Final Report - The Role of Innovation and Evidence-Based Decision Making in the 2016 U.S. Election

The Role of Innovation and Evidence-Based Decision Making in the 2016 U.S. Election

Zac Bodner, Spring 2017

Background There are two events that happened in my life that completely shifted my paradigm for understanding (political, and other types of) reality.

The first was 9/11. The second was Donald Trump being elected President of the United States.

For my project, I wanted to explore this issue within a framework of evidence based decision making, using themes from class and Moneyball to guide me.

2016 was a year like no other. It should have followed that the election taking place would be vastly different from previous ones. With all the madness surrounding the citizens of the US and the world, it’s no wonder people were clamoring for change.

There were so many factors (independent variables) that led to the ultimate outcome of the election (dependent variable), that one could write a book on it. Some of these variables include: •Fake News •Lack of Hillary appeal •The global rise of Nationalism/Populism •Obama Backlash •A rapidly changing media and tech landscape •Xenophobia and Racism •Shifting national demographics •The Russians!

•The decline of Legacy Institutions •Economic uncertainty •Weak Republican opponents

•Echo Chambers •Anti-Establishment sentiment •Bots •Sensationalist, gossipy news coverage •Media’s need for ratings/subscriptions •Were going to vote Republican no matter what •The “Make America Great Again” Brand •WikiLeaks •James Comey •Low voter turnout •Use and Misuse of Data Science

If we threw all these variables into a regression, it would be tough to nail down some accurate beta coefficients, due to some serious multicollinearity issues. There’s no doubt that each of these factors played off of all the others in some hard to define way or another. I’m a few doctorates and about twenty years away from being able to get into a real exploration of all that - so in terms of this paper, we’ll focus on the last of these - the use and misuse of data science.

Hillary and Class Themes At the beginning of the year, Dr. Davis (you!) gave us tips for approaching data science, life and pretty much everything in between. They are as follows.

1) Use impeccable language​ - Don’t leave any room for imagination when you need to explain something. We’re not novelists, we’re scientists! Say exactly you mean to say in the most awesome, crystalline, and concise terms as possible.

Unless you’re writing your novel. Then, by all means ignite the imagination and leave room for interpretation when you communicate.

2) It’s not personal​ - Self explanatory.

3) Don’t make assumptions​ - An assumption is a foregone conclusion of truth.

Assumption is the antithesis of the scientific method. Don’t make assumptions, make hypotheses - and then gather your data, perform your experiments, record and interpret your findings - and then go input what you’ve found back into a model or a new model and repeat.

4) Do your best​ - Self explanatory.

Of these rules, Hillary is guilty of breaking #3 more than any of the others. She made many assumptions during the course of her 2016 Presidential campaign. The results of these assumptions (combined with many of the previously listed factors) cost her the election.

She assumed that those that voted for Barak Obama would also vote for her. A good question to ask might have been, “How many of Barak’s voters had voted for other democratic candidates before him?” If they had never voted before, it might be possible that they wouldn’t vote again. This illustrates the importance of context and asking the right questions about our data, so that correlations become more than just correlations they become insights and answers.

She assumed that traditional campaigning and media tactics would work the way they always have. But as already discussed, this is a new era, with new methods of communicating. She was going up against a guy fast developing a rabid following using incendiary Twitter content, and getting an estimated 5 billion dollars in free, 24/7 news coverage. Hillary was guilty of not being in tune with the Zeitgeist of our times, which is one of the things she is largely accused of by her followers and detractors. She spent twice as much money as Donald on TV. But young voters, the type the carried Obama

to victory - don’t watch TV. Hillary assumed that the polls, which had her winning in a landslide, were accurate. The polls’ sampling methodology and assumptions were biased. They relied too much on voter history and the veracity of what was being reported back to them. They didn’t take into account newly inspired voters, or the new phenomenon of “Trump shame,” - failure to make public one’s intention to vote for Trump. This was not an ordinary election or an ordinary Republican candidate. Her team should have been cognizant of this.

Lastly and most importantly, she assumed that there was no possible way that any sane, self-respecting person could wind up voting for a candidate like Donald Trump.

Again, this falls under the category of Hillary being out of touch with the true sentiment of the American voter. Donald and camp were running online and offline surveys to gauge opinion weekly, up to 1,500 of them in each state. Hillary should have taken the same approach. This way, she could have familiarized herself with issues that people may have had with her and what’s important to them - so she could modify her approach.

So that’s a bit on how Hillary went against a major rule of class, life and data science which cost her dearly. Let’s get into some of the parallels between Moneyball and the Trump campaign of 2016.

