Discussion article for the meetup : Cincinnati/Columbus: Memorisation exercise (NB, time changed)

Discussion article for the meetup : Cincinnati/Columbus: Memorisation exercise (NB, time changed)

I have successfully confused myself about probability again. 

I am debugging an intermittent crash; it doesn't happen every time I run the program. After much confusion I believe I have traced the problem to a specific line (activating my debug logger, as it happens; irony...) I have tested my program with and without this line commented out. I find that, when the line is active, I get two crashes on seven runs. Without the line, I get no crashes on ten runs. Intuitively this seems like evidence in favour of the hypothesis that the line is causing the crash. But I'm confused on how to set up the equations. Do I need a probability distribution over crash frequencies? That was the solution the last time I was confused over Bayes, but I don't understand what it means to say "The probability of having the line, given crash frequency f", which it seems I need to know to calculate a new probability distribution. 

I'm going to go with my intuition and code on the assumption that the debug logger should be activated much later in the program to avoid a race condition, but I'd like to understand this math. 

It is really quite frustrating to discuss the intersection of physics and free will with a man who is capable of posting this as his opinion:

I regard atomic motions as determined, that is, as exactly defined to an infinite degree of precision, by the laws of physics, with nothing left over or left out of the explanation, and nothing else to explain.

and then, a day later, posts this as summing up mine:

[His] axiom that the motions of atoms are determined by and only by that description of non-deliberate physical reactions to outside forces called physics. [...] This axiom is not just false, it is obviously, outrageously false.

AAAGH! *Make up your damned mind, man!*

The result is from 2008, but it's new to me. Abstract:

A meta-study of repeated prisoner’s dilemma experiments run at numerous universities suggests that students cooperate 5% to 8% more often for every 100 point increase in the school’s average SAT score.

Some obvious points from my first five minutes of thinking about it:

• Meta-study or not, the sample still only covers humans. No implications for Friendly AI or intelligent aliens, which don't have our motivations.
• Even among humans the sample is WEIRD, and a subset of WEIRD at that; although there is obviously variation between universities, it's smaller than what you'd get if you extended the sample down into the working class. I also wonder what would happen if the PD was played between students and non-students. 
• Probably a point in favour of the Machiavellian intelligence hypothesis, in that we see those of higher intelligence doing better on a social problem.
• Presumably this implies that your best move, whatever your level of intelligence, is to surround yourself with the smartest people you can find, and then cooperate to ensure they don't throw you out.
• I'd like to know some details: Does intelligence also correlate with effective retaliation? With probing for suckers? What about cooperation in single games? (The study mentions one, in a footnote, which apparently did find higher intelligence correlated with greater cooperation even in one-shot games; but there's no metastudy.)

Suppose I have an exact simulation of a human. Feeling ambitious, I decide to print out a GLUT of the action this human will take in every circumstance; while the simulation of course works at the level of quarks, I have a different program that takes lists of quark movements and translates them into a suitably high-level language, such as "Confronted with the evidence that his wife is also his mother, the subject will blind himself and abdicate".

Now, one possible situation is "The subject is confronted with the evidence that his wife is also his mother, and additionally with the fact that this GLUT predicts he will do X". Is it clear that an accurate X exists? In high-level language, I would say that, whatever the prediction is, the subject may choose to do something different. More formally we can notice that the simulation is now self-referential: Part of the result is to be used as the input to the calculation, and therefore affects the result. It is not obvious to me that a self-consistent solution necessarily exists.

It seems to me that this is somehow reminiscent of the Halting Problem, and can perhaps be reduced to it. That is, it may be possible to show that an algorithm that can produce X for arbitrary Turing machines would also be a Halting Oracle. If so, this seems to say something interesting about limitations on what a simulation can do, but I'm not sure exactly what.

I just saw the letter icon under my username in orange, indicating that there should be something new in my inbox; but when I went to my inbox there was nothing there I hadn't already read. I wonder if this could be related to the recent trouble with the PM system? I sent a PM the other day and might have gotten a response to it which triggered the colour-the-icon code but not, perchance, the actual display-in-inbox code. Can I get a volunteer to receive a PM from me, or to PM me, and test whether the response shows up in the sender's inbox?

Facebook IPO'd at a price of 38 dollars a share, which apparently gave it a price-to-earnings ratio in the range of 100 - extremely, fantastically high. The price dropped pretty rapidly and is currently somewhere around 20 dollars; which still, presumably, gives it a very high P/E ratio somewhere in the forties. Now, suppose it had IPO'd at a more historically-reasonable P/E of, say, 20 - still high, but not stratospheric. That would put the initial share price somewhere around 10 or 12 dollars. Is there any strong reason to believe that the price would then have *risen* to where it is now? It is not obvious to me that the current price is supported by anything but the historical price - in other words, it's trading around 20 because it has recently traded around 25. 

My point: I can't help but wonder if someone connected to the IPO had read Kahneman on anchoring. Somebody, clearly, was buying the stock at 33, just as someone is still buying at 20; I wonder if the chain of thought had that apparently-arbitrary number "38" in it somewhere, making 33 look cheap - fundamentals be damned! And if this happened, who benefited, and what ought we to conclude about the efficiency of markets? 

Discussion article for the meetup : Monthly Ohio meetup

Discussion article for the meetup : Monthly Ohio meetup

In response to falenas108's "Ask an X" thread. I have a PhD in experimental particle physics; I'm currently working as a postdoc at the University of Cincinnati. Ask me anything, as the saying goes.

This is an experiment. There's nothing I like better than talking about what I do; but I usually find that even quite well-informed people don't know enough to ask questions sufficiently specific that I can answer any better than the next guy. What goes through most people's heads when they hear "particle physics" is, judging by experience, string theory. Well, I dunno nuffin' about string theory - at least not any more than the average layman who has read Brian Greene's book. (Admittedly, neither do string theorists.) I'm equally ignorant about quantum gravity, dark energy, quantum computing, and the Higgs boson - in other words, the big theory stuff that shows up in popular-science articles. For that sort of thing you want a theorist, and not just any theorist at that, but one who works specifically on that problem. On the other hand I'm reasonably well informed about production, decay, and mixing of the charm quark and charmed mesons, but who has heard of that? (Well, now you have.) I know a little about CP violation, a bit about detectors, something about reconstructing and simulating events, a fair amount about how we extract signal from background, and quite a lot about fitting distributions in multiple dimensions. 

Link

I'm increasingly impressed by the power of Zach Wiener's comic to demonstrate in a few images why hard problems are hard. It would be a vast task, but perhaps it would be useful to create an index of such problem-demonstrating comics to add to the Wiki, giving us something to point newbies at which would be less intimidating than formal Sequence postings. I get the impression that a common hurdle is just to get people to accept that problems of AI (and simulation, ethics, what have you) are actually difficult.