Archive for the ‘Physics for Doofuses’ Category

The First Law of Complexodynamics

Friday, September 23rd, 2011

A few weeks ago, I had the pleasure of attending FQXi’s Setting Time Aright conference, part of which took place on a cruise from Bergen, Norway to Copenhagen, Denmark.  (Why aren’t theoretical computer science conferences ever held on cruises?  If nothing else, it certainly cuts down on attendees sneaking away from the conference venue.)  This conference brought together physicists, cosmologists, philosophers, biologists, psychologists, and (for some strange reason) one quantum complexity blogger to pontificate about the existence, directionality, and nature of time.  If you want to know more about the conference, check out Sean Carroll’s Cosmic Variance posts here and here.

Sean also delivered the opening talk of the conference, during which (among other things) he asked a beautiful question: why does “complexity” or “interestingness” of physical systems seem to increase with time and then hit a maximum and decrease, in contrast to the entropy, which of course increases monotonically?

My purpose, in this post, is to sketch a possible answer to Sean’s question, drawing on concepts from Kolmogorov complexity.  If this answer has been suggested before, I’m sure someone will let me know in the comments section.

First, some background: we all know the Second Law, which says that the entropy of any closed system tends to increase with time until it reaches a maximum value.  Here “entropy” is slippery to define—we’ll come back to that later—but somehow measures how “random” or “generic” or “disordered” a system is.  As Sean points out in his wonderful book From Eternity to Here, the Second Law is almost a tautology: how could a system not tend to evolve to more “generic” configurations?  if it didn’t, those configurations wouldn’t be generic!  So the real question is not why the entropy is increasing, but why it was ever low to begin with.  In other words, why did the universe’s initial state at the big bang contain so much order for the universe’s subsequent evolution to destroy?  I won’t address that celebrated mystery in this post, but will simply take the low entropy of the initial state as given.

The point that interests us is this: even though isolated physical systems get monotonically more entropic, they don’t get monotonically more “complicated” or “interesting.”  Sean didn’t define what he meant by “complicated” or “interesting” here—indeed, defining those concepts was part of his challenge—but he illustrated what he had in mind with the example of a coffee cup.  Shamelessly ripping off his slides:

Entropy increases monotonically from left to right, but intuitively, the “complexity” seems highest in the middle picture: the one with all the tendrils of milk.  And same is true for the whole universe: shortly after the big bang, the universe was basically just a low-entropy soup of high-energy particles.  A googol years from now, after the last black holes have sputtered away in bursts of Hawking radiation, the universe will basically be just a high-entropy soup of low-energy particles.  But today, in between, the universe contains interesting structures such as galaxies and brains and hot-dog-shaped novelty vehicles.  We see the pattern:

 

In answering Sean’s provocative question (whether there’s some “law of complexodynamics” that would explain his graph), it seems to me that the challenge is twofold:

  1. Come up with a plausible formal definition of “complexity.”
  2. Prove that the “complexity,” so defined, is large at intermediate times in natural model systems, despite being close to zero at the initial time and close to zero at late times.

To clarify: it’s not hard to explain, at least at a handwaving level, why the complexity should be close to zero at the initial time.  It’s because we assumed the entropy is close to zero, and entropy plausibly gives an upper bound on complexity.  Nor is it hard to explain why the complexity should be close to zero at late times: it’s because the system reaches equilibrium (i.e., something resembling the uniform distribution over all possible states), which we’re essentially defining to be simple.  At intermediate times, neither of those constraints is operative, and therefore the complexity could become large.  But does it become large?  How large?  How could we predict?  And what kind of “complexity” are we talking about, anyway?

After thinking on and off about these questions, I now conjecture that they can be answered using a notion called sophistication from the theory of Kolmogorov complexity.  Recall that the Kolmogorov complexity of a string x is the length of the shortest computer program that outputs x (in some Turing-universal programming language—the exact choice can be shown not to matter much).  Sophistication is a more … well, sophisticated concept, but we’ll get to that later.

