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Dissertations / Theses on the topic 'Center for Research in Social Systems'
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1
Maes, Pauline. "Engaging Content Experience- Utilizing the Strossle recommendation capabilities, across publishers’ websites." Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-21487.
Full textAbstract:
The project aims at exploring the process of designing recommender systems from a users’ perspective. Recommendations are the systems that can help users navigate in the overload of information, that is currently available online. This project focuses on the recommender network of Strossle, which provides article recommendations across various publishers’ websites. User-centered research has been performed to understand the current system and how that influences the users’ perceived experience. The goal was to develop a more engaging content experience for the Strossle recommendation system. This is done by means of participatory design methods. As people tend to use recommendations very sporadic and they often do not really know what they are looking for. The emphasis was on finding the balance between exploratory browsing and navigating towards the users’ preferences. In order to achieve this, a more dynamic widget has been developed that offers navigation in various related topics.
2
Dreser, Melanie. "Design, Fun and Sustainability: Utilizing Design Research Methods to Develop an Application to Inform and Motivate Students to Make Sustainable Consumer Choices." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1322669294.
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Eschenfeldt, Patrick Clark. "Multiserver queueing systems in heavy traffic." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/108834.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.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 107-109).
In the study of queueing systems, a question of significant current interest is that of large scale behavior, where the size of the system increases without bound. This regime has becoming increasingly relevant with the rise of massive distributed systems like server farms, call centers, and health care management systems. To minimize underutilization of resources, the specific large scale regime of most interest is one in which the work to be done increases as processing capability increases. In this thesis, we characterize the behavior of two such large scale queueing systems. In the first part of the thesis we consider a Join the Shortest Queue (JSQ) policy in the so-called Halfin-Whitt heavy traffic regime. We establish that a scaled process counting the number of idle servers and queues of length two weakly converges to a two-dimensional reflected Ornstein-Uhlenbeck process, while processes counting longer queues converge to a deterministic system decaying to zero in constant time. This limiting system is similar to that of the traditional Halfin-Whitt model in its basic performance measures, but there are key differences in the queueing behavior of the JSQ model. In particular, only a vanishing fraction of customers will have to wait, but those who do will incur a constant order waiting time. In the second part of the thesis we consider a widely studied so-called "supermarket model" in which arriving customers join the shortest of d randomly selected queues. Assuming rate n[lambda]n Poisson arrivals and rate 1 exponentially distributed service times, our heavy traffic regime is described by [lambda]n 1 as n --> [infinity]. We give a simple expectation argument establishing that queues have steady state length at least i* = logd 1/1-[lambda]n with probability approaching one as n [infinity] 8. Our main result for this system concerns the detailed behavior of queues with length smaller than i*. Assuming [lambda]n converges to 1 at rate at most [square root of]n, we show that the dynamics of such queues does not follow a diffusion process, as is typical for queueing systems in heavy traffic, but is described instead by a deterministic infinite system of linear differential equations, after an appropriate rescaling.
by Patrick Clark Eschenfeldt.
Ph. D.
4
Bailey-Shimizu, Pamelalee. "First Nations Tribal Library and Social Research Center." CSUSB ScholarWorks, 2000. https://scholarworks.lib.csusb.edu/etd-project/1952.
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Goldberg, David Alan Ph D. Massachusetts Institute of Technology. "Large scale queueing systems : asymptotics and insights." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/67765.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 195-203).
Parallel server queues are a family of stochastic models useful in a variety of applications, including service systems and telecommunication networks. A particular application that has received considerable attention in recent years is the analysis of call centers. A feature common to these models is the notion of the 'trade-off' between quality and efficiency. It is known that if the underlying system parameters scale together according to a certain 'square-root scaling law', then this trade-off can be precisely quantified, in which case the queue is said to be in the Halfin-Whitt regime. A common approach to understanding this trade-off involves restricting one's models to have exponentially distributed call lengths, and restricting one's analysis to the steady-state behavior of the system. However, these are considered shortcomings of much work in the area. Although several recent works have moved beyond these assumptions, many open questions remain, especially w.r.t. the interplay between the transient and steady-state properties of the relevant models. These questions are the primary focus of this thesis. In the first part of this thesis, we prove several results about the rate of convergence to steady-state for the A/M/rn queue, i.e. n-server queue with exponentially distributed inter-arrival and processing times, in the Halfini-Whitt regime. We identify the limiting rate of convergence to steady-state, discover an asymptotic phase transition that occurs w.r.t. this rate, and prove explicit bounds on the distance to stationarity. The results of the first part of this thesis represent an important step towards understanding how to incorporate transient effects into the analysis of parallel server queues. In the second part of this thesis, we prove several results regarding the steadystate G/G/n queue, i.e. n-server queue with generally distributed inter-arrival and processing times, in the Halfin-Whitt regime. We first prove that under minor technical conditions, the steady-state number of jobs waiting in queue scales like the square root of the number of servers. We then establish bounds for the large deviations behavior of this model, partially resolving a conjecture made by Gamarnik and Momcilovic in [431. We also derive bounds for a related process studied by Reed in [91]. We then derive the first qualitative insights into the steady-state probability that an arriving job must wait for service in the Halfin-Whitt regime, for generally distributed processing times. We partially characterize the behavior of this probability when a certain excess parameter B approaches either 0 or oo. We conclude by studying the large deviations of the number of idle servers, proving that this random variable has a Gaussian-like tail. We prove our main results by combining tools from the theory of stochastic comparison [99] with the theory of heavy-traffic approximations [113]. We compare the system of interest to a 'modified' queue, in which all servers are kept busy at all times by adding artificial arrivals whenever a server would otherwise go idle, and certain servers can permanently break down. We then analyze the modified system using heavy-traffic approximations. The proven bounds hold for all n, have representations as the suprema of certain natural processes, and may prove useful in a variety of settings. The results of the second part of this thesis enhance our understanding of how parallel server queues behave in heavy traffic, when processing times are generally distributed.
by David Alan Goldberg.
Ph.D.
6
Papush, Anna. "Data-driven methods for personalized product recommendation systems." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115655.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2018.Cataloged from PDF version of thesis.
Includes bibliographical references.
The online market has expanded tremendously over the past two decades across all industries ranging from retail to travel. This trend has resulted in the growing availability of information regarding consumer preferences and purchase behavior, sparking the development of increasingly more sophisticated product recommendation systems. Thus, a competitive edge in this rapidly growing sector could be worth up to millions of dollars in revenue for an online seller. Motivated by this increasingly prevalent problem, we propose an innovative model that selects, prices and recommends a personalized bundle of products to an online consumer. This model captures the trade-off between myopic profit maximization and inventory management, while selecting relevant products from consumer preferences. We develop two classes of approximation algorithms that run efficiently in real-time and provide analytical guarantees on their performance. We present practical applications through two case studies using: (i) point-of-sale transaction data from a large U.S. e-tailer, and, (ii) ticket transaction data from a premier global airline. The results demonstrate that our approaches result in significant improvements on the order of 3-7% lifts in expected revenue over current industry practices. We then extend this model to the setting in which consumer demand is subject to uncertainty. We address this challenge using dynamic learning and then improve upon it with robust optimization. We first frame our learning model as a contextual nonlinear multi-armed bandit problem and develop an approximation algorithm to solve it in real-time. We provide analytical guarantees on the asymptotic behavior of this algorithm's regret, showing that with high probability it is on the order of O([square root of] T). Our computational studies demonstrate this algorithm's tractability across various numbers of products, consumer features, and demand functions, and illustrate how it significantly out performs benchmark strategies. Given that demand estimates inherently contain error, we next consider a robust optimization approach under row-wise demand uncertainty. We define the robust counterparts under both polynomial and ellipsoidal uncertainty sets. Computational analysis shows that robust optimization is critical in highly constrained inventory settings, however the price of robustness drastically grows as a result of pricing strategies if the level of conservatism is too high.
by Anna Papush.
Ph. D.
7
Zarybnisky, Eric J. (Eric Jack) 1979. "Maintenance scheduling for modular systems-models and algorithms." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68972.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 185-188).
