Interpersonal interaction over short time scales is frequently understood in terms of actions, which can be thought of as discrete events in which one individual emits a behavior directed at one or more other entities in his or her environment (possibly including him or herself). Here, we introduce a highly flexible framework for modeling actions within social settings, which permits likelihood-based inference for behavioral mechanisms with complex dependence. The utility of the framework is illustrated via an application to dynamic modeling of responder radio communications during the early hours of the World Trade Center disaster.

Although it is well-known that there is a relationship between socio-physical dis- tance and edge probability in interpersonal networks, the predictive power of such distances for total network structure has not been established. Here, it is shown that upper bounds on the marginal edge probabilities for farflung dyads can be used to place a lower bound on the predictive power of distance, and one such bound is de- rived. Application of this bound to the special case of uniformly placed vertices on the plane suggests that only modest constraints are required for distance effects to dominate at large physical scales.

Exponential family models for random graphs (ERGs, also known as p∗ models) are an increasingly popular tool for the analysis of social networks. ERGs allow for the parameterization of complex dependence among edges within a likelihood-based framework, and are often used to model local influences on global structure. This paper introduces a family of cycle statistics, which allow for the modeling of long-range dependence within ERGs. These statistics are shown to arise from a family of partial conditional dependence assumptions based on an extended form of reciprocity, here called reciprocal path dependence. Algorithms for computing cycle statistic changescores and the cycle census are provided, as are analytical expressions for the first and approximate second moments of the cycle census under a Bernoulli null model. An illustrative application of ERG modeling using cycle statistics is also provided.

Taking a relational and systemic approach, this dissertation offers theoretical, methodological, and empirical advancements in understanding the social forces that drive or inhibit human migration. We consider migration flows among geographical areas as a network system, analyzing its dynamics using the exponential-family random graph models (ERGMs) and simulation methods. Chapter 2 grapples with the computational hurdle for modeling valued/weighted networks using ERGMs. We propose and implement an efficient parallelizable subsampled Maximum Pseudo-Likelihood Estimation (MPLE) scheme, which enables fast and accurate computation of ERGMs for big valued networks with high edge variance. The comparative simulation experiments further show whether and how the performance of existing computational approaches vary by the network size and the variance of edge values, providing guidelines for choosing and tuning those methods for different use cases. Chapter 3 applies the implemented method to study intercounty migration in the United States (U.S.), whose migration rates have declined for decades and reached a historical low. We found a pattern of "segmented immobility," where fewer people migrate between counties with dissimilar political contexts, levels of urbanization, and racial compositions. We also propose a "knockout experiment" framework to quantify the impact of segmentation on population immobility. The chapter reveals the social and political cleavages that underlie population immobility in the U.S., bridging the scholarly domains of (im)mobility, segregation, and polarization. Motivated by debates about California’s net migration loss ("California Exodus"), Chapter 4 examines the scale of and the mechanisms behind the migration-induced population redistribution among U.S. states. We combine ERGMs, knockout experiments, and a protocol for functional form visualization to understand the complex effects of political climates, housing costs, racial dynamics, and urbanization. The chapter offers an analytical framework for migration-induced population redistribution and demonstrates how generative statistical models can provide mechanistic insights beyond hypothesis-testing.

This dissertation investigates coordination as a key component of social systems, from a network of international environmental governance to a localized response to disaster. Chapter 2 is a study of international environmental agreement (IEA) co-ratification. I focus specifically on mixing effects and how these demonstrate a shift in the global configuration of cooperative behavior within this context. Chapter 3 dives deeper into this network, looking more closely at the structural factors influencing ratification of IEAs, including factors at the agreement level. The goal of this chapter was to better understand the formation of new ratification ties over time, while making several methodological contributions as well. Finally, Chapter 4 is a study of dynamic communication patterns across 17 localized first responder networks in the midst of a disaster. We utilized a recently developed tool for relational event model simulation to study the resilience of these networks as they reorganized in the face of varied disruption. This dissertation pushes for the view of social systems as interconnected and specifically demonstrates the advantages of studying coordination from this perspective. I hope it spurs future research on these topics, especially in the realm of environmental degradation as it is ever more often encompassing both regulation and disaster response.

Time-varying networks and techniques developed to study them have been used to analyze dynamic systems in social, computational, biological, and other contexts. Significant progress has been made in this area in recent years, resulting from a combination of statistical advances and improved computational resources, giving rise to a range of new research questions. This thesis addresses problems related to three lines of inquiry involving dynamic networks: data collection designs; the conditions needed for structural stability of an evolving network; and the computational scalability of statistical models for network dynamics. The first contribution involves a commonly neglected problem concerning data collection protocols for dynamic network data: the impact of in-design missingness. A systematic formalization is offered for the widely used class of retrospective life history designs, and it is shown that design parameters have nontrivial effects on both the quantity of missingness and the impact of such missingness on network modeling and reconstruction. Using a simulation study, we also show how the consequences of design parameters for inference vary as a function of look-back time relative to the time of measurement. The second contribution of this thesis is related to a fundamental question of network dynamics: when or where are changes in a network most likely to occur? A novel approach is taken to this question, by exploring its complement -- what factors stabilize a network (or subgraphs thereof) and make it resistant to change? For networks whose behavior can be parameterized in exponential family form, a formal characterization of the graph-stabilizing region of the parameter space is shown to correspond to a convex polytope in the parameter space. A related construction can be used to find subgraphs that are or are not stable with respect to a given parameter vector, and to identify edge variables that are most vulnerable to perturbation. Finally, the third contribution of this thesis is to scalable parameter estimation for a class of temporal exponential family random graph models (TERGM) from sampled data. An algorithm is proposed that allows accurate approximation of maximum likelihood estimates for certain classes of TERGMs from egocentrically sampled retrospective life history data, without requiring simulation of the underlying network (a major bottleneck when the network size is large). Estimation time for this algorithm scales with the data size, and not with the size of the network, allowing it to be employed on very large populations.

A field study was carried out to assess the impact of installing a desktop task/ambient conditioning (TAC) system at 42 selected workstations within three San Francisco office buildings occupied by a large financial institution. In this study, field measurements, including subjective surveys and physical monitoring, were performed both before and after the TAC system installation to evaluate the impact of the TAC system on occupant satisfaction and thermal comfort, as well as the thermal environments within the office buildings. For comparative purposes within each building, a control group, consisting of workers who did not receive a desktop TAC unit, was studied concurrently. During the follow-up field tests, performed three months after the TAC system installation, measurements were repeated under three different room temperature setpoint conditions (normal, set-up, and set-down) to investigate the ability of the occupants to use the desktop TAC units to control their local environment in response to a wider range of ambient temperatures.

Survey results show that among the six building assessment categories investigated, installation of the desktop TAC system provided the largest increases in overall occupant satisfaction for thermal quality, acoustical quality, and air quality. In terms of specific environmental factors, increased occupant satisfaction levels among the TAC group were strongly significant in comparison to changes within the control group for both temperature and temperature control. A large majority of the workers in the control group indicated a preference for higher air movement at operative temperatures of 73°F (23°C) and above. The percentage preferring higher air movement within the TAC group was significantly lower. Workers in the TAC group had the ability to use their TAC units to adjust the air movement in their workstations in response to changes in the ambient temperature. Over the range of operative temperatures covered by this field study, air movement preference and thermal sensation votes by workers in the control group indicated that they were more than twice as sensitive to changes in temperature as those in the TAC group.