<p>Technological systems contain complex elements and processes with a diverse set of agents and problem-solving arrangements. They often interact with and influence multi-lateral stakeholders with varying interests and incentives. Recent technological developments and engineering advancements such as digital marketplaces and high-tech networks create both new challenges and opportunities to understand further about effective mechanism designs. This dissertation attempts to answer corporate-level mechanism design issues in two different technological systems: high-tech biopharmaceutical networks and the online peer-to-peer lending industry.</p><p><br></p><p>The first part of the dissertation focuses on identifying the emergence and evolution of near decomposable systems in interorganizational relationships. To do so, first I conceptually discuss how near decomposable systems can emerge in interfirm relationships. Second, leveraging advancements in network science, I empirically analyze a detailed biopharmaceutical alliance data set and find that strategic alliance networks of biopharmaceutical firms exhibit near decomposable characteristics. I identify an emerging evolutionary pattern with smaller networks of subcommunities organizing hierarchically over time into a larger network structure, with the subcommunities generally exhibiting local clustering. A salient finding, compared to previous studies in the field of strategic management, is the identification of nested clusters formed in hierarchical fashion within this interfirm network. I find the potential for simultaneous evolutionary processes to be in play in various subnetworks within the overall industry-level network. The accrual of local changes impacting the structural processes of the subnetworks slowly diffuses to the larger, less integrated modules of the network. Finally, with the help of a simulation model, I identify how fitness heterogeneity among firms, fitness heterogeneity among partnerships and the rate of growth of partnerships impact the emergence of near decomposability in varying degrees.</p><p><br></p><p>The second study focuses on understanding an important market access control mechanism: platform owners granting priority access to a subset of supply-side complementors to grow the marketplace and remove potential demand-side bottlenecks. Platform governance mechanisms, such as market access control, help to align all market players towards a specific value proposition. I study the interplay between priority access and the variation in expertise of the complementors. Leveraging a randomized priority access given to expert institutional investors in the online peer-to-peer lending industry, I show that it creates negative spillover effects on the performance of crowd retail investors. I provide evidence in support of two mechanisms in driving the impact of priority access, the intensity of priority access and cream skimming by institutional complementors, on the retail crowd market. Again using simulation to extend the analysis, I find that the brunt of negative impacts is likely borne by more risk-averse retail investors.</p>

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第20893号 / 理博第4345号 / 新制||理||1624(附属図書館) / 京都大学大学院理学研究科物理学・宇宙物理学専攻 / (主査)准教授 篠本 滋, 教授 佐々 真一, 教授 川上 則雄 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

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学位プログラム名: 京都大学大学院思修館 / 京都大学 / 新制・課程博士 / 博士(総合学術) / 甲第23344号 / 総総博第17号 / 新制||総総||3(附属図書館) / 京都大学大学院総合生存学館総合生存学専攻 / (主査)教授 池田 裕一, 特定教授 武田 英俊, 教授 諸富 徹 / 学位規則第4条第1項該当 / Doctor of Philosophy / Kyoto University / DFAM

One of the ways that a vascular network grows is through the process of angiogenesis, wherebya new blood vessel forms as a branch from an existing vessel towards an area which isstimulating vascular growth. Due to the demands for nutrients and waste transport, growingtumour cells will access the surrounding vascular network by inducing angiogenesis. Once thetumour is connected with the vascular system it can grow further and colonize distant organs.Given the critical nature of this step in tumour development, there is a demand for mathematicaland computational models to provide an understanding of the process for treatment in predictivemedicine. These models allow us to generate vascular networks that demonstrate similarbehaviour to that of the observed networks; however, there is a lack of quantifiable measures ofsimilarity between generated networks, or, of a generated and real network. Furthermore, thereis not an established way to determine which measures hold the most relevance todistinguishing similarity. To construct such a measure we transform our generated vascularnetworks into an abstract graph representation which allows exploration of the plethora of graphcentralities. We propose to determine the relevance of a centrality by finding one that acts as asynthetic likelihood function for estimating the model's parameters with minimal error.Evaluating the relevance of many centralities, it is then possible to suggest which centralitiesshould be used to quantitatively determine similarity. This allows for a way to measure howrealistic a model's growth is, and if given sufficient data, to distinguish between regular andtumour-induced angiogenesis and use it within cancer screening.

The advancement of modern technologies has resulted in an explosive growth of complex systems, such as the Internet, biological, social, and various infrastructure networks, which have, in turn, contributed to the rise of massive networks. During the past decade, analyzing and mining of these networks has become an emerging research area with many real-world applications. The most relevant problems in this area include: collecting and managing networks, modeling and generating random networks, and developing network mining algorithms. In the era of big data, speed is not an option anymore for the effective analysis of these massive systems, it is an absolute necessity. This motivates the need for parallel algorithms on modern high-performance computing (HPC) systems including multi-core, distributed, and graphics processor units (GPU) based systems. In this dissertation, we present distributed memory parallel algorithms for generating massive random networks and a novel GPU-based algorithm for index searching. This dissertation is divided into two parts. In Part I, we present parallel algorithms for generating massive random networks using several widely-used models. We design and develop a novel parallel algorithm for generating random networks using the preferential-attachment model. This algorithm can generate networks with billions of edges in just a few minutes using a medium-sized computing cluster. We develop another parallel algorithm for generating random networks with a given sequence of expected degrees. We also design a new a time and space efficient algorithmic method to generate random networks with any degree distributions. This method has been applied to generate random networks using other popular network models, such as block two-level Erdos-Renyi and stochastic block models. Parallel algorithms for network generation pose many nontrivial challenges such as dependency on edges, avoiding duplicate edges, and load balancing. We applied novel techniques to deal with these challenges. All of our algorithms scale very well to a large number of processors and provide almost linear speed-up. Dealing with a large number of networks collected from a variety of fields requires efficient management systems such as graph databases. Finding a record in those databases is very critical and typically is the main bottleneck for performance. In Part II of the dissertation, we develop a GPU-based parallel algorithm for index searching. Our algorithm achieves the fastest throughput ever reported in the literature for various benchmarks. / Ph. D.

