Machine Errors and Undervotes in Florida 2006 Revisited

The 2006 election for U.S. House of Representatives District 13 in Sarasota County, Florida, attracted extensive controversy because an unusually high proportion of the ballots cast lacked a vote for that office, and the unusual number of undervotes probably changed the election outcome. Intensive technical studies based on examining software and hardware from the iVotronic touchscreen voting machines used to conduct the election failed to find mechanical flaws sufficient to explain the undervotes. Studies that examined the ballots used in Sarasota and in some other counties concluded the high undervote rate was caused by peculiar features of the ballot\u27s format that confused many voters. I show that recorded events involving power failures and problems with the Personalized Electronic Ballots used with the machines correlate significantly with undervote rates in several Florida counties. The relationships between machine events and undervotes are sufficiently substantial and varied to make it unreasonable to discount the likelihood that mechanical failures contributed substantially to the high numbers of undervotes

Genetic Optimization Using Derivatives: The rgenoud Package for R

genoud is an R function that combines evolutionary algorithm methods with a derivative-based (quasi-Newton) method to solve difficult optimization problems. genoud may also be used for optimization problems for which derivatives do not exist. genoud solves problems that are nonlinear or perhaps even discontinuous in the parameters of the function to be optimized. When the function to be optimized (for example, a log-likelihood) is nonlinear in the model's parameters, the function will generally not be globally concave and may have irregularities such as saddlepoints or discontinuities. Optimization methods that rely on derivatives of the objective function may be unable to find any optimum at all. Multiple local optima may exist, so that there is no guarantee that a derivative-based method will converge to the global optimum. On the other hand, algorithms that do not use derivative information (such as pure genetic algorithms) are for many problems needlessly poor at local hill climbing. Most statistical problems are regular in a neighborhood of the solution. Therefore, for some portion of the search space, derivative information is useful. The function supports parallel processing on multiple CPUs on a single machine or a cluster of computers.

Frauds, strategies and complaints in Germany

Many statistical methods that use low-level election vote count data to detect election frauds have the limitation that they have a hard time distinguishing distortions in vote counts that stem from voters’ strategic behavior from distortions that originate with election frauds. Identifying latent components that underlie election forensics statistics and other contextual variables can help show the extent to which the statistics measure fraudulent as opposed to strategic behavior. We use an active-learning procedure with a support vector machine to classify complaints about German federal elections during 1949–2009 to show the diversity of the complaints, which we use as contextual data. We also use variables that measure strategic voting in those elections. For the elections of 2005 and 2009 we use latent variable methods to assess whether the parameters of a positive empirical model of frauds connect through latent variable structure to either the complaints or the strategic variables. Geographic ambiguity about the locations at which some complaints occur motivates embedding a geographic mixture structure in the latent variable model. The “fraud” parameters connect to both complaints and strategic behavior

Active learning approaches for labeling text: review and assessment of the performance of active learning approaches

Supervised machine learning methods are increasingly employed in political science. Such models require costly manual labeling of documents. In this paper, we introduce active learning, a framework in which data to be labeled by human coders are not chosen at random but rather targeted in such a way that the required amount of data to train a machine learning model can be minimized. We study the benefits of active learning using text data examples. We perform simulation studies that illustrate conditions where active learning can reduce the cost of labeling text data. We perform these simulations on three corpora that vary in size, document length, and domain. We find that in cases where the document class of interest is not balanced, researchers can label a fraction of the documents one would need using random sampling (or “passive” learning) to achieve equally performing classifiers. We further investigate how varying levels of intercoder reliability affect the active learning procedures and find that even with low reliability, active learning performs more efficiently than does random sampling

Cuing and Coordination in American Elections #

Cuing and Coordination in American Elections I use evolutionary game models based on pure imitation to reexamine recent findings that strategic coordination characterizes the American electorate. Imitation means that voters who are dissatisfied with their strategy adopt the strategy of the first voter they encounter who is similar to them. In the replicator dynamics such imitation implies, everyone ultimately uses the coordinating strategy, but I study what happens over time spans that are relevant for voters. I consider three evolutionary models, including two that involve partisan cuing. Simulations using National Election Studies data from presidential years 1976--96 suggest that many voters use an unconditional strategy, usually a strategy of voting a straight ticket matching their party identification. I then estimate a choice model that incorporates an approximation to the evolutionary dynamics. The results support partisan cuing and confirm that most voters vote unconditionally. The estimates also support previous findings regarding policy moderation and institutional balancing