When I was but a little lad, my father, a PhD physicist, warned me sternly against meddling in the affairs of physicists; he said that it was hopeless to try to comprehend physics without the formal math. Period. No escape clauses. But I had read in Feynman’s popular books that if you really understood physics, you ought to be able to explain it to a nonphysicist. I believed Feynman instead of my father, because Feynman had won the Nobel Prize and my father had not.
It was not until later—when I was reading the Feynman Lectures, in fact— that I realized that my father had given me the simple and honest truth. No math = no physics.
By vocation I am a Bayesian, not a physicist. Yet although I was raised not to meddle in the affairs of physicists, my hand has been forced by the occasional gross misuse of three terms: simple, falsifiable, and testable.
The foregoing introduction is so that you don’t laugh, and say, “Of course I know what those words mean!” There is math here. What follows will be a restatement of the points in Belief in the Implied Invisible, as they apply to quantum physics.
Let’s begin with the remark that started me down this whole avenue, of which I have seen several versions; paraphrased, it runs:
The many-worlds interpretation of quantum mechanics postulates that there are vast numbers of other worlds, existing alongside our own. Occam’s Razor says we should not multiply entities unnecessarily.
Now it must be said, in all fairness, that those who say this will usually also confess:
But this is not a universally accepted application of Occam’s Razor; some say that Occam’s Razor should apply to the laws governing the model, not the number of objects inside the model.
So it is good that we are all acknowledging the contrary arguments, and telling both sides of the story—
But suppose you had to calculate the simplicity of a theory.
The original formulation of William of Ockham stated:
Lex parsimoniae: Entia non sunt multiplicanda praeter necessitatem.
“The law of parsimony: Entities should not be multiplied beyond necessity.”
But this is qualitative advice. It is not enough to say whether one theory seems more simple, or seems more complex, than another—you have to assign a number; and the number has to be meaningful, you can’t just make it up. Crossing this gap is like the difference between being able to eyeball which things are moving “fast” or “slow,” and starting to measure and calculate velocities.
Suppose you tried saying: “Count the words—that’s how complicated a theory is.”
Robert Heinlein once claimed (tongue-in-cheek, I hope) that the “simplest explanation” is always: “The woman down the street is a witch; she did it.” Eleven words—not many physics papers can beat that.
Faced with this challenge, there are two different roads you can take.
First, you can ask: “The woman down the street is a what?” Just because English has one word to indicate a concept doesn’t mean that the concept itself is simple. Suppose you were talking to aliens who didn’t know about witches, women, or streets—how long would it take you to explain your theory to them? Better yet, suppose you had to write a computer program that embodied your hypothesis, and output what you say are your hypothesis’s predictions—how big would that computer program have to be? Let’s say that your task is to predict a time series of measured positions for a rock rolling down a hill. If you write a subroutine that simulates witches, this doesn’t seem to help narrow down where the rock rolls—the extra subroutine just inflates your code. You might find, however, that your code necessarily includes a subroutine that squares numbers.
Second, you can ask: “The woman down the street is a witch; she did what?” Suppose you want to describe some event, as precisely as you possibly can given the evidence available to you—again, say, the distance/time series of a rock rolling down a hill. You can preface your explanation by saying, “The woman down the street is a witch,” but your friend then says, “What did she do?,” and you reply, “She made the rock roll one meter after the first second, nine meters after the third second…” Prefacing your message with “The woman down the street is a witch,” doesn’t help to compress the rest of your description. On the whole, you just end up sending a longer message than necessary—it makes more sense to just leave off the “witch” prefix. On the other hand, if you take a moment to talk about Galileo, you may be able to greatly compress the next five thousand detailed time series for rocks rolling down hills.
If you follow the first road, you end up with what’s known as Kolmogorov complexity and Solomonoff induction. If you follow the second road, you end up with what’s known as Minimum Message Length.
Ah, so I can pick and choose among definitions of simplicity?
No, actually the two formalisms in their most highly developed forms were proven equivalent.
And I suppose now you’re going to tell me that both formalisms come down on the side of “Occam means counting laws, not counting objects.”