Maintenance scheduling is an integral part of many complex systems. For instance, without effective maintenance scheduling, the combined effects of preventative and corrective maintenance can have severe impacts on the availability of those systems. Based on current Air Force trends including maintenance manpower, dispersed aircraft basing, and increased complexity, there has been a renewed focus on preventative maintenance. To address these concerns, this thesis develops two models for preventative maintenance scheduling for complex systems, the first of interest in the system concept development and design phase, and the second of interest during operations. Both models are highly complex and intractable to solve in their original forms. For the first model, we develop approximation algorithms that yield high quality and easily implementable solutions. To address the second model, we propose a decomposition strategy that produces submodels that can be solved via existing algorithms or via specialized algorithms we develop. While much of the literature has examined stochastically failing systems, preventative maintenance of usage limited systems has received less attention. Of particular interest is the design of modular systems whose components must be repaired/replaced to prevent a failure. By making cost tradeoffs early in development, program managers, designers, engineers, and test conductors can better balance the up front costs associated with system design and testing with the long term cost of maintenance. To facilitate such a tradeoff, the Modular Maintenance Scheduling Problem provides a framework for design teams to evaluate different design and operations concepts and then evaluate the long term costs. While the general Modular Maintenance Scheduling Problem does not require maintenance schedules with specific structure, operational considerations push us to consider cyclic schedules in which components are maintained at a fixed frequency. In order to efficiently find cyclic schedules, we propose the Cycle Rounding algorithm, which has an approximation guarantee of 2, and a family of Shifted Power-of-Two algorithms, which have an approximation guarantee of 1/ ln(2) ~ 1.4427. Computational results indicate that both algorithms perform much better than their associated performance guarantees providing solutions within 15%-25% of a lower bound. Once a modular system has moved into operations, manpower and transportation scheduling become important considerations when developing maintenance schedules. To address the operations phase, we develop the Modular Maintenance and System Assembly Model to balance the tradeoffs between inventory, maintenance capacity, and transportation resources. This model explicitly captures the risk-pooling effects of a central repair facility while also modeling the interaction between repair actions at such a facility. The full model is intractable for all but the smallest instances. Accordingly, we decompose the problem into two parts, the system assembly portion and module repair portion. Finally, we tie together the Modular Maintenance and System Assembly Model with key concepts from the Modular Maintenance Scheduling Problem to propose an integrated methodology for design and operation.
by Eric Jack Zarybnisky.
Ph.D.
8
Werner, Loren M. (Loren Michael) 1977. "Analysis and design of closed loop manufacturing systems." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/82688.
Full textAbstract:
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2001.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 89-90).
by Loren M. Werner.
S.M.
9
Chhaochhria, Pallav. "Forecast-driven tactical planning models for manufacturing systems." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68700.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011.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 (p. 243-247).
Our work is motivated by real-world planning challenges faced by a manufacturer of industrial products. In the first part of the thesis, we study a multi-product serial-flow production line that operates in a low-volume, long lead-time environment. The objective is to minimize variable operating costs, in the face of forecast uncertainty, raw material arrival uncertainty and in-process failure. We develop a dynamic-programming-based tactical model to capture the key uncertainties and trade-offs, and to determine the minimum-cost operating tactics. The tactics include smoothing production to reduce production-related costs, and segmenting the serial-flow line with decoupling buffers to protect against variance propagation. For each segment, we specify a work release policy and a production control policy to manage the work-in-process inventory within the segment and to maintain the inventory targets in the downstream buffer. We also optimize the raw material ordering policy with fixed ordering times, long lead-times and staggered deliveries. In the second part of the thesis, we examine a multi-product assembly system that operates in a high-volume, short lead- time environment. The operating tactics used here include determining a fixed-length cyclic schedule to control production, in addition to smoothing production and segmenting the system with decoupling buffers. We develop another dynamic-programming-based tactical model that determines optimal policies for production planning and scheduling, inventory, and raw material ordering; these policies minimize the operating cost for the system in the face of forecast and raw material arrival uncertainty. We tested these models on both hypothetical and actual factory scenarios. The results confirmed our intuition and also helped develop new managerial insights on the application of these operating tactics. Moreover, the tactical model's factory performance predictions were found to be within 10% of simulation results for the testbed systems, thus validating the models.
by Pallav Chhaochhria.
Ph.D.
10
Achy-Brou, Aristide C. E. 1976. "A new approach to multistage serial inventory systems." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/8776.
Full textAbstract:
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2001.Includes bibliographical references (leaves 61-62).
We consider a single product multistage serial inventory system with several installations, say N - I, ... , l. Installation N - I intakes exogenous supply of a single commodity. For i E {I, ... N - 2}, installation i is supplied by shipments from installation i + 1. Demands for the finished good occur at installation l. Demands that cannot be filled immediately are backlogged. We assume holding costs at each installation which are linear functions of inventory, as well as a constant cost for each unit of backlogged demand, per period. Clark and Scarf {1960) showed that over a finite horizon an echelon basestock policy is optimal. Federgruen and Zipkin (1984) extend their result to the infinite-horizon case for both discounted and average costs. We present a new approach to this multistage serial inventory management problem, and give new proofs of these results by introducing and solving a simple Travel Time problem, using Dynamic Programming. This approach is motivated by the fact that the exact cost-to-go function of the related Travel Time problem can be easily computed using a straightforward recursive procedure (instead of using the typical value iteration or policy iteration methods). Moreover, this cost-to-go function gives various insights useful for a group of more complex multistage inventory problems. In this regard, we discuss how this cost-to-go function can be used to develop good Approximate Dynamic Programming algorithms for a number of complex multistage serial inventory problems. The results obtained suggest that the idea of introducing a related "Travel Time" problem and our algorithm to solve this problem can be used as a building block of a new approach to solve large scale multistage inventory management problems. This thesis was part of a research effort to find a fast algorithm to get very good robust suboptimal solutions to large scale multistage inventory management problems.
by Aristide C.E. Achy-Brou.
S.M.
11
Lamperski, Jourdain Bernard. "Structural and algorithmic aspects of linear inequality systems." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128971.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020Cataloged from PDF version of thesis.
Includes bibliographical references (pages 167-170).
Linear inequality systems play a foundational role in Operations Research, but many fundamental structural and algorithmic questions about linear inequality systems remain unanswered. This thesis considers and addresses some of these questions. In the first chapter, we reconsider the ellipsoid algorithm applied to solving a system of linear inequalities. Unlike the simplex method and interior point methods, the ellipsoid algorithm has no mechanism for proving that a system is infeasible (in the real model of computation). Motivated by this, we develop an ellipsoid algorithm that produces a solution to a system or provides a simple proof that no solution exists. Depending on the dimensions and on other natural condition measures, the computational complexity of our algorithm may be worse than, the same as, or better than that of the standard ellipsoid algorithm.. In the second chapter, we reduce the problem of solving a homogeneous linear inequality system to the problem of finding the unique sink of a unique sink orientation (USO) in the vertex evaluation model of computation. We show the USOs of interest satisfy a local property that is not satisfied by all USOs that satisfy the Holt-Klee property. This addresses an open question that is motivated by the idea that such local structure could be leveraged algorithmically to develop faster algorithms or a strongly polynomial algorithm. In the third chapter, we make progress on a conjecture about a particular class of linear inequality systems that have balanced constraint matrices. A balanced matrix is a 0-1 matrix that does not contain a square submatrix of odd order with two ones per row and column. The conjecture asserts that every nonzero balanced matrix contains an entry equal to 1, which upon setting to 0, leaves the matrix balanced.
by Jourdain Bernard Lamperski.
Ph. D.
Ph.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center
12
Sun, Xu Andy. "Advances in electric power systems : robustness, adaptability, and fairness." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/68971.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 151-157).
The electricity industry has been experiencing fundamental changes over the past decade. Two of the arguably most significant driving forces are the integration of renewable energy resources into the electric power system and the creation of the deregulated electricity markets. Many new challenges arise. In this thesis, we focus on two important ones: How to reliably operate the power system under high penetration of intermittent and uncertain renewable resources and uncertain demand: and how to design an electricity market that considers both efficiency and fairness. We present some new advances in these directions. In the first part of the thesis, we focus on the first issue in the context of the unit commitment (UC) problem, one of the most critical daily operations of an electric power system. Unit commitment in large scale power systems faces new challenges of increasing uncertainty from both generation and load. We propose an adaptive robust model for the security constrained unit commitment problem in the presence of nodal net load uncertainty. We develop a practical solution methodology based on a combination of Benders decomposition type algorithm and outer approximation techniques. We present an extensive numerical study on the real-world large scale power system operated by the ISO New England (ISO-NE). Computational results demonstrate the advantages of the robust model over the traditional reserve adjustment approach in terms of economic efficiency, operational reliability, and robustness to uncertain distributions. In the second part of the thesis, we are concerned with a geometric characterization of the performance of adaptive robust solutions in a multi-stage stochastic optimization problem. We study the notion of finite adaptability in a general setting of multi-stage stochastic and adaptive optimization. We show a significant role that geometric properties of uncertainty sets, such as symmetry, play in determining the power of robust and finitely adaptable solutions. We show that a class of finitely adaptable solutions is a good approximation for both the multi-stage stochastic as well as the adaptive optimization problem. To the best of our knowledge, these are the first approximation results for multi-stage problems in such generality. Moreover, the results and the proof techniques are quite general and extend to include important constraints such as integrality and linear conic constraints. In the third part of the thesis, we focus on how to design an auction and pricing scheme for the day-ahead electricity market that achieves both economic efficiency and fairness. The work is motivated by two outstanding problems in the current practice - the uplift problem and equitable selection problem. The uplift problem is that the electricity payment determined by the electricity price cannot fully recover the production cost (especially the fixed cost) of some committed generators, and therefore the ISOs make side payments to such generators to make up the loss. The equitable selection problem is how to achieve fairness and integrity of the day-ahead auction in choosing from multiple (near) optimal solutions. We offer a new perspective and propose a family of fairness based auction and pricing schemes that resolve these two problems. We present numerical test result using ISO-NE's day-ahead market data. The proposed auction- pricing schemes produce a frontier plot of efficiency versus fairness, which can be used as a vaulable decision tool for the system operation.
by Xu Andy Sun.
