Segregation, Gerrymandering, and Schelling’s Model

[This note is more about modeling some of the mathematics behind political events than politics themselves. And there are pretty pictures.]

Gerrymandering has become a recurring topic in the news. The Supreme Court of the US, as well as more state courts and supreme courts, is hearing multiple cases on partisan gerrymandering (all beginning with a case in Wisconsin).

Intuitively, it is clear that gerrymandering is bad. It allows politicians to choose their voters, instead of the other way around. And it allows the majority party to quash minority voices.

But how can one identify a gerrymandered map? To quote Justice Kennedy in his Concurrence the 2004 Supreme Court case Vieth v. Jubelirer:

When presented with a claim of injury from partisan gerrymandering, courts confront two obstacles. First is the lack of comprehensive and neutral principles for drawing electoral boundaries. No substantive definition of fairness in districting seems to command general assent. Second is the absence of rules to limit and confine judicial intervention. With uncertain limits, intervening courts–even when proceeding with best intentions–would risk assuming political, not legal, responsibility for a process that often produces ill will and distrust.

Later, he adds to the first obstacle, saying:

The object of districting is to establish “fair and effective representation for all citizens.” Reynolds v. Sims, 377 U.S. 533, 565—568 (1964). At first it might seem that courts could determine, by the exercise of their own judgment, whether political classifications are related to this object or instead burden representational rights. The lack, however, of any agreed upon model of fair and effective representation makes this analysis difficult to pursue.

From Justice Kennedy’s Concurrence emerges a theme — a “workable standard” of identifying gerrymandering would open up the possibility of limiting partisan gerrymandering through the courts. Indeed, at the core of the Wisconsin gerrymandering case is a proposed “workable standard”, based around the efficiency gap.


Thomas Schelling and Segregation

In 1971, American economist Thomas Schelling (who later won the Nobel Prize in Economics in 2005) published Dynamic Models of Segregation (Journal of Mathematical Sociology, 1971, Vol 1, pp 143–186). He sought to understand why racial segregation in the United States seems so difficult to combat.

He introduced a simple model of segregation suggesting that even if each individual person doesn’t mind living with others of a different race, they might still choose to segregate themselves through mild preferences. As each individual makes these choices, overall segregation increases.

I write this post because I wondered what happens if we adapt Schelling’s model to instead model a state and its district voting map. In place of racial segregation, I consider political segregation. Supposing the district voting map does not change, I wondered how the efficiency gap will change over time as people further segregate themselves.

It seemed intuitive to me that political segregation (where people who had the same political beliefs stayed largely together and separated from those with different political beliefs) might correspond to more egregious cases of gerrymandering. But to my surprise, I was (mostly) wrong.

Let’s set up and see the model.

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The Hawaiian Missile Crisis

I read an article from Doug Criss on CNN yesterday with the title “Hawaii’s governor couldn’t correct the false missile alert sooner because he forgot his Twitter password.”1 It turns out that Governor Ige knew within two minutes that the alert was a false alarm, but (in the words of the article) “he couldn’t hop on Twitter and tell everybody — because he didn’t know his password.”

There are a couple of different ways to take this story. The most common response I have seen is to blame the employee who accidentally triggered the alarm, and to forgive the Governor his error because who could have guessed that something like this would happen? The second most common response I see is a certain shock that the key mouthpiece of the Governor in this situation is apparently Twitter.

There is some merit to both of these lines of thought. Considering them in turn: it is pretty unfortunate that some employee triggered a state of hysteria by pressing an incorrect button (or something to that effect). We always hope that people with great responsibilities act with extreme caution (like thermonuclear war).

How about a nice game of global thermonuclear war?

So certainly some blame should be placed on the employee.

As for Twitter, I wonder whether or not a sarcasm filter has been watered down between the Governor’s initial remarks and my reading it in Doug’s article for CNN. It seems likely to me that this comment is meant more as commentary on the status of Twitter as the President’s preferred 2 medium of communicating with the People. It certainly seems unlikely to me that the Governor would both frequently use Twitter for important public messages and forget his Twitter credentials. Perhaps this is code for “I couldn’t get in touch with the person who manages my Twitter account” (because that person was hiding in a bunker?), but that’s not actually important. Continue reading

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Slides from a talk at the Joint Math Meetings 2018

I’m in San Diego, and it’s charming here. (It’s certainly much nicer outside than the feet of snow in Boston. I’ve apparently brought some British rain with me, though).

