Category Archives: Uncategorized

Whiteness

(Yes indeed, today we’re talking race.)

Yesterday I saw someone online describe herself as “half Canadian”, which has to be the lamest self-proclaimed heritage I’ve ever seen. We all know White People who self-identify as “one quarter Irish” or “one third German” or whatever.

Hello, folks: you are WHITE.

If you are of European ancestry, and most of your family has been in the US more than 2 generations you are white.  Go celebrate Casimir Pulaski Day and wear green in March all you want, you are white. End of story.

I got an amazing response that made me think about this more deeply:

Johann makes a great point: by identifying themselves with a specific national heritage, white people free themselves from the implications of Whiteness — free themselves from having to think about race. Because when white people are just French/Irish/German/Swedish/whatever, race doesn’t matter and there’s no point in talking about it. “We all have our identities, we’re all something-Americans, and everything is great.” But if you are white you have to grapple with what that *means*, what kind of treatment you get in the world, what your individual culpability is.

A similar point is made at greater length in the paper that Johann linked to. There’s a bundle of worldview that white people have which allows them to exist in a de-racialized world — and they/we react negatively to attempts to re-racialize the way they see things (the paper calls this “white fragility”). Because whiteness is unmarked in our society, white people walk around with a view that “I’m just a person, other people have races.” Or people appeal to universalism — “we’re all people” — or individualism — “we’re all our own individuals” — which is closest to the heritage approach.

Of course, turning an eye inwards, I see myself doing the same thing. I think of myself primarily as Jewish, not as white. My dad is an immigrant, my grandparents were survivors of genocide and then displaced persons and then immigrants twice over. But of course I **read** as white in the world, which for many intents and purposes is what whiteness is all about.

:siren: Inner conflict alert :siren:

 

Anyway, what’s the right name for white people being a little too proud of their ethnic/national heritage?

Seven Year Exercise Windows

Most startup option grants come with a 90-day exercise window when an employee leaves the company (voluntarily or not). This is standard practice. Essentially the problem is that this can force employees to take a major personal financial hit to exercise their options when they leave or are fired, and perversely this is worse the more the company has grown during their time there. (Exercising options comes with a tax hit proportional to the amount the stock has increased in value since the option was granted.)

There’s an easy solution: when an employee leaves, convert their ISOs to NSOs and extend the exercise window.

Today my employer TrueAccord extended our exercise window to seven years for people with two years or more at the company. I’m proud of what this means — this is a progressive move and it shows a dedication to the employees of the company. In general I’m skeptical about equity as a carrot for startup employees (see here for an extremely skeptical view), but this is the right way to do it.

For more, see
https://github.com/holman/extended-exercise-windows
and https://zachholman.com/posts/fuck-your-90-day-exercise-window/
and point (2) in http://blog.samaltman.com/employee-equity

AI Clears New Hurdle; beats European Go champion 5-0

Today Google’s DeepMind team announced that they built a Go-playing AI which beat the European Go champion 5 games to 0. This has been a long time coming! Look at timeline of games conquered by computers:

tic tac toe: 1952
checkers: 1994
chess: 1997
go: 2016

Let’s make a simplistic assumption that the game-playing ability of computers varies as Moore’s Law, which means that it doubles every 18 months. Software has improved as well, so this is a lower bound

So beating checkers required a computer 2^63 times as powerful as beating tic-tac-toe. And chess required a computer 2^4.5 times as powerful as checkers, a factor of 22.6. But the Go program was 379625062 (or 2^28.5) times more powerful than Deep Blue.

These differences are shockingly large, and it turns out that checkers and chess are virtually indistinguishable on the spectrum of human cognition. Both are about 70% of the way between tic tac toe and Go — but who knows how far the spectrum goes?

Amazing.

Political Sunday Morning

Some links that will fill you with outrage:

Police Carelessness Wakes Local Citizen

Tuesday morning I was woken up at 5AM by a police siren. As I lay awake, I started thinking. I couldn’t be the only one woken up. Should police cars use their sirens at night? It seems like a classic example of diffuse costs and easy-to-see benefits. But is it worth it? Let’s make a quick back-of-the-envelope calculation.