Ph.D.
13
Katz-Rogozhnikov, Dmitriy A. "Algorithmic issues in queueing systems and combinatorial counting problems." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45945.
Full textAbstract:
Includes bibliographical references (leaves 111-118).Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008.
(cont.) However, these randomized algorithms can never provide proven upper or lower bounds on the number of objects they are counting, but can only give probabilistic estimates. We propose a set of deterministic algorithms for counting such objects for three classes of counting problems. They are interesting both because they give an alternative approach to solving these problems, and because unlike MCMC algorithms, they provide provable bounds on the number of objects. The algorithms we propose are for special cases of counting the number of matchings, colorings, or perfect matchings (permanent), of a graph.
Multiclass queueing networks are used to model manufacturing, computer, supply chain, and other systems. Questions of performance and stability arise in these systems. There is a body of research on determining stability of a given queueing system, which contains algorithms for determining stability of queueing networks in some special cases, such as the case where there are only two stations. Yet previous attempts to find a general characterization of stability of queueing networks have not been successful.In the first part of the thesis, we contribute to the understanding of why such a general characterization could not be found. We prove that even under a relatively simple class of static buffer priority scheduling policies, stability of deterministic multiclass queueing network is, in general, an undecidable problem. Thus, there does not exist an algorithm for determining stability of queueing networks, even under those relatively simple assumptions. This explains why such an algorithm, despite significant efforts, has not been found to date. In the second part of the thesis, we address the problem of finding algorithms for approximately solving combinatorial graph counting problems. Counting problems are a wide and well studied class of algorithmic problems, that deal with counting certain objects, such as the number of independent sets, or matchings, or colorings, in a graph. The problems we address are known to be #P-hard, which implies that, unless P = #P, they can not be solved exactly in polynomial time. It is known that randomized approximation algorithms based on Monte Carlo Markov Chains (MCMC) solve these problems approximately, in polynomial time.
by Dmitriy A. Katz-Rogozhnikov.
Ph.D.
14
Rikun, Alexander Anatolyevich. "Applications of robust optimization to queueing and inventory systems." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/67768.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2011.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 105-111).
This thesis investigates the application of robust optimization in the performance analysis of queueing and inventory systems. In the first part of the thesis, we propose a new approach for performance analysis of queueing systems based on robust optimization. We first derive explicit upper bounds on performance for tandem single class, multiclass single server, and single class multi-server queueing systems by solving appropriate robust optimization problems. We then show that these bounds derived by solving deterministic optimization problems translate to upper bounds on the expected steady-state performance for a variety of widely used performance measures such as waiting times and queue lengths. Additionally, these explicit bounds agree qualitatively with known results. In the second part of the thesis, we propose methods to compute (s,S) policies in supply chain networks using robust and stochastic optimization and compare their performance. Our algorithms handle general uncertainty sets, arbitrary network topologies, and flexible cost functions including the presence of fixed costs. The algorithms exhibit empirically practical running times. We contrast the performance of robust and stochastic (s,S) policies in a numerical study, and we find that the robust policy is comparable to the average performance of the stochastic policy, but has a considerably lower standard deviation across a variety of networks and realized demand distributions. Additionally, we identify regimes when the robust policy exhibits particular strengths even in average performance and tail behavior as compared with the stochastic policy.
by Alexander Anatolyevich Rikun.
Ph.D.
15
Owen, Zachary Davis. "Revenue management and learning in systems of reusable resources." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119283.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 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 183-186).
Many problems in revenue management and operations management more generally can be framed as problems of resource allocation. This thesis focuses on developing policies and guarantees for resource allocation problems with reusable resources and on learning models for personalized resource allocation. First, we address the problem of pricing and assortment optimization for reusable resources under time-homogeneous demand. We demonstrate that a simple randomized policy achieves at least one half of the optimal revenue in both the pricing and assortment settings. Further, when prices are fixed a priori, we develop a method to compute the optimal randomized state-independent assortment policy. The performance of our policies is evaluated in numerical experiments based on arrival rate and parking time data from a municipal parking system. Though our algorithms perform well, our computational results suggest that dynamic pricing strategies are of limited value in the face of a consistent demand stream. Motivated in part by the computational results of the previous section, in the second section, we consider the problem of pricing and assortment optimization for reusable resource under time-varying demand. We develop a time-discretization strategy that yields a constant-factor performance guarantee relative to the optimal policy continuous-time policy. Additionally, we develop heuristic methods that implement a bid-price strategy between available resources based on pre-computed statistics that is computable in real-time. These methods effectively account for the future value of resources that in turn depend on the future patterns of demand. We validate our methods on arrival patterns derived from real arrival rate patterns in a parking context. In the third part, we consider the problem of learning contextual pricing policies more generally. We propose a framework for making personalized pricing decisions based on a multinomial logit model with features based on both customer attributes, item attributes, and their interactions. We demonstrate that our modeling procedure is coherent and in the well specified setting we demonstrate finite sample bounds on the performance of our strategy based on the size of the training data.
by Zachary Davis Owen.
Ph. D.
16
Herrling, Austin Donald First Lieutenant. "Optimization of micro-coaxial wire routing in complex microelectronic systems." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/119285.
Full textAbstract:
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 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 109-111).
In this thesis, we explore wire routing strategies for new paradigms in chip design. Where current chip design techniques involve multi-layered techniques to prevent wire crossings and electrical interference, we work with new technology that utilizes coaxial wires, allowing the construction of single-layered chips. Though the single layer lends itself well to optimization techniques, this approach generates novel challenges of its own. We design and implement multiple global routing algorithms appropriate for the new technology, and we discuss how these algorithms address technical constraints introduced by dierent variations of the routing problem. We cover three approaches using dierent techniques; these include simulated annealing, local heuristics, and global mixed-integer optimization. We demonstrate the performance of these algorithms on physical chip designs and existing layouts, including metrics of total wire length, overall routability, and running time. We also discuss our process of algorithm design, specically in context of satisfying engineering requirements decided by an external technical team. Finally, we describe our ideas for future areas of research, tailored towards improvement of our approaches and addressing technical problems that will be introduced as the new technology develops.
by Austin Donald Herrling.
S.M.
17
Roels, Guillaume. "Information and decentralization in inventory, supply chain, and transportation systems." Thesis, Massachusetts Institute of Technology, 2006. http://hdl.handle.net/1721.1/36228.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2006.Includes bibliographical references (p. 199-213).
This thesis investigates the impact of lack of information and decentralization of decision-making on the performance of inventory, supply chain, and transportation systems. In the first part of the thesis, we study two extensions of a classic single-item, single-period inventory control problem: the "newsvendor problem." We first analyze the newsvendor problem when the demand distribution is only partially specified by some moments and shape parameters. We determine order quantities that are robust, in the sense that they minimize the newsvendor's maximum regret about not acting optimally, and we compute the maximum value of additional information. The minimax regret approach is scalable to solve large practical problems, such as those arising in network revenue management, since it combines an efficient solution procedure with very modest data requirements. We then analyze the newsvendor problem when the inventory decision-making is decentralized. In supply chains, inventory decisions often result from complex negotiations among supply partners and might therefore lead to a loss of efficiency (in terms of profit loss).
(cont.) We quantify the loss of efficiency of decentralized supply chains that use price-only contracts under the following configurations: series, assembly, competitive procurement, and competitive distribution. In the second part of the thesis, we characterize the dynamic nature of traffic equilibria in a transportation network. Using the theory of kinematic waves, we derive an analytical model for traffic delays capturing the first-order traffic dynamics and the impact of shock waves. We then incorporate the travel-time model within a dynamic user equilibrium setting and illustrate how the model applies to solve a large network assignment problem.
by Guillaume Roels.
Ph.D.
18
Guo, Jingqiang Charles. "Estimation of sell-up potential in airline revenue management systems." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45800.
Full textAbstract:
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2008.Includes bibliographical references (p. 69-71).