Today I give a talk on counting lattice points on one-sheeted hyperboloids. These are the shapes described by
$$ X_1^2 + \cdots + X_{d-1}^2 = X_d^2 + h,$$
where $h > 0$ is a positive integer. The question is: how many lattice points $x$ are on such a hyperboloid with $| x |^2 \leq R$; or equivalently, how many lattice points are on such a hyperboloid and contained within a ball of radius $\sqrt R$ centered at the origin?

I describe my general approach of transforming this into a question about the behavior of modular forms, and then using spectral techniques from the theory of modular forms to understand this behavior. This becomes a question of understanding the shifted convolution Dirichlet series
$$ \sum_{n \geq 0} \frac{r_{d-1}(n+h)r_1(n)}{(2n + h)^s}.$$
Ultimately this comes from the modular form $\theta^{d-1}(z) \overline{\theta(z)}$, where
$$ \theta(z) = \sum_{m \in \mathbb{Z}} e^{2 \pi i m^2 z}.$$

Here are the slides for this talk. Note that this talk is based on chapter 5 of my thesis, and (hopefully) soon a preprint of this chapter ready for submission will appear on the arXiv.

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We begin bombing Korea in five minutes: Parallels to Reagan in 1984

On a day when President and Commander-in-Chief Donald Trump tweets belligerent messages aimed at North Korea, I ask: “Have we seen anything like this ever before?” In fact, we have. Let’s review a tale from Reagan.

August 11, 1984: President Reagan is preparing for his weekly NPR radio address. The opening line of his address was to be

My fellow Americans, I’m pleased to tell you that today I signed legislation that will allow student religious groups to begin enjoying a right they’ve too long been denied — the freedom to meet in public high schools during nonschool hours, just as other student groups are allowed to do.1

During the sound check, President Reagan joked

My fellow Americans, I’m pleased to tell you today that I’ve signed legislation that will outlaw Russia forever. We begin bombing in five minutes.

This was met with mild chuckles from the audio technicians, and it wasn’t broadcast intentionally. But it was leaked, and reached the Russians shortly thereafter.

They were not amused.

The Soviet army was placed on alert once they heard what Reagan joked during the sound check. They dropped their alert later, presumably when the bombing didn’t begin. Over the next week, this gaffe drew a lot of attention. Here is NBC Tom Brokaw addressing “the joke heard round the world”

The Pittsburgh Post-Gazette ran an article containing some of the Soviet responses five days later, on 16 August 1984.2 Similar articles ran in most major US newspapers that week, including the New York Times (which apparently retyped or OCR’d these statements, and these are now available on their site).

The major Russian papers Pravda and Izvestia, as well as the Soviet News Agency TASS, all decried the President’s remarks. Of particular note are two paragraphs from TASS. The first is reminiscent of many responses on Twitter today,

Tass is authorized to state that the Soviet Union deplores the U.S. President’s invective, unprecedentedly hostile toward the U.S.S.R. and dangerous to the cause of peace.

The second is a bit chilling, especially with modern context,

This conduct is incompatible with the high responsibility borne by leaders of states, particularly nuclear powers, for the destinies of their own peoples and for the destinies of mankind.

In 1984, an accidental microphone gaffe on behalf of the President led to public outcry both foreign and domestic; Soviet news outlets jumped on the opportunity to include additional propaganda3. It is easy to confuse some of Donald Trump’s deliberate actions today with others’ mistakes. I hope that he knows what he is doing.

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Advent of Code: Day 4

This is a very short post in my collection working through this year’s Advent of Code challenges. Unlike the previous ones, this has no mathematical comments, as it was a very short exercise. This notebook is available in its original format on my github.

Day 4: High Entropy Passphrases

Given a list of strings, determine how many strings have no duplicate words.

This is a classic problem, and it’s particularly easy to solve this in python. Some might use collections.Counter, but I think it’s more straightforward to use sets.