Donaldball

Moneyball is all about using evidence, not feelings or instincts to make decisions. What really wins baseball games? Is it handsome 5 tool guys with square jaws? No! It’s runs.

And outs. Figure out what the data tells you about what really makes runs and outs and you’re gonna be on to something. That’s what Billy Beane did.

Donald and company took a few pages out of Billy’s playbook, and they used these pages to help propel them to victory on Election Day. The first was, they did an excellent job of ​taking advantage of market inefficiencies​.

In my opinion, there has never been a greater market inefficiency than the electoral college itself. The system itself is anti-democratic (which is okay I guess, since America isn’t a true democracy, it’s a democratic republic) and inefficient. Think about, you don’t even need a majority of votes to win the Presidency of the United States. And if more people vote differently than you in the state where you live (looking at you Texas), then your vote basically doesn’t count. As mine basically didn’t. Come on!

Donald and team used this market inefficiency to focus their energy not on mass communication (which they didn’t have the money or scale for), but on targeted messaging in the states and and with the voters they knew could swing the election for them.

As previously discussed, they also took advantage of the inefficiencies in our media machine. Why pay 200 million for TV ads when you can say crazy stuff and get free 24/7 advertisement via sensationalist, ratings-hungry news outlets? And from your own free, personal Twitter account? Disturbing, but well played, Donald.

The second way they mirrored Moneyball was by ​challenging conventional wisdom, strategy and tactics. ​They decided to run a social media first campaign. They did this because they knew this would be the fastest, most cost efficient, most responsive way they could communicate with and persuade their audience - by adding constant feedback (from the treasure trove of data that the online and social world provide) to their model and optimizing it in real time.

They decided to use bombastic, sensationalist and incendiary rhetoric and themes.

Racism? Xenophobia? Sexism? Whatever gains headlines! No press is bad press. It sounds ridiculous to say it aloud in this context, but the decision proved prescient.

Donald positioned himself in a way unlike most politicians by speaking and behaving like John Q. (obnoxious) Everyman - saying whatever he feels and being unapologetic about it.

The third idea that Trump and Moneyball share is ​seeking fresh perspectives and employing alternative expertise​. Donald himself had never been a politician. He wasn’t part of “the swamp,” he was gonna drain it. And just like Billy Beane had Paul DePodesta, Trump used the alternative expertise and fresh perspectives of Kushner (a real estate maven and investor), Bannon (a political strategist and rabble rouser) and Alexander Nix (CEO of Cambridge Analytica) to help him come up with innovative approaches to solve a seemingly insurmountable problem. And that is - how to get a billionaire douchebag who’s not particularly intelligent, compassionate, articulate, stately, or decent elected President.

I’m sorry that wasn’t nice of me to say.

Let’s transition to the last part of this project, and the most innovative and controversial way Donald used data to solve his problem. Let’s talk about the behavioral microtargeting firm he (Jared Kushner) hired at the end of the Summer to help them make their last stand.

Cambridge Analytica

Donald actually made many shrewd marketing moves utilizing evidence based decision making while on the campaign trail - as any candidate must if they wish to advance through the primaries. But employing Cambridge Analytica was by far the most impactful, controversial and innovative of them all.

Microtargeting uses consumer data (from multiple sources), demographics, and even psychographics to identify the interests of individuals or groups to influence them. This is the groundwork for most digital and social media marketing and advertising in the digital age, and its usage is nothing new.

Cambridge Analytica, who also worked on the Brexit campaign, claims to take microtargeting one step further by adding a personality trait assessment to the mix.

Take everything from this point on with a grain of salt, because the actual effect of Cambridge Analytica on the Trump campaign (and the Brexit campaign) and persuading voters is the subject of much debate.

For a bit of context, let’s start at the beginning.

In 2008, Michael Kosinski was a researcher at the Psychometrics Centre at Cambridge University. He and his partner began experimenting with Facebook quizzes focusing on the “Big Five” personality traits. The O.C.E.A.N. Model. The quiz takers could opt in to sharing the results of these quizzes with the researchers, and to their surprise - millions of people did.

Kosinski began correlating this personality data with a person’s likes on Facebook. By 2012, Kosinski had produced a model proving that on the basis of 68 “likes” it was possible to predict a person’s skin color (with 95% accuracy), their sexual orientation (88% accuracy), and their political affiliation (85%).

It didn’t stop there. The model could also determine intelligence, religion, alcohol and drug use, even parental divorce. He found that “liking” Wu Tang Clan was one of the biggest indicators of heterosexuality, while “liking” Lady Gaga correlated highly with extroversion.

