Resilience to Climate Change: An Ethnographic Approach

Global projections for climate change impacts produce a startling picture of the future for low-lying coastal communities. The United States’ Chesapeake Bay region and especially marginalized and rural communities will be severely impacted by sea level rise and other changes over the next one hundred years. The concept of resilience has been theorized as a measure of social-ecological system health and as a unifying framework under which people can work together towards climate change adaptation. But it has also been critiqued for the way in which it does not adequately take into account local perspective and experiences, bringing into question the value of this concept as a tool for local communities. We must be sure that the concerns, weaknesses, and strengths of particular local communities are part of the climate change adaptation, decision-making, and planning process in which communities participate. An example of this type of planning process is the Deal Island Marsh and Community Project (DIMCP), a grant funded initiative to build resilience within marsh ecosystems and communities of the Deal Island Peninsula area of Maryland (USA) to environmental and social impacts from climate change. I argue it is important to have well-developed understandings of vulnerabilities and resiliencies identified by local residents and others to accomplish this type of work. This dissertation explores vulnerability and resilience to climate change using an engaged and ethnographic anthropological perspective. Utilizing participant observation, semi-structured and structured interviews, text analysis, and cultural domain analysis I produce an in-depth perspective of what vulnerability and resilience means to the DIMCP stakeholder network. Findings highlight significant vulnerabilities and resiliencies inherent in the local area and how these interface with additional vulnerabilities and resiliencies seen from a nonlocal perspective. I conclude that vulnerability and resilience are highly dynamic and context-specific for the local community. Vulnerabilities relate to climate change and other social and environmental changes. Resilience is a long-standing way of life, not a new concept related specifically to climate change. This ethnographic insight into vulnerability and resilience provides a basis for stronger engagement in collaboration and planning for the future.

Joint Optimization for Social Content Delivery in Wireless Networks

Over the last decade, success of social networks has significantly reshaped how people consume information. Recommendation of contents based on user profiles is well-received. However, as users become dominantly mobile, little is done to consider the impacts of the wireless environment, especially the capacity constraints and changing channel. In this dissertation, we investigate a centralized wireless content delivery system, aiming to optimize overall user experience given the capacity constraints of the wireless networks, by deciding what contents to deliver, when and how. We propose a scheduling framework that incorporates content-based reward and deliverability. Our approach utilizes the broadcast nature of wireless communication and social nature of content, by multicasting and precaching. Results indicate this novel joint optimization approach outperforms existing layered systems that separate recommendation and delivery, especially when the wireless network is operating at maximum capacity. Utilizing limited number of transmission modes, we significantly reduce the complexity of the optimization. We also introduce the design of a hybrid system to handle transmissions for both system recommended contents ('push') and active user requests ('pull'). Further, we extend the joint optimization framework to the wireless infrastructure with multiple base stations. The problem becomes much harder in that there are many more system configurations, including but not limited to power allocation and how resources are shared among the base stations ('out-of-band' in which base stations transmit with dedicated spectrum resources, thus no interference; and 'in-band' in which they share the spectrum and need to mitigate interference). We propose a scalable two-phase scheduling framework: 1) each base station obtains delivery decisions and resource allocation individually; 2) the system consolidates the decisions and allocations, reducing redundant transmissions. Additionally, if the social network applications could provide the predictions of how the social contents disseminate, the wireless networks could schedule the transmissions accordingly and significantly improve the dissemination performance by reducing the delivery delay. We propose a novel method utilizing: 1) hybrid systems to handle active disseminating requests; and 2) predictions of dissemination dynamics from the social network applications. This method could mitigate the performance degradation for content dissemination due to wireless delivery delay. Results indicate that our proposed system design is both efficient and easy to implement.

VOX POPULI: THREE ESSAYS ON THE USE OF SOCIAL MEDIA FOR VALUE CREATION IN SERVICES

Prior research shows that electronic word of mouth (eWOM) wields considerable influence over consumer behavior. However, as the volume and variety of eWOM grows, firms are faced with challenges in analyzing and responding to this information. In this dissertation, I argue that to meet the new challenges and opportunities posed by the expansion of eWOM and to more accurately measure its impacts on firms and consumers, we need to revisit our methodologies for extracting insights from eWOM. This dissertation consists of three essays that further our understanding of the value of social media analytics, especially with respect to eWOM. In the first essay, I use machine learning techniques to extract semantic structure from online reviews. These semantic dimensions describe the experiences of consumers in the service industry more accurately than traditional numerical variables. To demonstrate the value of these dimensions, I show that they can be used to substantially improve the accuracy of econometric models of firm survival. In the second essay, I explore the effects on eWOM of online deals, such as those offered by Groupon, the value of which to both consumers and merchants is controversial. Through a combination of Bayesian econometric models and controlled lab experiments, I examine the conditions under which online deals affect online reviews and provide strategies to mitigate the potential negative eWOM effects resulting from online deals. In the third essay, I focus on how eWOM can be incorporated into efforts to reduce foodborne illness, a major public health concern. I demonstrate how machine learning techniques can be used to monitor hygiene in restaurants through crowd-sourced online reviews. I am able to identify instances of moral hazard within the hygiene inspection scheme used in New York City by leveraging a dictionary specifically crafted for this purpose. To the extent that online reviews provide some visibility into the hygiene practices of restaurants, I show how losses from information asymmetry may be partially mitigated in this context. Taken together, this dissertation contributes by revisiting and refining the use of eWOM in the service sector through a combination of machine learning and econometric methodologies.