Scholarship concerning the importance of understanding audiences and venues for music has developed a great deal over the last two decades. This thesis examines one element of this research: the importance of the venue as a space for culture. The Royal Albert Hall, a world-famous but little- understood venue, acts as case study for this text. Through a mixed-methods approach, this thesis seeks to answer four questions concerning the relationship between a public space and the events it hosts in the case of the RAH explicitly: What factors have affected the identity of the RAH as a public venue? How have these changed during the Hall's existence? How do these factors affect the events which the Hall hosts? Does a space affect what happens inside it? These questions will allow us to gain a deeper understanding of how a fixed cultural space can be repeatedly reshaped by multiple, often overlooked, factors as well as the extent to which these factors can affect the identity of a venue.

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 151-163). / Water supply infrastructure planning faces many uncertainties. Uncertainty in short-term in rainfall and runoff, groundwater storage, and long-term climate change impacts water supply forecasts. Population and economic growth drive urban water demand growth at rapid but uncertain rates. Overbuilding infrastructure can lead to expensive stranded assets and unnecessary environmental impacts, while under building can cause reliability outages with impacts on the economy, ecosystems, and human health. This dissertation assesses the potential for Bayesian learning about uncertainty to enable flexible, adaptive approaches in which infrastructure can be changed over time to reduce cost risk while achieving reliability targets. It develops a novel planning framework that: 1) classifies uncertainties and applies appropriate, differentiated uncertainty analysis tools, 2) applies Bayesian inference to physical models of hydrology and climate to develop dynamic uncertainty estimates, and 3) uses stochastic dynamic programming and engineering options analysis to assess the value of flexibility in mitigating cost and reliability risk. This framework is applied to three applications. Chapter 3 evaluates the potential for modular desalination design to manage multiple, diverse uncertainties -- streamflow, demand growth, and the cost of water shortages -- in Melbourne, Australia. Chapter 4 addresses uncertainty in groundwater resources in desalination planning in Riyadh, Saudi Arabia, and Chapter 5 addresses model uncertainty in climate change projections in a dam design problem in Mombasa, Kenya. Across all three applications, we find value in flexible infrastructure planning with a 9-28% reduction in expected cost. However, the performance of flexible approaches compared to traditional robust approaches varies considerably and is influenced by technology choice, economies of scale, discounting, the presence of irreducible stochastic variability, and the value society places on water reliability. / by Sarah Marie Fletcher. / Ph. D. in Engineering Systems

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 168-171). / Space is one of the most scarce, expensive, and difficult to manage resources in urban retail establishments. A typical retail space broadly consists of two areas, the customer facing frontroom area and the backroom area, which is used for inventory storage and other support activities. While frontrooms have received considerable amount of attention from both academics and practitioners, backrooms are an often neglected area of retail space management and design. However, the allocation of space to the backroom and its management impact multiple operational aspects of retail establishments. These include in-store labor utilization, delivery schedules, product packaging, and inventory management. Therefore, the backroom area directly affects the performance of the store because it impacts stock-outs, customer service levels, and labor productivity. Moreover, extant literature suggests that backroom related operations contribute to a large fraction of the total retail supply chain costs. Thus, optimizing the management of backroom spaces is an important lever for store performance improvement. We address the gap in the extant literature related to space management of retail backrooms by investigating the following three questions: First, what is the effect of pack size on inventory levels and space needs in the backroom? Second, how can a given backroom space be efficiently utilized through optimal inventory control? Third, what is the optimal amount of space that should be allocated to the backroom in a given retail establishment? To address the first question, we evaluate the effect of two discrete pack sizes, order pack size (OPS) and storable pack size (SPS), on inventory levels and storage space requirements in the backrooms. While SPS drives the space needs for a given inventory level, OPS drives the amount of excess inventory and therefore, the space needs. Using inventory theory and probability theory, we quantify the amount of excess inventory and the expected stock-out probability for a given OPS in the case of a normally distributed demand. To address the second question, we discuss an inventory-theoretic approach to efficiently manage a given backroom space within a limited service restaurant. Specifically, we formulate a mathematical optimization model using mixed-integer linear programing with the objective of maximizing store profit. Applying this optimization model to real store data in collaboration with a major US retailer reveals cost implications related to constrained backroom space and the sensitivity of backroom space requirements to changes in OPS and SPS. The proposed model can serve as a decision support tool for various real-world use cases. For instance, the tool can help the retailers to identify (i) items whose contribution to the store profit does not justify their space needs in the backroom, and (ii) stores that are constrained in their profitability growth by backroom space limitations. To address the third question, we introduce the notion of interdependency between the frontroom and the backroom of a retail establishment. Such interdependencies yield nontrivial trade-offs inherent to the optimal retail space allocation. Demand can be lost due to unavailability of inventory (or inventory stock-out), which is a result of scarce amount of backroom space, or due to unavailability of sufficient frontroom space (or space stock-out). Furthermore, constrained backroom spaces increase in-store labor cost and the ordering costs incurred per unit of revenue generated in a retail establishment. The strategic decision model formulated in this chapter accounts for revenue, inventory cost, labor cost and ordering cost to determine the optimal amount of backroom space that should be allocated within a retail establishment. Sensitivity analyses with respect to the change in input parameters is used to connect the backroom space allocation and its impact on store profit to the different supply chain levers that can be managed by the retailers. / by Lita Das. / Ph. D. in Engineering Systems

