Saturday, July 21, 2012


Nothing to add, just so well put...

(Also, did you know that home improvements worth over $500 have to be performed a licensed contractor in the state of CA?)

Friday, July 20, 2012

finding Pluto

I have a bunch of astronomy-related things I should've blogged awhile ago, but I haven't had time to edit the related images yet, so that'll have to wait even longer. In the meantime, reporting from the Golden Gate Star Party, near Adin, CA...

I found Pluto! Which is just barely visible in my ten-inch telescope, at magnitude 14.04. Right now it's near the center of the galaxy and right on the edge of an open cluster, so there are literally hundreds of stars in the field of view, and you have to pick out just about the dimmest of them, and that's Pluto:

Which one is Pluto?

That one, of course!

You'd think (or at least, I thought...) that the incredibly rich background field of stars would make it really hard to pick out Pluto. Actually, though, since I had a very good finder chart (just like the images above) that I printed from Stellarium (awesome free software with libraries of stars you can download up to any limiting magnitude you could want!), I could use those hundreds of stars as a super fine grid to look exactly where I was supposed to. And after staring just next to that spot for about 15 minutes, I'd held Pluto in steady vision for about a cumulative total of one minute, but that was good and consistent enough to convince me it wasn't my imagination. Victory! I certainly couldn't have discovered Pluto in that manner, but I only wanted to find it, so good enough.

(If you are inclined to ask "So why are you so excited to look at a tiny barely-visible dot, amongst hundreds of other dots?", I unfortunately can't answer that; you're just not the target audience :)

Friday, July 13, 2012

introspection; altruism over unknown social image

Introspection seems like a flimsy basis for science, but in the social sciences, it's just another source of data. If you notice a strange data point, of course that single data point doesn't prove anything (and it might be completely misleading), but it can certainly point towards interesting avenues for research.

This happens to me constantly. That's what's so fun about being a behavioral economist.

For example, awhile ago, a friend of mine who is a wonderfully sweet and considerate person made an accidental faux pas in front of other people who don't know her. My immediate reaction was, oh no, now they're going to be annoyed at her because they don't realize it was an honest mistake by someone who would never do it intentionally. Despite the fact she wasn't even aware of the incident, I don't think, and probably won't interact with those people again, I felt bad for her for accidentally establishing a negative social image.

Why? I don't know. Humans can rationalize anything but I'm puzzled by this when I think about it from the perspective of a social preferences researcher. If she wasn't a friend, would I have had the same reaction? Probably not, but then I also wouldn't have been so confident that she's such a nice person and was sending a false signal. What if it was a friend who isn't so exceptionally nice all the time? I'm pretty sure I wouldn't have had the same reaction. I also imagine if a mean person was mistakenly seen as a very nice person, I would have a similarly inverted reaction, so it's not that I care about the annoyance level of the audience. But there are no consequences to having a bad image among people you'll never meet again, not even feeling bad about it herself! since she didn't notice it happen. So why do I care?

Thursday, July 12, 2012

mathematical modeling

Mathematical modeling's most important purpose is to formalize logic, so that lines of reasoning can't be led astray by false intuition. Game theoretic signaling models, for example, are often so hard to think about intuitively that math is flat out necessary to understand what's going on.

Through modeling, I may have convinced myself, over the past month or so, that the costly signaling story I've been in love* with for the last year is misleading, nearly to the point of being wrong. By exploring what happens when I change one set of assumptions in various ways, correcting an early error in my original model**, and formalizing one more aspect of what's going on, it seems like costliness of signals is a minor issue in only certain circumstances.

That, however, leads to new insights, and hopefully new models of those insights. That's progress. And hopefully that will make up for the time lost pursuing something that wasn't quite right.

I love the objectivity of math. It's hard, and a brutal judge, but there's less room for error, less room for pointless pursuit of invalid arguments, less room for arguing over reasoning that people interpret differently, and it's easier to pinpoint mistakes and move on from sunk costs.

*Huh, I feel like I've blogged on that topic a lot more than once, but I can't find them.

**Doh. I really need a mathematical collaborator who can check my work, and vice versa. It's phenomenal how much time I waste over simple math mistakes, and I hear I'm definitely not alone in this. I can only hope it gets better with experience.