The growth of Low Fare Carriers (LFCs) has encouraged many airlines to remove fare restrictions (such as advance purchase requirements and Saturday-night stays) on many of their fare class products, leading to the simplification of fare structures in competitive markets. In the most extreme case, these markets have fare structures that are unrestricted; the fare class products differ only by price since they AL1 lack restrictions. In these unrestricted markets, passengers buy the lowest possible fare product since there are no longer any restrictions that prevent them from doing so. A forecasting method known as "Q-forecasting" takes into account the sell- up potential of passengers in forecasting the demand in each of the fare products in such markets. Sell-up occurs when passengers upon being denied their original fare class choice, decide to pay more for the next available fare class so long as the price remains below their maximum willingness to pay. Quantifying this sell-up potential either using estimated or input values is thus crucial in helping airlines increase revenues when competing in unrestricted fare markets. A simulation model known as the Passenger Origin-Destination Simulator (PODS) contains the following 3 sell-up estimation methods: (i) Direct Observation (DO), (ii) Forecast Prediction (FP), and (iii) Inverse Cumulative (IC). The goal of this thesis is thus to investigate and compare the revenue performance of the 3 sell-up estimation methods. These methods are tested in a 2-airline (consisting of AL1 and AL2) unrestricted network under different RM fare class optimization scenarios.
(cont.) Both estimated and input sell-up values are tested on AL1 whereas only input sell-up values are tested on AL2. The findings of the simulations indicate that using FP typically results in the highest revenues for AL1 among AL1 3 sell-up estimation methods. When compared against simple RM fare class threshold methods that do not consider sell-up, using FP results in up to a 3% revenue gain for AL1. Under some fare class optimization scenarios, using FP instead of input sell-up values even results in a revenue increase of close to 1%. These findings suggest that FP is robust enough under a range of fare class optimizers to be used by airlines as a sell-up estimator in unrestricted fare environments so as to raise revenues.
by Jingqiang Charles Guo.
S.M.
19
Sun, Wei Ph D. Massachusetts Institute of Technology. "Price of anarchy in supply chains, congested systems and joint ventures." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/77823.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 169-174).
This thesis studies the price of anarchy in supply chains, congested systems and joint ventures. It consists of three main parts. In the first part, we investigate the impact of imperfect competition with nonlinear demand. We focus on a distribution channel with a single supplier and multiple downstream retailers. To evaluate the performance, we consider several metrics, including market penetration, total profit, social welfare and rent extraction. We quantify the performance with tight upper and lower bounds. We show that with substitutes, while competition improves the efficiency of a decentralized supply chain, the asymmetry among the retailers deteriorates the performance. The reverse happens when retailers carry complements. We also show that efficiency of a supply chain with concave (convex) demand is higher (lower) than that with affine demand. The second part of the thesis studies the impact of congestion in an oligopoly by incorporating convex costs. Costs could be fully self-contained or have a spillover component, which depends on others' output. We show that when costs are fully self-contained, the welfare loss in an oligopoly is at most 25% of the social optimum, even in the presence of highly convex costs. With spillover cost, the performance of an oligopoly depends on the relative magnitude of spillover cost to the marginal benefit to consumers. In particular, when spillover cost outweighs the marginal benefit, the welfare loss could be arbitrarily bad. The third part of the thesis focuses on capacity planning with resource pooling in joint ventures under demand uncertainties. We distinguish heterogeneous and homogeneous resource pooling. When resources are heterogeneous, the effective capacity in a joint venture is constrained by the minimum individual contribution. We show that there exists a unique constant marginal revenue sharing scheme which induces the same outcome in a Nash equilibrium, Nash Bargaining and the system optimum. The optimal scheme rewards every participant proportionally with respect to his marginal cost. When resources are homogeneous, we show that the revenue sharing ratio should be inversely proportional to a participant's marginal cost.
by Wei Sun.
Ph.D.
20
Lin, Maokai. "Optimization and equilibrium in dynamic networks and applications in traffic systems." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97776.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015.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 171-178).
This thesis discusses optimization problems and equilibrium in networks. There are three major parts of the thesis. In the first part, we discuss optimization in dynamic networks. We focus on two fundamental optimization problems in dynamic networks: the quickest flow problem and the quickest transshipment problem. The quickest flow problem is to find a minimum time needed to send a given amount of flow from one origin to one destination in a dynamic network. The quickest transshipment problem is similar to the quickest flow problem except with multiple origins and multiple destinations. We derive optimality conditions for the quickest flow problems and introduce simplified and more efficient algorithms for the quickest flow problems. For the quickest transshipment problem, we develop faster algorithms for several special cases and apply the approach to approximate an optimal solution more efficiently. In the second part, we discuss equilibrium in dynamic networks. We extend equilibrium results in static networks into dynamic networks and show that equilibria exist in a network where players either have the same origin or the same destination. We also introduce algorithms to compute such an equilibrium. Moreover, we analyze the average convergence speed of the best-response dynamics and connect equilibria in discrete network models to equilibria in continuous network models. In the third part, we introduce a new traffic information exchange system. The new system resolves the dilemma that broadcasting traffic predictions might affect drivers' behaviors and make the predictions inaccurate. We build game theoretic models to prove that drivers have incentives to use this system. In order to further test the effectiveness of such system, we run a series of behavioral experiments through an online traffic game. Experimental results show that drivers who use the system have a lower average travel time than the general public, and the system can help improve the average travel time of all drivers as the number of drivers who use this system increases.
by Maokai Lin.
Ph. D.
21
Liu, Jessamyn. "Anomaly detection methods for detecting cyber attacks in industrial control systems." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129055.
Full textAbstract:
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, September, 2020Cataloged 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
22
Marks, Christopher E. (Christopher Edward). "Analytic search methods in online social networks." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112012.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.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 175-185).
This thesis presents and evaluates methods for searching and analyzing social media data in order to improve situational awareness. We begin by proposing a method for network vertex search that looks for the target vertex by sequentially examining the neighbors of a set of "known" vertices. Using a dynamic programming approach, we show that there is always an optimal "block" search policy, in which all of the neighbors of a known vertex are examined before moving on to another vertex. We provide a precise characterization of the optimal policy in two specific cases: (1) when the connections between the known vertices and the target vertex are independent, and (2) when the target vertex is connected to at most one known vertex. We then apply this result to the problem of finding new accounts belonging to Twitter users whose previous accounts had been suspended for extremist activity, quantifying the performance of our optimal search policy in this application against other policies. In this application we use thousands of Twitter accounts related to the Islamic State in Iraq and Syria (ISIS) to develop a behavioral models for these extremist users. These models are used to identify new extremist accounts, identify pairs of accounts belonging to the same user, and predict to whom a user will connect when opening an account. We use this final model to inform our network search application. Finally, we develop a more general application of network search and classification that obtains a set of social media users from a specified location or group. We propose an expand -- classify methodology which recursively collects users that have social network connections to users inside the target location, and then classifies all of the users by maximizing the probability over a factor graph model. This factor graph model accounts for the implications of both observed user profile features and social network connections in inferring location. Using geo-located data to evaluate our method, we find that our classification method typically outperforms Twitter's native search methods in building a dataset of Twitter users in a specific location.
by Christopher E. Marks.
Ph. D.
23
Lobel, Ilan. "Social networks : rational learning and information aggregation." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/54232.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2009.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 (p. 137-140).
This thesis studies the learning problem of a set of agents connected via a general social network. We address the question of how dispersed information spreads in social networks and whether the information is efficiently aggregated in large societies. The models developed in this thesis allow us to study the learning behavior of rational agents embedded in complex networks. We analyze the perfect Bayesian equilibrium of a dynamic game where each agent sequentially receives a signal about an underlying state of the world, observes the past actions of a stochastically-generated neighborhood of individuals, and chooses one of two possible actions. The stochastic process generating the neighborhoods defines the network topology (social network). We characterize equilibria for arbitrary stochastic and deterministic social networks and characterize the conditions under which there will be asymptotic learning -- that is, the conditions under which, as the social network becomes large, the decisions of the individuals converge (in probability) to the right action. We show that when private beliefs are unbounded (meaning that the implied likelihood ratios are unbounded), there will be asymptotic learning as long as there is some minimal amount of expansion in observations. This result therefore establishes that, with unbounded private beliefs, there will be asymptotic learning in almost all reasonable social networks. Furthermore, we provide bounds on the speed of learning for some common network topologies. We also analyze when learning occurs when the private beliefs are bounded.
(cont.) We show that asymptotic learning does not occur in many classes of network topologies, but, surprisingly, it happens in a family of stochastic networks that has infinitely many agents observing the actions of neighbors that are not sufficiently persuasive. Finally, we characterize equilibria in a generalized environment with heterogeneity of preferences and show that, contrary to a nave intuition, greater diversity (heterogeneity) 3 facilitates asymptotic learning when agents observe the full history of past actions. In contrast, we show that heterogeneity of preferences hinders information aggregation when each agent observes only the action of a single neighbor.
by Ilan Lobel.
Ph.D.
24
Cohen, Maxime C. "Pricing for retail, social networks and green technologies." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/101291.
Full textAbstract:
Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015.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.
