Made available in DSpace on 2014-06-12T15:07:11Z (GMT). No. of bitstreams: 2 arquivo7213_1.pdf: 539249 bytes, checksum: 73d17b279ef19b26e6be9d08de14c08a (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2004 / Esta dissertação aborda algumas práticas juvenis associadas ao tempo do lazer, mais especificamente, o cotidiano de meninas com idade entre 13 e 18 anos, predominantemente, freqüentadoras de dois shopping centers de Recife PE. Através de observação participante, práticas como a freqüentação ao shopping, o consumo, as relações de gênero e de amizade e a construção de identidades de grupo se mostraram como aspectos centrais nas vidas das meninas que participaram da pesquisa. Foi possível perceber que havia um discurso que permeava e definia estas práticas, o que eu chamo de discurso do bom-comportamento, articulado para classificar os comportamentos em adequados ou inadequados, nomeando-os de diversas formas, e para localizar outras/os jovens entre nós e as/os outras/os . Dessa forma, surgem rótulos como o de maloqueiros , usado para classificar genericamente todos aqueles com comportamentos considerados inadequados, como o uso de vestimentas consideradas inapropriadas para determinados lugares, e hábitos como fumar e beber; galinhas para meninos e meninas que obedeciam a regras mais liberais nas relações de gênero, como o ficar descomprometido com diversos/as jovens; e patricinhas ou cocotinhas , articulado para definir meninas em geral bem-comportadas e que se preocupam com a aparência. Sendo que maloqueiros e galinhas são sempre jovens com determinados comportamentos que não fazem parte do círculo de amizade de quem aplica essas categorias, e que a patricinha ora é a boa-moça bemvestida (o nós ), ora a menina com excesso de frescuras (a outra ), percebe-se que o discurso do bom-comportamento é, antes de tudo, um discurso, no qual uma mesma característica poderá ser definida positiva ou negativamente. A análise e interpretação das práticas e discursos foi feita a partir do uso da categoria de gênero, portanto com uma perspectiva relacional; a noção de microcultura juvenil, através da qual a cultura dos jovensnão é vista como algo separado de uma cultura adulta. Além disso, nesse trabalho os jovens são abordados enquanto sujeitos ativos, com identidades, produtores de sentidos que vão além de práticas desviantes e espetaculares associadas a um possível período de rebeldia, estando inseridos em um contexto maior que lhes serve de acervo

One of the key themes of this research document examines the changes in architecture and the urban environment in relation to an event-oriented society. The urban entertainment culture of the 21st century has spawned a host of event worlds. The marketing of cities and regions with spectacular presentations of history and culture and the redevelopment of city centers to create event spaces are an eloquent expression of this development. In ever fewer cases is this process of urban re-organisation the product of town planning schemes devised by the public authorities. The commercializations of cities is being accompanied by a loss of public space, increasing controls and a growing exclusion of the poor and destitute. The commercializations of culture is celebrating its triumphal march in the concept of the “event city”. / Dissertation (MArch(Prof))--University of Pretoria, 2008. / Architecture / unrestricted

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020 / Cataloged from student-submitted PDF of thesis. / Includes bibliographical references (pages 261-268). / Sales promotions are a popular type of marketing strategy. When undertaking a sales promotion, products are promoted using short-term price reductions to stimulate their demand and increase their sales. These sales promotions are widely used in practice by retailers. When undertaking a sales promotion, retailers must take into consideration both the direct and indirect effects of price promotions on consumers, and as a result, on the demand. In this thesis, we consider the impact of two of these indirect effects on the planning process of promotions. First, we consider the problem of the promotion planning process for fast-moving consumer goods. The main challenge when considering the promotion planning problem for fast-moving consumer goods is the negative indirect effect of promotions on future sales. While temporary price reductions substantially increase demand, in the following periods after a temporary price reduction, retailers observe a slowdown in sales. / To capture this post promotion slowdown, we suggest a new set of past prices (namely, the last seen as well as the minimum price seen within a limited number of past periods) as features in the demand model. We refer to demand models that use this set of past prices as Bounded Memory Peak-End models. When tested on realworld data, our suggested demand model improved the estimation quality relative to a traditional estimation approach through a relative improvement in WMAPE by approximately 1 - 19%. In addition to the improvement in prediction accuracy, we analyze the sensitivity of our proposed Bounded Memory Peak-End demand model to demand misspecification. Through statistical analysis, and using principles from duality theory, we establish that even in the face of demand misspecification, the proposed Bounded Memory Peak-End model can capture the demand with provably low estimation error, and