Predicting Problematic Approach Behavior Toward Politicians: Exploring the Potential Contributions of Control Theory

The potential merits of Carver and Scheier's (1981) control theory in the prediction of targeted violence are reviewed and several novel indicators of risk that are consistent with this theory are suggested for study. It was hypothesized that: (a) similarity between inappropriate contact with politicians and extremist group literature and writings; (b) the temporal proximity to violent or otherwise criminal actions and notable anniversaries of such groups; (c) detailed specification of a plan to engage in problematic approach behavior, and; (d) self-focus, will be significant predictors of problematic approach behavior. A sample of 506 individuals who engaged in threatening or otherwise inappropriate contact toward members of the United States Congress was drawn from the case files of the United States Capitol Police. Results of the present research indicated that detailed specification of a plan to engage in problematic approach behavior was strongly predictive of actually engaging in problematic approach. Furthermore, high self-focus was significantly related to problematic approach between-persons, although within-person, higher-than-average self-focus showed no such relation. Neither temporal proximity to notable acts of extremist violence nor similarity to known extremist group writings was found to be associated with problematic approach in this sample.

Applications of Linear Programming to Coding Theory

Maximum-likelihood decoding is often the optimal decoding rule one can use, but it is very costly to implement in a general setting. Much effort has therefore been dedicated to find efficient decoding algorithms that either achieve or approximate the error-correcting performance of the maximum-likelihood decoder. This dissertation examines two approaches to this problem. In 2003 Feldman and his collaborators defined the linear programming decoder, which operates by solving a linear programming relaxation of the maximum-likelihood decoding problem. As with many modern decoding algorithms, is possible for the linear programming decoder to output vectors that do not correspond to codewords; such vectors are known as pseudocodewords. In this work, we completely classify the set of linear programming pseudocodewords for the family of cycle codes. For the case of the binary symmetric channel, another approximation of maximum-likelihood decoding was introduced by Omura in 1972. This decoder employs an iterative algorithm whose behavior closely mimics that of the simplex algorithm. We generalize Omura's decoder to operate on any binary-input memoryless channel, thus obtaining a soft-decision decoding algorithm. Further, we prove that the probability of the generalized algorithm returning the maximum-likelihood codeword approaches 1 as the number of iterations goes to infinity.