Data from Canadian 700MHz and 2500MHz spectrum auctions

The 700MHz (2014) and 2500MHz (2015) spectrum auctions generated revenues of 5,270,636,002 CAD from 302 licenses and 755,371,001 CAD from 97 licenses. Both auctions used a combinatorial clock auction (CCA) format involving an ascending clock phase followed by a sealed-bid supplementary stage where bids could be made on packages of products. Final prices were determined using Vickrey pricing with a core-adjustment. An activity rule was used which required bidders to make bids or lose eligibility to bid in later clock rounds, along with a revealed preference rule which allows the eligibility limit to be exceeded as long as consistency checks are satisfied. For full details on the auction formats see the official documentation (700MHz rules, 700MHz additional details, 2500MHz rules); and the record of bids placed is here for 700MHz and here for 2500MHz.

Bid consistency

The revealed preference rule prevents some inconsistent behavior but not all. By “truthful”, we mean bids that are true indications of subjective value, and by “consistent” we mean bids that are indications of some fixed set of valuations, possibly not the bidder’s actual valuations.

The following table gives the values of Afriat’s critical cost efficiency index (CCEI) for the 700MHz auction. Recall that for a CCEI value \(x\), if \(x < 1\) there is at least some intransitivity in preferences (i.e. inconsistent bidding) and \(1-x\) can be interpreted as the fraction of expenditure wasted making inefficient choices (see this by S. Kariv for more).

Kroemer et al. conclude for the 700MHz auction, “the numbers suggest that bidders deviated substantially from straightforward bidding” in the clock rounds. But “it is not unreasonable to believe that bidders tried to bid up to their true valuation in the supplementary stage” because of higher bid amounts compared to the clock rounds.

The next table gives CCEI values for the 2500MHz auction. We extend the definition of CCEI to apply to supplementary bids as in Kroemer’s paper.

Kroemer et al. (Sec. 5.2) also point out that the total number of bids submitted in the 700MHz auction was much smaller than the number of possible bids, which probably indicates untruthful bidding since an omitted package must have valuation less than or equal to its (low) opening price. The same observation holds for the 2500MHz auction. More exactly, the auction formats enforced a limit on the number of packages bidders were allowed to submit, which was in the hundreds, and bidders generally did not reach the limit.

Ideally, we would determine whether the bids made are consistent with a non-truthful strategy incorporating gaming and/or coordination. The papers Janssen and Karamychev - “Raising Rivals’ Cost in Multi-unit Auctions” and Janssen and Kasberger - “On the Clock of the Combinatorial Auction” derive Bayesian Nash equilibria under gaming preferences and conclude that GARP is not violated in equilibrium gaming strategies. We note that the assumptions in these game models do not include all features of the CCAs under consideration e.g. discrete products, many bidders, public aggregate excess demand, revealed preference rule, initial EP limit, supplementary package limit, 50% mid-auction deposits.

Bids, budgets, and final prices

Bidders may have a notion of a budget – the maximum they are willing to spend. But how should this correspond to the maximum they should bid? In general, bidders may end up paying the exact amount of their highest bid, but looking at the data we see bid prices and final prices can be very different in practice. The following tables show figures from both auctions that illustrate this difference. All prices are given in CAD.

700MHz auction: highest bid placed. Average ratio: 0.192. Max ratio: 0.766.

700MHz auction: (highest) bid placed on package eventually won. Average ratio: 0.447. Max ratio: 0.766.

2500MHz auction: highest bid placed. Average ratio: 0.135. Max ratio: 0.277.

2500MHz auction: (highest) bid placed on package eventually won. Average ratio: 0.235. Max ratio: 0.410.

Across both auctions, we see that bidders paid an average of 16% of their maximum bid placed, where each bidder is equally weighted.

Misc. notes

Researchers design approximation algorithms for winner and price determination in auctions (in e.g. this paper) because exact optimization can be intractable as the number of bids grows, at least in the worst case. However, in these recent auction instances, theoretical intractability did not present a problem because the solution was computable in a small amount of time. The 2500MHz allocation stage involved 2,239 bids and a GLPK-powered solver finds the winners and final prices in a couple minutes on a standard computer. Simulations involving well over 30,000 random bids still take a feasible amount of time.

In the 2500MHz auction, there were 4 pairs of package submissions where the lower-priced package had strictly higher quantities of products. In this case the lower-priced package is superfluous. This table shows the packages, submitted by Bell.

Acknowledgement: Thanks to Z. Gao for pointers.