As a first step, let’s use Kolmogorov complexity to define entropy.  Already it’s not quite obvious how to do that.  If you start, say, a cellular automaton, or a system of billiard balls, in some simple initial configuration, and then let it evolve for a while according to dynamical laws, visually it will look like the entropy is going up.  But if the system happens to be deterministic, then mathematically, its state can always be specified by giving (1) the initial state, and (2) the number of steps t it’s been run for.  The former takes a constant number of bits to specify (independent of t), while the latter takes log(t) bits.  It follows that, if we use Kolmogorov complexity as our stand-in for entropy, then the entropy can increase at most logarithmically with t—much slower than the linear or polynomial increase that we’d intuitively expect.

There are at least two ways to solve this problem.  The first is to consider probabilistic systems, rather than deterministic ones.  In the probabilistic case, the Kolmogorov complexity really does increase at a polynomial rate, as you’d expect.  The second solution is to replace the Kolmogorov complexity by the resource-bounded Kolmogorov complexity: the length of the shortest computer program that outputs the state in a short amount of time (or the size of the smallest, say, depth-3 circuit that outputs the state—for present purposes, it doesn’t even matter much what kind of resource bound we impose, as long as the bound is severe enough).  Even though there’s a computer program only log(t) bits long to compute the state of the system after t time steps, that program will typically use an amount of time that grows with t (or even faster), so if we rule out sufficiently complex programs, we can again get our program size to increase with t at a polynomial rate.

OK, that was entropy.  What about the thing Sean was calling “complexity”—which, to avoid confusion with other kinds of complexity, from now on I’m going to call “complextropy”?  For this, we’re going to need a cluster of related ideas that go under names like sophistication, Kolmogorov structure functions, and algorithmic statistics.  The backstory is that, in the 1970s (after introducing Kolmogorov complexity), Kolmogorov made an observation that was closely related to Sean’s observation above.  A uniformly random string, he said, has close-to-maximal Kolmogorov complexity, but it’s also one of the least “complex” or “interesting” strings imaginable.  After all, we can describe essentially everything you’d ever want to know about the string by saying “it’s random”!  But is there a way to formalize that intuition?  Indeed there is.

First, given a set S of n-bit strings, let K(S) be the number of bits in the shortest computer program that outputs the elements of S and then halts.  Also, given such a set S and an element x of S, let K(x|S) be the length of the shortest program that outputs x, given an oracle for testing membership in S.  Then we can let the sophistication of x, or Soph(x), be the smallest possible value of K(S), over all sets S such that

  1. x∈S and
  2. K(x|S) ≥ log2(|S|) – c, for some constant c.  (In other words, one can distill all the “nonrandom” information in x just by saying that x belongs that S.)

Intuitively, Soph(x) is the length of the shortest computer program that describes, not necessarily x itself, but a set S of which x is a “random” or “generic” member.  To illustrate, any string x with small Kolmogorov complexity has small sophistication, since we can let S be the singleton set {x}.  However, a uniformly-random string also has small sophistication, since we can let S be the set {0,1}n of all n-bit strings.  In fact, the question arises of whether there are any sophisticated strings!  Apparently, after Kolmogorov raised this question in the early 1980s, it was answered in the affirmative by Alexander Shen (for more, see this paper by Gács, Tromp, and Vitányi).  The construction is via a diagonalization argument that’s a bit too complicated to fit in this blog post.

But what does any of this have to do with coffee cups?  Well, at first glance, sophistication seems to have exactly the properties that we were looking for in a “complextropy” measure: it’s small for both simple strings and uniformly random strings, but large for strings in a weird third category of “neither simple nor random.”  Unfortunately, as we defined it above, sophistication still doesn’t do the job.  For deterministic systems, the problem is the same as the one pointed out earlier for Kolmogorov complexity: we can always describe the system’s state after t time steps by specifying the initial state, the transition rule, and t.  Therefore the sophistication can never exceed log(t)+c.  Even for probabilistic systems, though, we can specify the set S(t) of all possible states after t time steps by specifying the initial state, the probabilistic transition rule, and t.  And, at least assuming that the probability distribution over S(t) is uniform, by a simple counting argument the state after t steps will almost always be a “generic” element of S(t).  So again, the sophistication will almost never exceed log(t)+c.  (If the distribution over S(t) is nonuniform, then some technical further arguments are needed, which I omit.)