with low impact on the resulting optimal pricing policy. / The structure of the new proposed demand model allows us to derive fast algorithms that can find the optimal solution to the problem of promotion planning for a single item. For the case of promotion planning for multiple items, although we show that the problem is NP-hard in the strong sense, we propose a Polynomial Time Approximation Scheme that can solve the problem efficiently. Overall, we show that using our proposed approach, the retailer can obtain an increase of 4 - 15.6% in profit compared to current practice. Second, we consider the promotion targeting problem for trendy commodities. In the case of trendy commodities, the demand is driven, among other factors, by social trends. Examples of trendy commodities include fashion items, wearable electronics, and smartphones. To capture the demand with high accuracy, retailers must understand how the purchasing behavior of customers can impact the future purchasing behavior of other customers. / Social media can be instrumental in learning how consumers can impose trends on one another. Unfortunately, many retailers are unable to obtain this information due to high costs and privacy issues. This has motivated us to develop a model that detects customer relationships based only on transaction data history. Incorporating the customer to customer trend in the demand estimation, we observe a significant improvement of 12% in the WMAPE forecasting metric. The proposed customer to customer trend-based demand model subsequently allows us to formulate the promotion targeting optimization problem in a way that consider the indirect effect of targeted promotions through trends. We show that the problem of finding the personalized promotion policy that would maximize the profit function is NP-hard. Nonetheless, we introduce an adaptive greedy algorithm that is intuitive to implement and can find a provably near-optimal personalized promotion policy. / We tested our approach on Oracle data and observed a 5-12% improvement in terms of profit. / by Tamar Cohen-Hillel. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 119-123). / Industrial control systems (ICS) are pervasive in modern society and increasingly under threat of cyber attack. Due to the critical nature of these systems, which govern everything from power and wastewater plants to refineries and manufacturing, a successful ICS cyber attack can result in serious physical consequences. This thesis evaluates multiple anomaly detection methods to quickly and accurately detect ICS cyber attacks. Two fundamental challenges in developing ICS cyber attack detection methods are the lack of historical attack data and the ability of attackers to make their malicious activity appear normal. The goal of this thesis is to develop methods which generalize well to anomalies that are not included in the training data and to increase the sensitivity of detection methods without increasing the false alarm rate. The thesis presents and analyzes a baseline detection method, the multivariate Shewhart control chart, and four extensions to the Shewhart chart which use machine learning or optimization methods to improve detection performance. Two of these methods, stationary subspace analysis and maximized ratio divergence analysis, are based on dimensionality reduction techniques, and an additional model-based method is implemented using residuals from LASSO regression models. The thesis also develops an ensemble method which uses an optimization formulation to combine the output of multiple models in a way that minimizes detection delay. When evaluated on 380 samples from the Kasperskey Tennessee Eastman process dataset, a simulated chemical process that includes disruptions from cyber attacks, the ensemble method reduced detection delay on attack data by 12% (55 minutes) on average when compared to the baseline method and was 9% (42 minutes) faster on average than the method which performed best on training data. / by Jessamyn Liu. / S.M. / S.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 127-134). / During the past decade an increasing trend in the Data Center Network's traffic has been observed. This traffic is characterized mostly by many small bursty flows (mice) that last for less than few milliseconds as well as a few heavier more persistent (elephant) flows between certain number of nodes. As a result many relatively underutilized network links become momentarily hotspots with increased chance of packet loss. A potential solution could be given by Reconfigurable Optical Data Centers, due to higher traffic aggregation links and topology adaptation capabilities. An example is a novel two level hierarchical WDM-Based scalable Data Center Network architecture, RHODA, which is based on the interconnection of high speed equal sized clusters of Racks. We study the traffic based dynamic cluster membership reconfiguration of the Racks. Main goal is to maintain a near optimal network operation with respect to minimization of the inter cluster traffic, while emphasising better link utilization and network scalability. We present four algorithms, two deterministic greedy and two stochastic iterative, and discuss the tradeoffs of their use. Our results draw two main conclusion: 1) Stochastic iterative algorithms are more suitable for dynamic traffic based reconfiguration 2) Fast algorithmic deployments come at a price of reduced optimality / by Georgia Dimaki. / S.M. / S.