Statistical detection of systematic election irregularities – three essays on supervised and unsupervised machine learning approaches and the attitudinal consequences of exposing cheating
election forensics , electoral integrity , Monte Carlo Simulation , machine learning , survey experiment
Abstract:
This dissertation (i) develops statistical methodology for the detection of systematic irregularities in fine-graded election results (ii) and seeks to enhance our understanding of the attitudinal consequences of exposing individuals to information on electoral malpractice. While the range of methods for statistical fraud detection—a field often referred to as 'election forensics'—is constantly expanding, the same holds for the knowledge and strategies of micro- and macro-level agents of interference. Hence, this dissertation aims to contribute to the need of continuous innovation in the methodology of electoral anomaly detection. Without doubt, communicating
negative findings about the integrity of electoral events is in itself likely to lead to a legitimacy loss of political institutions among the citizenry. This is
why—next to making methodological contributions—this dissertation empirically investigates these decays in support, and hence also reflects on the role of statistical fraud detection in the tension between safeguarding democracy and producing democratic backlashes.
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