You are $2n better off if you have the smaller envelope and switch, and are 2n-1 worse off if you initially had the larger envelope and switch.

So your change in expected value by switching instead of staying is:

∆EU = $ ⅓ (1⅓)n – $ ⅓ ¼ (1⅓)n+1
= $ ⅓ (1⅓)n (1 – ¼ · 1⅓)
= $ ⅓ (1⅓)n (1 – ⅓) > 0

So if you are holding $1, you are better off switching. And if you are holding more than $1, you are better off switching. In other words, switching is always better than staying, regardless of how much money you are holding.

And yet this exact same argument applies once you’ve switched envelopes, so you are led to an infinite process of switching envelopes back and forth. Your decision theory tells you that as you’re doing this, your expected value is exponentially growing, so it’s worth it to you to keep on switching ad infinitum – it’s not often that you get a chance to generate exponentially large amounts of money!

The problem this time can’t be the prior – we are explicitly given the prior in the problem, and verified that it was normalized just in case.

So what’s going wrong?




(once again, recommend that you sit down and try to figure this out for yourself before reading on)




Recall that in my post yesterday, I claimed to have proven that no matter what your prior distribution over money amounts in your envelope, you will always have a net zero expected value. But apparently here we have a statement that contradicts that.

The reason is that my proof yesterday was only for continuous prior distributions over all real numbers, and didn’t apply to discrete distributions like the one in this variant. And apparently for discrete distributions, it is no longer the case that your expected value is zero.

The best solution to this problem that I’ve come across is the following: This problem involves comparing infinite utilities, and decision theory can’t handle infinities.

There’s a long and fascinating precedent for this claim, starting with problems like the Saint Petersburg paradox, where an infinite expected value leads you to bet arbitrarily large amounts of money on arbitrarily unlikely scenarios, and including weird issues in Boltzmann brain scenarios. Discussions of Pascal’s wager also end up confronting this difficulty – comparing different levels of infinite expected utility leads you into big trouble.

And in this variant of the problem, both your expected utility for switching and your expected utility for staying are infinite. Both involve a calculation of a sum of (⅔)n (the probability) times 2n, which diverges.

This is fairly unsatisfying to me, but perhaps it’s the same dissatisfaction that I feel when confronting problems like Pascal’s wager – a mistaken feeling that decision theory should be able to handle these problems, ultimately rooted in a failure to internalize the hidden infinities in the problem.