Michelangelo D’Agostino: Physics and presidential politics


  • harvard undergrad, berkeley PhD, worked on Icecube (neutrino detection)


  • neural networks and support vector machines to score liklihood of particular outcomes (neutrinos or voter stats)
  • Data collection — mass profile data from Facebook / Twitter.

Campaign organization

Tech department

  • voter calling tool from web interface
  • dashboard – online field office to match up activists
  • database maintenance

digital team

  • email fundraising
  • social media pages for the president
  • digital advertising


  • cave “learned from physics how to be in a closed windowless room with 40 people”
  • modeling / polling / experiments

Building models

  • using historical data make models for predictions.
  • how likely is someone to donate / change vote in response to door knock / volunteer?
  • use time and money to target persuadable people and infrequent voters.
  • all individuals in battle ground states get support, turnout, persuasion, and getout-the-vote.


  • states provide pubically for all registered voters how much they vote, age, etc (not who they voted for)
  • political parties also collect similar data.
  • Match this data to 5-10K people you’ve called on the phone. Use to train models.
  • have statistical models of liklihood of effect, rather than gut feeling and attack ads.
  • cable TV collects data on what channels people are watching. correlate with data files. Intermediary controls so that no individual has all the data on a given individual (what person X watches and how X votes).
  • can advertize to persuadable voters on cheap niche channels rather than big public channels — make money go further.

Fund raising

  • 504 million of over 1 billion raised came from digital: data driven analysis of email campaign.
  • randomized control experiments. Starts as a dozen different version sent out to small random control group. Analyze returns (donations/clicks/unsubscription). Pick winning email send to list.
  • difference in returns millions of dollars per email, 4-10 fold difference in return rate.
  • How much to ask for? different emails for new vs. repeat contacts?
  • people respond better when you spam them then if you show restraint.
  • optimize form appearance: fast loading helps. ask people to enter 1 thing at a time rather than a whole form.

Monte Carlo simulations

  • How big should a test group be? (optimization of selecting the best and not burning through too much of the list)

social media

  • 50% of 18-29 yr olds are unreachable on phone: no number or refuse to pick up. Only getting worse.
  • 85% of them are on Facebook.
  • 24 million Twitter followers
  • Targeted sharing on facebook. Authorize campaign’s app: gives access to email/name/etc + photos + friends birthdays, likes and locations.
  • write to you to reach out to your friends in battleground states with a personal message to ask them to vote, (in particular if model predicts they are unlikely to get out to vote).
  • high response rate on people sending messages (10% clickback). 1 million users authorized app, 100 million people get access.
  • Twitter
  • sent targeted direct messages from the president’s account / first lady’s account, asking people to contact their followers.
  • people really liked getting messages directly from VP/pres etc.
    • use peoples tweet words + SVM to ID if individual is conservative or progressive.
    • conservatives tweeted more about Obama than progressives. Both tweeted about Romney at similar rates.
    • don’t use known info about contacts to assign political connections.

Data Science


  • predictions – well predicted but well sealed, don’t let predictions alter response.
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