More or less. In Minimum Message Length, so long as you can tell your friend an exact recipe they can mentally follow to get the rolling rock’s time series, we don’t care how much mental work it takes to follow the recipe. In Solomonoff induction, we count bits in the program code, not bits of RAM used by the program as it runs. “Entities” are lines of code, not simulated objects. And as said, these two formalisms are ultimately equivalent.
Now before I go into any further detail on formal simplicity, let me digress to consider the objection:
So what? Why can’t I just invent my own formalism that does things differently? Why should I pay any attention to the way you happened to decide to do things, over in your field? Got any experimental evidence that shows I should do things this way?
Yes, actually, believe it or not. But let me start at the beginning.
The conjunction rule of probability theory states:
P(X,Y)≤P(X)
For any propositions X and Y, the probability that “X is true, and Y is true,” is less than or equal to the probability that “X is true (whether or not Y is true).” (If this statement sounds not terribly profound, then let me assure you that it is easy to find cases where human probability assessors violate this rule.)
You usually can’t apply the conjunction rule P(X,Y)≤P(X) directly to a conflict between mutually exclusive hypotheses. The conjunction rule only applies directly to cases where the left-hand-side strictly implies the right-hand-side. Furthermore, the conjunction is just an inequality; it doesn’t give us the kind of quantitative calculation we want.
But the conjunction rule does give us a rule of monotonic decrease in probability: as you tack more details onto a story, and each additional detail can potentially be true or false, the story’s probability goes down monotonically. Think of probability as a conserved quantity: there’s only so much to go around. As the number of details in a story goes up, the number of possible stories increases exponentially, but the sum over their probabilities can never be greater than 1. For every story “X and Y,” there is a story “X and ¬Y.” When you just tell the story “X,” you get to sum over the possibilities Y and ¬Y.
If you add ten details to X, each of which could potentially be true or false, then that story must compete with 210−1 other equally detailed stories for precious probability. If on the other hand it suffices to just say X, you can sum your probability over 210 stories
((X and Y and Z and …) or (X and ¬Y and Z and …) or …) .
The “entities” counted by Occam’s Razor should be individually costly in probability; this is why we prefer theories with fewer of them.
Imagine a lottery which sells up to a million tickets, where each possible ticket is sold only once, and the lottery has sold every ticket at the time of the drawing. A friend of yours has bought one ticket for $1—which seems to you like a poor investment, because the payoff is only $500,000. Yet your friend says, “Ah, but consider the alternative hypotheses, ‘Tomorrow, someone will win the lottery’ and ‘Tomorrow, I will win the lottery.’ Clearly, the latter hypothesis is simpler by Occam’s Razor; it only makes mention of one person and one ticket, while the former hypothesis is more complicated: it mentions a million people and a million tickets!”
To say that Occam’s Razor only counts laws, and not objects, is not quite correct: what counts against a theory are the entities it must mention explicitly, because these are the entities that cannot be summed over. Suppose that you and a friend are puzzling over an amazing billiards shot, in which you are told the starting state of a billiards table, and which balls were sunk, but not how the shot was made. You propose a theory which involves ten specific collisions between ten specific balls; your friend counters with a theory that involves five specific collisions between five specific balls. What counts against your theories is not just the laws that you claim to govern billiard balls, but any specific billiard balls that had to be in some particular state for your model’s prediction to be successful.
If you measure the temperature of your living room as 22 degrees Celsius, it does not make sense to say: “Your thermometer is probably in error; the room is much more likely to be 20 °C. Because, when you consider all the particles in the room, there are exponentially vastly more states they can occupy if the temperature is really 22 °C—which makes any particular state all the more improbable.” But no matter which exact 22 °C state your room occupies, you can make the same prediction (for the supervast majority of these states) that your thermometer will end up showing 22 °C, and so you are not sensitive to the exact initial conditions. You do not need to specify an exact position of all the air molecules in the room, so that is not counted against the probability of your explanation.
On the other hand—returning to the case of the lottery—suppose your friend won ten lotteries in a row. At this point you should suspect the fix is in. The hypothesis “My friend wins the lottery every time” is more complicated than the hypothesis “Someone wins the lottery every time.” But the former hypothesis is predicting the data much more precisely.