What is the right price to charge your customers? Many retailers and researchers are facing this question. In the last three decades, tremendous progress was made, both in the academic and business worlds. In this thesis, we investigate four novel pricing applications. In the first part, we study the promotion optimization problem for supermarket retailers. One needs to decide which products to promote, the depth of price discounts and when to schedule the promotions. To capture the stockpiling behavior of consumers, we propose two general classes of demand functions that can be easily estimated from data. We then develop an approximation that allows us to solve the problem efficiently and derive analytical results on its accuracy. The second part is motivated by the ubiquity of social networking platforms. We consider a setting where a monopolist sells an indivisible good to consumers embedded in a social network. First, the firm designs prices to maximize its profits. Subsequently, consumers choose whether to purchase the item or not. Assuming positive externalities, we show the existence of a pure Nash equilibrium. Using duality theory and integer programming techniques, we reformulate the problem into a linear mixed-integer program. We then derive efficient ways of optimally solving the problem for discriminative and uniform pricing strategies. The third part considers the problem of pricing a product for which demand information is very limited. We impose minimal assumptions on the problem: that is, only the constant marginal cost and the maximal price the firm can set are known. We propose a simple way of pricing the product by approximating the true inverse demand by a linear function. Surprisingly, we show that this approximation yields a good profit performance for a wide range of demand curves. In the final part, we consider green technology products such as electric vehicles. We propose a Stackelberg model where the government offers consumer subsidies in order to encourage the technology adoption, whereas the supplier decides price and production to maximize profits. We address the question: How does demand uncertainty affect the government, the industry and the consumers, when designing policies.
by Maxime C. Cohen.
Ph. D.
25
Rizzo, Ludovica. "Price incentives for online retailers using social media." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98563.
Full textAbstract:
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015.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 139-141).
In the era of Big Data, online retailers have access to a large amount of data about their customers. This data can include demographic information, shopping carts, transactions and browsing history. In the last decade, online retailers have been leveraging this data to build a personalized shopping experience for their customers with targeted promotions, discounts and personalized item recommendations. More recently, some online retailers started having access to social media data: more accurate demographic and interests information, friends, social interactions, posts and comments on social networks, etc. Social media data allows to understand, not only what customers buy, but also what they like, what they recommend to their friends, and more importantly what is the impact of these recommendations. This work is done in collaboration with an online marketplace in Canada with an embedded social network on its website. We study the impact of incorporating social media data on demand forecasting and we design an optimized and transparent social loyalty program to reward socially active customers and maximize the retailer's revenue. The first chapter of this thesis builds a demand estimation framework in a setting of heterogeneous customers. We want to cluster the customers into categories according to their social characteristics and jointly estimate their future consumption using a distinct logistic demand function for each category. We show that the problem of joint clustering and logistic regression can be formulated as a mixed-integer concave optimization problem that can be solved efficiently even for a large number of customers. We apply our algorithm using the actual online marketplace data and study the impact of clustering and incorporating social features on the performance of the demand forecasting model. In the second chapter of this thesis, we focus on price sensitivity estimation in the context of missing data. We want to incorporate a price component in the demand model built in the previous chapter using recorded transactions. We face the problem of missing data: for the customers who make a purchase we have access to the price they paid, but for customers who visited the website and decided not to make a purchase, we do not observe the price they were offered. The EM (Expectation Maximization) algorithm is a classical approach for estimation with missing data. We propose a non-parametric alternative to the EM algorithm, called NPM (Non-Parametric Maximization). We then show analytically the consistency of our algorithm in two particular settings. With extensive simulations, we show that NPM is a robust and flexible algorithm that converges significantly faster than EM. In the last chapter, we introduce and study a model to incorporate social influence among customers into the demand functions estimated in the previous chapters. We then use this demand model to formulate the retailer' revenue maximization problem. We provide a solution approach using dynamic programming that can deal with general demand functions. We then focus on two special structures of social influence: the nested and VIP models and compare their performance in terms of optimal prices and profit. Finally, we develop qualitative insights on the behavior of optimal price strategies under linear demand and illustrate computationally that these insights still hold for several popular non-linear demand functions.
by Ludovica Rizzo.
S.M.
26
Bimpikis, Kostas. "Strategic delay and information exchange in endogenous social networks." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/62405.
Full textAbstract:
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 160-165).
This thesis studies optimal stopping problems for strategic agents in the context of two economic applications: experimentation in a competitive market and information exchange in social networks. The economic agents (firms in the first application, individuals in the second) take actions, whose payoffs depend on an unknown underlying state. Our framework is characterized by the following key feature: agents time their actions to take advantage of either the outcome of the actions of others (experimentation model) or information obtained over time by their peers (information exchange model). Equilibria in both environments are typically inefficient, since information is imperfect and, thus, there is a benefit in being a late mover, but delaying is costly. More specifically, in the first part of the thesis, we develop a model of experimentation and innovation in a competitive multi-firm environment. Each firm receives a private signal on the success probability of a research project and decides when and which project to implement. A successful innovation can be copied by other firms. We start the analysis by considering the symmetric environment, where the signal quality is the same for all firms. Symmetric equilibria (where actions do not depend on the identity of the firm) always involve delayed and staggered experimentation, whereas the optimal allocation never involves delays and may involve simultaneous rather than staggered experimentation. The social cost of insufficient experimentation can be arbitrarily large. Then, we study the role of simple instruments in improving over equilibrium outcomes. We show that appropriately-designed patents can implement the socially optimal allocation (in all equilibria) by encouraging rapid experimentation and efficient ex post transfer of knowledge across firms. In contrast to patents, subsidies to experimentation, research, or innovation cannot typically achieve this objective. We also discuss the case when signal quality is private information and differs across firms. We show that in this more general environment patents again encourage experimentation and reduce delays. In the second part, we study a model of information exchange among rational individuals through communication and investigate its implications for information aggregation in large societies. An underlying state (of the world) determines which action has higher payoff. Agents receive a private signal correlated with the underlying state. They then exchange information over their social network until taking an (irreversible) action. We define asymptotic learning as the fraction of agents taking an action that is close to optimal converging to one in probability as a society grows large. Under truthful communication, we show that asymptotic learning occurs if (and under some additional conditions, also only if) in the social network most agents are a short distance away from "information hubs", which receive and distribute a large amount of information. Asymptotic learning therefore requires information to be aggregated in the hands of a few agents. We also show that while truthful communication is not always optimal, when the communication network induces asymptotic learning (in a large society), truthful communication is an equilibrium. Then, we discuss the welfare implications of equilibrium behavior. In particular, we compare the aggregate welfare at equilibrium with that of the optimal allocation, which is defined as the strategy profile a social planner would choose, so as to maximize the expected aggregate welfare. We show that when asymptotic learning occurs all equilibria are efficient. A partial converse is also true: if asymptotic learning does not occur at the optimal allocation and an additional mild condition holds at an equilibrium, then the equilibrium is inefficient. Furthermore, we discuss how our learning results can be applied to several commonly studied random graph models, such as preferential attachment and Erdos-Renyi graphs. In the final part, we study strategic network formation in the context of information exchange. In particular, we relax the assumption that the social network over which agents communicate is fixed, and we let agents decide which agents to form a communication link with incurring an associated cost. We provide a systematic investigation of what types of cost structures and associated social cliques (consisting of groups of individuals linked to each other at zero cost, such as friendship networks) ensure the emergence of communication networks that lead to asymptotic learning. Our result shows that societies with too many and sufficiently large social cliques do not induce asymptotic learning, because each social clique would have sufficient information by itself, making communication with others relatively unattractive. Asymptotic learning results if social cliques are neither too numerous nor too large, in which case communication across cliques is encouraged.
by Kostas Bimpikis.
Ph.D.
27
Mesnards, Nicolas Guenon des. "Identifying and assessing coordinated influence campaigns on social networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122385.
Full textAbstract:
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 82-90).
Social networks have given us the ability to spread messages and influence large populations very easily. Malicious actors can take advantage of social networks to manipulate opinions using artificial accounts, or bots. It is suspected that the 2016 U.S. presidential election was the victim of such social network interference, potentially by foreign actors. Foreign influence bots are also suspected of having attacked European elections. The bots main action was the sharing of politically polarized content in an effort to shift opinions. In this work we present a method to identify coordinated influence campaigns, and quantify the impact of bots on the opinions of users in a social network. First, we provide evidence that modern bots in the social network Twitter coordinate their attacks. They do not create original content, but rather amplify certain human users by disproportionately retweeting them. We design a new algorithm for bot detection, and utilize the Ising model from statistical physics to model the network structure and bot labels. Then, we leverage a model for opinion dynamics in a social network, which we validate by showing that the user opinions predicted by the model align with the opinions of these users' based on their social media posts. Finally, we use the opinion model to calculate how the opinions shift when we remove the bots from the network. Our high level finding is that a small number of bots can have a disproportionate impact on the network opinions.
by Nicolas Guenon des Mesnards
S.M.
