I'm far from an expert on LOESS (in fact, I hadn't heard the term before now), but it looks like it doesn't perform a comparable function to MIC. LOESS seems to be an algorithm for producing a non-linear regression while MIC is an algorithm to measure the strength of a relationship between two variables.

In the paper (figure 2A), they compare it to Pearson correlation coefficient, Spearman rank correlation, mutual information, CorGC, and maximal correlation on data in a variety of shapes. Basically, it is effective on a wider range of shapes than any of them.

Recently I've been using Evernote to organize my notes. It has a nice phone app that I can use to take quick notes while away from my computer, a computer program, and a browser plugin that lets me clip articles. When it comes to notes I try to think that every time I record an idea I would have forgotten, it is roughly equivalent to thinking of one new idea.

I tend to write out outlines after I finish books or some interesting articles partially to see the arguments more clearly and to refer back to in the future.

It's always interesting/fun to go back browse old ideas I've forgotten about.

Took it!

For the probability questions, I think it might have been useful for people to be able to specify confidence in their estimate. An estimate of X% from someone who is familiar with almost all of the relevant arguments and evidence is different from an estimate of X% by someone with only a cursory understanding of the issue. Then we can target the subjects people are most uncertain about to produce the most informative discussions.

Here is a (contrived) situation where a satisficer would need to rewrite.

Sally the Satisficer gets invited to participate on a game show. The game starts with a coin toss. If she loses the coin toss, she gets 8 paperclips. If she wins, she gets invited to the Showcase Showdown where she will first be offered a prize of 9 paperclips. If she turns down this first showcase, she is offered the second showcase of 10 paper clips (fans of The Price is Right know the second showcase is always better).

When she first steps on stage she considers whether she should switch to maximizer mode or stick with her satisficer strategy. As a satisficer, she knows that if she wins the coin toss she won't be able to refuse the 9 paperclip prize since it satisfies her target expected utility of 9. So her expected utility as a satisficer is (1/2) 8 + (1/2) 9 = 8.5. If she won the flip as a maximizer, she would clearly pass on the first showcase and receive the second showcase of 10 paperclips. Thus her expected utility as a maximizer is (1/2) 8 + (1/2) 10 = 9. Switching to maximizer mode meets her target while remaining a satisficer does not, so she rewrites herself to be a maximizer.

Hi. I'm a Caltech student in math/econ.