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A standard probability formalism is introduced, including a definition of the probability density function (PDF) and its first four moments. Most basic PDFs such as the uniform and Gaussian PDFs, are defined. The fundamentals of the Bayes’ formula derivation and its formulation in terms of PDFs are also presented. More importantly, data assimilation is described as a recursive Bayes’ formula, which connects the standard Bayes’ formula from different analysis times by using transition PDFs. A basic introduction to Shannon information theory is presented, followed by a definition of uncertainty in terms of entropy, and therefore establishing a mathematical basis for interpreting data assimilation in terms of information processing that is used throughout this book. The multivariate Gaussian data assimilation framework, most often used in practice, is described. Common analysis solutions that include maximum a posteriori and minimum variance methods are derived, which include a formulation of the cost function and posterior probability.
Children are more vulnerable than adults but have greater adaptive potential. They need thinking about in the context of their families and communities even if separated from them. Effects of adversity vary according to the developmental stage at which they occur, and subsequent development can also be compromised. Key developmental stages are considered, attending to what harms can be sustained, and what may be protective.
Post-migration factors affecting outcome are also reviewed, including poor housing, poverty, bullying, racism, isolation, and prolonged uncertainty.
Intervention requires building a relationship and a network of support, and possibly sequential episodes of care with incremental and ‘portable’ gains: meeting basic needs first by attending to accommodation, safety, links with schools and language skills. Age-appropriate explanations are essential. Some specific interventions are reviewed.
The chapter concludes by revisiting the need for support and protection, and understanding developmental needs and family dynamics. This can promote healthy development and reduce intergenerational transmission of trauma.
This book has had two main aims. The first has been to make the case for a particular approach to understanding the concept of security – namely, by suspending prior belief as far as possible and attempting to rethink it from philosophical first principles. The second has been to demonstrate that approach by reconstructing a detailed account of security in precisely that vein and exploring its real-world implications.
Chapter 1 accounts for the goals and limits of the book and provides an overview of the content, uncertainty, and development of Article 13 in the Court's case law. It describes how Article 13 has developed into a proper right encompassing both procedural rights of access to justice and a substantive right to redress. It, also, explains how Article 13 not only grants a right for the individual, but is an important expression of the principle of subsidiarity upon which the system for the protection of human rights under the Convention is based. Yet, even though the case law concerning Article 13 is abundant, there is still considerable uncertainty concerning the content and scope of the obligations arising therefrom. Further, the case law reveals diverging opinions among judges as to how Article 13 could be further developed. This is connected to the Court's case load and the calls for the improvement of domestic remedies and a more subsidiary protection of human rights, most notably, in the process of reform of the Court and Convention system. How Article 13 could and should be a tool (or not) to mitigate such systemic concerns is a central part of the normative goal of the book.
This chapter examines the role of imagination in enabling economic actors to make sense of the world and decide how to act and the part played by metaphorical thinking and analytical imagination within the discipline of economics. It starts from the assumption that modern capitalism is a quintessentially creative and imaginative system, characterized by constant novelty and radical uncertainty. The authors argue that economic behavior is therefore necessarily guided by working fictions and, in particular, by fictional expectations that combine individual imaginaries and social narratives with calculation. Building on insights from literary theory, the chapter examines the structuring and performative role of narratives and models and concludes that market power rests with those able to make their narratives and imaginaries count. Championing a new form of narrative economics, the authors propose that economists should employ discourse analysis to read the contingent interpretations that economic actors use to navigate uncertain futures.
Over the last twenty years, New Institutional Economics (NIE) has been a highly influential model in the study of the Greek and Roman economy. Although both its assumptions and methods are controversial, NIE approaches have changed the agenda of ancient economic history. The overall goal of neo-institutional economic history is to explain economic development, and notably growth, in line with a much-quoted phrase by the Nobel-prize winning economist Douglass North, that it is the task of economic history to explain the structure and performance of economies through time. NIE approaches and methods have therefore stimulated quite specific research directions in ancient economic history. This chapter suggests that NIE offers a fruitful conceptual matrix for asking new questions – with or without the answers necessarily staying within the NIE model. By contrast, the aim of the NIE method to predict and quantify outcomes, and the broader implications of the approach, are far more difficult to accept and defend. Particularly problematic is its commitment to certain kinds of growth as the desirable outcome of economic development, together with the assumption of the universal benefits of that growth, with its end point and golden standard explicitly or implicitly based on successful economies of the modern West.