The key idea is that the set of words in a sentence will not include duplicates. So if taking the set of a sentence reduces its length, then there was a duplicate word.

In [1]:
with open("input.txt", "r") as f:
    lines = f.readlines()
def count_lines_with_unique_words(lines):
    num_pass = 0
    for line in lines:
        s = line.split()
        if len(s) == len(set(s)):
            num_pass += 1
    return num_pass


I think this is the first day where I would have had a shot at the leaderboard if I’d been gunning for it.

Part 2

Let’s add in another constraint. Determine how many strings have no duplicate words, even after anagramming. Thus the string

abc bac

is not valid, since the second word is an anagram of the first. There are many ways to tackle this as well, but I will handle anagrams by sorting the letters in each word first, and then running the bit from part 1 to identify repeated words.

In [2]:
with open("input.txt", "r") as f:
    lines = f.readlines()
sorted_lines = []
for line in lines:
    sorted_line = ' '.join([''.join(l) for l in map(sorted, line.split())])

['bddjjow acimrv bcjjm anr flmmos fiosv',
 'bcmnoxy dfinyzz dgmp dfgioy hinrrv eeklpuu adgpw kqv']
In [3]:
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Advent of Code: Day 3

This is the third notebook in my posts on the Advent of Code challenges. The notebook in its original format can be found on my github.

Day 3: Spiral Memory

Numbers are arranged in a spiral

17  16  15  14  13
18   5   4   3  12
19   6   1   2  11
20   7   8   9  10
21  22  23---> ...

Given an integer n, what is its Manhattan Distance from the center (1) of the spiral? For instance, the distance of 3 is $2 = 1 + 1$, since it’s one space to the right and one space up from the center.

Here’s my idea. The bottom right corner of the $k$th layer is the integer $(2k+1)^2$, since that’s how many integers are contained within that square. The other three corners in that layer are $(2k+1)^2 – 2k, (2k+1)^2 – 4k$, and $(2k+1)^2 – 6k$. Finally, the closest spot on the $k$th layer to the origin is at distance $k$: these are the four “axis locations” halfway between the corners, at $(2k+1)^2 – k, (2k+1)^2 – 3k, (2k+1)^2 – 5k$, and $(2k+1)^2 – 7k$.

For instance when $k = 1$, the bottom right is $(2 + 1)^2 = 9$, and the four “axis locations” are $9 – 1, 9 – 3, 9-5$, and $9-7$. The “axis locations” are $k$ away, and the corners are $2k$ away.

So I will first find which layer the number is on. Then I’ll figure out which side it’s on, and then how far away it is from the nearest “axis location” or “corner”.

My given number happens to be 289326.

In [1]:
import math

def find_lowest_larger_odd_square(n):
    upper = math.ceil(n**.5)
    if upper %2 == 0:
        upper += 1
    return upper
In [2]:
assert find_lowest_larger_odd_square(39) == 7
assert find_lowest_larger_odd_square(26) == 7
assert find_lowest_larger_odd_square(25) == 5
In [3]:
In [4]:
539**2 - 289326

It happens to be that our integer is very close to an odd square.
The square is $539^2$, and the distance to that square is $538$ from the center.

Note that $539 = 2(269) + 1$, so this is the $269$th layer of the square.
The previous corner to $539^2$ is $539^2 – 538$, and the previous corner to that is $539^2 – 2\cdot538 = 539^2 – 1076$.
This is the nearest corner.
How far away from the square is this corner?

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Advent of Code: Day 2

This is the second notebook in my posts on the Advent of Code challenges. This notebook in its original format can be found on my github.

Day 2: Corruption Checksum, part I

You are given a table of integers. Find the difference between the maximum and minimum of each row, and add these differences together.

There is not a lot to say about this challenge. The plan is to read the file linewise, compute the difference on each line, and sum them up.