S.M. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center
28
Rajagopalan, Krishnan S. M. Sloan School of Management. "Interacting with users in social networks : the follow-back problem." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/105000.
Full textAbstract:
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016.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 69-71).
An agent wants to form a connection with a predetermined set of target users over social media. Because forming a connection is known as "following" in social networks such as Twitter, we refer to this as the follow-back problem. The targets and their friends form a directed graph which we refer to as the "friends graph." The agent's goal is to get the targets to follow it, and it is allowed to interact with the targets and their friends. To understand what features impact the probability of an interaction resulting in a follow-back, we conduct an empirical analysis of several thousand interactions in Twitter. We build a model of the follow-back probabilities based upon this analysis which incorporates features such as the friend and follower count of the target and the neighborhood overlap of the target with the agent. We find optimal policies for simple network topologies such as directed acyclic graphs. For arbitrary directed graphs we develop integer programming heuristics that employ network centrality measures and a graph score we define as the follow-back score. We show that these heuristic policies perform well in simulation on a real Twitter network.
by Krishnan Rajagopalan.
S.M.
29
Webb, Matthew Robert. "Inferring user location from time series of social media activity." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112082.
Full textAbstract:
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 121-123).
Combining social media posts with known user locations can lead to unique insights with applications ranging from tracking diffusion of sentiment to earthquake detection. One approach used to determine a user's home location is to examine the timing of their posts, but the precision of existing time-based location predictors is limited to discrimination among time zones. In this thesis, we formulate a general time-based geolocation algorithm that has greater precision, using knowledge of a social media user's real world activities derived from his or her membership in a particular class. Our activity-based model discriminates among locations within a time zone, with city-level accuracy. We also develop methods to solve two related inference tasks. The first method detects when a user travels, allowing us to exclude posts when a user is away from his or her home location. Our other method classifies an account as belonging to a particular user group based on the time series of posts and a known user location. Finally, we test the performance of our geolocation model and related methods using Twitter accounts belonging to Muslims. Using Islamic prayer activity to inform our model, we are able to infer the locations of Muslim accounts. We are also able to accurately determine if an account belongs to a Muslim or non-Muslim using their activity patterns and location. Our work challenges the accepted practices used to protect online privacy by demonstrating that timing of user activity can provide specific location or group membership information.
by Matthew Robert Webb.
S.M.
30
Evans, Jane A. (Jane Amanda). "Modeling social response to the spread of an infectious disease." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/72647.
Full textAbstract:
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.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 (p. 85-88).
With the globalization of culture and economic trade, it is increasingly important not only to detect outbreaks of infectious disease early, but also to anticipate the social response to the disease. In this thesis, we use social network analysis and data mining methods to model negative social response (NSR), where a society demonstrates strain associated with a disease. Specifically, we apply real world biosurveillance data on over 11,000 initial events to: 1) describe how negative social response spreads within an outbreak, and 2) analytically predict negative social response to an outbreak. In the first approach, we developed a meta-model that describes the interrelated spread of disease and NSR over a network. This model is based on both a susceptible-infective- recovered (SIR) epidemiology model and a social influence model. It accurately captured the collective behavior of a complex epidemic, providing insights on the volatility of social response. In the second approach, we introduced a multi-step joint methodology to improve the detection and prediction of rare NSR events. The methodology significantly reduced the incidence of false positives over a more conventional supervised learning model. We found that social response to the spread of an infectious disease is predictable, despite the seemingly random occurrence of these events. Together, both approaches offer a framework for expanding a society's critical biosurveillance capability.
by Jane A. Evans.
S.M.
31
Howard, Nicholas J. (Nicholas Jacob). "Finding optimal strategies for influencing social networks in two player games." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/67772.
Full textAbstract:
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, June 2011.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 141).
This thesis considers the problem of optimally influencing social networks in Afghanistan as part of ongoing counterinsurgency efforts. The social network is analyzed using a discrete time agent based model. Each agent has a belief [-0.5,0.5] and interacts stochastically pairwise with their neighbors. The network converges to a set of equilibrium beliefs in expectation. A 2-player game is formulated in which the players control a set of stubborn agents whose beliefs never change, and who wield significant influence in the network. Each player chooses how to connect their stubborn agents to maximally influence the network. Two different payoff functions are defined, and the pure Nash equilibrium strategy profiles are found in a series of test networks. Finding equilibrium strategy profiles can be difficult for large networks due to exponential increases in the strategy space but a simulated annealing heuristic is used to rapidly find equilibria using best response dynamics. We demonstrate through experimentation that the games formulated admit pure Nash equilibrium strategy profiles and that best response dynamics can be used to find them. We also test a scenario based on the author's experience in Afghanistan to show how nonsymmetric equilibria can naturally emerge if each player weights the value of agents in the network differently.
by Nicholas J Howard.
S.M.
32
Oei, Hong Lim. "The influence of social factors on the performance of a center: a case study of the "University Research Center"." Thesis, Virginia Tech, 1989. http://hdl.handle.net/10919/43104.
Full textAbstract:
Factors involved in the development and performance of interdisciplinary university based research centers were
investigated by an analytical case study of one such center, the "University Research Center" (URC). A description of URC's life cycle and the various factors that affected its performance is presented. The sociocognitive ideals of
university-based research centers emphasized the promotion
of interdisciplinary research and education. The
organizational reality, however, showed that a variety of
other factors, both internal and external to a center, may
significantly influence its operations. Factors internal to
URC included leadership, support of loyal participants and
their motivations to participate in interdisciplinary research. External factors included the effects of
university departments and the availability of funds. The
interpretation of these factors made it possible to
construct generalizations about the organizational
characteristics of university-based research centers. In
order to function effectively, a university-based research
center must manage its sociocognitive ideals and its organizational characteristics simultaneously and with some degree of balance.Master of Science
33
Bradwick, Matthew E. (Matthew Edward). "Belief propagation analysis in two-player games for peer-influence social networks." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/72645.
Full textAbstract:
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2012.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 (p. 152-153).
This thesis considers approaches to influencing population opinions during counterinsurgency efforts in Afghanistan. A discrete time, agent-based threshold model is developed to analyze the propagation of beliefs in the social network, whereby each agent has a belief and a threshold value, which indicts the willingness to be influenced by the peers. Agents communicate in stochastic pairwise interactions with their neighbors. A dynamic, two player game is formulated whereby each player strategically controls the placement of one stubborn agent over time in order to maximally influence the network according to one of two different payoff functions. The stubborn agents have opposite, immutable beliefs and exert significant influence in the network. We demonstrate the characteristics of strategies chosen by the players to improve their payoffs through simulation. Determining strategies for the players in large, complex networks in which each stubborn agent has multiple connections is difficult due to exponential increases in the strategy space that is searched. We implement two heuristic methods which are shown to significantly reduce the run time needed to find strategies without significantly reducing the quality of the strategies. Lastly, we introduce population-focused actions, such as economic stimulus projects, which when used by the players result in long-lasting changes in the beliefs of the agents in the network.
by Matthew E. Bradwick.
S.M.
34
Hung, Benjamin W. K. (Benjamin Wei Kit). "Optimization-based selection of influential agents in a rural Afghan social network." Thesis, Massachusetts Institute of Technology, 2010. http://hdl.handle.net/1721.1/61193.
Full textAbstract:
Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2010.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 177-185).
This work considers the nonlethal targeting assignment problem in counterinsurgency in Afghanistan, the problem of deciding on the people whom US forces should engage through outreach, negotiations, meetings, and other interactions in order to ultimately win the support of the population in their area of operations. We developed three models: 1) the Afghan COIN social influence model, to represent how attitudes of local leaders are affected by repeated interactions with other local leaders, insurgents, and counter-insurgents, 2) the network generation model, to arrive at a reasonable representation of a Pashtun district-level, opinion leader social network, and 3) the nonlethal targeting model, a nonlinear programming (NLP) optimization formulation that identifies the k US agent assignment strategy producing the greatest arithmetic mean of the expected long-term attitude of the population. We demonstrate in experiments the merits of the optimization model in nonlethal targeting, which performs significantly better than both doctrine-based and random methods of assignment in a large network.
by Benjamin W. K. Hung.
S.M.
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Spigner, Dominique Deshay. "Roles of Social Workers at a Dialysis Center: An Action Research Project." ScholarWorks, 2017. https://scholarworks.waldenu.edu/dissertations/4564.
