A dual-process framework argues that both intuition and reflection interact to produce moral decisions. The present dissertation integrated moral identity and moral judgment to explain moral behavior from the dual-process model and its account was tested by three studies. A typical everyday moral behavior of interest in the present research was honest behavior. Participants were introduced to use their intuitive ability to predict the dice number demonstrated on a computer. The reward will base on their self-reported accuracy. Studies examined cheating behavior of individuals who had a chance to lie for money. In study 1, sixty participants with diversified background were recruited in a laboratory study. The results supported that honest behavior was more an intuitive result than a reflective outcome. Honest behavior resulted from the absence of temptation and priming moral constructs increased honest behavior. Study 2 contained two parts, in the first part, the researcher developed a Chinese version of moral identity based on Aquino and Reed’s (2002) work, in the second part, fifty-eight participants’ moral identity was investigated by the instrument in the first part. Their honest behavior was measured in the same task adopted in study 1. The result confirmed that different mechanisms led different people to behave ethically. For people who had strong moral identity, honesty resulted from the absence of temptation, while for individual with weak moral identity, honest behavior resulted from the active resistance of temptation. In study 3, moral identity and moral judgment were integrated to explain moral behavior. A Web-based survey with 437 subjects showed that the relationship between moral identity and moral judgment was significant. Individuals who viewed themselves as moral people preferred formalistic ideals to utilitarian framework when making moral judgment. The follow-up experimental study demonstrated that moral identity and moral judgment interacted together to determine moral behavior. When formalism was coupled with the motivational power of moral identity, individuals were most likely to behave morally.

With the recent advent of Web 2.0, mobile computing, and pervasive sensing technologies, human activities can readily be logged, leaving digital traces of di.erent forms. For instance, human communication activities recorded in online social networks allow user interactions to be represented as “network” data. Also, human daily activities can be tracked in a smart house, where the log of sensor triggering events can be represented as “sequence” data. This thesis research aims to develop computational data mining algorithms using the generative modeling approach to extract salient patterns (motifs) embedded in such network and sequence data, and to apply them for human behavior analysis. Motifs are de.ned as the recurrent over-represented patterns embedded in the data, and have been known to be e.ective for characterizing complex networks. Many motif extraction methods found in the literature assume that a motif is either present or absent. In real practice, such salient patterns can appear partially due to their stochastic nature and/or the presence of noise. Thus, the probabilistic approach is adopted in this thesis to model motifs. For network data, we use a probability matrix to represent a network motif and propose a mixture model to extract network motifs. A component-wise EM algorithm is adopted where the optimal number of stochastic motifs is automatically determined with the help of a minimum message length criterion. Considering also the edge occurrence ordering within a motif, we model a motif as a mixture of .rst-order Markov chains for the extraction. Using a probabilistic approach similar to the one for network motif, an optimal set of stochastic temporal network motifs are extracted. We carried out rigorous experiments to evaluate the performance of the proposed motif extraction algorithms using both synthetic data sets and real-world social network data sets and mobile phone usage data sets, and obtained promising results. Also, we found that some of the results can be interpreted using the social balance and social status theories which are well-known in social network analysis. To evaluate the e.ectiveness of adopting stochastic temporal network motifs for not only characterizing human behaviors, we incorporate stochastic temporal network motifs as local structural features into a factor graph model for followee recommendation prediction (essentially a link prediction problem) in online social networks. The proposed motif-based factor graph model is found to outperform signi.cantly the existing state-of-the-art methods for the prediction task. For extract motifs from sequence data, the probabilistic framework proposed for the stochastic temporal network motif extraction is also applicable. One possible way is to make use of the edit distance in the probabilistic framework so that the subsequences with minor ordering variations can .rst be grouped to form the initial set of motif candidates. A mixture model can then be used to determine the optimal set of temporal motifs. We applied this approach to extract sequence motifs from a smart home data set which contains sensor triggering events corresponding to some activities performed by residents in the smart home. The unique behavior extracted for each resident based on the detected motifs is also discussed. Keywords: Stochastic network motifs, .nite mixture models, expectation maxi­mization algorithms, social networks, stochastic temporal network motifs, mixture of Markov chains, human behavior analysis, followee recommendation, signed social networks, activity of daily living, smart environments

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This dissertation consists of three research papers in Applied Microeconomics. Each paper uses an econometric technique to analyze a problem related to human behavior. The first paper examines the separate effects of time and location of the School Breakfast Program on participation and consumption of breakfast by elementary school children in northern Nevada. Controlling for potential selection bias and unobserved individual fixed effects with a panel version of the Heckman sample selection model, it is shown that extra time allowed for breakfast leads to an approximately 20% increase in average participation, and the transition from cafeteria to classroom adds another 40% for the typical student. The second paper uses the Hedonic Property Valuation Method to quantify the willingness-to-pay of residents in the Dan River region for three dimensions of an improved food environment---availability, accessibility, and acceptability of food. This paper accounts for potential omitted variables issue in the hedonic analysis by applying a spatial-lag model, and finds an overall negative or null preference of residents in this region for an improved food environment. The third paper investigates the effects of characteristics of human interpreters and images on the accuracy of cloud interpretation for satellite images in an online experiment, using a fractional logit model. The results indicate that an image with higher cloud coverage and/or larger brightness is more likely to receive higher accuracy, and the more time spent on the image and more image completed are also beneficial for improving the accuracy. This paper also uses a logistic regression model to compare the performance of human interpreters to that of an automated algorithm, and finds that human interpreters outperform the automated algorithm for an average satellite image out of our twelve selected images. / Ph. D.