Animate Texts: Hieroglyphic Reading Practices in Early Modern England, 1564-1658

Sixteenth- and seventeenth-century hieroglyphs have rarely been studied as a distinct category, yet they offer a new venue to deepen and complicate our understanding of how contemporary readers, writers, and theatrical audiences conceived of their own engagement with multimodal texts. My dissertation argues that early modern authors and audiences conceived of reading such symbols not as passive consumption of a static text but rather as an active, embodied experience of transformation as well as interpretation. Situating my argument within the early modern intellectual contexts of emblem theory and spiritual alchemy, I suggest that hieroglyphic reading can be understood as a dynamic process thought to transmute both individual and collective identities, refining the reader as well as forging new bonds among groups of elite reader-participants. My investigation tracks this notion of transformative reading across discursive domains and somatic zones, beginning with a unitary, self-contained symbol in Elizabethan polymath John Dee's alchemical writing, and ending with Sir Thomas Browne's quincunx, an expansive hieroglyph that fully contains, describes, and embodies humanity's capacity to perceive and interpret the world. In John Dee's Monas Hieroglyphica, the private letters of New England colonist John Winthrop, Jr., the court masques of Ben Jonson, and Sir Thomas Browne's Garden of Cyrus, I consider how hieroglyphic texts work upon their readers in contexts both public and private, both published and manuscript, both dramatic and non-dramatic. Although new criticism on reading practices has begun to map the material, cognitive, and affective dimensions of book use, my project revises our understanding of reading in the period as an active, reciprocal endeavor with profound epistemological and ontological resonances.Doctor of Philosoph

Estimating Project Performance through a System Dynamics Learning Model

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this recordMonitoring of the technical progression of projects is highly difficult, especially for complex projects where the current state may be obscured by the use of traditional project metrics. Late detection of technical problems leads to high resolution costs and delayed delivery of projects. To counter this, we report on the development of a updated technical metrics process designed to help ensure the on-time delivery, to both cost and schedule, of high quality products by a U.K. Systems Engineering Company. Published best practice suggests the necessity of using planned parameter profiles crafted to support technical metrics; but these have proven difficult to create due to the variance in project types and noise within individual project systems. This paper presents research findings relevant to the creation of a model to help set valid planned parameter profiles for a diverse range of system engineering products; and in establishing how to help project users get meaningful use out of these planned parameter profiles. We present a solution using a System Dynamics (SD) model capable of generating suitable planned parameter profiles. The final validated and verified model overlays the idea of a learning “S-curve” abstraction onto a rework cycle system archetype. Once applied in SD this matched the mental models of experienced engineering managers within the company, and triangulates with validated empirical data from within the literature. This has delivered three key benefits in practice: the development of a heuristic for understanding the work flow within projects, as a result of the interaction between a project learning system and defect discovery; the ability to produce morphologically accurate performance baselines for metrics; and an approach for enabling teams to generate benefit from the model via the use of problem structuring methodology.Engineering and Physical Sciences Research Council (EPSRC

Macro-financial linkages and bank behaviour: evidence from the second-round effects of the global financial crisis on East Asia

This paper studies the link between macro-financial variability and bank behaviour, which justifies the second-round effects of the global financial crisis on East Asia. Following Gallego et al. (The impact of the global economic and financial crisis on Central Eastern and South Eastern Europe (CESEE) and Latin America, 2010), the second round effects are defined as the adverse feedback loop from the slumps in economic activities and sharp financial market deterioration, which may influence the financial performance of bank, inter alia via deteriorating credit quality, declining profitability and increasing problems in retaining necessary capitalization. Differentiating itself from other research, this study stresses adjustments in four dimensions of bank performance and behaviour: asset quality, profitability, capital adequacy, and lending behaviour, assuming that any change in a bank-specific characteristic is induced by endogenous adjustments of the others. The empirical results based on partial adjustment models and two-step system GMM estimation show that bank’s adjustment behaviour is subject to the variation in the macro-financial environment and the stress condition in the global financial market. There is no convincing evidence to support the effectiveness of policy rate cut to boots bank lending and to avoid a financial accelerator effect

Banks’ Risk Endogenous to Strategic Management Choices

Use of variability of profits and other accounting-based ratios in order to estimate a firm's risk of insolvency is a well-established concept in management and economics. This paper argues that these measures fail to approximate the true level of risk accurately because managers consider other strategic choices and goals when making risky decisions. Instead, we propose an econometric model that incorporates current and past strategic choices to estimate risk from the profit function. Specifically, we extend the well-established multiplicative error model to allow for the endogeneity of the uncertainty component. We demonstrate the power of the model using a large sample of U.S. banks, and show that our estimates predict the accelerated bank risk that led to the subprime crisis in 2007. Our measure of risk also predicts the probability of bank default both in the period of the default, but also well in advance of this default and before conventional measures of bank risk