Computer scientists, including us, have long been skeptical of electronic voting systems. E-voting systems are computers, with all of the attendant problems. If something goes wrong, can the problem be detected? Can it be fixed? Some e-voting systems are much riskier than others. As the 2012 Presidential election approaches, we decided to evaluate the risk of a “meltdown scenario” in which problems with electronic voting equipment cause a state to cast the deciding electoral college vote that would flip the election winner from one candidate to the other. We’re interested in the risk of these technological problems, weighted by the relative voting power of each voter. So for example, here in New Jersey we use direct-recording electronic voting machines that have been found by a court to be inadequate, but with Obama polling at +14% it’s not likely that a snafu with these machines could change the entire state’s outcome. But in swing states that poll closer to even, like Virginia (where your voting machines can be modified to play Pac-Man), an electronic voting mix-up could have a much bigger impact. So, which states have the greatest risk of an e-voting meltdown affecting the result of the 2012 Presidential election?
A meltdown scenario is very unlikely, of course. A knife-edge election is highly improbable. Still, we can evaluate the relative risk of a worst-case scenario in each state. Here is how we did it: First, we created a model of electronic voting risk, using data from the recent Counting Votes 2012: A State by State Look at Election Preparedness report and the VerifiedVoting.org Verifier database. Our risk model takes information on every county in the US and combines it into a state-by-state risk score. We took into account which voting technology each county uses, whether paper records are used (and whether those records are marked directly by voters or are machine generated), which procedures are in place if machines fail, how ballots or electronic media are physically protected and accounted for, and what kind of auditing is in place to detect problems after the election. We then weighted our per-state risk calculation by the number of registered voters in the state to estimate the probability, per voter in a given state, that a bad e-voting event will take place.
Next, we combined these risk scores with the meta-polling analysis performed by our Princeton colleague Professor Sam Wang. Professor Wang’s meta-analysis provides a measure of the relative power of a single vote in each state based on the number of electoral votes it affects and the current polls. Professor Wang also provides a prediction of how the election would go were it held today (assuming that the polls are accurate). His predictions are updated four times each day based on the newest polls, so you can follow along. We refresh with his latest vote power data periodically, so the map and top-ten list in this post will update automatically. The most-red states on the map are those most at risk, according to our model. The scores you see are simply a relative measure: the model is normalized so that the highest-risk state scores exactly 100.
Full Article: Which States have the Highest Risk of an E-Voting Meltdown?.