Science posts

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I had trouble finding this on the internet, so here is a scan of the hard copy that came in.

pdf

Its important because Camerer and others have cited Kahneman's surprise at convergence in the market entry game, or what he calls the N* game. In his words "Observing the regularity of behavior in these markets was a bewildering experience -- to a psychologist, it looked almost like magic."

Besides summarizing the market entry results---in which psychologists are subverted by economics---he describes "reluctance to trade" and other cases of economists being subverted by psychology. The paper is short and sweet; full of lots of other good quotes. For example, he succinctly asserts a claim that Gode and Sunder fleshed out a decade later: "Robots programmed to obey simple dominance would establish the optimal market price."

Kahneman, D., (1986) Experimental economics: A psychological perspective

presented in Bounded Rational Behavior in experimental games and markets: Proceedings of the Fourth Conference on Experimental Econoimcs, Bielefeld, West Germany, Spetember 21-25, 1986. eds. R. Tietz, W. Albers, R. Selten published in Lecture notes in Economics and Mathematical Systems, Eds M. Beckmann and W. Krelle

Abstract:

In a recent review Bruno Frey (1986) listed three assumptions of standard economic analyses: economic agents are supposed to be rational, to be selfish, and to have unchanging tastes. These assumptions contradict common sense knowledge of human nature as well as the conclusions of other social sciences. Economists know this, of course. Their position of deliberate o unconcern to for the validity of assumptions about economic agents was brilliantly stated in Friedman's (1953) classic essay, and additional arguments in its defense are added from time to time. For social scientists reared in other sultures, the intellectual position of economics presents a dual challenge. First, the position must be understood -- which is not easy to do across the cultural gap. Second, the limits of its validity must be established. The economic predictions sometimes succeed in ways that are surprising to other social scientists, and sometime fail in ways that are surprising to economics --- or would be surprising if the failure were admitted, which is not always the case. In the following comments I briefly describe two sets of recent experimental observations that bear on these issues. The first project is concerned with a situation in t which an economic prediction is upheld, in a manner that is quite surprising for a psychologist. The second documents a failure of an important assumption in economic analyses of exchanges.

extended quote:

Observing the regularity of behavior in these markets was a bewildering experience --- to a psychologist, it looked almost like magic.  The bewilderment was not eased by the debriefing conversations in which we engaged participants after the experiment.  They described a large variety of strategies and expectations as guiding their behavior.  Most of the strategies were completely unfounded (as they must have been, since the equilibrium effectively precluded any successful strategy).  Furthermore, in at least one case there was evidence that the strategy which the individual described did not in fact account for his choices.  The equilibrium outcome (which would be generated by the optimal policies of rational players) was produced in this case by a group of excited and confused people, who simply did not seem to know what they were doing.
Psychologists are trained to believe that aggregate phenomena can be explained by finding relevant regularity in individual behavior.  The N* game provided me with  first -hand experience of a clear failure of this belief.  The only solid explanation of the results of the N* game belongs to a type that is quite familiar to economists, but not to other social scientists.  As J. Brander  pointed out, E had to be close to N* because there a was no stable alternative.  Any systematic deviation from near-equality would have been obvious to the participants, and their attempt to take advantage of it would have ce cancelled it.  The achievement of global equilibrium in such a situation requires  very little  intelligence from the participants: just enough to recognize apparent regularities in the group response to variations f of N*, and to move to counter them.  It is very likely that all the participants had the intelligence to detect arbitrage opportunities -- and as a result there were none.

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note: This is dated. I hear you can get away with jags and rjags now.

specifically: Bayesian Data Analysis on a Mac with R and OpenBUGS via BRugs

Bayesian data analysis is a way of figuring out what you believe given what you just saw, but in math. R is a free computer language for calculating statistics. OpenBUGS is a flexible program for doing Bayesian data analysis. BRugs is a bridge between R and BUGS. Macs are why Steve Jobs wears black turtlenecks.

This post is solving a very narrow problem. R is cross-platform, but OpenBUGS only works on Windows. The same holds either for the cousins of OpenBUGS or for their bridges with R. So it is hard for Mac users to do Bayesian data analysis in R with the flexibility offered by BUGS.

My colleague Jack Harris has a fix that doesn't take any fiddling. Its great. If you know what you are doing, instructions are below. If you wish you knew what you were doing, read a book.

Instructions for using BRugs on a Mac

• Be a Mac user of R (if you are on Windows then you don't have a problem).
• Download this.
• Extract the tarball or (if you don't know what that means) hope that your computer magically turns the file into one that ends with .app .
• Open the app, wait a bit. There will be a benign error, just hit OK.
• After R loads, type

install.packages("BRugs")

• Download this test file and type

setwd("~/whereeverthefilewent")
source("TutorialBRugsTest.R")

You should get pretty graphs. That means it worked. If you want more, the samples from Kruschke's book are here. The samples should all run fine if you download everything to one spot. The book has puppies on the cover to signal its friendliness to the 'fraid-of-math.

additional notes

There are other workarounds to get BUGS in R on a Mac. This one is the easiest to get going and I've been told that it has some other advantages (thanks Drew):

• In this wine environment copy/paste/save work (but with the control key instead of the command key on my Mac keyboard).
• Control-R actually runs highlighted text.
• Downside: shift-arrows does not highlight, instead it overwrites text in this version.

update

I wrote this before Papers2 and any Mendeley updates. In the meantime they've both come up a long way. I can't speak as well for Mendeley except that I saw it recently, briefly over someone's shoulder and it looked flashier---less like straight Java. Papers2 fixes all the relative weaknesses below, except that it is still Mac only. And since release, about two months ago, they've released eight updates. That said, I've used few of its newest features. And below, where I said I've only bought Papers once. Well, I've bought it twice now (heavily discounted).

original post

Papers and Mendeley are two competing PDF viewer/organizers intended for scientists. I've never really bought much software, but if you are on a Mac, I recommend that you give Papers a try over Mendeley. It must be purchased after 30 days, but is totally worth it. I use it to take all of my notes, build all of my reference lists, and of course organize all of my papers.