In the Minimum Message Length formalism, saying “There is a single person who wins the lottery every time” at the beginning of your message compresses your description of who won the next ten lotteries; you can just say “And that person is Fred Smith” to finish your message. Compare to, “The first lottery was won by Fred Smith, the second lottery was won by Fred Smith, the third lottery was…”
In the Solomonoff induction formalism, the prior probability of “My friend wins the lottery every time” is low, because the program that describes the lottery now needs explicit code that singles out your friend; but because that program can produce a tighter probability distribution over potential lottery winners than “Someone wins the lottery every time,” it can, by Bayes’s Rule, overcome its prior improbability and win out as a hypothesis.
Any formal theory of Occam’s Razor should quantitatively define, not only “entities” and “simplicity,” but also the “necessity” part.
Minimum Message Length defines necessity as “that which compresses the message.”
Solomonoff induction assigns a prior probability to each possible computer program, with the entire distribution, over every possible computer program, summing to no more than 1. This can be accomplished using a binary code where no valid computer program is a prefix of any other valid computer program (“prefix-free code”), e.g. because it contains a stop code. Then the prior probability of any program P is simply 2−L(P) where L(P) is the length of P in bits.
The program P itself can be a program that takes in a (possibly zero-length) string of bits and outputs the conditional probability that the next bit will be 1; this makes P a probability distribution over all binary sequences. This version of Solomonoff induction, for any string, gives us a mixture of posterior probabilities dominated by the shortest programs that most precisely predict the string. Summing over this mixture gives us a prediction for the next bit.
The upshot is that it takes more Bayesian evidence—more successful predictions, or more precise predictions—to justify more complex hypotheses. But it can be done; the burden of prior improbability is not infinite. If you flip a coin four times, and it comes up heads every time, you don’t conclude right away that the coin produces only heads; but if the coin comes up heads twenty times in a row, you should be considering it very seriously. What about the hypothesis that a coin is fixed to produce HTTHTT… in a repeating cycle? That’s more bizarre—but after a hundred coinflips you’d be a fool to deny it.
Standard chemistry says that in a gram of hydrogen gas there are six hundred billion trillion hydrogen atoms. This is a startling statement, but there was some amount of evidence that sufficed to convince physicists in general, and you particularly, that this statement was true.
Now ask yourself how much evidence it would take to convince you of a theory with six hundred billion trillion separately specified physical laws.
Why doesn’t the prior probability of a program, in the Solomonoff formalism, include a measure of how much RAM the program uses, or the total running time?
The simple answer is, “Because space and time resources used by a program aren’t mutually exclusive possibilities.” It’s not like the program specification, that can only have a 1 or a 0 in any particular place.
But the even simpler answer is, “Because, historically speaking, that heuristic doesn’t work.”
Occam’s Razor was raised as an objection to the suggestion that nebulae were actually distant galaxies—it seemed to vastly multiply the number of entities in the universe. All those stars!
Over and over, in human history, the universe has gotten bigger. A variant of Occam’s Razor which, on each such occasion, would label the vaster universe as more unlikely, would fare less well under humanity’s historical experience.
This is part of the “experimental evidence” I was alluding to earlier. While you can justify theories of simplicity on mathy sorts of grounds, it is also desirable that they actually work in practice. (The other part of the “experimental evidence” comes from statisticians / computer scientists / Artificial Intelligence researchers, testing which definitions of “simplicity” let them construct computer programs that do empirically well at predicting future data from past data. Probably the Minimum Message Length paradigm has proven most productive here, because it is a very adaptable way to think about real-world problems.)
Imagine a spaceship whose launch you witness with great fanfare; it accelerates away from you, and is soon traveling at 0.9c. If the expansion of the universe continues, as current cosmology holds it should, there will come some future point where—according to your model of reality—you don’t expect to be able to interact with the spaceship even in principle; it has gone over the cosmological horizon relative to you, and photons leaving it will not be able to outrace the expansion of the universe.
If you believe that Occam’s Razor counts the objects in a model, then yes, you should. Once the spaceship goes over your cosmological horizon, the model in which the spaceship instantly disappears, and the model in which the spaceship continues onward, give indistinguishable predictions; they have no Bayesian evidential advantage over one another. But one model contains many fewer “entities”; it need not speak of all the quarks and electrons and fields composing the spaceship. So it is simpler to suppose that the spaceship vanishes.