Link of the day: Journal of Craptology

http://www.anagram.com/jcrap/

The prestigious Journal of Craptology is an open-access publication in the area of cryptology.

U of Toronto study space info

(a partial list)

(Above: U of T cannons pointed at Ryerson)

Libraries

Robarts Library

Richard Charles Lee Canada-Hong Kong Library

Gerstein Science Information Centre

Sanford Fleming Library

Mathematics Library

EJ Pratt Library

Architecture Library (Eberhard Zeidler Library)

Kelly Library

The Inforum (Faculty of Information)

East Asian Library

Centre for Reformation and Renaissance Studies

Local Toronto public library branches

Toronto Reference Library

Lillian H. Smith public library

Other areas with seating

Tables in Bahen Centre

Markov of Chain: Automating Weird Sun tweets

Let’s use python to train a Markov chain generator using all the tweets from a certain list of users, say this one. We’ll use the following libraries.

from functional import seq
import markovify
import re
import tweepy
import unidecode

To use the Twitter API, we need to authenticate ourselves. Register for your personal keys at https://apps.twitter.com/ and then create a config.json file that looks like this

{
  "consumer_key":    "...",
  "consumer_secret": "...",
  "access_key":      "...",
  "access_secret":   "..."
}

Now we can initialize the Twitter API provided by tweepy.

config = seq.json('config.json').dict()
auth = tweepy.OAuthHandler(
    config['consumer_key'], config['consumer_secret'])
auth.set_access_token(config['access_key'], config['access_secret'])
api = tweepy.API(auth)

First we write the following function (based on this gist) which returns the most recent tweets of a given user. The API limits us to at most 3240 tweets per user.

def get_user_tweets(screen_name):
    alltweets = []

    #  200 is the maximum allowed count
    # 'extended' means return full unabridged tweet contents
    new_tweets = api.user_timeline(screen_name=screen_name, count=200,
                                  tweet_mode='extended')

    alltweets.extend(new_tweets)

    # save the id of the oldest tweet less one
    oldest_id = alltweets[-1].id - 1

    # keep grabbing tweets until there are no tweets left to grab
    while len(new_tweets) > 0:
        # since we're grabbing 200 at a time, we use `max_id` to
        #   ask for a certain range of tweets
        new_tweets = api.user_timeline(
                screen_name = screen_name, count=200,
                tweet_mode='extended', max_id=oldest_id)

        alltweets.extend(new_tweets)

        #update the id of the oldest tweet less one
        oldest_id = alltweets[-1].id - 1

        print("...{} tweets downloaded so far".format(len(alltweets)))

    # put each tweet on a single line
    tweet_texts = [re.sub(r'\s*\n+\s*', ' ', tweet.full_text)
                   for tweet in alltweets]

    return tweet_texts

The other interaction with Twitter we need to perform is get all users in a list. We’ll write a function that fetches the usernames and calls get_user_tweets on each:

def get_list_tweets(screen_name, list_name):
    '''
    params: `screen_name` is the username of the owner of the list,
    `list_name` is the name of the list found in the URL
    '''

    # get list of all users in list
    user_names = []
    for user in tweepy.Cursor(
            api.list_members,
            screen_name,
            list_name).items():
        user_names.append(user.screen_name)

    # for each user, get their tweets
    list_tweets = []
    for user_name in user_names:
        list_tweets += get_user_tweets(user_name)
    print('Found {1} tweets from @{2}.'
        .format(len(list_tweets), user_name))
    return list_tweets

Let’s run get_list_tweets and save the output to a file.

tweets = get_list_tweets('Grognor', 'weird-sun-twitter')

with open('data/tweetdump.txt', 'w') as f:
    f.write('\n'.join(tweets))

With all of the raw data saved, we’re done with the Twitter API and we can process the data and auto-generate tweets offline. Assuming the file tweetdump.txt has a set of tweets, one per line, we load them as a list of strings tweets.

tweets = open('data/tweetdump.txt').readlines()

Some processing needs to be done in order to get high quality text from the tweets. The next function process_tweet is called on each one.

def process_tweet(tweet):
    # convert to ASCII
    tweet = unidecode.unidecode(tweet)
    # remove URLs
    tweet = re.sub(r'http\S+', '', tweet)
    # remove mentions
    tweet = re.sub(r'@\S+', '', tweet)

    tweet = tweet.strip()

    # append terminal punctuation if absent
    if len(tweet) > 0:
        last_char = tweet[-1]
        if last_char not in '.!?':
            tweet += '.'

    return tweet

processed_tweets = [ process_tweet(tweet) for tweet in tweets ]