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020 / Cataloged from student-submitted PDF of thesis. / Includes bibliographical references (pages 187-203). / Networks offer a unified, conceptual formalism for reasoning about complex, relational systems. While pioneering work in network science focused primarily on the ability of "universal" models to explain the features of observed systems, contemporary research increasingly focuses on challenges and opportunities for data analysis in complex systems. In this thesis we study four problems, each of which is informed by the need for theory-informed modeling in network data science. The first chapter is a study of binary-state adaptive voter models (AVMs). AVMs model the emergence of global opinion-based network polarization from localized decision-making, doing so through a simple coupling of node and edge states. This coupling yields rich behavior, including phase transitions and low-dimensional quasistable manifolds. However, the coupling also makes these models extremely difficult to analyze. / Exploiting a novel asymmetry in the local dynamics, we provide low-dimensional approximations of unprecedented accuracy for one AVM variant, and of competitive accuracy for another. In the second chapter, we continue our focus on fragmentation in social systems with a study of spatial segregation. While the question of how to measure and quantify segregation has received extensive treatment in the sociological literature, this treatment tends to be mathematically disjoint. This results in scholars often re-proving the same results for special cases of measures, and grappling with incomparable methods for incorporating the role of space in their analyses. We provide contributions to address each of these issues. With respect to the first, we unify a large body of extant segregation measures through the calculus of Bregman divergences, showing that the most popular measures are instantiations of generalized mutual informations. / We then formulate a microscopic measure of spatial structure - the local information density - and prove a novel information-geometric result in order to measure it on real data in the common case in which the data is embedded in planar network. Using these tools, we are then able to formulate and evaluate several network-based regionalization algorithms for multiscale spatial analysis. We then take up two questions in null random graph modeling. The first of these develops a family of null random models for hypergraphs, the natural mathematical representation of polyadic networks in which multiple entities interact simultaneously. We formulate two distributions over spaces of hypergraphs subject to fixed node degree and edge dimension sequences, and provide Markov Chain Monte Carlo algorithms for sampling from them. We then conduct a sequence of experiments to highlight the role of hypergraph configuration models in the data science of polyadic networks. / We show that (a) the use of hypergraph nulls can lead to directionally different hypothesis-testing than the use of traditional nulls and that (b) polyadic nulls support richer and more complex measurements of graph structure. We close with a formulation of a novel measure of correlation in hypergraphs, as well as an asymptotic formula for estimating its expectations under one of our configuration models. In the final chapter, we study the expected adjacency matrix of a uniformly random multigraph with a fixed degree sequence. This matrix is an input into several common network analyses, including community-detection and mean-field theories of spreading properties on contact networks. The actual structure of this matrix, however, is not well understood. The main issues are (a) the combinatorial complexity of the space on which this random graph is defined and (b) an erroneous folk-theorem among network scientists which stems from confusion with related models. / By studying the dynamics of a Markov chain sampler, we prove a sequence of approximations that allow us to estimate the expected adjacency matrix - and other elementwise moments - using a fast numerical scheme with qualified uniqueness guarantees. We illustrate using a series of experiments on primary and secondary school contact networks, showing order-of-magnitude improvements over extant methods. We conclude with a description of several directions of future work. / by Philip S. Chodrow. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 197-203). / Stochastic traffic simulators are widely used in the transportation community to model real-world urban road networks in applications ranging from real-time congestion routing and control to traffic state prediction. Online calibration of these simulators plays a crucial role in achieving high accuracy in the replication and prediction of streaming traffic data (i.e., link flows, densities). In order to be relevant in a real-time context, the problem must also be solved within a strict computational budget. The primary goal of this thesis is to develop an algorithm that adequately solves the online calibration problem for high-dimensional cases and on large-scale networks. In the first half, a new online calibration algorithm is proposed that incorporates structural information from an analytical metamodel into a general-purpose extended Kalman filter framework. / The