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People with end-stage renal disease have higher rates of mental health diagnoses due to sudden changes in health status, lack of effective support systems, and diminished survival rates. The purpose of this action research study, and research question posed, centered on how dialysis clinical social workers perceive their roles in providing consultation to the interdisciplinary team members on how to identify and respond to patients with mental illnesses. An interview guide was used to gather data by facilitating 3 focus groups with 7 dialysis social workers in a rural town in Texas. The theory of planned behavior was used to inform clinical social workers' understanding of their roles and responsibilities when interfacing with patients displaying symptoms of mental illnesses. A thematic analysis coding technique was used to analyze the data collected. Solutions explored included (a) increasing education efforts with interdisciplinary team members on the importance of consulting with the social worker on ways to identify and respond to patients with mental illnesses, and (b) ways to increase teammate support within the dialysis setting. This study clarifies dialysis social workers' roles and responsibilities when responding to dialysis patients with mental illnesses and guides them to enhance the capacity of the multidisciplinary dialysis team by improving inter-professional communication. The implications for social change through enhanced continuing education efforts designed to increase social work engagement and effective communication strategies within interdisciplinary teams are discussed. These social change efforts aim to enhance the overall wellbeing of dialysis patients with co-occurring mental health illnesses in rural settings.
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Fast, Shannon M. (Shannon Marie). "Pandemic panic : a network-based approach to predicting social response during a disease outbreak." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/91406.
Full textAbstract:
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014.85
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 99-104).
Epidemic trajectories and associated social responses vary widely between populations, with severe reactions sometimes observed. When confronted with fatal or novel pathogens, people exhibit a variety of behaviors from anxiety to hoarding of medical supplies, overwhelming medical infrastructure and rioting. We developed a coupled network approach to understanding and predicting social response to disease spread. We couple the disease spread and panic spread processes and model them through local interactions between agents. The behavioral contagion process depends on the prevalence of the disease, its perceived risk and a global media signal. We verify the model by analyzing the spread of disease and social response during the 2009 H1N1 outbreak in Mexico City, the 2003 SARS and 2009 H1N1 outbreaks in Hong Kong and the 2012-2013 Boston influenza season, accurately predicting population-level behavior. The effect of interventions on the disease spread and social response is explored, and we implement an optimization study to determine the least cost intervention, taking into account the costs of the disease itself, the intervention and the social response. We show that the optimal strategy is dependent upon the relative costs assigned to infection with the disease, intervention and social response, as well as the perceived risk of infection. This kind of empirically validated model is critical to exploring strategies for public health intervention, increasing our ability to anticipate the response to infectious disease outbreaks.
by Shannon M. Fast.
S.M.
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Feiler, John F. "The establishment of a Management Information Systems research center at the Naval Postgraduate School." Thesis, Monterey, California. Naval Postgraduate School, 1989. http://hdl.handle.net/10945/27029.
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Lux, Jessica, Bob Downing, and Jack Sheldon. "DESIGN OF A MISSION DATA STORAGE AND RETRIEVAL SYSTEM FOR NASA DRYDEN FLIGHT RESEARCH CENTER." International Foundation for Telemetering, 2007. http://hdl.handle.net/10150/604506.
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ITC/USA 2007 Conference Proceedings / The Forty-Third Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2007 / Riviera Hotel & Convention Center, Las Vegas, NevadaThe Western Aeronautical Test Range (WATR) at the NASA Dryden Flight Research Center (DFRC) employs the WATR Integrated Next Generation System (WINGS) for the processing and display of aeronautical flight data. This report discusses the post-mission segment of the WINGS architecture. A team designed and implemented a system for the near- and long-term storage and distribution of mission data for flight projects at DFRC, providing the user with intelligent access to data. Discussed are the legacy system, an industry survey, system operational concept, high-level system features, and initial design efforts.
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Schorr, Raphael Avram 1976. "Marginal social cost auctions for congested airport facilities." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/84837.
Full textAbstract:
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; and, (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2002."September 2002."
Includes bibliographical references (p. 96-97).
by Raphael Avram Schorr.
S.M.
M.Eng.
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Kabat, Sarah S. "Organizational change of parking systems at University of Colorado Hospital." [Denver, Colo.] : Regis University, 2007. http://165.236.235.140/lib/SKabat2007.pdf.
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Kahtani, Abdullah S. Mossa (Abdullah Salem Mossa). "Plans for Establishing and Developing the Social Research Studies and Information Center Libraries in Saudi Arabia." Thesis, University of North Texas, 1990. https://digital.library.unt.edu/ark:/67531/metadc278822/.
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The problem was to define the present status of the Social Research Studies and Information Center libraries in Saudi Arabia and to suggest ways in which they could be improved. The purposes of the study were two-fold: (1) to analyze and evaluate the current status of these libraries and to develop and improve the role and functions of these libraries; and (2) to consider the possibility of cooperation between these libraries.
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Kahtani, Abdullah S. Mossa. "Plans for Establishing and Developing the Social Research Studies and Information Center Libraries in Saudi Arabia." Thesis, University of North Texas, 1990. https://digital.library.unt.edu/ark:/67531/metadc330932/.
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Apelman, Lisa, Raik Klawitter, and Simone Wenzel. "Organizations as Functioning Social Systems : A Review of Social Sustainability in Management and Organizational Research." Thesis, Blekinge Tekniska Högskola, Institutionen för strategisk hållbar utveckling, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2324.
Full textAbstract:
One of the reasons, why it is difficult to implement the concept of social sustainability into organizations, is its inherent complexity and vagueness. The new Social Sustainability Principles (SSPs) within the Framework for Strategic Sustainable Development (FSSD) offer a clear definition of success for the social system. This study aims to put the new SSPs into an organizational context. It investigates how people-related issues within organizations, discussed in six organizational and management journals, published between 2009 and 2014, are related to the SSPs. One fourth of the 3305 reviewed articles were found to relate to social sustainability. Most of the articles focused on improving performance through aspects related to social sustainability. The articles mainly discussed aspects related to barriers to the SSPs as problems, solutions or positive aspects that could remove barriers to the SSPs. The results show that for organizational research to be able to support organizations moving towards social sustainability, there is a need for a clear definition of success as well as a frame that takes the whole social system into consideration. The FSSD and the SSPs could help to structure the diverse topics, put research problems in a bigger context and discern relevant problems and solutions.
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Li, Fu Min. "Collecting web data for social science research." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3953492.
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Foster, Anthony G. "An investigation of the program curriculum leading to successful sobriety in a substance abuse residential treatment center in Florida." Thesis, Florida Atlantic University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10154937.
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Recovery from alcoholism and substance abuse has had an ignominious history. There does not appear to be any statistics that stand up to any rigorous fact-checking which show how well treatment centers do at helping their clients to stay sober. Statistics that are used to show success rates are not considered credible and they are shockingly low. Despite these issues, substance abuse research has failed to link the historical knowledge of why people stay sober for long periods of time with what is being taught in treatment centers in hopes of creating a better, more accurate outcome.
The qualitative, phenomenological research study was conducted to ascertain whether a treatment center was teaching the curriculum components that prior research studies had found allowed an addict or alcoholic to stay sober for 20 plus years. Twelve volunteer participants (i.e., nine clients and three counselors), at a treatment center located in Southeast Florida, were interviewed regarding their perceptions of the curricula being taught in the treatment center.
Patterns emerged with the clients regarding their perceptions of spirituality and 12-step programs, believing that spirituality and 12-step programs were significantly emphasized in treatment and that they were very important to their recovery when they left treatment. Counselors agreed with this finding, but felt stronger about the importance of family and social support than did the clients. Overall, the clients felt that what was emphasized in treatment was important to their recovery and intended to use their new knowledge in helping them stay sober. Implications for treatment centers and recommendations for future studies are discussed.
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Mullarkey, Matthew T. "Inter-Organizational Social Network Information Systems: Diagnosing and Design." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5279.
Full textAbstract:
While IS research into on-line Inter-Personal (IP) Social Networks (SN) is highly visible, there has been surprisingly little focus on the use of on-line social networks for Inter-Organizational (IO) communications, interactions, and goal achievement. We explore the issues and challenges facing organizations in their design and use of inter-organizational social network information systems (IO SNIS). Artifact design principles are drawn from a new and insightful model that contrasts the advantages of existing innovative inter-personal (IP) SNIS artifacts with Social Network Theory on differences between IP and IO Social Networks. This research extends the existing streams of IS social networking research into the inter-organizational domain and encourages additional IS research into the analysis, design, and build of artifacts that animate the social behavior of organizations. We develop a key design concept for IO SNIS and establish the design principles underlying the general artifact design and the specific design features that apply the design constructs to an exemplar IO social domain. This dissertation uses Action Design Research (ADR) approach within the Design Science Research (DSR) paradigm to formulate the research opportunity and anticipate a practice-inspired and theory-ingrained artifact. The researcher works with a practitioner team in the domain of mid-market private equity (MMPE) to explore the model and evaluate existing on-line inter-organizational artifacts to establish specific design features for an IO SNIS artifact. We find that the design principles can generalize from the IO SNIS Design Concept Model to other IO Social domains and that the design features can be used to build an instantiation of IO SNIS in the Private Equity domain.