Thursday, July 5, 2012

Berkeley in one sentence

Berkeley: where the homeless people refuse food that isn't vegan.

(I am beside myself at how perfect that is :D)

Wednesday, July 4, 2012

useful math

Math I liked in college: abstract algebra, combinatorics, topology
Math I didn't like: analysis, statistics

(All of the above most broadly speaking; the exceptions being differential topology, which should go in the 2nd category, and chunks of classical analysis such as measure theory, which should go in the first.)

You could just as easily label those lists "not useful" and "useful", respectively.


Tuesday, July 3, 2012

coding in economics

Well, it's a huge stretch to say that "doing research is writing software", but it is writing code, that's true. And this is a great guide any economist (or any scientist who writes code) should read.

But writing readable code is one thing and writing good, efficient code is another thing, and much more involved than an enumerable list of do's and don't's. More frequently in economics, as we estimate complicated structural models and other computationally intensive things like that, we're going to need to get better at programming, above and beyond simple Stata scripts. Naively writing Matlab code that takes weeks to estimate a model isn't the best way to go about these things in the long run. We should learn C (for example), learn what happens behind the scenes when we write a line of code so we can avoid doing stupidly inefficient things, and learn good algorithms (or at least how they work, so we can use the appropriate pre-existent library for the task.)

I'm as guilty as anyone. And probably these kinds of problems where efficient code is really necessary are still pretty rare, and it's probably hard to know going into it that it will be necessary, so it's still easy to avoid thinking about. But now is a good time to put in the investment. As a profession we seem to have dug ourselves a Stata-lined hole (of infinite despair...) when it comes to regression analysis, let's not also dig ourselves a Matlab-lined hole for structural estimation :)

Monday, July 2, 2012


Swann's Way by Marcel Proust - I only read a third of this, actually, before I couldn't take it anymore. If this guy had anything at all interesting to talk about he would be the most amazing writer I've possibly ever encountered, and for certain ten-page increments that were even vaguely relatable I did believe that. But the rest of the time he's an infuriatingly whiny spineless narcissist recounting his spoiled upper-crusty childhood experience in the kind of excruciating detail (Proustian recall became a common phrase for a very good reason) that, again, is astoundingly wonderful when applied to something of interest, but when not, causes my brain to slowly boil. If any of the later volumes of In Search of Lost Time, or even later bits of this volume, are less painful, please let me know, because I really want to read anything in which his stunning talent with words is put to good use...

Machine of Death - Collection of short stories on the theme of what the world would be like if there existed a machine that could tell you how you would die. Free at the link! Many of the stories were pointedly amateur (I didn't realize until I read this how easy it is to tell bad fiction writing from good fiction writing...) but they were weirdly addicting nonetheless and I finished them all despite my intention to just read a few for book club. Some had fantastic premises, most were just ok.

Transit of Venus: 1631 to the Present by Nick Lomb - Huh, I guess I still haven't blogged about the transit. Mental note. Anyway, I read this book before hand, which goes through all the history and explains the science behind it and the scientific importance (it was how they measured the solar system originally), all with countless stunning photographs. It made viewing the actual transit much more enjoyable; without appreciating the significance I'm sure five minutes at the eyepiece would've satiated my interest. Highly recommended except for the fact that there won't be another transit until 2117...

Sunday, July 1, 2012

social preferences towards the experimenter

Periodically I run into the objection towards interpretations/designs of social preferences experiments that the experimenter is ignored as a player. That is, taking money from the experimenter is transferring it from taxpayers to the participants, and that might be something participants care about, in addition to transfers between participants.

Frankly I don't really care about that possibility. It certainly is something that warrants careful study, but I find it very unlikely and therefore not so interesting, personally. (And, it's only a substantial criticism for certain experiments, not usually the kind I think about.)

It occurs to me that that's probably because I view social preferences as largely driven by norms and image. We do things because we're expected to do things. We share money in laboratory dictator games because it's implied that we're expected to and sharing norms say we should and we don't want to look bad; we don't give money to random strangers on the subway because there is no such expectation or norm. If that's the case, it's pretty safe to ignore the experimenter as a player, because almost no experiment ever even hints at the experimenter's money as something the participants should care about. And, the mere fact that they're running the experiment tells participants that they want to use that money to see how people use it within a game among themselves. The expectation is, if anything, against having social preferences towards the experimenter.