Alternatively, you could say: “Over numerous experiments, I have generalized certain laws that govern observed particles. The spaceship is made up of such particles. Applying these laws, I deduce that the spaceship should continue on after it crosses the cosmological horizon, with the same momentum and the same energy as before, on pain of violating the conservation laws that I have seen holding in every examinable instance. To suppose that the spaceship vanishes, I would have to add a new law, ‘Things vanish as soon as they cross my cosmological horizon.’ ”
The decoherence (a.k.a. many-worlds) version of quantum mechanics states that measurements obey the same quantum-mechanical rules as all other physical processes. Applying these rules to macroscopic objects in exactly the same way as microscopic ones, we end up with observers in states of superposition. Now there are many questions that can be asked here, such as
“But then why don’t all binary quantum measurements appear to have 50⁄50 probability, since different versions of us see both outcomes?”
However, the objection that decoherence violates Occam’s Razor on account of multiplying objects in the model is simply wrong.
Decoherence does not require the wavefunction to take on some complicated exact initial state. Many-worlds is not specifying all its worlds by hand, but generating them via the compact laws of quantum mechanics. A computer program that directly simulates quantum mechanics to make experimental predictions, would require a great deal of RAM to run—but simulating the wavefunction is exponentially expensive in any flavor of quantum mechanics! Decoherence is simply more so. Many physical discoveries in human history, from stars to galaxies, from atoms to quantum mechanics, have vastly increased the apparent CPU load of what we believe to be the universe.
Many-worlds is not a zillion worlds worth of complicated, any more than the atomic hypothesis is a zillion atoms worth of complicated. For anyone with a quantitative grasp of Occam’s Razor that is simply not what the term “complicated” means.
As with the historical case of galaxies, it may be that people have mistaken their shock at the notion of a universe that large, for a probability penalty, and invoked Occam’s Razor in justification. But if there are probability penalties for decoherence, the largeness of the implied universe, per se, is definitely not their source!
The notion that decoherent worlds are additional entities penalized by Occam’s Razor is just plain mistaken. It is not sort-of-right. It is not an argument that is weak but still valid. It is not a defensible position that could be shored up with further arguments. It is entirely defective as probability theory. It is not fixable. It is bad math. 2+2=3.
Decoherence is Simple
An epistle to the physicists:
When I was but a little lad, my father, a PhD physicist, warned me sternly against meddling in the affairs of physicists; he said that it was hopeless to try to comprehend physics without the formal math. Period. No escape clauses. But I had read in Feynman’s popular books that if you really understood physics, you ought to be able to explain it to a nonphysicist. I believed Feynman instead of my father, because Feynman had won the Nobel Prize and my father had not.
It was not until later—when I was reading the Feynman Lectures, in fact— that I realized that my father had given me the simple and honest truth. No math = no physics.
By vocation I am a Bayesian, not a physicist. Yet although I was raised not to meddle in the affairs of physicists, my hand has been forced by the occasional gross misuse of three terms: simple, falsifiable, and testable.
The foregoing introduction is so that you don’t laugh, and say, “Of course I know what those words mean!” There is math here. What follows will be a restatement of the points in Belief in the Implied Invisible, as they apply to quantum physics.
Let’s begin with the remark that started me down this whole avenue, of which I have seen several versions; paraphrased, it runs:
Now it must be said, in all fairness, that those who say this will usually also confess:
So it is good that we are all acknowledging the contrary arguments, and telling both sides of the story—
But suppose you had to calculate the simplicity of a theory.
The original formulation of William of Ockham stated:
“The law of parsimony: Entities should not be multiplied beyond necessity.”
But this is qualitative advice. It is not enough to say whether one theory seems more simple, or seems more complex, than another—you have to assign a number; and the number has to be meaningful, you can’t just make it up. Crossing this gap is like the difference between being able to eyeball which things are moving “fast” or “slow,” and starting to measure and calculate velocities.
Suppose you tried saying: “Count the words—that’s how complicated a theory is.”