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West, Simon. "Meaning and Action in Sustainability Science : Interpretive approaches for social-ecological systems research." Doctoral thesis, Stockholms universitet, Stockholm Resilience Centre, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-135463.
Full textAbstract:
Social-ecological systems research is interventionist by nature. As a subset of sustainability science, social-ecological systems research aims to generate knowledge and introduce concepts that will bring about transformation. Yet scientific concepts diverge in innumerable ways when they are put to work in the world. Why are concepts used in quite different ways to the intended purpose? Why do some appear to fail and others succeed? What do the answers to these questions tell us about the nature of science-society engagement, and what implications do they have for social-ecological systems research and sustainability science? This thesis addresses these questions from an interpretive perspective, focusing on the meanings that shape human actions. In particular, the thesis examines how meaning, interpretation and experience shape the enactment of four action-oriented sustainability concepts: adaptive management, biosphere reserves, biodiversity corridors and planetary boundaries/reconnecting to the biosphere. In so doing, the thesis provides in-depth empirical applications of three interpretive traditions – hermeneutic, discursive and dialogical – that together articulate a broadly interpretive approach to studying social-ecological complexity. In the hermeneutic tradition, Paper I presents a ‘rich narrative’ case study of a single practitioner tasked with enacting adaptive management in an Australian land management agency, and Paper II provides a qualitative multi-case study of learning among 177 participants in 11 UNESCO biosphere reserves. In the discursive tradition, Paper III uses Q-method to explore interpretations of ‘successful’ biodiversity corridors among 20 practitioners, scientists and community representatives in the Cape Floristic Region, South Africa. In the dialogical tradition, Paper IV reworks conventional understandings of knowledge-action relationships by using three concepts from contemporary practice theory – ‘actionable understanding,’ ‘ongoing business’ and the ‘eternally unfolding present’ – to explore the enactment of adaptive management in an Australian national park. Paper V explores ideas of human-environment connection in the concepts planetary boundaries and reconnecting to the biosphere, and develops an ‘embodied connection’ where human-environment relations emerge through interactivity between mind, body and environment over time. Overall, the thesis extends the frontiers of social-ecological systems research by highlighting the meanings that shape social-ecological complexity; by contributing theories and methods that treat social-ecological change as a relational and holistic process; and by providing entry points to address knowledge, politics and power. The thesis contributes to sustainability science more broadly by introducing novel understandings of knowledge-action relationships; by providing advice on how to make sustainability interventions more useful and effective; by introducing tools that can improve co-production and outcome assessment in the global research platform Future Earth; and by helping to generate robust forms of justification for transdisciplinary knowledge production. The interventionist, actionable nature of social-ecological systems research means that interpretive approaches are an essential complement to existing structural, institutional and behavioural perspectives. Interpretive research can help build a scientifically robust, normatively committed and critically reflexive sustainability science.At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 2: Manuscript. Paper 4: Manuscript.
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Borremans, Lieve. "The development of agroforestry systems in Flanders. A farming systems research approach to social, institutional and economic inquiry." Doctoral thesis, Universite Libre de Bruxelles, 2019. https://dipot.ulb.ac.be/dspace/bitstream/2013/281527/3/TOC.pdf.
Full textAbstract:
Because of the multiple values and services that trees deliver to society, agroforestry is increasingly interpreted as an agricultural innovation that can help to address challenges in modern agriculture. Despite its potential opportunities in Flanders, many farmers remain skeptical though, resulting in adoption rates that are lagging behind. Therefore the objective of this thesis is to gain a better understanding of the unfavorable environment for agroforestry adoption and development making use of a farming systems research approach (FSR). In Chapter 2 we explain FSR as our general research approach, which implies the consideration of three key characteristics, i.e. systems thinking, interdisciplinarity and a participatory approach. Taking into account the general FSR characteristics, Chapter 3, 4 and 5 “diagnose” the agroforestry implementation gap more in detail. In chapter 3, we gain some first insights by assessing farmers’ intentions to engage in agroforestry and by giving an overview of the current agroforestry acreage in Flanders. In Chapter 4, we use the Agricultural Innovation Systems concept as general framework to identify the different stakeholders and their respective roles, and to give an overview of the different merits and failures with respect to agroforestry development. Afterwards Chapter 5 elucidates the different perspectives that exist on agroforestry systems among Flemish stakeholders, and links them with general discourses on agriculture in Flanders. Diagnostic analyses were followed up by design exercises in Chapter 6, which looks into different instruments that may give economic incentives to farmers to adopt agroforestry. Taking into account the gathered insights, we present in the main discussion chapter five development pathways to further stimulate agroforestry adoption and development: (1) the science and technology pathway, which stands for investing in research, especially targeting the productivity and compatibility of agroforestry systems, and this in active collaboration with farmers; (2) the market and financial pathway, which implies the creation of market mechanisms in which landscape and biodiversity aspects are valued, while stimulating private investments and consumer demand; (3) the policy and institutional pathway, which aims for the creation of a fully-fledged legal landscape for agroforestry, which is clear and steadfast into the future, and which should be complemented with an attractive and effective subsidy program; (4) the educational and organizational pathway, which stimulates the use of multiple communication and education channels to inform the relevant actors and familiarize them with agroecological practices and their benefits for society; and (5) the social and behavioral pathway, which encourages strengthening the dialogue between influential groups to restore mutual confidence, build up common visions, and open up collaboration opportunities. Through further systemic, interdisciplinary and participatory research, the identified development pathways should be translated into concrete action plans to eliminate adoption barriers and close the agroforestry implementation gap in Flanders.Doctorat en Sciences agronomiques et ingénierie biologique
info:eu-repo/semantics/nonPublished
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Criqui, Joseph E. "A confirmatory factor analysis of two competing social power measurement systems." PDXScholar, 1990. https://pdxscholar.library.pdx.edu/open_access_etds/4168.
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The main purpose of this study is to analyze a measurement instrument developed by Frost & Stahelski (1988) to measure French & Raven's (1959) bases of social power. The measurement instrument of a competing typology of social influence tactics (Kipnis, Schmidt, & Wilkinson, 1980) was also administered to the same managerial population (N=108). Confirmatory factor analyses using LISREL (Joreskog & Sorbom, 1986) were performed on each scale. Possible relationships between the two typologies were explored. Results include confirming a modified Frost & Stahelski scale and no confirmation of the Kipnis et al. scale. Canonical correlation yielded two dimensions where Coercive Power and Expert Power relate to Assertiveness and Rationality respectively. Exploratory factor analysis of the composite scores from both typologies yielded two factors called Positive Power and Negative Power. Implications and future research are briefly discussed.
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Nakrani, Sunil. "Biomimetic and autonomic server ensemble orchestration." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.534214.
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This thesis addresses orchestration of servers amongst multiple co-hosted internet services such as e-Banking, e-Auction and e-Retail in hosting centres. The hosting paradigm entails levying fees for hosting third party internet services on servers at guaranteed levels of service performance. The orchestration of server ensemble in hosting centres is considered in the context of maximising the hosting centre's revenue over a lengthy time horizon. The inspiration for the server orchestration approach proposed in this thesis is drawn from nature and generally classed as swarm intelligence, specifically, sophisticated collective behaviour of social insects borne out of primitive interactions amongst members of the group to solve problems beyond the capability of individual members. Consequently, the approach is self-organising, adaptive and robust. A new scheme for server ensemble orchestration is introduced in this thesis. This scheme exploits the many similarities between server orchestration in an internet hosting centre and forager allocation in a honeybee (Apis mellifera) colony. The scheme mimics the way a honeybee colony distributes foragers amongst flower patches to maximise nectar influx, to orchestrate servers amongst hosted internet services to maximise revenue. The scheme is extended by further exploiting inherent feedback loops within the colony to introduce self-tuning and energy-aware server ensemble orchestration. In order to evaluate the new server ensemble orchestration scheme, a collection of server ensemble orchestration methods is developed, including a classical technique that relies on past history to make time varying orchestration decisions and two theoretical techniques that omnisciently make optimal time varying orchestration decisions or an optimal static orchestration decision based on complete knowledge of the future. The efficacy of the new biomimetic scheme is assessed in terms of adaptiveness and versatility. The performance study uses representative classes of internet traffic stream behaviour, service user's behaviour, demand intensity, multiple services co-hosting as well as differentiated hosting fee schedule. The biomimetic orchestration scheme is compared with the classical and the theoretical optimal orchestration techniques in terms of revenue stream. This study reveals that the new server ensemble orchestration approach is adaptive in a widely varying external internet environments. The study also highlights the versatility of the biomimetic approach over the classical technique. The self-tuning scheme improves on the original performance. The energy-aware scheme is able to conserve significant energy with minimal revenue performance degradation. The simulation results also indicate that the new scheme is competitive or better than classical and static methods.