The cost of running a siren at night can be modeled by the following equation (I love Fermi problems!):
LengthDriven * (2 * SirenDistance) * Density * PctWoken *
DailyIncome * ProductivityLoss

where:

  • we start with the LengthDriven with the siren on
  • multiply by twice the SirenDistance, how far away on each side of the police car a person can hear the siren, to get the geographic area affected
  • multiply by the Density to get the number of people potentially affected
  • multiply by the PctWoken to get the number of people who were woken up
  • multiply by the average DailyIncome to see how much economic value those people create each day
  • multiply by the ProductivityLoss (as a percent) that they experience when groggy to see how much economic value was lost by the siren

This model makes some assumptions. It assumes the police car drives in a straight line, that the density is uniform, that the PctWoken is constant within the SirenDistance and zero outside of it, that everyone works (so ignoring children), that everyone works a day shift, that the ProductivityLoss is independent of DailyIncome, etc. But it seems like a reasonable first step.

Let’s plug in some values:

LengthDriven = 0.25 miles

SirenDistance = 400 feet \approx 0.08 miles

Density = 18,187 people/square mile in SF (from Wikipedia)

PctWoken = 5% of people. I made this up out of nowhere.

DailyIncome = $45000 yearly per-capita income in SF / 200 work days per year = $225 per day

ProductivityLoss = 25%, I made this up too

This gives us an economic cost of $1023 every time a police officer flips a siren on at night.

Even if this only happens once per night in SF, it creates a cost of $375,000 over the course of the year — equivalent to the salary of about 4 police officers, or about 1% of the SFPD’s budget. Use of sirens also appears to be dangerous. I wonder what the benefits are — how much additional public safety is provided for this cost?

I reached out to the SFPD asking if they have any guidelines about siren use — stay tuned.

What is an order of magnitude?

I’ve always wondered exactly how to define an order of magnitude. At ideas42 I had a particular colleague with a PhD in Econ; one time I mentioned that I think use it in a “fuzzy” way, and he responded that he always uses it in an exact way.

So what is the exact way? I think there are actually two ways to use the phrase. Is it a property or is it only a relation?

  1. Property: every number has an order of magnitude that is equivalent to its power of ten (so the OOM of 153 is 2 because 153 = 1.53 * 10^2). This implies a relation: two numbers have the same order of magnitude if each of them has the same order of magnitude, and you calculate the difference in their OOMs by subtracting one from the other.
  2. Relation: two numbers x and y, with x < y, differ by N orders of magnitude if

    (x * 10^N) < y < ( x* 10^(N-1) )

Notice that these two definitions have different “predictions”! Example: is 153 an order of magnitude more than 53? Definition (1) would say yes, but Definition (2) would say no.

The Wikipedia page actually uses both definitions without trying to explain the contradiction — the first sentence is a statement of definition (1) but the John Baez quote is definition (2).

The general principle is that objective-sounding, mathematical terms get used for rhetoric even when they are loosely defined; dig deep and nothing is as precise as it seems.

How do you use the phrase?

Get Broader

Convene five of your friends who collectively have some expertise in each of the following:

  • Physics
  • Chemistry
  • Medicine
  • International Politics*
  • Literature
  • Economics

Every two months, read one book / paper by each of that year’s Nobelists.  Meet and discuss. Make the economist wear a special badge.

*For the peace prize, of course. I couldn’t figure out what it means to be an expert in peace.

Uber Gets Insightful

Uber is sharing anonymized data with Boston policymakers.

The data will provide new insights to help manage urban growth, relieve traffic congestion, expand public transportation, and reduce greenhouse gas emissions.

This is an interesting dataset for an urban planner. And Uber did well to anonymize to ZCTA instead of giving individual addresses.1 NYC made a mess of things in June 2014 when trying to do something similar.

But using Uber’s data to actually make decisions is ludicrous. Urban planning, like most policymaking, is about how to best distribute scarce resources. It is political. Look at Uber’s example uses:

Uber’s transportation policy

If Boston actually uses Uber’s data to decide which potholes to fill first, they are not going to fill many potholes in poor neighborhoods. If Boston uses Uber’s data to add additional metro stops, they will not add metro stops in poor neighborhoods.

The first questions any data analyst should ask with a new dataset are:

  1. How was this data collected?
  2. What are its blind spots?

And the blind spots here are both large and systematic. Note, however, Uber’s vacuous language above: this data will provide “insights”. They are too smart to say explicitly “this is the only data source you should use for your city planning.” But without a similarly rich dataset on the whole city, the data will only provide “insights” about how to help rich folks.

And, by the way, be wary of people peddling insights. If an insight held up to rigorous analysis, we would just call it a conclusion.

What do you think about Uber’s influence on urban policy?

  1. Though some interesting data is lost here. For example, is the rider’s destination on a busy avenue (to a store/restaurant) or a residential street (personal visit)? []