t-Mobile has these tiered refill cards for their prepaid mobile phones. The pricing table is here and reproduced below:

$10 for 30 minutes, expires in 90 days
$25 for 130 minutes, expires in 90 days
$40 for 208 minutes, expires in 90 days
$50 for 400 minutes, expires in 90 days
$100 for 1000 minutes, expires in 365 days

So which card should you buy? You could calculate a per minute cost and conclude that $100 for 1000 minutes is the most economical (plus it doesn’t expire for the longest time). Wrong!

It depends on how much you use the phone. The fact that the minutes expire makes the prepaid plan a virtual monthly plan in the regime where you do not use 1000 or more minutes per year, which is highly likely for people who choose prepaid phones to begin with (e.g. temporary visitors, odd occasions, emergencies, etc.). The constraint in that case is the expiration, not the number of minutes. If you blindly purchased $100 refills one after another, you’d have more and more unused minutes piling up. Sure, you could still use them, but even at $0.10/min. it is expensive compared to a straight monthly plan if you really mean to call that much. Of course you don’t, so now what?
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The Kendall Band at the subway station on campus had been rusting away, with only the chimes — the part they call “Pythagoras” — working. The other parts, “Kepler” and “Galileo” I have never seen working in all the years I have been here. Then one day “Pythagoras” too was gone for repairs. They posted this note for half a year until suddenly, it was back!

“Pythagoras” is two identical sets of eight pipes that could be struck by seven different mallets each. The mallets are controlled by a bar that could be swung back and forth by an attached handle which the user controls on the platform. Before the repairs, I had never paid attention to its intricacies, partly because there was not much time to play with them in the time before the next train arrived, and partly because the old rusty version didn’t make great sounds and I thought they were just some randomly sized pipes. Plus, the handle lacked fine control, and the best one could do was to hopefully transfer as much energy as possible to even get the thing going.

When it came back new, it was looking much like a real instrument and now I wondered what else you could do with it besides swinging the handle back and forth like most people do. Surely you could play an actual melody, right?
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Somebody was commenting here that they thought the sun would rise from the Boston (south) side of the river in the morning, but in fact it rises from the Cambridge (north) side these days. A little more than ten years ago, sitting in my northern-latitude abode watching the sun set into the northwest, I wondered the same confused thing: why does the sun appear to venture into the northern part of the sky? (For reference, the sun’s direct projection on the earth never crosses north of the tropic of cancer, and that is south of here.)
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MBTA has these “BIG RED” high-capacity cars now, but guess what? All they did was to remove all the seats. The trouble is, this form of so-called “high capacity” doesn’t actually add much capacity. You still have to hold on to these loops or bars and there are only so many of them. The trains look more spacious though… I think they’re confusing high personal space with high capacity.

(Why do “suit” and “purple” choose to stand?)

There is a better way to add capacity and reduce crowding in the subway car, however. Make the damned seats wider! What people do right now — due to some kind of human repelling force — is to skip every other seat unless the situation is really saturated, because it’s impolite and uncomfortable to squeeze next to/in between people. So MBTA ends up with half the designed seating capacity and the overflow just stand, sometimes right next to/blocking an empty seat. Just remove one seat per row and the number of seated people may increase dramatically. This applies to classrooms and other contexts, too.

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This, “How Goldman Sachs Created the Food Crisis” is already Frederick Kaufman’s third article on the subject in as many years. As the article indicates, here “Goldman Sachs” just stands for the financial speculators in general. He is milking this thesis a bit dry at this point — but he has a point, sort of.

There are those who argue that financial speculators, for their own good, closely track underlying supply and demand, and therefore stabilize — not destabilize — markets. But this is not the case. Experiments have shown that, even for an instrument with a perfectly known valuation, the existence of human players itself creates speculative intent which can at least temporarily unanchor market prices. Speculation itself certainly doesn’t stabilize markets, in fact it has to be disequilibriating — required for price discovery. What keeps prices stable are well structured instruments that anchor to real economic variables by some other mechanism, things like periodic reconciliation of contracts, payment of agreed-upon dividends, or taking delivery of commodities. Anything that needs no reconciliation for an indefinite period of time is asking for trouble.
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Where to Live to Avoid a Natural Disaster
Weather disasters and quakes: who’s most at risk? The analysis below, by Sperling’s Best Places, a publisher of city rankings, is an attempt to assess a combination of those risks in 379 American metro areas … and take(s) into account the relative infrequency of quakes, compared with weather events and floods.

I don’t know what exact metric they used here but it seems to be more or less expected value of “disaster points” accumulated during a unit period, for the lack of a better description. Here is the problem with these expected value based metrics, trying to summarize very different distributions: the variance matters! One catastrophic disaster isn’t quite the same as several smaller disasters costing the same number of disaster points. Yet “maximizing” survival using this map, humanity moves to the West Coast then becomes extinct in the next big earthquake. Even imposing a convex utility function on the distribution isn’t entirely satisfying. When it comes to decision making, tail risk is in a different bucket than central risk. Somehow an important aspect of the decision making process isn’t captured by “soft” metrics.
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