<p>Assessments of chemical health risks are performed by scientific experts. Their intended use is as bases for decisions. This thesis tries to answer the questions of how uncertainty is, and should be, communicated in such risk assessments. The thesis consists of two articles and an introductory essay.</p><p>Article I focuses on the linguistic aspect of the communication of uncertainty in risk assessments. The aim of the article is to elucidate how risk assessors actually indicate uncertainty in risk assessment reports. Because of the prevalent uncertainty in risk assessment, deriving from several sources, uncertainty is communicated in verbal, rather than numerical terms. A typology of uncertainty indicators – phrases used to express uncertainty – is proposed and applied to the reviewed reports. It is found that the use of such phrases is not transparent, and the article concludes by a number of recommendations for improving the practice.</p><p>Article II mainly deals with the content of the communication. The overall question treated is what a characterization of uncertainty should include if a decision made on the basis of the risk assessment information is to be as well-founded as possible. A set of conditions is put forward to be fulfilled by a characterization of uncertainty if it is to be adequate from a decision-making point of view.</p><p>The greater part of the introductory essay is devoted to the concept of uncertainty which, at the conceptual level, does not appear to have been much discussed by philosophers</p>

Includes bibliographical references (p. 81).

Thesis (Ph. D.)--Ohio State University, 2003. / Title from first page of PDF file. Document formatted into pages; contains xi, 141 p.; also includes graphics. Includes abstract and vita. Advisor: Jay B. Barney, Dept. of Business Administration. Includes bibliographical references (p. 130-141).

Thesis (M.S.)--Mississippi State University. Department of Mechanical Engineering. / Title from title screen. Includes bibliographical references.

Thesis (M.S. in Meteorology and Physical Oceanography)--Naval Postgraduate School, March 2003. / Thesis advisor(s): Wendell A. Nuss, David S. Brown. Includes bibliographical references (p. 77-78). Also available online.

This dissertation reconciles ostensibly conflicting evidence from prior research about the association between information uncertainty and post-earnings-announcement drift (PEAD). According to traditional PEAD studies there should be a positive association between PEAD and uncertainty about the implication of an earnings announcement for future earnings, referred to in this dissertation as "information uncertainty." Empirical studies have documented both positive and negative associations, however. In particular, studies that use analyst forecast dispersion as a proxy for information uncertainty report a negative association between information uncertainty and PEAD. Although the authors of those studies argue that their results are consistent with behavioral finance theories, a negative association between information uncertainty and PEAD is troubling because it is not consistent with the notion that more reliable information improves market efficiency. In fact, previous empirical studies that use proxies for information uncertainty other than analyst forecast dispersion find a positive association between information uncertainty and PEAD. This study argues that the negative association between analyst forecast dispersion and PEAD can be explained by "herding" behavior immediately after earnings announcements. I introduce an analyst-based proxy for information uncertainty that mitigates the effects of herding on forecast dispersion. I find that, after controlling for the effect of herding, there is a positive association between information uncertainty and PEAD even when analyst forecasts are used to measure information uncertainty.

This dissertation contains two themes. The first involves the relationship between economic uncertainty and employment/unemployment determination, the second involves the institution of firing costs, in particular its effect on employment over the business cycle. Chapter 2 introduces uncertainty about labour productivity into the dynamic turnover-training model of Hoon and Phelps (1992). This makes hiring an investment under uncertainty. I get an explicit solution for the Bellman equation for the representative firm's optimization problem and find the optimal path for employment at the industry level. It is shown that an increase in the level of uncertainty reduces the shadow price of employees, the hire rate and optimal wage, and shifts the path for industry employment to a lower level. Chapter 3 then moves on to test the hypothesis using both data for all the OECD countries and also panel data for British industries. I find that those industries which have experienced the most volatility - measured by their average share prices - also have had the slowest employment growth, other things equal. Similarly, epochs of high uncertainty, measured by inflation and exchange rate movements, have accompanied low job creation in the OECD countries. The policy implication is that predictable economic policies and stability may reduce the equilibrium rate of unemployment. Chapter 4 looks at the determinants of the effectiveness of the institution of statutory firing costs. In the past, measures of firing restrictions have only included the size of the redundancy payments. I show that this is seriously inadequate. Thus the effectiveness of firing restrictions depends on the persistence of shocks to labour demand, the rate of interest, the rate at which workers quit, the degree of uncertainty about future productivity and economic growth. The implication is that a given level of redundancy payments may either have a very large or a very small effect on the number of redundancies, depending on the economic environment.

This MS thesis studies the effect of uncertainty in the demand of finished products, supply of raw materials, and maintenance resources availability on the maintenance schedule of a manufacturing facility. A simulation model is formulated in order to realistically model manufacturing systems of various complexities, consisting of multiple interacting machines that degrade and fail over time, and are repaired using imperfectly available maintenance crews and resources. A design of experiments (DOE) based sensitivity study is conducted to find the system parameters that mostly affected the maintenance decisions and corresponding profits. / text

Groundwater models are often developed as tools for environmental decision-making. However, sparse data availability can limit a model's utility by confounding attempts to select a single structural representation of a system or to find a unique and optimal set of model parameters. As a result, estimates of prediction uncertainty and the value of further data collection may be important results of a modeling effort. The Discrimination/Inference to Reduce Expected Cost Technique (DIRECT) is a new method for developing an ensemble of models that collectively define prediction uncertainty in a manner that supports risk-based decision making and monitoring network design optimization. We apply aspects of DIRECT to a modeling investigation of an aquifer system in Central Utah where a major Coalbed Methane gas field is located and a new approach for stimulating gas production is being explored. In the first stage of this study we develop an ensemble of regional MODFLOW models and calculate their relative likelihood using a set of observation data. These regional results and likelihoods are then transferred to a regional MT3D residence time model and to a local advective transport model to provide further information for the well design. A cost function is applied to the transport results to assess the relative expected costs of several proposed well field designs. The set of hydrologic results and associated likelihoods from the ensemble are combined into cost curves that allow for the selection of designs that minimize expected costs. These curves were found to be a useful tool for visualizing the ways that design decisions and hydrologic results interact to generate costs. Furthermore, these curves reveal ways in which uncertainty can add to the cost of implementing a design. A final analysis explored the cost of having uncertain model results by applying and manipulating synthetic likelihood distributions to the transport results. These results suggest the value that may be added by reducing uncertainty through data collection. Overall, the application of DIRECT was found to provide a rich set of information that is not available when ensemble methods and cost consideration are omitted from a modeling study.

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