metamodel is built around a macroscopic network model that relates calibration parameters to field measurements in an analytical, computationally tractable, and differentiable way. Using the metamodel as an analytical approximation of the traffic simulator improves the computational efficiency of the linearization step of the extended Kalman filter, making it suitable for use in large-scale calibration problems. The embedded analytical network model provides a secondary benefit of making the algorithm more robust to simulator stochasticity compared with traditional black-box calibration methods. In the second half, the proposed algorithm is adapted for the case study of online calibration of travel demand as defined by a set of time-dependent origin-destination matrices. First, an analytical network model relating origin-destination demand to link measurements is formulated and validated on the Singapore expressway network. / Next, the proposed algorithm is validated on a synthetic toy network, where its flexibility in calibrating to multiple sources of field data is demonstrated. The empirical results show marked improvement over the baseline of offline calibration and comparable performance to multiple benchmark algorithms from the literature. Finally, the proposed algorithm is applied to a problem of dimension 4,050 on the Singapore expressway network to evaluate its feasibility for large-scale problems. Empirical results confirm the real-time performance of the algorithm in a real-world setting, with strong accuracy in the estimation of sensor counts. / by Kevin Zhang. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020 / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 213-219). / With the rapid advancement of information technology and accelerated development of data science, the importance of integrating data into decision-making has never been stronger. In this thesis, we propose data-driven algorithms to incorporate learning from data in three operations problems, concerning both online learning and offline learning settings. First, we study a single product pricing problem with demand censoring in an offline data-driven setting. In this problem, a retailer is given a finite level of inventory, and faces a random demand that is price sensitive in a linear fashion with unknown parameters and distribution. Any unsatisfied demand is lost and unobservable. The retailer's objective is to use offline censored demand data to find an optimal price, maximizing her expected revenue with finite inventories. / We characterize an exact condition for the identifiability of near-optimal algorithms, and propose a data-driven algorithm that guarantees near-optimality in the identifiable case and approaches best-achievable optimality gap in the unidentifiable case. Next, we study the classic multi-period joint pricing and inventory control problem in an offline data-driven setting. We assume the demand functions and noise distributions are unknown, and propose a data-driven approximation algorithm, which uses offline demand data to solve the joint pricing and inventory control problem. We establish a polynomial sample complexity bound, the number of data samples needed to guarantee a near-optimal profit. A simulation study suggests that the data-driven algorithm solves the dynamic program effectively. Finally, we study an online learning problem for product selection in urban warehouses managed by fast-delivery retailers. We distill the problem into a semi-bandit model with linear generalization. / There are n products, each with a feature vector of dimension T. In each of the T periods, a retailer selects K products to offer, where T is much greater than T or b. We propose an online learning algorithm that iteratively shrinks the upper confidence bounds within each period. Compared to the standard UCB algorithm, we prove the new algorithm reduces the most dominant regret term by a factor of d, and experiments on datasets from Alibaba Group suggest it lowers the total regret by at least 10%.. / by Li Wang. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020 / Page 236 blank. Cataloged from PDF version of thesis. / Includes bibliographical references (pages 223-235). / The upstream parts of the agricultural supply chain consists of millions of smallholder farmers who continue to suffer from extreme poverty. The first stream of research in this thesis focuses on online agri-platforms which have been launched to connect geographically isolated markets in many developing countries. This work is in close collaboration with the state government of Karnataka in India which launched the Unified Market Platform (UMP). Leveraging both public data and platform data, a difference-in-differences analysis in Chapter 2 suggests that the implementation of the UMP has significantly increased modal price of certain commodities (5.1%-3.5%), while prices for other commodities have not changed. The analysis provides evidence that logistical challenges, bidding efficiency, market concentration, and price discovery process are important factors explaining the variable impact of UMP on prices. / Based on the insights, Chapter 3 describes the design, analysis and field implementation of a new two-stage auction mechanism. From February to May 2019, commodities worth more than $6 million (USD) had been traded under the new auction. Our empirical analysis suggests that the