How can we fix this problem?  I think the key is to bring computational resource bounds into the picture.  (We already saw a hint of this in the discussion of entropy.)  In particular, suppose we define the complextropy of an n-bit string x to be something like the following:

the number of bits in the shortest computer program that runs in n log(n) time, and that outputs a nearly-uniform sample from a set S such that (i) x∈S, and (ii) any computer program that outputs x in n log(n) time, given an oracle that provides independent, uniform samples from S, has at least log2(|S|)-c bits, for some constant c.

Here n log(n) is just intended as a concrete example of a complexity bound: one could replace it with some other time bound, or a restriction to (say) constant-depth circuits or some other weak model of computation.  The motivation for the definition is that we want some “complextropy” measure that will assign a value close to 0 to the first and third coffee cups in the picture, but a large value to the second coffee cup.  And thus we consider the length of the shortest efficient computer program that outputs, not necessarily the target string x itself, but a sample from a probability distribution D such that x is not efficiently compressible with respect to D.  (In other words, x looks to any efficient algorithm like a “random” or “generic” sample from D.)

Note that it’s essential for this definition that we imposed a computational efficiency requirement in two places: on the sampling algorithm, and also on the algorithm that reconstructs x given the sampling oracle.  Without the first efficiency constraint, the complextropy could never exceed log(t)+c by the previous argument.  Meanwhile, without the second efficiency constraint, the complextropy would increase, but then it would probably keep right on increasing, for the following reason: a time-bounded sampling algorithm wouldn’t be able to sample from exactly the right set S, only a reasonable facsimile thereof, and a reconstruction algorithm with unlimited time could probably then use special properties of the target string x to reconstruct x with fewer than log2(|S|)-c bits.

But as long as we remember to put computational efficiency requirements on both algorithms, I conjecture that the complextropy will satisfy the “First Law of Complexodynamics,” exhibiting exactly the behavior that Sean wants: small for the initial state, large for intermediate states, then small again once the mixing has finished.  I don’t yet know how to prove this conjecture.  But crucially, it’s not a hopelessly open-ended question that one tosses out just to show how wide-ranging one’s thoughts are, but a relatively-bounded question about which actual theorems could be proved and actual papers published.

If you want to do so, the first step will be to “instantiate” everything I said above with a particular model system and particular resource constraints.  One good choice could be a discretized “coffee cup,” consisting of a 2D array of black and white pixels (the “coffee” and “milk”), which are initially in separated components and then subject to random nearest-neighbor mixing dynamics.  (E.g., at each time step, we pick an adjacent coffee pixel and milk pixel uniformly at random, and swap the two.)  Can we show that for such a system, the complextropy becomes large at intermediate times (intuitively, because of the need to specify the irregular boundaries between the regions of all-black pixels, all-white pixels, and mixed black-and-white pixels)?

One could try to show such a statement either theoretically or empirically.  Theoretically, I have no idea where to begin in proving it, despite a clear intuition that such a statement should hold: let me toss it out as a wonderful (I think) open problem!  At an empirical level, one could simply try to plot the complextropy in some simulated system, like the discrete coffee cup, and show that it has the predicted small-large-small behavior.   One obvious difficulty here is that the complextropy, under any definition like the one I gave, is almost certainly going to be intractable to compute or even approximate.  However, one could try to get around that problem the same way many others have, in empirical research inspired by Kolmogorov complexity: namely, by using something you can compute (e.g., the size of a gzip compressed file) as a rough-and-ready substitute for something you can’t compute (e.g., the Kolmogorov complexity K(x)).  In the interest of a full disclosure, a wonderful MIT undergrad, Lauren Oullette, recently started a research project with me where she’s trying to do exactly that.  So hopefully, by the end of the semester, we’ll be able to answer Sean’s question at least at a physics level of rigor!  Answering the question at a math/CS level of rigor could take a while longer.

PS (unrelated). Are neutrinos traveling faster than light?  See this xkcd strip (which does what I was trying to do in the Deolalikar affair, but better).

Physics for Doofuses: Why Beds Exist

Friday, September 3rd, 2010

I promised to blog more about research, and I will.  Unfortunately, in the one week between my world tour and the start of the fall semester, I’ve been spending less time on quantum complexity research than on sleeping on a new mattress that I bought.  This has provided ample time to ponder the following question, which I’ve decided to add to the Shtetl-Optimized Physics for Doofuses series:

Why is a soft bed more comfortable than a hard one?