Kosinski and his partner are now working on a new set of research, yet to be published, that addresses the effectiveness of these methods. Their early findings: using this model-based targeting, Facebook posts can attract up to 63 percent more clicks and 1,400 more conversions.

The details get a little murky here, and I encourage you to look into this on your own, because it’s pretty fascinating, but an assistant professor at the Psychometric Centre,

Aleksandr Kogan requested (and was denied by Kosinski) access to this database on behalf of an “Election Management Agency” in London later to be registered as Cambridge Analytica.

Whether it was stolen or not remains to be seen; what’s important is that the Cambridge Analytica model mirrors the one Kosinski developed in 2012.

So based on all this, here is how CA might target two different personality types with two different variations of a pro-gun rights ad. If they were speaking to an audience they had classified, based on their data and testing to be highly Neurotic and Conscientious they might show an ad similar to the one on the left. (see picture below) It is the threat of a burglary, with a gun serving as a kind of insurance policy against home intruders.

For an audience they classify as “Closed and Agreeable,” they might send them an ad that looks like the one on the right. It is a picture of a father and son in a field at sunset, duck hunting together. For this audience, a gun is a symbol of tradition, and a tool for establishing a connection between family members. For the previous audience - a gun is the thing keeping them safe in a world full of dangerous bad people who are looking to get them.

So now that we know what CA does, let’s take a look at what happened next. The Trump team merged their data with the data of Cambridge Analytica, who after extrapolating the results of their surveys to those with similar correlations in

independent variables across the United States - claimed to have a profile of every Adult American voter that contained up to 5000 data points, coupled with a corresponding personality assessment.

This combined super database was known as Project Alamo. And with the help of Cambridge Analytica, the Trump data team used it to come up with 32 distinct personality types in 17 swing states that shared similarities - both in their data footprints and their personality traits - to people who supported Trump.

The ultimate point of Cambridge Analytica, and the reason the Trump data team under Jared Kushner employed their services - was to use these profiles to effectively convince these potential voters to get out and vote for Trump.

Here’s how they did it.

A/B Testing They used 40,000-50,000 variations of targeted ads per day on each of the 32 sections of different personality profiles they come up with. They had an automated AI system that instantly measured feedback and responded.

For example, if a swing voter responded to the ad attacking Clinton’s negligence over her email server, then they would keep serving her more content that emphasizes her failures of responsibility. If not, the script would try a different headline that corresponds to another personality trait - like their tendency to be agreeable towards authority figures. For example, “Top Intelligence Officials Agree: Clinton’s emails jeopardized National Security.”

And the cycle repeats. If an ad repeatedly fails to engage, the system moves on to the next voter to save resources.

Based on responses, the Trump team could see which messages and issues were resonating and where.

This info was then used to determine many of Trump’s rally locations. If 73% of targeted voters in a Michigan county clicked on ad focused on bringing back jobs, they would schedule a rally close by focused on economic recovery.

Voter Suppression Based on their data (like voting history), they targeted certain voters with a combination of low propensity to vote, democratic affiliation, gender, race, nationality, etc. to try

discourage them from getting out and voting for Hillary. For certain blacks, they purchased radio spots and targeted them with Facebook dark posts (only visible to the user) about Hillary’s “Superpredator” line from 1996. For Haitians in Miami’s Little Haiti neighborhood, they used ads about the Clinton’s lack of relief effort after the Haiti earthquake. And for women, it was ads about Bill Clinton’s philandering and Hillary’s lack of response.

Ground game and Phone App They could match the personality profiles with the address information that they had on individual voters. This way, they could “flag” certain households that the model showed to be “highly receptive” to Trump’s messages. From here, canvassers could then communicate with these households, based on their given personality type - using a sort of communications template. They would then update the app, and the data team would know in real time where to continue their efforts and allocate resources. This made their GET OUT THE VOTE ground tactics faster and more efficient than Clinton’s; her canvassing operation was supposedly done using paper forms.

Afterword/Food for Thought The results are in. We have a new president. How much of this was due to Trump’s data operation? It’s hard to say, but the lessons learned are applicable, regardless.

The power and danger of data is real, and will be a primary theme throughout all of our lives moving forward. Nearly everything we do online, on our phones, and on our social media channels is tracked, recorded and analyzed. Just look at the effect that it had on this election.

That being said, it’s not data itself that has any power. It’s the methodologies we employ and the questions we ask. It’s these questions and methods that transform correlations to actual answers. Hillary asked the wrong questions, or didn’t ask any at all. Moving forward, we must remember what we learned in this class (remove as much bias as possible, don’t make assumptions, challenge our findings) or we will suffer the same fate.

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