The study of how social organizations work, change and develop is central to sociology and to our understanding of the social world and its transformations. At the same time, the underlying principles of organizational dynamics are extremely difficult to investigate. This is partly due to the difficulties of tracking organizations, individuals and their interactions over relatively long periods of time. But it is also due to limitations in the kinds of quantitative methods used to tackle these questions, which are for the most part based on regression analysis. This thesis seeks to improve our understanding of social organizing by using models to explore and describe the logics of the structures and mechanisms underlying organizational change. Particular emphasis is given to the modeling process, the use of new concepts and analogies, and the application of interdisciplinary methods to get new insights into classical sociological questions. The thesis consists of an introductory part and five studies (I-V). Using Swedish longitudinal data on employment in the Stockholm Region, the studies tackle different dimensions of organizational dynamics, from organizational structures and growth processes to labor mobility and employment trajectories. The introductory chapters contextualize the studies by providing an overview of theories, concepts and quantitative methods that are relevant for the modeling of organizational dynamics.  The five studies look into various aspects of organizational dynamics with the help of complementary data representations and non-traditional quantitative methods. Study I analyzes organizational growth statistics for different sectors and industries. The typically observed heavy-tailed statistical patterns for the size and growth rate distributions are broken down into a superposition of interorganizational movements. Study II models interorganizational movements as a labor flow network. Organizations tend to be more tightly linked if they belong to the same ownership sector. Additionally, public organizations have a more stable connection structure. Study III uses a similarity-based method called homogeneity analysis to map out the social space of large organizations in the Stockholm Region. A social distance is then derived within this space, and we find that the interorganizational movements analyzed in Studies I and II take place more often between organizations that are closer in social space and in the same network community. Study IV presents an approach to organizational dynamics based on sequences of employment states. Evidence for a positive feedback mechanism is found for large and highly sequence-diverse public organizations. Finally, Study V features an agent-based model where we simulate a social influence mechanism for organizational membership dynamics. We introduce a parameter analogous to a physical temperature to model contextual influence, and the familiar growth distributions are recovered as an intermediate case between extreme parameter values. The thesis as a whole provides suggestions for a more process-oriented modeling approach to social organizing that gives a more prominent role to the logics of organizational change. Finally, the series of methodological tools discussed can be useful for the analysis of many other social processes and more broadly for the development of quantitative sociological methods. / <p>At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 1: Manuscript. Paper 2: Manuscript. Paper 3: Manuscript. Paper 4: Manuscript.</p><p> </p>

Numerous studies on Queueing systems, such as Internet traffic flows, have shown to be bursty, self-similar and/or long-range dependent, because of the heavy (long) tails for the various distributions of interest, including intermittent intervals and queue lengths. Other studies have addressed vacation in no-customers’ queueing system or when the server fails. These patterns are important for capacity planning, performance prediction, and optimization of networks and have a negative impact on their effective functioning. Heavy-tailed distributions have been commonly used by telecommunication engineers to create workloads for simulation studies, which, regrettably, may show peculiar queueing characteristics. To cost-effectively examine the impacts of different network patterns on heavy- tailed queues, new and reliable analytical approaches need to be developed. It is decided to establish a brand-new analytical framework based on optimizing entropy functionals, such as those of Shannon, Rényi, Tsallis, and others that have been suggested within statistical physics and information theory, subject to suitable linear and non-linear system constraints. In both discrete and continuous time domains, new heavy tail analytic performance distributions will be developed, with a focus on those exhibiting the power law behaviour seen in many Internet scenarios. The exposition of two major revolutionary approaches, namely the unification of information geometry and classical queueing systems and unifying information length theory with transient queueing systems. After conclusions, open problems arising from this thesis and limitations are introduced as future work.

本篇論文將厚尾分配（Heavy-Tailed Distribution）應用在財務及保險精算上。本研究主要有三個部分：第一部份是用厚尾分配來重新建構Lee-Carter模型（1992），發現改良後的Lee-Carter模型其配適與預測效果都較準確。第二部分是將厚尾分配建構於具有世代因子（Cohort Factor）的Renshaw and Haberman模型（2006）中，其配適及預測效果皆有顯著改善，此外，針對英格蘭及威爾斯（England and Wales）訂價長壽交換（Longevity Swaps），結果顯示此模型可以支付較少的長壽交換之保費以及避免低估損失準備金。第三部分是財務上的應用，利用Schmidt等人（2006）提出的多元仿射廣義雙曲線分配（Multivariate Affine Generalized Hyperbolic Distributions; MAGH）於Boyle等人（2003）提出的低偏差網狀法（Low Discrepancy Mesh; LDM）來定價多維度的百慕達選擇權。理論上，LDM法的數值會高於Longstaff and Schwartz（2001）提出的最小平方法（Least Square Method; LSM）的數值，而數值分析結果皆一致顯示此性質，藉由此特性，我們可知道多維度之百慕達選擇權的真值落於此範圍之間。 / The thesis focus on the application of heavy-tailed distributions in finance and actuarial science. We provide three applications in this thesis. The first application is that we refine the Lee-Carter model (1992) with heavy-tailed distributions. The results show that the Lee-Carter model with heavy-tailed distributions provide better fitting and prediction. The second application is that we also model the error term of Renshaw and Haberman model (2006) using heavy-tailed distributions and provide an iterative fitting algorithm to generate maximum likelihood estimates under the Cox regression model. Using the RH model with non-Gaussian innovations can pay lower premiums of longevity swaps and avoid the underestimation of loss reserves for England and Wales. The third application is that we use multivariate affine generalized hyperbolic (MAGH) distributions introduced by Schmidt et al. (2006) and low discrepancy mesh (LDM) method introduced by Boyle et al. (2003), to show how to price multidimensional Bermudan derivatives. In addition, the LDM estimates are higher than the corresponding estimates from the Least Square Method (LSM) of Longstaff and Schwartz (2001). This is consistent with the property that the LDM estimate is high bias while the LSM estimate is low bias. This property also ensures that the true option value will lie between these two bounds.

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