At first glance, this question seems too doofusy even for a series such as this, which makes its target audience clear.  The trouble is that, while perfectly reasonable-sounding answers immediately suggest themselves, several of those answers can be shown to be wrong.

Let’s start with the most common answer: a soft bed is more comfortable than a hard bed because it molds to your shape.   The inadequacy of this answer can be seen by the following thought experiment: lie on a soft bed, and let it mold to your body.  Then imagine that the bed retains exactly the same molded shape, but is replaced by ceramic.  No longer so comfortable!

Ah, you reply, but that’s because a ceramic bed doesn’t change its shape as you shift positions throughout the night.  But this reply is still inadequate—since even if you’re lying as still as possible, it still seems clear that a soft bed is more comfortable than a hard one.

So it seems any answer needs to start from the observation that, even when you’re lying still, you’re not really lying still: you’re breathing in and out, there are tiny vibrations, etc.  The real point of a soft bed is to create a gentler potential well, which absorbs the shocks that would otherwise be caused by those sorts of small movements.

(I was tempted to say the point is to damp the movements, but that can’t be right: trampolines are designed for minimal damping, yet sleeping on a trampoline could actually be pretty comfortable.  So the essential thing a bed needs to do is simply to make way in response to small movements and vibrations.  How hard the bed tries to spring back to its original shape is a secondary question—the answer to which presumably influences, for example, whether you prefer an innerspring or a memory-foam mattress.)

So then why aren’t beds even softer than they are?  Well, the limit of infinite softness would be a bed that immediately collapsed to nothing when you lay on it, dropping you to the floor.  But even before that limit, a bed that was too soft would give you too much freedom to shift into awkward positions and thereby cause yourself back problems.  This suggests an answer to a question raised by a colleague: is the purpose of a bed to approximate, as well as possible on the earth’s surface, the experience of sleeping in zero gravity?  Unless I’m mistaken, the answer is no.  Sleeping in space would be like sleeping on a bed that was too soft, with the same potential for back problems and so forth.

Given that lying in bed is normally the least active thing we do, I find it ironic that the only reasons we lie in bed in the first place (as opposed to, say, on steel beams) are dynamical: they involve the way the bed responds to continual vibrations and movements.

I’ll be grateful if knowledgeable physicists, physiologists, or sleepers can correct any errors in the above account.  Meantime, the next time your spouse, partner, roommate, parent, etc. accuses you of lounging in bed all afternoon like a comatose dog, you can reply that nothing could be further from the truth: rather, inspired by a post on Shtetl-Optimized, you’re struggling to reconcile your modern understanding of the physics and biology of lying in bed with the prescientific, phenomenal experience of lying in bed, and thereby make yourself into a more enlightened human being.


How long could a black hole remain in the center of the earth?

Sunday, December 21st, 2008

The above question came up in conversation with Michael Vassar and some other nerds in New York City yesterday (before I went with relatives to see Gimpel Tam, an extraordinarily dark and depressing musical performed entirely in Yiddish).  Look, I know a massive black hole would swallow the earth extremely quickly, and I also know that a microscopic black hole would quickly evaporate as Hawking radiation.  So suppose we chose one of intermediate size so as to maximize the earth’s survival time—how long a time could we achieve?  (Does the answer depend on the viscosity of the magma or whatever else is in the earth’s core?)  Sure, I could try to calculate an answer myself, but why bother when so many physicists read this blog?  Pencils out!

Quantum Computing Since Democritus Lecture 20: Cosmology and Complexity

Thursday, August 21st, 2008

Come watch me attempt to explain the implications of a positive cosmological constant for computational complexity theory.  If this blog is about anything, it’s about me talking about subjects I don’t understand sufficiently well and thereby making a fool of myself.  But it’s also about experts taking the time to correct me.  The latter is the primary saving grace.

Physics for Doofuses: Mass vs. charge deathmatch

Sunday, July 15th, 2007

Back in high school, I was struck by the apparent symmetry between mass and charge. For the one you’ve got Newton’s F=Gm1m2/r2, for the other you’ve got Coulomb’s F=Kq1q2/r2. So then why, in our current understanding of the universe, are mass and charge treated so differently? Why should one be inextricably linked to the geometry of spacetime, whereas the other seems more like an add-on? Why should it be so much harder to give a quantum-mechanical treatment of one than the other? Notwithstanding that such questions occupied Einstein for the last decades of his life, let’s plunge ahead.