Robert Heinlein once claimed (tongue-in-cheek, I hope) that the “simplest explanation” is always: “The woman down the street is a witch; she did it.” Eleven words—not many physics papers can beat that.
Faced with this challenge, there are two different roads you can take.
First, you can ask: “The woman down the street is a what?” Just because English has one word to indicate a concept doesn’t mean that the concept itself is simple. Suppose you were talking to aliens who didn’t know about witches, women, or streets—how long would it take you to explain your theory to them? Better yet, suppose you had to write a computer program that embodied your hypothesis, and output what you say are your hypothesis’s predictions—how big would that computer program have to be? Let’s say that your task is to predict a time series of measured positions for a rock rolling down a hill. If you write a subroutine that simulates witches, this doesn’t seem to help narrow down where the rock rolls—the extra subroutine just inflates your code. You might find, however, that your code necessarily includes a subroutine that squares numbers.
Second, you can ask: “The woman down the street is a witch; she did what?” Suppose you want to describe some event, as precisely as you possibly can given the evidence available to you—again, say, the distance/time series of a rock rolling down a hill. You can preface your explanation by saying, “The woman down the street is a witch,” but your friend then says, “What did she do?,” and you reply, “She made the rock roll one meter after the first second, nine meters after the third second…” Prefacing your message with “The woman down the street is a witch,” doesn’t help to compress the rest of your description. On the whole, you just end up sending a longer message than necessary—it makes more sense to just leave off the “witch” prefix. On the other hand, if you take a moment to talk about Galileo, you may be able to greatly compress the next five thousand detailed time series for rocks rolling down hills.
If you follow the first road, you end up with what’s known as Kolmogorov complexity and Solomonoff induction. If you follow the second road, you end up with what’s known as Minimum Message Length.
No, actually the two formalisms in their most highly developed forms were proven equivalent.
More or less. In Minimum Message Length, so long as you can tell your friend an exact recipe they can mentally follow to get the rolling rock’s time series, we don’t care how much mental work it takes to follow the recipe. In Solomonoff induction, we count bits in the program code, not bits of RAM used by the program as it runs. “Entities” are lines of code, not simulated objects. And as said, these two formalisms are ultimately equivalent.
Now before I go into any further detail on formal simplicity, let me digress to consider the objection:
Yes, actually, believe it or not. But let me start at the beginning.
The conjunction rule of probability theory states:
For any propositions X and Y, the probability that “X is true, and Y is true,” is less than or equal to the probability that “X is true (whether or not Y is true).” (If this statement sounds not terribly profound, then let me assure you that it is easy to find cases where human probability assessors violate this rule.)
You usually can’t apply the conjunction rule P(X,Y)≤P(X) directly to a conflict between mutually exclusive hypotheses. The conjunction rule only applies directly to cases where the left-hand-side strictly implies the right-hand-side. Furthermore, the conjunction is just an inequality; it doesn’t give us the kind of quantitative calculation we want.
But the conjunction rule does give us a rule of monotonic decrease in probability: as you tack more details onto a story, and each additional detail can potentially be true or false, the story’s probability goes down monotonically. Think of probability as a conserved quantity: there’s only so much to go around. As the number of details in a story goes up, the number of possible stories increases exponentially, but the sum over their probabilities can never be greater than 1. For every story “X and Y,” there is a story “X and ¬Y.” When you just tell the story “X,” you get to sum over the possibilities Y and ¬Y.
If you add ten details to X, each of which could potentially be true or false, then that story must compete with 210−1 other equally detailed stories for precious probability. If on the other hand it suffices to just say X, you can sum your probability over 210 stories
The “entities” counted by Occam’s Razor should be individually costly in probability; this is why we prefer theories with fewer of them.
Imagine a lottery which sells up to a million tickets, where each possible ticket is sold only once, and the lottery has sold every ticket at the time of the drawing. A friend of yours has bought one ticket for $1—which seems to you like a poor investment, because the payoff is only $500,000. Yet your friend says, “Ah, but consider the alternative hypotheses, ‘Tomorrow, someone will win the lottery’ and ‘Tomorrow, I will win the lottery.’ Clearly, the latter hypothesis is simpler by Occam’s Razor; it only makes mention of one person and one ticket, while the former hypothesis is more complicated: it mentions a million people and a million tickets!”