And we remove any tweets that aren’t useful.

def is_excluded(tweet):
    ex = False
    # no RTs
    ex = ex or bool(re.match(r'^RT', tweet))
    # remove whitespace-only tweets
    ex = ex or bool(re.match(r'^\s*$', tweet))
    return ex

good_tweets = [ tweet for tweet in processed_tweets
               if not is_excluded(tweet) ]

We save the fully processed tweets for easy access later.

with open('data/processed_tweets.txt', 'w') as f:
    f.write('\n'.join(good_tweets))

The markovify library lets us train, and generate from, a Markov chain very easily. Just load the training text and set a state size.

text = open('data/processed_tweets.txt').read()

text_model = markovify.Text(text, state_size=3)

for x in range(5):
    print('* ' + text_model.make_short_sentence(140))

Some favorites:

• “It is no coincidence we call them gods because we suppose they are trying to convince Robin Hanson.”
• “Tell anyone who does not produce Scott Alexander.”
• “Weird sun is a costly signal of the ability to remember sources of information, not just the study of complex manifolds.”
• “If you read The Hobbit backwards, it’s about a layer of radioactive ash that develops the capacity to become larger.”
• “When you read a physical book, you get a dust speck in the eye.”
• “We all continuously scream about how the people in it are breaking the awkward silence.”
• “People are important, but so are lexicographic preferences.”
• “You don’t need an expert Bayesian Epistemologist to ensure it’s not a markov chain.”

Building a shell with JavaScript

ShellJS is a JS library that provides functions like cd() and ls() which you can use to write Node scripts instead of bash scripts. That’s great for scripts, but what about an interactive shell? Well, we could just run the Node repl and import ShellJS:

$ node
> require('shelljs/global');
{}
> pwd()
{ [String: '/tmp']
  stdout: '/tmp',
  stderr: null,
  code: 0,
  cat: [Function: bound ],
  exec: [Function: bound ],
  grep: [Function: bound ],
  head: [Function: bound ],
  sed: [Function: bound ],
  sort: [Function: bound ],
  tail: [Function: bound ],
  to: [Function: bound ],
  toEnd: [Function: bound ],
  uniq: [Function: bound ] }

Hmm, that’s a little verbose, and we might want to avoid manually importing ShellJS. We also might want more features than the Node repl offers, such as vi keybindings.

We can get vi keybindings with rlwrap, but then tab completion goes away. The solution is given in this SO answer. First we need to install an rlwrap filter that negotiates tab-completion with a Node repl. The filter file can be found at that link, where it’s called node_complete. Put node_complete in $RLWRAP_FILTERDIR, which should be the folder on your system containing the RlwrapFilter.pm Perl module. For me it’s /usr/share/rlwrap/filters.

Now rlwrap is ready to negotiate tab completion, but the Node repl isn’t. We’ll have to actually write our own Node repl, which is easy because the repl module gives us all the tools we need. We’ll create a file called, say, myrepl.js, the contents of which are also given in the SO answer, only 9 lines. This script starts a repl with a hook to negotiate tab completion with rlwrap. If myrepl.js is in ~/bin, now we can run

$ rlwrap -z node_complete -e '' -c ~/bin/myrepl.js

and have both JS tab completion and rlwrap features, such as vi keybindings if that’s what we’ve configured. Let’s create a file called mysh with the following contents:

#!/usr/bin/env bash
rlwrap -z node_complete -e '' -c ~/bin/myrepl.js

Assuming ~/bin is in our path variable, we can put mysh there and launch our shell anywhere by just running mysh. So far so good but we wanted to automatically import ShellJS. In myrepl.js, add the following:

var shell = require('shelljs');
Object.assign(myrepl.context, shell);

Those two lines add all the ShellJS functions to the JS global object inside the repl. We have:

$ mysh
> pwd()
{ [String: '/tmp']
  stdout: '/tmp',
  stderr: null,
  code: 0,
  cat: [Function: bound ],
  exec: [Function: bound ],
  grep: [Function: bound ],
  head: [Function: bound ],
  sed: [Function: bound ],
  sort: [Function: bound ],
  tail: [Function: bound ],
  to: [Function: bound ],
  toEnd: [Function: bound ],
  uniq: [Function: bound ] }