When we look for differences between mass and charge, we immediately notice several.

(1) Charge can be negative whereas mass can’t.

That’s why gravity is always attractive, whereas the Coulomb force is both attractive and repulsive. Since positive and negative charges tend to neutralize each other, this already explains why gravity is relevant to the large-scale structure of the universe while electromagnetism isn’t. It also explains why there can’t be any “charge black holes” analogous to gravitational black holes. (I don’t mean charged black holes; I mean “black holes” that are black because of electric charge.) Unfortunately, it still doesn’t explain why mass should be related to the geometry of spacetime.

(2) Charge appears to be quantized (coming in units of 1/3 of an electron charge), whereas mass appears not to be quantized, at least not in units we know.

(3) The apparent mass of a moving object increases Lorentzianly, whereas the charge is invariant.

These are interesting differences, but they also don’t seem to get us anywhere.

(4) Gravity is “many orders of magnitude weaker” than electromagnetism.

One hears this statement often; the trouble is, what does it mean? How does one compare the “intrinsic” strength of gravity and electromagnetism, without plugging in the masses and charges of typical particles that we happen to find in the universe? (Help me.)

(5) Gravity is transmitted by a spin-2 particle, whereas electromagnetism is transmitted by a spin-1 particle.

This difference is surely crucial; the trouble with it (to use a pomo word) is that it’s too “theory-laden.” Since no one has ever seen a graviton, the reason we know gravitons are spin-2 particles in the first place must have to do with more “basic” properties of gravity. So if we want a non-circular explanation for why gravity is different from the Coulomb force, it’d be better to phrase the explanation directly in terms of the more basic properties.

(6) Charge shows up in only one fundamental equation of physics — F=Kq1q2/r2 — whereas mass shows up in two equations: F=Gm1m2/r2 and F=ma.

Now we finally seem to be getting somewhere. Difference (6) was the basis for Einstein’s equivalence principle, which was one of the main steps on the road to general relativity.

But while the equivalence principle suggests the possibility of relating mass to spacetime geometry, I could never understand why it implies the necessity of doing so. If we wanted, why couldn’t we simply regard the equivalence of gravitational and inertial mass as a weird coincidence? Why are we forced to take the drastic step of making spacetime itself into a pseudo-Riemannian manifold?

The answer seems to be that we’re not! It’s possible to treat general relativity as just a complicated field theory on flat spacetime, involving a tensor at every point — and indeed, this is a perspective that both Feynman and Weinberg famously adopted at various times. It’s just that most people see it as simpler, more parsimonious, to interpret the tensors geometrically.

So the real question is: why should the field theory of Gmm/r2 involve these complicated tensors (which also turn out to be hard to quantize), whereas the field theory of Kqq/r2 is much simpler and easier to quantize?

After studying this question assiduously for years (alright, alright — I Googled it), I came across the following point, which struck me as the crucial one:

(7) Whereas the electric force is mediated by photons, which don’t themselves carry charge, the gravitational force is mediated by gravitons, which do themselves carry energy.

Photons sail past each other, ships passing in the night. They’re too busy tugging on the charges in the universe even to notice each other’s presence. (Indeed, this is why it’s so hard to build a quantum computer with photons as qubits, despite photons’ excellent coherence times.) Gravitons, by contrast, are constantly tugging at the matter in the universe and at each other. This is why Maxwell’s equations are linear whereas Einstein’s are nonlinear — and that, in turn, is related to why Einstein’s are so much harder than Maxwell’s to quantize.

When I ran this explanation by non-doofus friends like Daniel Gottesman, they immediately pointed out that I’ve ignored the strong nuclear force — which, while it’s also nonlinear, turns out to be possible to quantize in certain energy regimes, using the hack called “renormalization.” Incidentally, John Preskill told me that this hack only works in 3+1 dimensions: if spacetime were 5-dimensional, then the strong force wouldn’t be renormalizable either. And in the other direction, if spacetime were 3-dimensional, then gravity would become a topological theory that we do sort of know how to quantize.