To say that Occam’s Razor only counts laws, and not objects, is not quite correct: what counts against a theory are the entities it must mention explicitly, because these are the entities that cannot be summed over. Suppose that you and a friend are puzzling over an amazing billiards shot, in which you are told the starting state of a billiards table, and which balls were sunk, but not how the shot was made. You propose a theory which involves ten specific collisions between ten specific balls; your friend counters with a theory that involves five specific collisions between five specific balls. What counts against your theories is not just the laws that you claim to govern billiard balls, but any specific billiard balls that had to be in some particular state for your model’s prediction to be successful.
If you measure the temperature of your living room as 22 degrees Celsius, it does not make sense to say: “Your thermometer is probably in error; the room is much more likely to be 20 °C. Because, when you consider all the particles in the room, there are exponentially vastly more states they can occupy if the temperature is really 22 °C—which makes any particular state all the more improbable.” But no matter which exact 22 °C state your room occupies, you can make the same prediction (for the supervast majority of these states) that your thermometer will end up showing 22 °C, and so you are not sensitive to the exact initial conditions. You do not need to specify an exact position of all the air molecules in the room, so that is not counted against the probability of your explanation.
On the other hand—returning to the case of the lottery—suppose your friend won ten lotteries in a row. At this point you should suspect the fix is in. The hypothesis “My friend wins the lottery every time” is more complicated than the hypothesis “Someone wins the lottery every time.” But the former hypothesis is predicting the data much more precisely.
In the Minimum Message Length formalism, saying “There is a single person who wins the lottery every time” at the beginning of your message compresses your description of who won the next ten lotteries; you can just say “And that person is Fred Smith” to finish your message. Compare to, “The first lottery was won by Fred Smith, the second lottery was won by Fred Smith, the third lottery was…”
In the Solomonoff induction formalism, the prior probability of “My friend wins the lottery every time” is low, because the program that describes the lottery now needs explicit code that singles out your friend; but because that program can produce a tighter probability distribution over potential lottery winners than “Someone wins the lottery every time,” it can, by Bayes’s Rule, overcome its prior improbability and win out as a hypothesis.
Any formal theory of Occam’s Razor should quantitatively define, not only “entities” and “simplicity,” but also the “necessity” part.
Minimum Message Length defines necessity as “that which compresses the message.”
Solomonoff induction assigns a prior probability to each possible computer program, with the entire distribution, over every possible computer program, summing to no more than 1. This can be accomplished using a binary code where no valid computer program is a prefix of any other valid computer program (“prefix-free code”), e.g. because it contains a stop code. Then the prior probability of any program P is simply 2−L(P) where L(P) is the length of P in bits.
The program P itself can be a program that takes in a (possibly zero-length) string of bits and outputs the conditional probability that the next bit will be 1; this makes P a probability distribution over all binary sequences. This version of Solomonoff induction, for any string, gives us a mixture of posterior probabilities dominated by the shortest programs that most precisely predict the string. Summing over this mixture gives us a prediction for the next bit.
The upshot is that it takes more Bayesian evidence—more successful predictions, or more precise predictions—to justify more complex hypotheses. But it can be done; the burden of prior improbability is not infinite. If you flip a coin four times, and it comes up heads every time, you don’t conclude right away that the coin produces only heads; but if the coin comes up heads twenty times in a row, you should be considering it very seriously. What about the hypothesis that a coin is fixed to produce HTTHTT… in a repeating cycle? That’s more bizarre—but after a hundred coinflips you’d be a fool to deny it.
Standard chemistry says that in a gram of hydrogen gas there are six hundred billion trillion hydrogen atoms. This is a startling statement, but there was some amount of evidence that sufficed to convince physicists in general, and you particularly, that this statement was true.
Now ask yourself how much evidence it would take to convince you of a theory with six hundred billion trillion separately specified physical laws.
Why doesn’t the prior probability of a program, in the Solomonoff formalism, include a measure of how much RAM the program uses, or the total running time?