Thesis: Ph. D. in Engineering Systems, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018. / Cataloged from PDF version of thesis. "Some pages in the original document contain text that runs off the edge of the page"--Disclaimer Notice page. / Includes bibliographical references. / China's rapid economic growth in the twenty-first century has driven, and been driven by, concomitant motorization and growth of passenger and freight mobility, leading to greater energy demand and environmental impacts. In this dissertation I develop methods to characterize the evolution of passenger transport demand in a rapidly-developing country, in order to support projection and policy assessment. In Essay #1, I study the role that vehicle tailpipe and fuel quality standards ("emissions standards") can play vis-à-vis economy-wide carbon pricing in reducing emissions of pollutants that lead to poor air quality. I extend a global, computable general equilibrium (CGE) model resolving 30 Chinese provinces by separating freight and passenger transport subsectors, road and non-road modes, and household-owned vehicles; and then linking energy demand in these subsectors to a province-level inventory of primary pollutant emissions and future policy targets. While climate policy yields an air quality co-benefit by inducing shifts away from dirtier fuels, this effect is weak within the transport sector. Current emissions standards can drastically reduce transportation emissions, but their overall impact is limited by transport's share in total emissions, which varies across provinces. I conclude that the two categories of measures examined are complementary, and the effectiveness of emissions standards relies on enforcement in removing older, higher-polluting vehicles from the roads. In Essay #2, I characterize Chinese households' demand for transport by estimating the recently-developed, Exact affine Stone index (EASI) demand system on publicly-available data from non-governmental, social surveys. Flexible, EASI demands are particularly useful in China's rapidly-changing economy and transport system, because they capture ways that income elasticities of demand, and household transport budgets, vary with incomes; with population and road network densities; and with the supply of alternative transport modes. I find transport demand to be highly elastic ([epsilon][subscript x] = 1.46) at low incomes, and that income-elasticity of demand declines but remains greater than unity as incomes rise, so that the share of transport in households' spending rises monotonically from 1.6 % to 7.5 %; a wider, yet lower range than in some previous estimates. While no strong effects of city-level factors are identified, these and other non-income effects account for a larger portion of budget share changes than rising incomes. Finally, in Essay #3, I evaluate the predictive performance of the EASI demand system, by testing the sensitivity of model fit to the data available for estimation, in comparison with the less flexible, but widely used, Almost Ideal demand system (AIDS). In rapidly-evolving countries such as China, survey data without nationwide coverage can be used to characterize transport systems, but the omission of cities and provinces could bias results. To examine this possibility, I estimate demand systems on data subsets and test their predictions against observations for the withheld fraction. I find that simple EASI specifications slightly outperform AIDS under cross-validation; these offer a ready replacement in standalone and CGE applications. However, a trade-off exists between accuracy and the inclusion of policy-relevant covariates when data omit areas with high values of these variables. Also, while province-level fixed-effects control for unobserved heterogeneity across units that may bias parameter estimates, they increase prediction error in out-of-sample applications-revealing that the influence of local conditions on household transport expenditure varies significantly across China's provinces. The results motivate targeted transport data collection that better spans variation on city types and attributes; and the validation technique aids transport modelers in designing and validating demand specifications for projection and assessment. / by Paul Natsuo Kishimoto. / Ph. D. in Engineering Systems

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Thesis: Ph. D. Engineering Systems: Human-Systems Engineering, Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, 2018 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 146-152). / Current approaches to nuclear security can produce elegantly designed physical protection systems (PPS) that may be limited by untenable assumptions or well stated-albeit vague and imprecise-descriptions of how to improve nuclear security culture itself. According to one nuclear security culture expert: While the International Atomic Energy Agency has released methodologies on evaluating vulnerabilities and physical protection, it has not yet introduced guidelines on assessing the human-factor in detection, delay, and response. (Khripunov, 2014, pp. 39-40) (Emphasis added) This dissertation argues that such a link lies in understanding how organizational influences affect the completion of tasks required for PPS to meet expected nuclear security performance goals. In this dissertation, I propose the System-Theoretic Framework for Security (the STFS) for evaluating system-level interactions between PPS and human/organizational behaviors to describe overall security performance. / Invoking key tenets of systems theory and organization science, the STFS uses the concept of "security task completion" to explain how the interactions between PPS and human/organizational behaviors result in security performance at nuclear facilities. Yet, empirical data is needed to explore the efficacy of this approach for incorporating organizational influences into security performance. As such, my research objectives were to: 1. Improve the understanding of how PPS and human/organizational behaviors interact to produce security performance at nuclear facilities, 2. Identify a manageable (but not exhaustive) set of organizational influences on this interaction, and 3. Develop a framework for assessing these interactions and organizational influences on security performance at nuclear facilities. I used a mixed methods research design to develop the STFS. / My first study consisted of 18 narrative interviews across different areas of nuclear security expertise and my second study examined the case of the 2012 security incident at the Y-12 National Security Complex. These two studies provided evidence for the security task completion construct (as a new causal mechanism), behavioral performance requirements (assumptions on which the causal mechanism is based), a set of organizational influences and quality indicators related to nuclear security performance. While this framework does not address every aspect of achieving high security performance, the STFS offers a structured thought process and direction for further development regarding how technologies and organizations interact to affect individual behaviors that contribute to security at nuclear facilities. / by Adam D. Williams. / Ph. D. Engineering Systems: Human-Systems Engineering / Ph.D.EngineeringSystems:Human-SystemsEngineering Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society

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