<p> This dissertation presents a Bayesian generative modeling approach for complex dynamical systems for emotion-interaction patterns within multivariate data collected in social psychology studies. While dynamical models have been used by social psychologists to study complex psychological and behavior patterns in recent years, most of these studies have been limited by using regression methods to fit the model parameters from noisy observations. These regression methods mostly rely on the estimates of the derivatives from the noisy observation, thus easily result in overfitting and fail to predict future outcomes. A Bayesian generative model solves the problem by integrating the prior knowledge of where the data comes from with the observed data through posterior distributions. It allows the development of theoretical ideas and mathematical models to be independent of the inference concerns. Besides, Bayesian generative statistical modeling allows evaluation of the model based on its predictive power instead of the model residual error reduction in regression methods to prevent overfitting in social psychology data analysis. </p><p> In the proposed Bayesian generative modeling approach, this dissertation uses the State Space Model (SSM) to model the dynamics of emotion interactions. Specifically, it tests the approach in a class of psychological models aimed at explaining the emotional dynamics of interacting couples in committed relationships. The latent states of the SSM are composed of continuous real numbers that represent the level of the true emotional states of both partners. One can obtain the latent states at all subsequent time points by evolving a differential equation (typically a coupled linear oscillator (CLO)) forward in time with some known initial state at the starting time. The multivariate observed states include self-reported emotional experiences and physiological measurements of both partners during the interactions. To test whether well-being factors, such as body weight, can help to predict emotion-interaction patterns, We construct functions that determine the prior distributions of the CLO parameters of individual couples based on existing emotion theories. Besides, we allow a single latent state to generate multivariate observations and learn the group-shared coefficients that specify the relationship between the latent states and the multivariate observations. </p><p> Furthermore, we model the nonlinearity of the emotional interaction by allowing smooth changes (drift) in the model parameters. By restricting the stochasticity to the parameter level, the proposed approach models the dynamics in longer periods of social interactions assuming that the interaction dynamics slowly and smoothly vary over time. The proposed approach achieves this by applying Gaussian Process (GP) priors with smooth covariance functions to the CLO parameters. Also, we propose to model the emotion regulation patterns as clusters of the dynamical parameters. To infer the parameters of the proposed Bayesian generative model from noisy experimental data, we develop a Gibbs sampler to learn the parameters of the patterns using a set of training couples. </p><p> To evaluate the fitted model, we develop a multi-level cross-validation procedure for learning the group-shared parameters and distributions from training data and testing the learned models on held-out testing data. During testing, we use the learned shared model parameters to fit the individual CLO parameters to the first 80% of the time points of the testing data by Monte Carlo sampling and then predict the states of the last 20% of the time points. By evaluating models with cross-validation, one can estimate whether complex models are overfitted to noisy observations and fail to generalize to unseen data. I test our approach on both synthetic data that was generated by the generative model and real data that was collected in multiple social psychology experiments. The proposed approach has the potential to model other complex behavior since the generative model is not restricted to the forms of the underlying dynamics.</p>

<p> Many users of smart mobile devices (SMDs) are unaware that personally identifiable information (PII) is being sent from mobile applications to digital corporations for processing and use by third-parties. The severity of the PII disclosed includes location tracking, health status, friends lists, or detailed financial transactions, which increasingly involve SMDs. A total of 407 participants recruited from the eLancing ecosystem were randomly assigned to a scenario-based experimental survey to determine the extent that level-of-effort (LOE) moderated the privacy calculus. The privacy calculus is subjective based on an individual&rsquo;s own perceived privacy risks and perceived benefits; however, it is also subject to a level of engagement by an individual in the amount of effort the individual is willing to apply to the problem. Actively open-minded thinking (AOT) was used as a proxy for LOE. It has been shown that, independent of cognitive ability, AOT plays an important role in predicting the degree to which individuals reason rationally, independent of immediate experience, mood, or affect, and is a measure of good thinking. Partial least squares structural equation modeling was conducted, and results show that users LOE moderates the privacy calculus. Participants that use high-effort processing perceived risks to be much higher when trusting beliefs are low and perceive benefits to be lower when perceived risks are high. In the context of health data compared to location data, high-effort respondents perceive benefits to be lower than low-level processors when perceived risk is high but much higher than low-effort respondents when the perceived risks are low. In addition, this study provided a replication of prior findings that privacy concern has a third-order conceptualization using measures adapted for use with the Enhanced-APCO macro-model within the context of location-based and health-based mobile applications that have theoretical and practical application in the field of information privacy.</p><p>