Progress. Now, how do we clean up this output? The repl module allows us to define a custom writer. This is a function which takes the output of a line of JS and returns a string to represent the output in the repl. What we need to do is intercept objects like the one returned by pwd() above and only show the stderr and stdout properties. Add the following near the beginning of myrepl.js:

var util = require('util');

var myWriter = function(output) {
  var isSS = (
      output &&
      output.hasOwnProperty('stdout') &&
      output.hasOwnProperty('stderr'));
  if (isSS) {
    var stderrPart = output.stderr || '';
    var stdoutPart = output.stdout || '';
    return stderrPart + stdoutPart;
  } else {
    return util.inspect(output, null, null, true);
  }
};

And load this writer by changing

var myrepl = require("repl").start({terminal:false});

to

var myrepl = require("repl").start({
  terminal: false,
  writer: myWriter});

Now we get

$ mysh
> pwd()
/tmp

Much better. However, since the echo function prints its argument to the console and returns an object with it in the stdout property, we get this:

$ mysh
> echo('hi')
hi
hi

I haven’t solved this issue quite yet although I’d be surprised if there isn’t a reasonable solution out there. You can add to mysh and myrepl.js to get more features, such as colors, custom evaluation, custom pretty printing, other pre-loaded libraries, et cetera. The sky is the limit. I added an inspect function which allows us to see the full ShellJS output of a command if we really want it. My complete myrepl.js file is:

#!/usr/bin/env node

var util = require('util');
var colors = require('colors/safe');

var inspect = function(obj) {
  if (obj && typeof obj === 'object') {
    obj['__inspect'] = true;
  }
  return obj;
};

var myWriter = function(output) {
  var isSS = (
      output &&
      output.hasOwnProperty('stdout') &&
      output.hasOwnProperty('stderr') &&
      !output.hasOwnProperty('__inspect'));
  if (isSS) {
    var stderrPart = output.stderr || '';
    var stdoutPart = output.stdout || '';
    return colors.cyan(stderrPart + stdoutPart);
  } else {
    if (typeof output === 'object') {
      delete output['__inspect'];
    }
    return util.inspect(output, null, null, true);
  }
};

// terminal:false disables readline (just like env NODE_NO_READLINE=1):
var myrepl = require("repl").start({
  terminal: false,
  prompt: colors.green('% '),
  ignoreUndefined: true,
  useColors: true,
  writer: myWriter});

var shell = require('shelljs');
Object.assign(myrepl.context, shell);
myrepl.context['inspect'] = inspect;

// add REPL command rlwrap_complete(prefix) that prints a simple list
//   of completions of prefix
myrepl.context['rlwrap_complete'] =  function(prefix) {
  myrepl.complete(prefix, function(err,data) {
    for (x of data[0]) {console.log(x);}
  });
};

So this is basically what we wanted. We have a JS repl with convenient ShellJS commands. We also have vi keybindings, and tab completion for JS and filenames. It’s very rough around the edges, but it was really simple to make. GitHub user streamich built a more advanced form of this, called jssh which adds many features but lacks some too. The bottom line is, if you know JS, you might be surprised at what you can build.

Modeling aesthetics in mathematics

What exactly is beautiful math?

[A]bove all, adepts [of mathematics] find therein delights analogous to those given by painting and music. They admire the delicate harmony of numbers and forms; they marvel when a new discovery opens to them an unexpected perspective; and has not the joy they thus feel the esthetic character, even though the senses take no part therein? Only a privileged few are called to enjoy it fully, it is true, but is not this the case for all the noblest arts?

-Henri Poincaré, The Value of Science

One expects a mathematical theorem or a mathematical theory not only to describe and to classify in a simple and elegant way numerous and a priori disparate special cases. One also expects “elegance” in its “architectural”, structural makeup. Ease in stating the problem, great difficulty in getting hold of it and in all attempts at approaching it, then again some very surprising twist by which the approach, or some part of the approach, becomes easy, etc. Also, if the deductions are lengthy or complicated, there should be some simple general principle involved, which “explains” the complications and detours, reduces the apparent arbitrariness to a few simple guiding motivations, etc. These criteria are clearly those of any creative art.

-John von Neumann, The Mathematician

The moral: a good proof is one that makes us wiser.

-Yuri Manin, A Course in Mathematical Logic for Mathematicians

My hypothesis is that generally when people talk about beauty in mathematics they’re talking about things that teach us something useful for proving new facts. For example, proving a difficult but simple theorem is useful because its difficulty means it may imply other previously difficult theorems, and its simplicity means it may show up and be used often. A theorem that establishes a connection between two previously disparate areas of mathematics is considered beautiful, and such a connection allows knowledge from one are to be applied to the other, potentially cracking new problems. An unexpected proof – “an unexpected perspective” or “surprising twist” – offers something new to be learned, something that can then be used for other problems.