However, I see no reason to let these actual facts mar our tidy explanation. Think of it this way: if electromagnetism (being linear) is in P and gravity (being nonlinear) is NP-complete, then the strong force is Graph Isomorphism.

My physicist friends were at least willing to concede to me that, while the explanation I’ve settled on is not completely right, it’s not completely wrong either. And that, my friends, means that it more than meets the standards of the Physics for Doofuses series.

Physics for Doofuses: Understanding Electricity

Sunday, April 15th, 2007

Welcome to an occasional new Shtetl-Optimized series, where physicists get to amuse themselves by watching me struggle to understand the most basic concepts of their discipline. I’ll consider my post on black hole singularities to be retroactively part of this series.

Official motto: “Because if I talked about complexity, you wouldn’t understand it.”

Unofficial motto: “Because if I talked about climate change, I’d start another flamewar — and as much as I want to save civilization, I want even more for everyone to like me.”

Today’s topic is Understanding Electricity. First of all, what makes electricity confusing? Well, besides electricity’s evil twin magnetism (which we’ll get to another time), what makes it confusing is that there are six things to keep track of: charge, current, energy, power, voltage, and resistance, which are measured respectively in coulombs, amps, joules, watts, volts, and ohms. And I mean, sure you can memorize formulas for these things, but what are they, in terms of actual electrons flowing through a wire?

Alright, let’s take ‘em one by one.

Charge is the q in kqq/r2. Twice as many electrons, twice as much charge. ‘Nuff said.

Current is charge per unit time. It’s how many electrons are flowing through a cross-section of the wire every second. If you’ve got 100 amps coming out, you can send 50 this way and 50 that way, or π this way and 100-π that way, etc.

Energy … Alright, even I know this one. Energy is what we fight wars to liberate. In our case, if you have a bunch of electrons going through a wire, then the energy scales like the number of electrons times the speed of the electrons squared.

Power is energy per unit time: how much energy does your appliance consume every second? Duh, that’s why a 60-watt light bulb is environmentally-friendlier than a 100-watt bulb.

Voltage is the first one I had trouble with back in freshman physics. It’s energy per charge, or power per current. Intuitively, voltage measures how much energy gets imparted to each individual electron. Thus, if you have a 110-volt hairdryer and you plug it into a 220-volt outlet, then the trouble is that the electrons have twice as much energy as the hairdryer expects. This is what transformers are for: to ramp voltages up and down.

Incidentally, the ability to transform voltages is related to why what comes out of your socket is alternating current (AC) instead of direct current (DC). AC, of course, is the kind where the electrons switch direction 60 times or so per second, while DC is the kind where they always flow in the same direction. For computers and other electronics, you clearly want DC, since logic gates are unidirectional. And indeed, the earliest power plants did transmit DC. In the 1890’s, Thomas Edison fought vigorously against the adoption of AC, going so far as to electrocute dogs, horses, and even an elephant using AC in order to “prove” that it was unsafe. (These demonstrations proved about as much as D-Wave’s quantum computer — since needless to say, one can also electrocute elephants using DC. To draw any conclusions a comparative study is needed.)

So why did AC win? Because it turns out that it’s not practical to transmit DC over distances of more than about a mile. The reason is this: the longer the wire, the more power gets lost along the way. On the other hand, the higher the voltage, the less power gets lost along the way. This means that if you want to send power over a long wire and have a reasonable amount of it reach its destination, then you want to transmit at high voltages. But high voltages are no good for household appliances, for safety and other reasons. So once the power gets close to its destination, you want to convert back down to lower voltages.

Now, the simplest way to convert high voltages to low ones was discovered by Michael Faraday, and relies on the principle of electromagnetic induction. This is the principle according to which a changing electric current creates a changing magnetic field, which can in turn be used to drive another current. (Damn, I knew we wouldn’t get far without bumping into electricity’s evil and confusing magnetwin.) And that gives us a simple way to convert one voltage to another — analogous to using a small, quickly-rotating gear to drive a big, slowly-rotating gear.

So to make a long story short: while in principle it’s possible to convert voltages with DC, it’s more practical to do it with AC. And if you don’t convert voltages, then you can only transmit power for about a mile — meaning that you’d have to build millions of tiny power plants, unless you only cared about urban centers like New York.