The present thesis is devoted to an application of the ideas of complex networks theory for analysing, modelling, and, finally, optimising different processes that occur in transportation networks.

Between 1997 and 2009, 47 major defense acquisition programs experienced cost overruns of at least 15% or 30% over their current or original baseline estimates, respectively (GAO, 2011, p. 1). Known formally as a Nunn-McCurdy breach (GAO, 2011, p. 1), the reasons for this excessive growth are myriad, although nearly 70% of the cases identified engineering and design issues as a contributing factor (GAO, 2011, p. 5). Accordingly, Congress legislatively acknowledged the need for change in 2009 with the passage of the Weapon Systems Acquisition Reform Act (WSARA, 2009), which mandated additional rigor and accountability in early life cycle (or Pre-Milestone A) cost estimation. Consistent with this effort, the Department of Defense has recently required more system specification earlier in the life cycle, notably the submission of detailed architectural models, and this has created opportunities for new approaches. In this dissertation, I describe my effort to transform one such model (or view), namely the SV-3, into computational knowledge that can be leveraged in Pre-Milestone A cost estimation and risk analysis. The principal contribution of my work is Algorithm 3-a novel, network science-based method for estimating the cost of unforeseen architectural growth in defense programs. Specifically, using number theory, network science, simulation, and statistical analysis, I simultaneously find the best fitting probability mass functions and strengths of preferential attachment for an incoming subsystem's interfaces, and I apply blockmodeling to find the SV-3's globally optimal macrostructure. Leveraging these inputs, I use Monte Carlo simulation and the Constructive Systems Engineering Cost Model to estimate the systems engineering effort required to connect a new subsystem to the existing architecture. This effort is chronicled by the five articles given in Appendices A through C, and it is summarized in Chapter 2.In addition to Algorithm 3, there are several important, tangential outcomes of this work, including: an explicit connection between Model Based System Engineering and parametric cost modeling, a general procedure for organizations to improve the measurement reliability of their early life cycle cost estimates, and several exact and heuristic methods for the blockmodeling of one-, two-, and mixed-mode networks. More generally, this research highlights the benefits of applying network science to systems engineering, and it reinforces the value of viewing architectural models as computational objects.

Since 2008, more than half of the world population lives in cities. To cope with this rapid urbanization in a sustainable manner, transit systems all around the world are likely to grow. By studying 33 networks in the world, this thesis identifies the properties and effects of metro network designs by using a graph theory approach. After the literature review, a new methodology was introduced to translate networks into graphs; it notably accounts for various transit specificities (e.g., presence of lines). Metro networks were then characterised according to their State, Form, and Structure; where State relates to the development phase of metros; Form investigates the link between metros and the built environment; Structure examines the intrinsic properties of metros, by notably looking at their connectivity. Subsequently, the complexity and robustness of metros were studied; metros were found to possess scale-free and small-world features although showing atypical topologies; robustness emphasizes on the presence of alternative paths. Three network design indicators (coverage, directness and connectivity) were then related to ridership (annual boardings per capita), and positive relations were observed, which suggests that network design plays an important role in their success. Finally, these concepts were applied to the Toronto metro plans announced by the Toronto regional transportation authority, Metrolinx; it was found that the grid-pattern nature of the plans could hinder the success of the metro; seven possible improvements were suggested. Overall, the topology of metro networks can play a key role in their success. The concepts presented here can particularly be useful to transit planners; they should also be used along with conventional planning techniques. New transit projects could benefit greatly from an analysis of their network designs, which in turn may play a relevant role in the global endeavour for sustainability.

Simple network models that focus only on graph topology or, at best, basic interactions are often insufficient to capture all the aspects of a dynamic complex system. In this thesis, I explore those limitations, and some concrete methods of resolving them. I argue that, in order to succeed at interpreting and influencing complex systems, we need to take into account  slightly more complex parts, interactions and information flows in our models.This thesis supports that affirmation with five actual examples of applied research. Each study case takes a closer look at the dynamic of the studied problem and complements the network model with techniques from information theory, machine learning, discrete maths and/or ergodic theory. By using these techniques to study the concrete dynamics of each system, we could obtain interesting new information. Concretely, we could get better models of network walks that are used on everyday applications like journal ranking. We could also uncover asymptotic characteristics of an agent-based information propagation model which we think is the basis for things like belief propaga-tion or technology adoption on society. And finally, we could spot associations between antibiotic resistance genes in bacterial populations, a problem which is becoming more serious every day.