There is nothing in Copenhagen that forbids macroscopic superposition. The experimental results of macroscopic superposition in SQUIDs are usually calculated in terms of copenhagen (as are almost all experimental results).
EHeller
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How are you defining territory here? If the territory is ‘reality’ the only place where quantum mechanics connects to reality is when it tells us the outcome of measurements. We don’t observe the wavefunction directly, we measure observables.
I think the challenge of MWI is to make the probabilities a natural result of the theory, and there has been a fair amount of active research trying and failing to do this. RQM side steps this by saying “the observables are the thing, the wavefunction is just a map, not territory.”
How would this affect a frequentist?
It doesn’t the frequentist is already measuring with the sample distribution. That is how frequentism works.
I was mainly trying to convince you that nothing’s actually wrong with having 33% false positive rate in contrived cases.
I mean it’s not “wrong” but if you care about false positive rates and there is a method had has a 5% false positive rate, wouldn’t you want to use that instead?
No, there’s a limit on that as well. See http://www.ejwagenmakers.com/2007/StoppingRuleAppendix.pdf
I can check my simulation for bugs. I don’t have the referenced textbook to check the result being suggested.
It is my thesis that every optional stopping so-called paradox can be converted into a form without optional stopping, and those will be clearer as to whether the problem is real or not.
The first part of this is trivially true. Replace the original distribution with the sampling distribution from the stopped problem, and it’s not longer a stopped problem, it’s normal pulls from that sampling distribution.
I’m not sure it’s more clear,I think it is not. Your “remapped” problem makes it look like it’s a result of low-data-volume and not a problem of how the sampling distribution was actually constructed.
I think this is problem dependent.
In simulation, I start to asymptote to around 20%, with a coin flip, but estimating mean from a normal distribution (with the null being 0) with fixed variance I keep climbing indefinitely. If you are willing to sample literally forever it seems like you’d be able to convince the Bayesian that the mean is not 0 with arbitrary Bayes factor. So for large enough N in a sample, I expect you can get a factor of 3 for 99⁄100 of the Bayesians in cages (so long as that last Bayesian is really, really sure the value is 0).
But it doesn’t change the results if we switch and say we fool 33% of the Bayesians with Bayes factor of 3. We are still fooling them.
Before I analyse this case, can you clarify whether the hypothesis happens to be true, false, or chosen at random? Also give these Bayesians’ priors, and perhaps an example of the rule you’d use.
Again, the prior doesn’t matter, they are computing Bayes factors. We are talking about Bayes factors. Bayes factors. Prior doesn’t matter. Bayes factors. Prior.Doesn’t.Matter. Bayes factors. Prior.Doesn’t.Matter. Bayes.factor.
Let’s say the null is true, but the frequentist mastermind has devised some data generating process that (let’s say he has infinite data at his disposal) that can produce evidence in favor of competing hypothesis at a Bayes factor of 3, 99% of the time.
I’m saying that all inferences are still correct. So if your prior is correct/well calibrated, then your posterior is as well. If you end up with 100 studies that all found an effect for different things at a posterior of 95%, 5% of them should be wrong.
But that is based on the posterior.
When I ask for clarification, you seem to be doing two things:
changing the subject to posteriors
asserting that a perfect prior leads to a perfect posterior.
I think 2 is uncontroversial, other than if you have a perfect prior why do any experiment at all? But it is also not what is being discussed. The issue is that with optional stopping you bias the Bayes factor.
As another poster mentioned, expected evidence is conserved. So let’s think of this like a frequentist who has a laboratory full of bayesians in cages. Each Bayesian gets one set of data collected via a standard protocol. Without optional stopping, most of the Bayesians get similar evidence, and they all do roughly the same updates.
With optional stopping, you’ll create either short sets of stopped data that support the favored hypothesis or very long sets of data that fail to support the favored hypothesis. So you might be able to create a rule that fools 99 out of the 100 Bayesians, but the remaining Baysian is going to be very strongly convinced of the disfavored hypothesis.
Where the Bayesian wins over the frequentist is that if you let the Bayesians out of the cages to talk, and they share their likelihood ratios, they can coherently combine evidence and the 1 correct Bayesian will convince all the incorrect Bayesians of the proper update. With frequentists, fewer will be fooled, but there isn’t a coherent way to combine the confidence intervals.
So the issue for scientists writing papers is that if you are a Bayesian adopt the second, optional stopped experimental protocol (lets say it really can fool 99 out of 100 Bayesians) then at least 99 out of 100 of the experiments you run will be a success (some of the effects really will be real). The 1⁄100 that fails miserably doesn’t have to be published.
