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This dissertation contributes to the understanding of heterogeneity in rational learning. The first chapter proceeds from the observation that, in an environment of social learning under unobserved heterogeneity, those with opinions closest to one's own may in fact be the most informative for oneself. For example, a similar opinion of a restaurant may suggest similar tastes and similar political views may suggest a similarity in values or interpretations of evidence. In this environment, individuals will display rational forms of confirmation bias and other ostensibly anomalous patterns of behavior. The second chapter is a research collaboration with Aydin Mohseni studying learning by agents in social networks. Each agent has a preference for both accuracy (choosing the ``correct" action) and conformity (selecting the action taken by the majority of her neighbors) with the relative weight placed on these two concerns being heterogeneous between agents. Related literature finds the star network to possess optimality properties. In contrast, our analysis finds that agents in highly centralized networks, such as star networks, take longer to settle on the optimal action than agents in other standard networks. To combat the replication crisis in science, a group of prominent scholars has proposed {\it redefining statistical significance} by reducing the p-value significance threshold from 0.05 to 0.005. The third chapter shows that, if researchers can exercise their ``degrees of freedom" to obtain significance and if they are heterogeneous, then this proposal may exacerbate the problems with reproducibility. I provide an example demonstrating that even a small amount of researcher bias can produce this effect and give a general characterization of the conditions when it will occur.

Asthma is a chronic relapsing airways disease that represents a major public health problem worldwide. Intermittent exacerbations are provoked by airway mucosal exposure to pro-inflammatory stimuli, with RNA viral infections or inhaled allergens representing the two most common precipitants. In this setting, inducible signaling pathways the airway mucosa play a central role in the initiation of airway inflammation through production of antimicrobial peptides (defensins), cytokines, chemokines and arachidonic acid metabolites that coordinate the complex processes of vascular permeability, cellular recruitment, mucous hyper-secretion, bronchial constriction and tissue remodeling. These signals also are responsible for leukocytic infiltration into the submucosa, T helper-lymphocyte skewing, and allergic sensitization. Currently, it is well appreciated that asthma is a heterogeneous in terms of onset, exacerbants, severity, and treatment response. Current asthma classification methods are largely descriptive and focus on a single aspect or dimension of the disease. An active area of investigation on how to collect, use and visualize multidimensional profiling in asthma. This book will overview multidimensional profiling strategies and visualization approaches for phenotyping asthma. As an outcome, this work will facilitate the understanding of disease etiology, prognosis and/or therapeutic intervention.

This paper considers recent methodological developments in the treatment effects literature, describes their value for applied evaluation work, and suggests next steps. It pays particular attention to documenting the presence of treatment effect heterogeneity, to the quest to attach treatment effect heterogeneity to particular subgroups and other moderators, and to the recent application of machine learning methods in this domain.

Political parties field heterogeneous candidates and send a variety of messages about their policy positions. Yet most voting models maintain that office-seeking parties should enforce intraparty homogeneity and cultivate clear party reputations. This article reconciles theory with reality by identifying a strategic rationale for parties to pursue heterogeneity. I develop a model in which two parties each select a distribution of potential candidates to compete in an upcoming election. The model demonstrates that well-positioned parties should indeed offer homogeneous candidate teams, but that parties with platforms distant from the median voter should cast a wide net. Extensions allow for multiple candidate signals, voters who care about party platforms and candidates' positions, and voter uncertainty.

Political parties field heterogeneous candidates and send a variety of messages about their policy positions. Yet most voting models maintain that office-seeking parties should enforce intraparty homogeneity and cultivate clear party reputations. This article reconciles theory with reality by identifying a strategic rationale for parties to pursue heterogeneity. I develop a model in which two parties each select a distribution of potential candidates to compete in an upcoming election. The model demonstrates that well-positioned parties should indeed offer homogeneous candidate teams, but that parties with platforms distant from the median voter should cast a wide net. Extensions allow for multiple candidate signals, voters who care about party platforms and candidates' positions, and voter uncertainty.