Some observers have proposed that physicians may die differently compared with the average patient. Semi-structured interviews with family members of physicians who died offer an opportunity to better understand how patient preferences and wishes are perceived and acted on by family members at the end of life. The decision-making experiences of these family members for a loved one who was a physician may have implications for the lay person at end of life.
The Johns Hopkins Precursors Study includes individuals who matriculated into the graduating classes of 1948 to 1964 of the Johns Hopkins University School of Medicine. From this cohort, we interviewed 26 family members of physicians who died. Interviews were coded and analyzed using a comparative, iterative process.
We found that family members of physicians who died described the uncertainty at end of life. This overarching theme was organized into the following: (a) the certainty of uncertainty; (b) the preparation for uncertainty; and (c) brokering of decisions in the face of uncertainty. Despite careful end-of-life preparation by well-informed physicians, family members were still left to broker decisions as they navigated the wishes of the physician and what the family and medical care team believed to be in the best interest of the physician.
Significance of results
Our findings suggest that our family members were not immune to uncertainty. The clinical momentum at the end of life may contribute to challenges faced by patients and family members when brokering decisions. Normalizing uncertainty in medical training and for families may aid in addressing the stress of uncertainty at end of life.
With this chapter, we deal with the problem of research ‘uncertainty’: how it is defined and dealt with in the standard praxis of psychological research. It stresses that the idea of measurement ‘error’ (in the sense of variability) is predominantly valid under a substance ontology. The processual alternative is described, stemming from a complex dynamic systems framework, which embraces variability, a fuzziness of category boundaries, and multiplicity. As the notion of uncertainty is also inextricably linked with the fundamental concept of probability, we present a processual framework for understanding probability.
Conviction Narrative Theory (CNT) is a theory of choice under radical uncertainty—situations where outcomes cannot be enumerated and probabilities cannot be assigned. Whereas most theories of choice assume that people rely on (potentially biased) probabilistic judgments, such theories cannot account for adaptive decision-making when probabilities cannot be assigned. CNT proposes that people use narratives—structured representations of causal, temporal, analogical, and valence relationships—rather than probabilities, as the currency of thought that unifies our sense-making and decision-making faculties. According to CNT, narratives arise from the interplay between individual cognition and the social environment, with reasoners adopting a narrative that feels ‘right’ to explain the available data; using that narrative to imagine plausible futures; and affectively evaluating those imagined futures to make a choice. Evidence from many areas of the cognitive, behavioral, and social sciences supports this basic model, including lab experiments, interview studies, and econometric analyses. We propose 12 principles to explain how the mental representations (narratives) interact with four inter-related processes (explanation, simulation, affective evaluation, communication), examining the theoretical and empirical basis for each. We conclude by discussing how CNT can provide a common vocabulary for researchers studying everyday choices across areas of the decision sciences.
This paper addresses the problem of finding a robust optimal design when uncertain parameters in the form of crisp or interval sets are present in the optimization. Furthermore, in order to make the approach as general as possible, direct search methods with the help of sensitivity analysis techniques are employed to optimize the design. Consequently, the presented approach is suitable for black box models for which no, or very little, information of the equations governing the model is available. The design of an electric drivetrain is used to illustrate the benefits of the proposed method.
The integration of Sensing Machine Elements (SME) is a promising approach to obtain reliable data about relevant process and state variables of technical systems. However, the quality and reliability of the provided data strongly depends on the corresponding calculation model of the SME and the therein included uncertainty. Consequently, in this contribution, the calculation model of a sensory utilized rolling bearing, as exemplary SME, is systematically analyzed using existing methods and tools to identify uncertainty that critically affects the quality and reliability of the data provided.
This article offers an alternative conceptualisation of prudence as encompassing four normative components: reflective reasoning, experience, long-term well-being, and moderation. Prudence involves a pattern of reflective reasoning informed by experience in the pursuit of long-term well-being through moderate judgements and actions. This conceptualisation allows distilling a set of prescriptions for guiding deliberation and choice under uncertainty, which I name the Prudent Judgement Approach. An analysis of John F. Kennedy's deliberations at the start of the Cuban Missile Crisis uncovers evidence of prudent judgement and demonstrates the practical feasibility and value of this approach. Although the numerous cognitive and procedural sources of errors in decision-making under uncertainty are by now well understood, there are few prescriptive approaches for guiding the process of formulating judgements and making choices. This article shows how prudence can help improve the quality of deliberative processes and policy choices.