Even if it is published, if two experimentalists both average to the truth, the one who paints most of his results as experimental successes probably goes further in his career.
That paper only calculates what happens to the bayes factor when the null is true. There’s nothing that implies the inference will be wrong.
That is the practical problem for statistics (the null is true, but the experimenter desperately wants it to be false). Everyone wants their experiment to be a success. The goal of this particular form of p-hacking is to increase the chance that you get a publishable result. The goal of the p-hacker is to increase the probability of type 1 error. A publication rule based on Bayes factors instead of p-values is still susceptible to optional stopping.
You seem to be saying that a rule based on posteriors would not be susceptible to such hacking?
It depends only on the prior. I consider all these “stopping rule paradoxes” disguised cases where you give the Bayesian a bad prior, and the frequentist formula encodes a better prior.
Then you are doing a very confusing thing that isn’t likely to give much insight. Frequentist inference and Bayesian inference are different and it’s useful to at least understand both ideas(even if you reject frequentism).
Frequentists are bounding their error with various forms of the law of large numbers, they aren’t coherently integrating evidence. So saying the “frequentist encodes a better prior” is to miss the whole point of how frequentist statistics works.
And the point in the paper I linked has nothing to do with the prior, it’s about the bayes factor, which is independent of the prior. Most people who advocate Bayesian statistics in experiments advocate sharing bayes factors, not posteriors in order to abstract away the problem of prior construction.
In practice what p-hacking is about is convincing the world of an effect, so you are trying to create bias toward any data looking like a novel effect. Stopping rules/data peeking accomplish this just as much for Bayes as for frequentist inference (though if the frequentist knows about the stopping rule they can adjust in a way that bayesians can’t), which is my whole point.
Whether or not the Bayesian calibration is overall correct depends not just on the Bayes factor but the prior.
Reminds of this bit from a Wasserman paper http://ba.stat.cmu.edu/journal/2006/vol01/issue03/wasserman.pdf
van Nostrand: Of course. I remember each problem quite clearly. And I recall that on each occasion I was quite thorough. I interrogated you in detail, determined your model and prior and produced a coherent 95 percent interval for the quantity of interest.
Pennypacker: Yes indeed. We did this many times and I paid you quite handsomely.
van Nostrand: Well earned money I’d say. And it helped win you that Nobel.
Pennypacker: Well they retracted the Nobel and they took away my retirement savings.
… van Nostrand: Whatever are you talking about?
Pennypacker: You see, physics has really advanced. All those quantities I estimated have now been measured to great precision. Of those thousands of 95 percent intervals, only 3 percent contained the true values! They concluded I was a fraud.
van Nostrand: Pennypacker you fool. I never said those intervals would contain the truth 95 percent of the time. I guaranteed coherence not coverage!
What makes Bayesian “lose” in the cases proposed by Mayo and Simonsohn isn’t the inference, it’s the scoring rule. A Bayesian scores himself on total calibration, “number of times my 95% confidence interval includes the truth” is just a small part of it. You can generate an experiment that has a high chance (let’s say 99%) of making a Bayesian have a 20:1 likelihood ratio in favor of some hypothesis. By conservation of expected evidence, the same experiment might have 1% chance of generating close to a 2000:1 likelihood ratio against that same hypothesis. A frequentist could never be as sure of anything, this occasional 2000:1 confidence is the Bayesian’s reward. If you rig the rules to view something about 95% confidence intervals as the only measure of success, then the frequentist’s decision rule about accepting hypotheses at a 5% p-value wins, it’s not his inference that magically becomes superior.
Sometimes we might care about “total calibration” I guess, but sometimes we care about being actually calibrated in the rationalist sense. Sometimes we want a 95% confidence interval to mean that doing this 100 times will include the true value about 95 times.
My point was this idea that the stopping rule doesn’t matter is more complicated than calculating a Bayes factor and saying “look, the stopping rule doesn’t change the Bayes factor.”
If you look at the paper, what you call optional stopping is what the authors called “data peeking.”
In their simulations, the authors first took in a sample of 20 and calculated it, and then could selectively continue to add data up to 30 (stopping when they reach “effect” or 30 samples). The papers point is that this does skew the Bayes factor (doubles the chances of managing to get a Bayes factor > 3).