In [1]:
with open("input.txt", "r") as f:
    lines = f.readlines()
In [2]:
l = lines[0]
l = l.split()
In [3]:
def max_minus_min(line):
    '''Compute the difference between the largest and smallest integer in a line'''
    line = list(map(int, line.split()))
    return max(line) - min(line)

def sum_differences(lines):
    '''Sum the value of `max_minus_min` for each line in `lines`'''
    return sum(max_minus_min(line) for line in lines)
In [4]:
testcase = ['5 1 9 5','7 5 3', '2 4 6 8']
assert sum_differences(testcase) == 18
In [5]:

Mathematical Interlude

In line with the first day’s challenge, I’m inclined to ask what we should “expect.” But what we should expect is not well-defined in this case. Let us rephrase the problem in a randomized sense.

Suppose we are given a table, $n$ lines long, where each line consists of $m$ elements, that are each uniformly randomly chosen integers from $1$ to $10$. We might ask what is the expected value of this operation, of summing the differences between the maxima and minima of each row, on this table. What should we expect?

As each line is independent of the others, we are really asking what is the expected value across a single row. So given $m$ integers uniformly randomly chosen from $1$ to $10$, what is the expected value of the maximum, and what is the expected value of the minimum?


Expected Minimum

Let’s begin with the minimum. The minimum is $1$ unless all the integers are greater than $2$. This has probability
$$ 1 – \left( \frac{9}{10} \right)^m = \frac{10^m – 9^m}{10^m}$$
of occurring. We rewrite it as the version on the right for reasons that will soon be clear.
The minimum is $2$ if all the integers are at least $2$ (which can occur in $9$ different ways for each integer), but not all the integers are at least $3$ (each integer has $8$ different ways of being at least $3$). Thus this has probability
$$ \frac{9^m – 8^m}{10^m}.$$
Continuing to do one more for posterity, the minimum is $3$ if all the integers are at least $3$ (each integer has $8$ different ways of being at least $3$), but not all integers are at least $4$ (each integer has $7$ different ways of being at least $4$). Thus this has probability

$$ \frac{8^m – 7^m}{10^m}.$$

And so on.

Recall that the expected value of a random variable is

$$ E[X] = \sum x_i P(X = x_i),$$

so the expected value of the minimum is

$$ \frac{1}{10^m} \big( 1(10^m – 9^m) + 2(9^m – 8^m) + 3(8^m – 7^m) + \cdots + 9(2^m – 1^m) + 10(1^m – 0^m)\big).$$

This simplifies nicely to

$$ \sum_ {k = 1}^{10} \frac{k^m}{10^m}. $$

Expected Maximum

The same style of thinking shows that the expected value of the maximum is

$$ \frac{1}{10^m} \big( 10(10^m – 9^m) + 9(9^m – 8^m) + 8(8^m – 7^m) + \cdots + 2(2^m – 1^m) + 1(1^m – 0^m)\big).$$

This simplifies to

$$ \frac{1}{10^m} \big( 10 \cdot 10^m – 9^m – 8^m – \cdots – 2^m – 1^m \big) = 10 – \sum_ {k = 1}^{9} \frac{k^m}{10^m}.$$

Expected Difference

Subtracting, we find that the expected difference is

$$ 9 – 2\sum_ {k=1}^{9} \frac{k^m}{10^m}. $$

From this we can compute this for each list-length $m$. It is good to note that as $m \to \infty$, the expected value is $9$. Does this make sense? Yes, as when there are lots of values we should expect one to be a $10$ and one to be a $1$. It’s also pretty straightforward to see how to extend this to values of integers from $1$ to $N$.

Looking at the data, it does not appear that the integers were randomly chosen. Instead, there are very many relatively small integers and some relatively large integers. So we shouldn’t expect this toy analysis to accurately model this problem — the distribution is definitely not uniform random.
But we can try it out anyway.

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Advent of Code: Day 1

I thoroughly enjoyed reading through Peter Norvig’s extraordinarily clean and nice solutions to the Advent of Code challenge last year. Inspired by his clean, literate programming style and the convenience of jupyter notebook demonstrations, I will look at several of these challenges in my own jupyter notebooks.

My background and intentions aren’t the same as Peter Norvig’s: his expertise dwarfs mine. And timezones are not kind to those of us in the UK, and thus I won’t be competing for a position on the leaderboards. These are to be fun. And sometimes there are tidbits of math that want to come out of the challenges.

Enough of that. Let’s dive into the first day.