The simple answer is, “Because space and time resources used by a program aren’t mutually exclusive possibilities.” It’s not like the program specification, that can only have a 1 or a 0 in any particular place.
But the even simpler answer is, “Because, historically speaking, that heuristic doesn’t work.”
Occam’s Razor was raised as an objection to the suggestion that nebulae were actually distant galaxies—it seemed to vastly multiply the number of entities in the universe. All those stars!
Over and over, in human history, the universe has gotten bigger. A variant of Occam’s Razor which, on each such occasion, would label the vaster universe as more unlikely, would fare less well under humanity’s historical experience.
This is part of the “experimental evidence” I was alluding to earlier. While you can justify theories of simplicity on mathy sorts of grounds, it is also desirable that they actually work in practice. (The other part of the “experimental evidence” comes from statisticians / computer scientists / Artificial Intelligence researchers, testing which definitions of “simplicity” let them construct computer programs that do empirically well at predicting future data from past data. Probably the Minimum Message Length paradigm has proven most productive here, because it is a very adaptable way to think about real-world problems.)
Imagine a spaceship whose launch you witness with great fanfare; it accelerates away from you, and is soon traveling at 0.9c. If the expansion of the universe continues, as current cosmology holds it should, there will come some future point where—according to your model of reality—you don’t expect to be able to interact with the spaceship even in principle; it has gone over the cosmological horizon relative to you, and photons leaving it will not be able to outrace the expansion of the universe.
Should you believe that the spaceship literally, physically disappears from the universe at the point where it goes over the cosmological horizon relative to you?
If you believe that Occam’s Razor counts the objects in a model, then yes, you should. Once the spaceship goes over your cosmological horizon, the model in which the spaceship instantly disappears, and the model in which the spaceship continues onward, give indistinguishable predictions; they have no Bayesian evidential advantage over one another. But one model contains many fewer “entities”; it need not speak of all the quarks and electrons and fields composing the spaceship. So it is simpler to suppose that the spaceship vanishes.
Alternatively, you could say: “Over numerous experiments, I have generalized certain laws that govern observed particles. The spaceship is made up of such particles. Applying these laws, I deduce that the spaceship should continue on after it crosses the cosmological horizon, with the same momentum and the same energy as before, on pain of violating the conservation laws that I have seen holding in every examinable instance. To suppose that the spaceship vanishes, I would have to add a new law, ‘Things vanish as soon as they cross my cosmological horizon.’ ”
The decoherence (a.k.a. many-worlds) version of quantum mechanics states that measurements obey the same quantum-mechanical rules as all other physical processes. Applying these rules to macroscopic objects in exactly the same way as microscopic ones, we end up with observers in states of superposition. Now there are many questions that can be asked here, such as
“But then why don’t all binary quantum measurements appear to have 50⁄50 probability, since different versions of us see both outcomes?”
However, the objection that decoherence violates Occam’s Razor on account of multiplying objects in the model is simply wrong.
Decoherence does not require the wavefunction to take on some complicated exact initial state. Many-worlds is not specifying all its worlds by hand, but generating them via the compact laws of quantum mechanics. A computer program that directly simulates quantum mechanics to make experimental predictions, would require a great deal of RAM to run—but simulating the wavefunction is exponentially expensive in any flavor of quantum mechanics! Decoherence is simply more so. Many physical discoveries in human history, from stars to galaxies, from atoms to quantum mechanics, have vastly increased the apparent CPU load of what we believe to be the universe.
Many-worlds is not a zillion worlds worth of complicated, any more than the atomic hypothesis is a zillion atoms worth of complicated. For anyone with a quantitative grasp of Occam’s Razor that is simply not what the term “complicated” means.
As with the historical case of galaxies, it may be that people have mistaken their shock at the notion of a universe that large, for a probability penalty, and invoked Occam’s Razor in justification. But if there are probability penalties for decoherence, the largeness of the implied universe, per se, is definitely not their source!
The notion that decoherent worlds are additional entities penalized by Occam’s Razor is just plain mistaken. It is not sort-of-right. It is not an argument that is weak but still valid. It is not a defensible position that could be shored up with further arguments. It is entirely defective as probability theory. It is not fixable. It is bad math. 2+2=3.