It is true that optional stopping won’t change Bayes rule updates (which is easy enough to show). It’s also true that optional stopping does affect frequentist tests (different sampling distributions). The broader question is “which behavior is better?”
p-hacking is when statisticians use optional stopping to make their results look more significant (by not reporting their stopping rule). As it turns out you in fact can “posterior hack” Bayesians—http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2374040
Edit: Also Debrah Mayo’s Error Statistics book contains a demonstration that optional stopping can cause a Bayesian to construct confidence interval that never contain the true parameter value. Weirdly, those Bayesians can be posterior hacked even if you tell them about the stopping rule, because they don’t think it matters.
The existence of the Higg’s is one of the rare bits of physics that doesn’t average out under renormalization.
The reason is that the Higgs is deeply related to the overall symmetry of the whole standard model- you start with a symmetry group SU(2)xU(1) and then the Higgs messes with the symmetry so you end up with just U(1) symmetry. What the theory predicts is relationships between the Higgs, the W and Z boson, but not the absolute scale. The general rule is RG flow respects symmetries, but other stuff gets washed out.
This is why the prediction was actually “at least 1 scalar particle that interacts with W and Z bosons”. But there are lots of models consistent with this- it could have been a composite particle made of new quark-like-things (technicolor models), there could be multiple Higgs (2 in SUSY, dozens in some grand unified models),etc. So it’s sort of an existence proof with no details.
I think I’m communicating a little poorly. So start with atomic level physics- it’s characterized by energy scales of 13.6 eV or so. Making measurements at that scale will tell you a lot about atomic level physics, but it won’t tell you anything about lower level physics- there is an infinite number of of lower level physics theories that will be compatible with your atomic theory (which is why you don’t need the mass of the top quark to calculate the hydrogen energy levels- conversely you can’t find the mass of the top quark by measuring those levels).
So you build a more powerful microscope, now you can get to 200*10^6 eV. Now you’ll start creating all sorts of subatomic particles and you can build QCD up as a theory (which is one of the infinitely many theories compatible with atomic theory). But you can’t infer anything about the physics that might live at even lower levels.
So you build a yet more powerful microscope, now you can get 10^14 eV, and you start to see the second generation of quarks,etc.
At every new level you get to, there might be yet more physics below that length scale. The fundamental length scale is maybe the planck scale, and we are still 13 orders of magnitude above that.
Edit: this author is sort of a dick overall, but this was a good piece on the renormalization group- http://su3su2u1.tumblr.com/post/123586152663/renormalization-group-and-deep-learning-part-1
The point of RG is that “higher level” physics is independent of most “lower level” physics. There are infinitely many low level theories that could lead to a plane flying.
There are infinitely many lower level theories that could lead to quarks behaving as they do,etc. So 1. you can’t deduce low level physics from high level physics (i.e. you could never figure out quarks by making careful measurements of tennis balls), and you can never know if you have truly found the lowest level theory (there might be a totally different theory if you only had the ability to probe higher energies).
This is super convenient for us- we don’t need to know the mass of the top quark to figure out the hydrogen atom,etc. Also, it’s a nice explanation for why the laws of physics look so simple- the laws of physics are the fixed points of renormalization group flow.
The whole point of the renormalization group is that lower level models aren’t more accurate, the lower level effects average out.
The multiple levels of reality are “parallel in a peculiar way” governed by RG. It might be “more complex” but it’s also the backbone of modern physics.
Heck climate scientists aren’t even that sparing about basic facts. They’ll mention that CO2 is a greenhouse gas, but avoid any more technical questions. For example, I only recently found out that (in the absence of other factors or any feedback) temperature is a logarithmic function of CO2 concentration.
So this seems like you’ve never cracked open any climate/atmospheric science textbook? Because that is pretty basic info. It seems like you’re determined to be skeptical despite not really spending much time learning about the state of the science. Also it sounds like you are equivocating between “climate scientist” and “person on the internet who believes in global warming.”
My background is particle physics, if someone asked me about the mass of a muon, I’d have to make about a hundred appeals to authority to give them any relevant information, and I suspect climate scientists are in the same boat when talking to people who don’t understand some of the basics. I’ve personally engaged with special relativity crackpots who ask you to justify everything, and keep saying this or that basic fact from the field is an appeal to authority. There is no convincing a determined skeptic, so it’s best not to engage.
If you are near a university campus, wait until there is a technical talk on climate modelling and go sit and listen (don’t ask questions, just listen). You’ll probably be surprised at how vociferous the debate is- climate modelers are serious scientists working hard on perfecting their models.
So there are obviously a lot of different things you could mean by “Copenhagen” or “in the back of a lot of copenhagenist minds” but the way it’s usually used by physicists nowadays is to mean “the Von Neumann axioms” because that is what is in 90+% of the textbooks.