When I went from undergrad to grad school, the introduction of a monthly stipend into my life gave me a natural unit in which to measure my monthly consumption. I never did itemized budgeting, but net positive savings was definitely a goal. This goal was easily reached, mostly due to my realization that scrimping on rent got me very far. As of a month ago, I started working, and for the first time ever, have substantial savings.

This introduces a degree of freedom, and with that, one more thing to optimize. There were a few decisions necessitated by the living environment, like eating out and using the wash-n-fold service instead of the laundromat. Other than that, however, it seems like the other decisions are going to be made by my choice of company more than anything else. Tonkatsu curry is the most delicious dish I can think of, and I really don't think anything will change that. I do follow along when people go to fancy restaurants though.

I've stopped reading the news recently because I think it is terribly time-consuming given how little knowledge it conveys. The credibility of news depends a lot on social proof, I find - just because everyone around you has the same sources and believes the same things about far away worlds doesn't mean those beliefs are more dependable.

News informs belief choice, and is most useful when surprising. It can be unsurprising because it is predictable, or because it is noise. In either case, uninteresting because the candidate beliefs are not differentiated by the evidence.

My particular candidate beliefs at this moment mean that news does not interest me.

There are two classes of mistakes one can make when using models - one can either be using the wrong model, or using the correct model but assigning an erroneous solution. It is important to differentiate between the two types of mistakes, because doing so would allow you to better modularize reality, detect the places in which that reality repeats, and apply the ideas one has.

When using the wrong model, the elementary objects in reality do not actually have the properties required for the model to be relevant. When solving a given model wrongly, the space of possibilities and consequences represented by the model are not searched thoroughly enough to find the best solution.

For a given level of simplicity, models that apply to many different situations are more profitable to solve. For models that apply to the same number of situations, I prefer to solve the simpler models first, with the strength of the preference proportional to my model-specific intelligence. Therefore, if I feel like my comparative (not competitive?) advantage is in intelligence, I will be constantly trying to search for models of wide applicability and high complexity. I will value the ability to generalize primitives to identify common games.