Day 1: Inverse Captcha, Part 1

Given a sequence of digits, find the sum of those digits which match the following digit. The sequence is presumed circular, so the first digit may match the last digit.

This would probably be done the fastest by looping through the sequence.

In [1]:
with open('input.txt', 'r') as f:
    seq =
seq = seq.strip()
In [2]:
def sum_matched_digits(s):
    "Sum of digits which match following digit, and first digit if it matches last digit"
    total = 0
    for a,b in zip(s, s[1:]+s[0]):
        if a == b:
            total += int(a)
    return total

They provide a few test cases which we use to test our method against.

In [3]:
assert sum_matched_digits('1122') == 3
assert sum_matched_digits('1111') == 4
assert sum_matched_digits('1234') == 0
assert sum_matched_digits('91212129') == 9

For fun, this is a oneline version.

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A Jupyter Notebook from a SageMath tutorial

I gave an introduction to sage tutorial at the University of Warwick Computational Group seminar today, 2 November 2017. Below is a conversion of the sage/jupyter notebook I based the rest of the tutorial on. I said many things which are not included in the notebook, and during the seminar we added a few additional examples and took extra consideration to a few different calls. But for reference, the notebook is here.

The notebook itself (as a jupyter notebook) can be found and viewed on my github (link to jupyter notebook). When written, this notebook used a Sage 8.0.0.rc1 backend kernel and ran fine on the standard Sage 8.0 release , though I expect it to work fine with any recent official version of sage. The last cell requires an active notebook to be seen (or some way to export jupyter widgets to standalone javascript or something; this either doesn’t yet exist, or I am not aware of it).

I will also note that I converted the notebook for display on this website using jupyter’s nbconvert package. I have some CSS and syntax coloring set up that affects the display.

Good luck learning sage, and happy hacking.


Sage (also known as SageMath) is a general purpose computer algebra system written on top of the python language. In Mathematica, Magma, and Maple, one writes code in the mathematica-language, the magma-language, or the maple-language. Sage is python.

But no python background is necessary for the rest of today’s guided tutorial. The purpose of today’s tutorial is to give an indication about how one really uses sage, and what might be available to you if you want to try it out.

I will spoil the surprise by telling you upfront the two main points I hope you’ll take away from this tutorial.

  1. With tab-completion and documentation, you can do many things in sage without ever having done them before.
  2. The ecosystem of libraries and functionality available in sage is tremendous, and (usually) pretty easy to use.

Lightning Preview

Let’s first get a small feel for sage by seeing some standard operations and what typical use looks like through a series of trivial, mostly unconnected examples.

In [1]:
# Fundamental manipulations work as you hope


You can also subtract, multiply, divide, exponentiate…

>>> 3-2
>>> 2*3
>>> 2^3
>>> 2**3 # (also exponentiation)

There is an order of operations, but these things work pretty much as you want them to work. You might try out several different operations.

Sage includes a lot of functionality, too. For instance,

In [2]:
-1 * 2^4 * 3^2 * 7
In [3]:
[(2, 4), (3, 2), (7, 1)]

In the background, Sage is actually calling on pari/GP to do this factorization. Sage bundles lots of free and open source math software within it (which is why it’s so large), and provides a common access point. The great thing here is that you can often use sage without needing to know much pari/GP (or other software).

Sage knows many functions and constants, and these are accessible.

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Having no internet for four half weeks isn’t necessarily all bad

I moved to the UK to begin a postdoc with John Cremona at the University of Warwick. And for the last four weeks, I have had no internet at my home. This wasn’t by choice — it’s due to the reluctance of my local gigantitelecom to press a button that says “begin internet service.” I could write more about that, but that’s not the purpose of this note.

The purpose of this note is to describe the large effects of having no internet at my home for the last four weeks. I’m at my home about half the time, leading to the title.

I don't talk about it, but I do know *exactly* where I can get other's wifi in my place. Sadly, no linksys.


I have become accustomed to having the internet at all times. I now see that many various habits of mine involved the internet. In the mornings and evenings, I would check HackerNews, longform, and reddit for interesting reads. Invariably there are more interesting seeming things than I would read, and my Checkout bookmarks list is a hundreds-of-items long growing list of maybe interesting stuff. In the middle times throughout the day, I would checkout a few of these bookmarks.