Resistance is the trickiest of the six concepts. Basically, resistance is the thing you need to cut in half, if you want to send twice as much current through a wire at the same voltage. If you have two appliances hooked up serially, the total resistance is the sum of the individual resistances: Rtot = R1 + R2. On the other hand, if you have two appliances hooked up in parallel, the reciprocal of the total resistance is the sum of the reciprocals of the individual resistances: 1/Rtot = 1/R1 + 1/R2. If you’re like me, you’ll immediately ask: why should resistance obey these identities? Or to put it differently, why should the thing that obeys one or both of these identities be resistance, defined as voltage divided by current?

Well, as it turns out, the identities don’t always hold. That they do in most cases of interest is just an empirical fact, called Ohm’s Law. I suspect that much confusion could be eliminated in freshman physics classes, were it made clear that there’s nothing obvious about this “Law”: a new physical assumption is being introduced. (Challenge for commenters: can you give me a handwaving argument for why Ohm’s Law should hold? The rule is that your argument has to be grounded in terms of what the actual electrons in a wire are doing.)

Here are some useful formulas that follow from the above discussion:

Power = Voltage2/Resistance = Current2 x Resistance = Voltage x Current
Voltage = Power/Current = Current x Resistance = √(Power x Resistance)
Resistance = Voltage/Current = Power/Current2 = Voltage2/Power
Current = Power/Voltage = Voltage/Resistance = √(Power/Resistance)

Understand? Really? Take the test!

Update (4/16): Chad Orzel answers my question about Ohm’s Law.

The event horizon’s involved, but the singularity is committed

Thursday, March 22nd, 2007

Lenny Susskind — the Stanford string theorist who Shtetl-Optimized readers will remember from this entry — is currently visiting Perimeter Institute to give a fascinating series of lectures on “Black Holes and Holography.”

After this morning’s lecture (yes, I’m actually getting up at 10am for them), the following question occurred to me: what’s the connection between a black hole having an event horizon and its having a singularity? In other words, once you’ve clumped enough stuff together that light can’t escape, why have you also clumped enough together to create a singularity? I know there’s a physics answer; what I’m looking for is a conceptual answer.

Of course, one direction of the correspondence — that you can’t have a singularity without also having an event horizon — is the famous Cosmic Censorship Hypothesis popularized by Hawking. But what about the other direction?

When I posed this question at lunch, Daniel Gottesman patiently explained to me that singularities and event horizons just sort of go together, “like bacon and eggs.” However, this answer was unsatisfying to me for several reasons — one of them being that, with my limited bacon experience, I don’t know why bacon and eggs go together. (I have eaten eggs with turkey bacon, but I wouldn’t describe their combined goodness as greater than the sum of their individual goodnesses.)

So then Daniel gave me a second answer, which, by the time it lodged in my brain, had morphed itself into the following. By definition, an event horizon is a surface that twists the causal structure in its interior, so that none of the geodesics (paths taken by light rays) lead outside the horizon. But geodesics can’t just stop: assuming there are no closed timelike curves, they have to either keep going forever or else terminate at a singularity. In particular, if you take a causal structure that “wants” to send geodesics off to infinity, and shoehorn it into a finite box (as you do when creating a black hole), the causal structure gets very, very angry — so much so that it has to “vent its anger” somewhere by forming a singularity!

Of course this can’t be the full explanation, since why can’t the geodesics just circle around forever? But if it’s even slightly correct, then it makes me much happier. The reason is that it reminds me of things I already know, like the hairy ball theorem (there must be a spot on the Earth’s surface where the wind isn’t blowing), or Cauchy’s integral theorem (if the integral around a closed curve in the complex plane is nonzero, then there must be a singularity in the middle), or even the Nash equilibrium theorem. In each of these cases, you take a geometric structure with some global property, and then deduce that having that property makes the structure “angry,” so that it needs a special point (a singularity, an equilibrium, or whatever) to blow off some steam.

So, question for the relativistas: is there a theorem in GR anything like my beautiful story, or am I just talking out of my ass as usual?

Update (3/22): Well, it turns out that I was ignorantly groping toward the famous Penrose-Hawking singularity theorems. Thanks to Dave Bacon, Sean Carroll, and ambitwistor for immediately pointing this out.