Description: Governments and individuals take actions and adopt policies and behavior to deal with current needs and also to have protection against some future dangers. Future dangers may come in two different forms: those that, at least in principle, can be statistically predicted; and those that are too uncertain, in time and in intensity, to be predicted statistically. For the first, called “risky events,” policies and markets can be developed, and actions can be taken to deal with them. The “uncertain events” are difficult to deal with. J. M. Keynes and Frank Knight, in 1921, each wrote a books that somehow dealt with this distinction. It has continued to be difficult to deal with “uncertain events,” those that include various natural disasters, pandemics, and global warming. Because of that, societies tend to overinvest in protection against risky events and to underinvest in protection against “uncertain events.”
Chapter 7 is the conclusion. We provide a short and selective synopsis of our argument and briefly review, and elaborate on, the empirical illustrations from previous chapters. Theoretically, we suggest that cross-class solidarity, which has sometimes been linked to dense networks of civic associations, is likely to originate in low information and encompassing social insurance programs. The chapter also discusses promising avenues for future research.
Chapter 1 introduces the topic and motivates our study. It explains the general logic of our argument and introduces the methods and evidence we rely on. The chapter gives an overview of the book’s organization and main insights and hence serves as a preview.
A core principle of the welfare state is that everyone pays taxes or contributions in exchange for universal insurance against social risks such as sickness, old age, unemployment, and plain bad luck. This solidarity principle assumes that everyone is a member of a single national insurance pool, and it is commonly explained by poor and asymmetric information, which undermines markets and creates the perception that we are all in the same boat. Living in the midst of an information revolution, this is no longer a satisfactory approach. This book explores, theoretically and empirically, the consequences of 'big data' for the politics of social protection. Torben Iversen and Philipp Rehm argue that more and better data polarize preferences over public insurance and often segment social insurance into smaller, more homogenous, and less redistributive pools, using cases studies of health and unemployment insurance and statistical analyses of life insurance, credit markets, and public opinion.
In this chapter, we present an overview of enterprise risk management (ERM). We begin by discussing the concepts of risk and uncertainty. We then review some of the more important historical developments in different areas of risk management, propose a definition for ERM, and show how ERM has its origins in all of the individual areas of risk management that came before. We discuss how ERM can be implemented as an ongoing process, which is optimally built into the operations of an organization from the top down, through a risk governance framework. Stages of the ERM cycle include risk identification and analysis, risk evaluation, and risk treatment. Each of these stages is introduced in this chapter, and then developed in more detail in subsequent chapters.
In this special issue, we have collected 13 articles that offer new vantage points for research on dynamic capabilities. We offer a selection of thought-provoking papers that advance current thinking on dynamic capabilities and provide directions for new inquiries using the dynamic capability framework. The microfoundations of dynamic capabilities have increasingly received interest. This special issue offers a range of conceptual methodological approaches to deepen our understanding of the issues surrounding the microfoundations of dynamic capabilities.
How organizations utilize capabilities to achieve competitive advantage and improve performance has received an abundance of scholarly attention. Both ordinary and dynamic capabilities (DC) enable organizations to achieve higher performance when leveraged appropriately and under favorable conditions. The complexity of an organization's motives for why and how different capabilities are acquired drives us further to explore what complementarities organizations might achieve and under what contexts. Specifically, we explore how firms engaging in mergers and acquisitions (M&A) to acquire dynamic and/or ordinary capabilities experience different market reactions and levels of short- and long-run value creation given environmental uncertainty. Our results support the acquisition of ordinary capabilities for predicting positive short-term market reactions and of DC for longer-run firm performance post-M&A, with uncertainty factors moderating these relationships. We discuss both the theoretical and practical implications of uncertainty and acquisitions of these capabilities and offer suggestions for future research.
This book revisits a distinction introduced in 1921 by economists Frank Knight and John Maynard Keynes: that between statistically predictable future events ('risks') and statistically unpredictable, uncertain events ('uncertainties'). Governments have generally ignored the latter, perceiving phenomena such as pandemics, natural disasters and climate change as uncontrollable Acts of God. As a result, there has been little if any preparation for future catastrophes. Our modern society is more interconnected and more globalized than ever. Dealing with uncertain future events requires a stronger and more globally coordinated government response. This book suggests a larger, more global government role in dealing with these disasters and keeping economic inequalities low. Major institutional changes, such as regulating the private sector for the common good and dealing with special harms, risks and crises, especially those concerning climate change and pandemics, are necessary in order to achieve any semblance of future progress for humankind.