When I want ideas for a paper I'm writing, I look at what I've already

written on the article. When I want to know what journals to submit to, I sort my paper collection by journal to see what I read the most in what category. When I want to know what Japanese researcher to work with this summer, I type in the word Japan to search PDFs, notes, journal titles, and whatever else to see what institutions I am reading work from. When I am writing a paper, I collect everything relevant to it in a folder. When I want the bibtex for that library of papers, I export it (though I've had to use Bibdesk as an intermediary to clean the .tex). When I want to share a paper, I drag it into Gmail.

At this point Mendeley does some things better than Papers. It is currently

• better suited for people who write in tex (Paper's accommodation of metadata is a little weak),
• it stores non-PDFs,
• it is cross platform, and
• free (but not necc. for long, see below)

Papers also does some things better than Mendeley. In my opinion

• search is + better in Papers,
• note taking is ++ better,
• reading is ++,
• interface is +++, and
• speed is ++ or +++)

There are also ways that the two are just different:

• the way they handle auto-filling of metadata and
• Mendeley's social network thing (I'm not going to go ahead and call it an advantage just because it has the words social and network. I tried it this summer, and it is currently too crippled to be as useful as I'd like).

Until a week or so ago I was a little concerned that my preference for Papers over Mendeley was just the sunk costs speaking. It is hard to switch once you have invested so much time into your notes, which I don't think are easily transferable. I tried anyway and moved my entire paper collection over. I gave Mendeley a really fair try, and came out decisively in favor of Papers. No more regret.

Regarding the downsides, the

developers are fairly transparent about their future plans in the forums and it looks like my biggest qualms with the software are on their todo list. Updates come out about once every six months. I know it costs money ($40 after a 30 day free trial), and Mendeley doesn't, but I also know that the Mendeley folks are crippling certain features so that they can start charging soon as well. Even worse, their plan is for a subscription service, which means that to get the most of Mendeley, you will have to keep buying it every month. They said so in an email to me. I'm not sure what the subscription will unlock, but I'd guess that every currently free feature will stay free. Regardless, I only bought Papers once.

So yeah, consider Papers by mekentosj.

I was simultaneously excited and suspicious of this paper, which made the cover of Nature:

http://www.nature.com/nature/journal/v464/n7290/abs/nature08891.html http://hal.elte.hu/pigeonflocks/index.html

Watching the videos I got the sense that the pigeons that were identified as "leaders" were also the most boring. They didn't seem to turn as much as the other pigeons. What if their "leadership" is just "laziness"? Take a spring model, in which every bird wants to be a little close and a little far from every other bird. Now get a lazy ("massive" in the analogy) bird, and the flock's behavior will be determined by his stubborness.

How to test this? Well if, from moment to moment, a given bird doesn't turn very much, I predict that he will appear as the leader of the group. To test this, I calculated the differences between the angles at each time step, and found the variance in these differences. straight flying bird will have low variance, wildly turning bird will have high variance. This variance should vary inversely with leadership position.

To avoid recalculating their hierarchies for each flight of the pigeons, I used the hierarchy the the authors published for the whole flock, over all recorded flights. It is on page 892 of the article. I did a paired test of ranked sums for each of the (in this case) 9 birds, comparing their laziness and their calculated position in the pecking order. My results were null: by this analysis they are unrelated.

But I like a few things about having done this:

• I got the data from the experimenters less than a day after emailing them for it.
• It took about three hours to do the analysis, and should take less and less as I become fluent in R.
• I was able to answer a question I had.
• My blog is a nice outlet for a quick report of the [null] result. Might be interesting to someone.

Null results ("ummm, nothing happened") have a history of being underreported in science. Naturally, people only want to share (and can usually only get published) when something Happened. There is actually a really neat history here, and there are some classic results that got published due to this bias.

As for lazy pigeon bosses, it still might be the case: I didn't compare one individual flights (because I'm avoiding the crazy calculations they did to infer hierarchies for each of the 15 flights constituting the data set). I cut some other corners too (ignored third dimension, velocity and other data). Also, I used a pretty low power statistical analysis, one that I was forced to use because of the coarse hierarchy data published in the paper. But I'm not going to keep trying, I'm pretty satisfied.

In summary, I was unable to support my pet hypothesis that leader pigeons are lazy pigeons. The authors propose a number of more exciting possibilities instead. For example: leadership role corresponded to accuracy in individual navigation back home, though they point out that the causation could go either way (leadership -> accuracy or accuracy -> leadership).

I wanted to use vim to develop R code. I am on a Mac. R lets you specify an external editor (go from R->vim), but you need Applescript as glue to go the other way, vim->R, and execute code from vim in R. The Applescript below, saved in Applescript Editor, creates a script that can be run from the command line and will execute the first command line argument as a source file. It should be pretty easy to run any other program besides R that allows this kind of thing.

-- in shell "osascript RunR.scpt ~/path/script.r"
-- from vim: ":! osascript ~/bin/RunRb.scpt `pwd`/% "
-- in .vimrc: "cmap runr ! osascript ~/bin/RunRb.scpt `pwd`/% "

on run argv --for command line
	tell application "R"
		activate
		with timeout of 90000 seconds
			cmd "source(" & quoted form of POSIX path of item 1 of argv & ")" --R code
		end timeout
	end tell
end run