Data Science for Democracy
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This project aims to provide quantitative insights to phenomenon and problems in democracy and election science.

We accomplish this using the basic principles of the scientific method:
1) ask objective, pragmatic, coherent questions regarding elections and other political science matters.
2) use quantitative tools to determine data-driven answers to those questions.
3) communicate any compelling, pragmatic new information in a coherent story to the public.


Data Science for Democracy began with the desire to answer some basic questions about the US electoral college and to determine which electoral mechanisms contributed most to the difference between popular vote and electoral vote observed in the 2016 US presidential election.  In other words, how to determine the true reasons why candidates sometimes win elections but lose the popular vote, and assess the contribution of each.  As with any good research project, the question and answer process was a tumultuous path with many unexpected turns, but certain concepts emerged with clarity by the end.  The resulting story is discussed in the working paper, "Quantifying the Difference between Popular Vote and Electoral Vote in US Presidential Elections: How Noise is Amplified in the Electoral College". 

After writing the draft of this paper, the author decided to seek peer-review feedback from a local independent research organization, the San Diego Wet Lab, and open it up to them to make additional scientific contributions and broaden the scope.   We hope to grow the project and gain more contributors who dare to ask pragmatic compelling questions about democracy and seek data-driven solutions. 

Where Your Donation Goes

Your donation is tax deductible and goes towards:

Progress and Outcomes To Date

Working paper -  Factors Driving the Difference between Popular Vote and Electoral Vote in US Presidential Elections: How Noise is Amplified in the Electoral College

This working paper provides an objective summary of the election practices which drove the difference between the popular vote and electoral vote in the 2016 US Presidential election as well as the other presidential elections over the past few decades.

The main contributions of this paper are as follows:

  • Trump won by an unusually large electoral vote margin given the popular vote margin.  This likelihood of this outcome is very rare, estimated at 2.4%, based on election results from the past 100 years.
  • Three election practices drive 96% of the difference between electoral vote (EV) and popular vote (PV) and 93% of the discrepancy seen in the 2016 election.
  • The census districting policy typically benefits republicans, contrary to current theory that it benefits democrats.
  • Additional data supporting the argument that census-based districting incentivizes voter disenfranchisement by violating the concept of 'one person one vote'
  • New method for estimating PV margin uncertainty based on voting machine error rates and published fraud data.
  • Narrow victory margins drive most of the difference between PV and EV and states with narrow victories can often be with estimated ranges of PV margin uncertainty.
  • New analysis quantifying a growing gap in voter turnout between left and right that may lead to more discrepancies between popular vote and electoral vote in the future.
  • Recommend policy changes to address the above issues.

Presentation at International Studies Association (ISA) Conference

I've been invited to present the paper at the ISA conference in San Francisco, April 4-7th, 2018!   The ISA conference brings together thousands of political science researchers and experts each year.   Looking forward to sharing these results with this audience to raise awareness and drive change.   Please support this effort by making a donation to cover travel and conference costs!

Lecture at the Riford Library, La Jolla CA

As part of the Wet Lab's science lecture series, I presented preliminary results at the La Jolla Library in March 2017 - The Role of Chance in US Presidential Elections

Upcoming Goals

  • Publish a pre-print of the paper on
  • Approach the MIT/Caltech Voting Technology Project for collaboration
  • Consider peer-review publication
  • Assist local vote-auditing organizations with analytic needs
  • Grow project with additional volunteers or collaborators

Who We Are

David Forney is a professional scientist and MIT graduate.  His research has spanned numerous topics including ecology, microbiology, climate and energy, autonomous vehicles, and became interested in election science after the 2016 election. 

The San Diego Wet Lab is a platform for independent scientific research and science outreach. It provides researchers with a low-overhead research space, shared equipment, shared resources, access to a community of volunteers, and fundraising opportunities, giving project leaders the means to quickly test, iterate and grow their project. It also provides professional scientists with an outreach platform to educate and give back to the public via scientific lectures and educational workshops.

Additional Donation Info

If you found this paper new, informative, and compelling, or would like to support further discussion of quantitative democracy and election science, then please make a donation!

If you’d like more information about this project or would like to get involved, please contact us here on fundrazr, or check for updates on the project page and reach out there.

5013c info:

The Wet Lab is a  tax-deductible public charity  in the state of California.  
EIN #81-4247481

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