It is true that some/many Rolex AD’s will allocate the most desirable watches to customers with an existing purchase history, and that some customers therefore buy less desirable models in order to earn goodwill with the AD.

However, it is not the case that the most desirable watches are necessarily (or even on average) the most expensive models. For instance, it is generally the steel models that are the most desirable and command the highest markup from MSRP on the secondary market. The Submariner, the Daytona, the GMT-Master II: almost all of Rolex’s most iconic, most in-demand, most "flippable" watches are the full steel versions, which are the cheapest versions of those model families.

To give a concrete example, it is generally considered easier to get a full-gold GMT (~$43k) or a two-tone (half steel, half gold) GMT (~$18k) at an Authorized Dealer than it is to get the full steel version ($11k).

  # Download and extract zip file
  import requests
  import zipfile
  import io

  # Get raw data from Clark County website
  zip_url = "https://elections.clarkcountynv.gov/electionresultsTV/cvr/24G/24G_CVRExport_NOV_Final_Confidential.zip"

  # Download the zip file
  response = requests.get(zip_url)
  zip_file = zipfile.ZipFile(io.BytesIO(response.content))

  # Extract to the current working directory
  zip_file.extractall()

  # Close the zip file
  zip_file.close()

  import pandas as pd
  import matplotlib.pyplot as plt
  import numpy as np

  # Read the actual data, skipping the first three header rows and excluding downballot races
  df = pd.read_csv('/content/24G_CVRExport_NOV_Final_Confidential.csv', skiprows=3, usecols=range(21), low_memory=False)

  # Find the Trump and Harris columns
  trump_col = "REP"
  harris_col = "DEM"

  # Convert to numeric
  df[trump_col] = pd.to_numeric(df[trump_col], errors='coerce')
  df[harris_col] = pd.to_numeric(df[harris_col], errors='coerce')

  # Filter for early voting
  early_voting = df[df['CountingGroup'] == 'Early Voting']

  # Group by tabulator and calculate percentages
  tabulator_stats = early_voting.groupby('TabulatorNum').agg({
      harris_col: 'sum',
      trump_col: 'sum'
  }).reset_index()

  # Calculate total votes and percentages
  tabulator_stats['total_votes'] = tabulator_stats[harris_col] + tabulator_stats[trump_col]
  tabulator_stats['harris_pct'] = tabulator_stats[harris_col] / tabulator_stats['total_votes'] \* 100
  tabulator_stats['trump_pct'] = tabulator_stats[trump_col] / tabulator_stats['total_votes'] \* 100

  # Create subplots
  fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 8))

  # Plot Harris histogram
  ax1.hist(tabulator_stats['harris_pct'], bins=50, edgecolor='black', color='blue', alpha=0.7)
  ax1.set_title('Distribution of Harris Votes by Tabulator (Early Voting Only)')
  ax1.set_xlabel('Percentage of Votes for Harris')
  ax1.set_ylabel('Number of Tabulators')

  # Plot Trump histogram
  ax2.hist(tabulator_stats['trump_pct'], bins=50, edgecolor='black', color='red', alpha=0.7)
  ax2.set_title('Distribution of Trump Votes by Tabulator (Early Voting Only)')
  ax2.set_xlabel('Percentage of Votes for Trump')
  ax2.set_ylabel('Number of Tabulators')

  plt.tight_layout()
  plt.show()

...but you’re missing the point of my comment, which is simply to acknowledge and honor (my late dear friend) Peter.

These harms can be diffuse at massive scale, and acute at small scale.

One example of each: (1) https://www.science.org/doi/abs/10.1126/science.aax2342 One of USA’s largest health insurers builds ML system for patient triage. It optimizes for a proxy metric of health need (namely, cost) rather than health need itself; consequently it deprioritizes and systematically excludes millions of people from access to health care.

(2) https://en.wikipedia.org/wiki/Death_of_Elaine_Herzberg Autonomous Uber car builds their braking system on top of a vision model that optimizes for object classification accuracy using categories of {"pedestrian", "cyclist", "vehicle", "debris"}; consequently it fails to determine how to classify a woman walking a bicycle across the street, as a result killing her.

In both cases, optimizing for a naively sensible proxy metric of the thing that was truly desired turned out to be catastrophic.

https://www.nytimes.com/2021/06/04/opinion/ezra-klein-podcas...?

https://www.nytimes.com/2021/06/04/podcasts/transcript-ezra-...

Happy to take any questions, etc., if folks are interested! AMA.

Comments URL: https://news.ycombinator.com/item?id=27396473

Points: 9

# Comments: 5

Essentially you are "both right". > 50% of total spending is Social Security, Medicare and Medicaid. Whether that is "social services" is another semantic question.

And > 50% of the discretionary budget is defense.

But have a look for yourself: e.g., https://en.wikipedia.org/wiki/United_States_federal_budget

'For the last year, the National Association of Black Journalists (NABJ) has been integrating the capitalization of the word "Black" into its communications.

However, it is equally important that the word is capitalized in news coverage and reporting about Black people, Black communities, Black culture, Black institutions, etc.

NABJ's Board of Directors has adopted this approach, as well as many of our members, and recommends that it be used across the industry.

We are updating the organization's style guidance to reflect this determination. The organization believes it is important to capitalize "Black" when referring to (and out of respect for) the Black diaspora.

NABJ also recommends that whenever a color is used to appropriately describe race then it should be capitalized, including White and Brown.'

https://www.nabj.org/news/512370/NABJ-Statement-on-Capitaliz...

This appears to have been part of what prompted a large number of newspapers to change their style guides this past week, including USA Today, NBC News, MSNBC, the LA Times, the Seattle Times, the Boston Globe, the San Diego Union-Tribune, and the Washington Post.

Please look at the trendlines for "heroin deaths" and "synthetic opiod deaths" in the following graph of US CDC data, and note that from the turn of the millennium to 2017, opioid-related deaths in the US have increased more than 10x:

https://www.drugabuse.gov/related-topics/trends-statistics/o...

(Of course that's not the entire story on autonomous cars, for instance, because they will also get into fewer accidents, react to changing traffic conditions faster, can potentially drive more tightly at high speed, etc.)

Anyone interested in selfish routing should check out Braess's paradox, which is wonderfully unintuitive and strange:

https://en.wikipedia.org/wiki/Braess's_paradox