All in all, I would spend an enormous amount of time reading random interesting tidbits, even though much of this time was spread out in the “in-betweens” in my day.


I still remember modem sounds. This is a defining aspect of my generation. My postdoc advisor was telling me about the difference between 100 and 300 baud teletypes. Things change, you know?


When I didn’t have internet at my home, I had to fill all those “in-between” moments, as well as my waking and sleeping moments, with something else. Faced with the necessity of doing something, I filled most of these moments with reading books. Made out of paper. (The same sort of books whose sales are rising compared to ebooks, contrary to most predictions a few years ago).

I’d forgotten how much I enjoyed reading a book in large chunks, in very few sittings. I usually have an ebook on my phone that I read during commutes, and perhaps most of my idle reading over the last several years has been in 20 page increments. The key phrase here is “idle reading”. I now set aside time to “actively read”, in perhaps 100 page increments. Reading enables a “flow state” very similar to the sensation I get when mathing continuously, or programming continuously, for a long period of time. I not only read more, but I enjoy what I’m reading more.

As a youth, I would read all the time. Fun fact: at one time, I’d read almost every book in the Star Wars expanded universe. There were over a hundred, and they were all canon (before Disney paved over the universe to make room). I learned to love reading by reading science fiction, and the first novel I remember reading was a copy of Andre Norton’s “The Beastmaster” (… which is great. A part telepath part Navajo soldier moves to another planet. Then it’s a space western. What’s not to love?).

I have also been known to open hackernews, think there's nothing interesting on the front page, close the tab, and then go immediately to hackernews to see if there's something interesting. There isn't. This reminds me of opening the fridge, hoping for tastier food to have appeared since I last didn't put anything in there.


My primary source of books is the library at the University of Warwick. Whether through differences in continental taste or simply a case of different focus, the University Library doesn’t have many books in its fiction collection that I’ve been intending to read. I realize now that most of the nonfiction I read originates on the internet, while much of the fiction I read comes from books. Now, encouraged by a lack of alternatives, I picked up many more and varied nonfiction books than I would otherwise have.

As an unexpected side effect, I found that I would also carefully download some of the articles I identified as “interesting” a bit before I headed home from the office. Without internet, I read far more of my checkout bookmarks than I did with internet. Weird. Correspondingly, I found that I would spend a bit more time cutting down the false-positive rate — I used to bookmark almost anything that I thought might be interesting, but which I wasn’t going to read right then. Now I culled the wheat from the chaff, as harvesting wheat takes time. (Perhaps this is something I should do more often. I recognize that there are services or newsletters that promise to identify great materials, but somehow none of them have worked better to my tastes than hackernews or longform. But these both have questionable signal to noise.).

The result is that I’ve goofed off reading probably about the same amount of time, but in fewer topics and at greater depth in each. It’s easy to jump from 10 page article to 10 page article online; when the medium is books, things come in larger chunks.

I feel more productive reading a book, even though I don’t actually attribute much to the difference. There may be something to the act of reading contiguously and continuously for long periods of time, though. This correlated with an overall increase my “chunking” of tasks across continuous blocks of time, instead of loosely multitasking. I think this is for the better.

I now have internet at my flat. Some habits will slide back, but there are other new habits that I will keep. I’ll keep my bedroom computer-free. In the evening, this means I read books before I sleep. In the morning, this means I must leave and go to the other room before I waste any time on online whatevers. Both of these are good. And I’ll try to continue to chunk time.

To end, I’ll note what I read in the last month, along with a few notes about each.


From best to worse.

