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In belief change, message discrepancy is the difference between the belief position advocated in a message and a message receiver’s initial belief position. Psychological discrepancy is the message discrepancy experienced by the receiver. Existing literature had assumed that a high level of psychological discrepancy discounted the weight of a message, which could make the message less effective. However, there were three alternative assumptions about the role of psychological discrepancy. The problem that this dissertation examined was: Does psychological discrepancy affect the weight of a message only, affect the scale value of the message only, affect neither, or affect both? To find out which of these four assumptions was more plausible, this dissertation derived competing hypotheses based on four mathematical models either through an analytic proof alone or a combination of an analytic proof and a computational approximation. This dissertation tested these hypotheses in an experiment with a 3 (high vs. moderate vs. low message scale value) ´ 3 (high vs. moderate vs. low upper bound) between-subjects design (N = 448 Mechanical Turk workers). The results showed that the weight-discounting model had the most supported hypotheses and fit the data the best, which indicated that psychological discrepancy affected the weight of a message only. This dissertation improves the understanding of the mechanism that leads to the outcomes posited by discrepancy models in persuasion research and provides additional empirical evidence for the scale value constancy assumption in information integration theory. / Media & Communication

This research developed a system, Rx, to provide early identification and effective control of network stealth worms in digital networks through techniques based on biological epidemiology. Network stealth worms comprise a class of surreptitious, self-propagating code that spread over network connections by exploiting security vulnerabilities in hosts. Past outbreaks due to traditional worms subverted hundreds of thousands of machines. Network stealth worms exacerbate that threat by using clandestine methods to maintain a persistent presence in the network. Biological epidemiology was shown to support the real-time detection, characterization, forecasting, and containment of network stealth worms. Epidemiology describes a scientific methodology in biology that seeks to understand, explain, and control disease. Bio-mathematical modeling led to the development of a mechanism for digital networks to identify worm infection behavior buried in anomaly data, to characterize a worm, and to forecast the temporal spread of a worm. Demographic analysis of the infected hosts revealed the subset of vulnerable machines within the population. The automated response of advanced quarantine used this information to control the spread of an identified worm by isolating both infected and vulnerable machines. The novel contributions of this research included the identification of a network stealth worm at the network-level based on end-host reports while simultaneously characterizing and forecasting the spread of the worm. Additionally, this task offered the technique of advanced quarantine through demographic analysis of the population. This work resulted in a scalable, fault-tolerant strategy that dramatically enhanced the survival rate of network hosts under attack by a stealth worm. Moreover, this approach did not require new hardware, changes to existing protocols, or participation outside the implementing organization. This research showed application to a wider range of challenges. The bio-mathematical models are extensible, allowing Rx to respond to variations on the self-propagating code presented here. The approach is applicable to other forms of malware beyond self-propagating code by interchanging the epidemic model with one more appropriate. Lastly, the strategy allowed anomaly detectors to be sensitive to lower reporting thresholds and a variety of often benign yet potentially useful events. / Ph. D.