implementation has yielded a significant 4.7% price increase with an impact on farmer profitability ranging 60%-158%, affecting over 10,000 farmers who traded in the treatment market. The second stream of research work in the thesis turns to consumer welfare and identifies effective policies to tackle structural challenges of food safety and food security that arise in traditional agricultural markets. In Chapter 4, we develop a new modeling framework to investigate how quality uncertainty, supply chain dispersion, and imperfect testing capabilities jointly engender suppliers' adulteration behavior. / The results highlight the limitations of only relying on end-product inspection to deter EMA and advocate a more proactive approach that addresses fundamental structural problems in the supply chain. In Chapter 5, we analyze the issue of artificial shortage, the phenomenon that leads to food security risks where powerful traders strategically withhold inventory of essential commodities to create price surge in the market. The behavioral game-theoretic models developed allow us to examine the effectiveness of common government interventions. The analysis demonstrates the disparate effects of different interventions on artificial shortage; while supply allocation schemes often mitigate shortage, cash subsidy can inadvertently aggravate shortage in the market. Further, using field data from onion markets of India, we structurally estimate that 10% of the total supply is being hoarded by the traders during the lean season. / by Somya Singhvi. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020 / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 228-238). / .Retail operations have experienced a transformational change in the past decade with the advent and adoption of data-driven approaches to drive decision making. Granular data collection has enabled firms to make personalized decisions that improve customer experience and maintain long-term engagement. In this thesis we discuss important problems that retailers face in practice, before, while and after a product is introduced in the market. In Chapter 2, we consider the problem of estimating sales for a new product before retailers release the product to the customer. We introduce a joint clustering and regression method that jointly clusters existing products based on their features as well as their sales patterns while estimating their demand. Further, we use this information to predict demand for new products. Analytically, we show an out-of-sample prediction error bound. / Numerically, we perform an extensive study on real world data sets from Johnson & Johnson and a large fashion retailer and find that the proposed method outperforms state-of-the-art prediction methods and improves the WMAPE forecasting metric between 5%-15%. Even after the product is released in the market, a customer's decision of purchasing the product depends on the right recommendation personalized for her. In Chapter 3, we consider the problem of personalized product recommendations when customer preferences are unknown and the retailer risks losing customers because of irrelevant recommendations. We present empirical evidence of customer disengagement through real-world data. We formulate this problem as a user preference learning problem. We show that customer disengagement can cause almost all state-of-the-art learning algorithms to fail in this setting. / We propose modifying bandit learning strategies by constraining the action space upfront using an integer optimization model. We prove that this modification can keep significantly more customers engaged on the platform. Numerical experiments demonstrate that our algorithm can improve customer engagement with the platform by up to 80%. Another important decision a retailer needs to make for a new product, is its pricing. In Chapter 4, we consider the dynamic pricing problem of a retailer who does not have any information on the underlying demand for the product. An important feature we incorporate is the fact that the retailer also seeks to reduce the amount of price experimentation. / We consider the pricing problem when demand is non-parametric and construct a pricing algorithm that uses piecewise linear approximations of the unknown demand function and establish when the proposed policy achieves a near-optimal rate of regret (Õ)( [square root of] T), while making O(log log T) price changes. Our algorithm allows for a considerable reduction in price changes from the previously known O(log T) rate of price change guarantee found in the literature. Finally, once a purchase is made, a customer's decision to return to the same retailer depends on the product return polices and after-sales services of the retailer. As a result, in Chapter 5, we focus on the problem of reducing product returns. Closely working with one of India's largest online fashion retailers, we focus on identifying the effect of delivery gaps (total time that customers have to wait for the product they ordered to arrive) and customer promise dates on product returns. / We perform an extensive empirical analysis and run a large scale Randomized Control Trial (RCT) to estimate these effects. Based on the insights from this empirical analysis, we then develop an integer optimization model to optimize delivery speed targets. / by Divya Singhvi. / Ph. D. / Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center