  • The best fiction I read was The Three Body Problem, by Cixin Liu. I’d heard lots about this book. It’s Chinese scifi, and much of the story takes place against the backdrop of the Chinese cultural revolution… which I know embarassingly little about. The moral and philosophical underpinnings of this book are interesting and atypical (to me). At its core are various groups of people who have lost faith in aspects of science, or humanity, or both. I was unprepared for the many (hundreds?) of pages of philosophizing in the book, but I understood why it was there. This aspect reminded me of the last half of Anathem by Stephenson (perhaps the best book I’ve read in the last few years), which also had many (also hundreds?) of pages of philosophizing. I love this book, I recommend it. And I note that I read it in four sittings. There are two more books completing a trilogy, and I will read them once I can get my hands on them. [No library within 50 miles of me has them. I did buy the first one, though. Perhaps I’ll buy the other two.]
  • The second best was The Lathe of Heaven by Ursula Le Guin. This is some classic fantasy, and is pretty mindbending. I think the feel of many books of Ursula Le Guin is very similar — there are many interesting ideas throughout the book, but the book deliberately loses coherence as the flow and fury of the plot reaches a climax. I like The Lathe of Heaven more than The Wizard of Earthsea and about the same as The Left Hand of Darkness, also by Le Guin. I read this book in three sittings.
  • I read three of the Witcher books, by Andzej Sapkowski. Namely, The Sword of Destiny, Blood of Elves, and Time of Concempt. These are fun, not particularly deep reads. There is a taste of moral ambiguity that I like as it’s different from what I normally find. On the other hand, Sapkowski often uses humor or ambiguity in place of a meaningful, coherent plot. The Sword of Destiny is a collection of short tales, and I think his short tales are better than his novels — entirely because one doesn’t need or expect coherence from short stories.

I’m currently reading Confusion by Neal Stephenson, book two of the Baroque trilogy. Right now, I am exactly 1 page in.


I rank these from those I most enjoyed to those I least enjoyed.

  • How Equal Temperament Ruined Harmony, by Duffin. This was told to me as an introduction to music theory [in fact, I noted this from a comment thread on hackernews somewhere], but really it is a treatise on the history of tuning and temparaments. It turns out that modern equal termperament suffers from many flaws that aren’t commonly taught. When I got back to the office after reading this book, I spent a good amount of time on youtube listening to songs in mean tone tuning and just intonation. There is a difference! I read this book in 2 sittings — it’s short, pretty simple, and generally nice. However there are several long passages that are simply better to skip. Nonetheless I learned a lot.
  • A Random Walk down Wall Street, by Burton Malkiel. I didn’t know too much about investing before reading this book. I wouldn’t actually say that I know too much after reading it either, but the book is about investing. I was warned that reading this book would make me think that the only way to really invest is to purchase index funds. And indeed, that is the overwhelming (and explicit) takeawar from the book. But I found the book surprisingly readable, and read it very quickly. I find that some of the analysis is biased towards long-term investing even as a basis of comparison.
  • Guesstimation, by Weinstein. Ok, perhaps it is not fair to say that one “reads” this book. It consists of many Fermi-style questions (how many golf balls does it take to fill up a football stadium type questions), followed by their analysis. So I read a question and then sit down and do my own analysis. And then I compare it against Weinstein’s. I was stunned at how often the analyses were tremendously similar and got essentially the same order of magnitude at the end. [But not always, that’s for sure. There are also lots of things that I estimate very, very poorly]. There’s a small subgenre of “popular mathematics for the reader who is willing to take out a pencil and paper” (which can’t have a big readership, but which I thoroughly enjoy), and this is a good book within that subgenre. I’m currently working through its sequel.
  • Natures Numbers, by Ian Stewart. This is a pop math book. Ian Stewart is an emeritus professor at my university, so it seemed appropriate to read something of his. This is a surprisingly fast read (I read it in a single sitting). Stewart is known for writing approachable popular math accounts, and this fits.
  • The Structure of Scientific Revolutions, by Thomas Kuhn. This is metascience. I read the first half of this book/essay very quickly, and I struggled through its second half. This came highly recommended to me, but I found the signal to noise ratio to be pretty low. It might be that I wasn’t very willing to navigate the careful treading around equivocation throughout. However, I think many of the ideas are good. I don’t know if someone has written a 30 page summary, but I think this may be possible — and a good alternative to the book/essay itself.

I’m now reading Grit, by Angela Duckworth. Another side effect of reading more is that I find myself reading one fiction, one non-fiction, and one “simple” book at the same time.

Written while on a bus without internet to Heathrow, minus the pictures (which were added at Heathrow).

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