8/13/2019 Rayid Ghani - Using Data and Analytics Powers for Good
#Oct2013
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USING DATA AND ANALYTICS POWERS FOR GOOD
RAYID GHANI, TYPED BY TOM LAGATTA
Any errors, inconsistencies, and unclear rambles in the notes
are entirely the fault of the typist.
Affiliations: University of Chicago, Edgeflip
Objective function: maximize probability of winning 270 votes.
Emphasis: winner takes all.
2-2.5 million volunteers. (approximately .075% of U.S.
population)
Main data source: voter file, database of every registered voter
in country. More precisely, every state has
its own voter file. Obama campaign consolidated these voter
files into a single database.
Most people in database (e.g., emails) arenot
identified to an entry in the voter file.
Essential quantities:
Support: how likely is somebody to support our side?
Turnout: how likely is somebody to turn out and vote?
Persuasion: how likely is somebody to vote for each side?
Central theme: better than random. Use the data to make
estimates that are better than random, and
using these estimates, take actions to influence and affect the
outcome. Key point: justify the costs of those
actions.
Support model. You have some data on who supports whom (e.g.,
party registration). Augment these data
with polling. The central use of polling was to prime the priors
for the model.
Inputs to the model: Demographics, voting history, email
history, fundraising history, calling history.
Constraints to model. Accuracy: get good ranked list of
supportive people, in order to target actions. Need
probabilities to line up with frequencies: if I am a 40% Obama
supporter, then 40/100 of people like me
should be Obama supporters.
Number of features for each person: roughly hundreds. Total
database: 10-20 terabytes, very manageable.
Interesting: the Narwhal backend was for web apps, and had
little to do with data or analytics. Also:
investment for the future.
To be data-driven is to be rational: change actions based on
available data. Most organizations are not
rational in this sense: they still make decisions based on their
guts.1
Several channels of communication: direct mail, TV ads, knocking
on doors, 5 billion emails.
Persuasion scripts for volunteers: are you going to vote? wheres
your polling place? how are you going to
get there? when are you going?
Date: October 2013.1nb: this is a particular definition of
rationality, and not agreed upon by the whole community.
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8/13/2019 Rayid Ghani - Using Data and Analytics Powers for Good
#Oct2013
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USING DATA AND ANALYTICS POWERS FOR GOOD 2
Goal: identify that small number of people who are
persuadable.
DIfferent channels have different purposes: emails and online
ads are for fundraising; TV ads are for persua-
sion.
Primary variables for support: saying yes I support Obama,
evidence of past support, donations to cam-
paign.
Fundamentally: this is a ranking problem. Identify supporters by
degree.
In an ideal world, we do something more game theoretic with
regards to persuasion. This is not that world
(yet).
There is always a tension between people who are comfortable
with data and people who are not. The way
to settle this tension is by experiments.
Surprising: Facebook Pages do not have access to full lists of
the users whove liked them.
Built a tool called Targeted Sharing. Authorize our Facebook
App, to access their social graph and certain
attributes. Try to match them to voter database (30-40%).
Influence model. How likely is your friend to take an action
given that you do? They had approx 1 million
people authorize the app, and used this to get data on 200
million people. Small world phenomenon.
There was a sharp correlation between the level of
personalization of emails and the level of engagement.
Lots of A/B tests.
A mistake that nonprofits and campaigns make is that they dont
send enough emails!
Clever: give us $23, but option is $25. These numbers are
carefully optimized.
Prediction is great, but how can we influence behavior?
