Interpretational variability of structural traps : implications for exploration risk and volume uncertainty

We would like to acknowledge and thank Frank Peel and Seb Turner for taking time to review this paper. Also thanks to Marguerite Fleming for perpetual encouragement. From: Richards, F. L., Richardson, N. J., Rippington, S. J., Wilson, R. W. & Bond, C. E. (eds) 2015. Industrial Structural Geology: Principles, Techniques and Integration. Geological Society, London,

Economic growth and inequality patterns in the presence of costly technology adoption and uncertainty

We develop a stochastic endogenous growth model to explain the diversity in growth and inequality patterns and the non-convergence of incomes in transitional economies where an underdeveloped financial sector imposes an implicit, fixed cost on the diversification of idiosyncratic risk. In the model endogenous growth occurs through physical and human capital deepening, with the latter being the more dominant element. We interpret the fixed cost as a ‘learning by doing’ cost for entrepreneurs who undertake risk in the absence of well developed financial markets and institutions that help diversify such risk. As such, this cost may be interpreted as the implicit returns foregone due to the lack of diversification opportunities that would otherwise have been available, had such institutions been present. The analytical and numerical results of the model suggest three growth outcomes depending on the productivity differences between the projects and the fixed cost associated with the more productive project. We label these outcomes as poverty trap, dual economy and balanced growth. Further analysis of these three outcomes highlights the existence of a diversity within diversity. Specifically, within the ‘poverty trap’ and ‘dual economy’ scenarios growth and inequality patterns differ, depending on the initial conditions. This additional diversity allows the model to capture a richer range of outcomes that are consistent with the empirical experience of several transitional economies.

Spatial distribution of soil heavy metal pollution estimated by different interpolation methods: Accuracy and uncertainty analysis

Mapping the spatial distribution of contaminants in soils is the basis of pollution evaluation and risk control. Interpolation methods are extensively applied in the mapping processes to estimate the heavy metal concentrations at unsampled sites. The performances of interpolation methods (inverse distance weighting, local polynomial, ordinary kriging and radial basis functions) were assessed and compared using the root mean square error for cross validation. The results indicated that all interpolation methods provided a high prediction accuracy of the mean concentration of soil heavy metals. However, the classic method based on percentages of polluted samples, gave a pollution area 23.54-41.92% larger than that estimated by interpolation methods. The difference in contaminated area estimation among the four methods reached 6.14%. According to the interpolation results, the spatial uncertainty of polluted areas was mainly located in three types of region: (a) the local maxima concentration region surrounded by low concentration (clean) sites, (b) the local minima concentration region surrounded with highly polluted samples; and (c) the boundaries of the contaminated areas. (C) 2010 Elsevier Ltd. All rights reserved.

Risk: A Study of Its Origins, History and Politics

Over a period of several centuries, the academic study of risk has evolved as a distinct body of thought, which continues to influence conceptual developments in fields such as economics, management, politics and sociology. However, few scholarly works have given a chronological account of cultural and intellectual trends relating to the understanding and analysis of risks. Risk: A Study of its Origins, History and Politics aims to fill this gap by providing a detailed study of key turning points in the evolution of society's understanding of risk. Using a wide range of primary and secondary materials, Matthias Beck and Beth Kewell map the political origins and moral reach of some of the most influential ideas associated with risk and uncertainty at specific periods of time. The historical focus of the book makes it an excellent introduction for readers who wish to go beyond specific risk management techniques and their theoretical underpinnings, to gain an understanding of the history and politics of risk.

Relative Uncertainty and Additively Representable Set Rankings

This paper proposes a definition of relative uncertainty aversion for decision models under complete uncertainty. It is shown that, for a large class of decision rules characterized by a set of plausible axioms, the new criterion yields a complete ranking of those rules with respect to the relative degree of uncertainty aversion they represent. In addition, we address a combinatorial question that arises in this context, and we examine conditions for the additive representability of our rules.

Does uncertainty cause inertia in decision making? An experimental study of the role of regret aversion and indecisiveness

Previous research has shown that often there is clear inertia in individual decision making---that is, a tendency for decision makers to choose a status quo option. I conduct a laboratory experiment to investigate two potential determinants of inertia in uncertain environments: (i) regret aversion and (ii) ambiguity-driven indecisiveness. I use a between-subjects design with varying conditions to identify the effects of these two mechanisms on choice behavior. In each condition, participants choose between two simple real gambles, one of which is the status quo option. I find that inertia is quite large and that both mechanisms are equally important.

Risk, ambiguity, and diversification

Attitudes toward risk influence the decision to diversify among uncertain options. Yet, because in most situations the options are ambiguous, attitudes toward ambiguity may also play an important role. I conduct a laboratory experiment to investigate the effect of ambiguity on the decision to diversify. I find that diversification is more prevalent and more persistent under ambiguity than under risk. Moreover, excess diversification under ambiguity is driven by participants who stick with a status quo gamble when diversification among gambles is not feasible. This behavioral pattern cannot be accommodated by major theories of choice under ambiguity.

Prediction of postprandial blood glucose under intra-patient variability and uncertainty and its use in the design of insulin dosing strategies for type 1 diabetic patients

In this thesis I propose a novel method to estimate the dose and injection-to-meal time for low-risk intensive insulin therapy. This dosage-aid system uses an optimization algorithm to determine the insulin dose and injection-to-meal time that minimizes the risk of postprandial hyper- and hypoglycaemia in type 1 diabetic patients. To this end, the algorithm applies a methodology that quantifies the risk of experiencing different grades of hypo- or hyperglycaemia in the postprandial state induced by insulin therapy according to an individual patient’s parameters. This methodology is based on modal interval analysis (MIA). Applying MIA, the postprandial glucose level is predicted with consideration of intra-patient variability and other sources of uncertainty. A worst-case approach is then used to calculate the risk index. In this way, a safer prediction of possible hyper- and hypoglycaemic episodes induced by the insulin therapy tested can be calculated in terms of these uncertainties.

Global hydrology modelling and uncertainty: running multiple ensembles with a campus grid.

Uncertainties associated with the representation of various physical processes in global climate models (GCMs) mean that, when projections from GCMs are used in climate change impact studies, the uncertainty propagates through to the impact estimates. A complete treatment of this ‘climate model structural uncertainty’ is necessary so that decision-makers are presented with an uncertainty range around the impact estimates. This uncertainty is often underexplored owing to the human and computer processing time required to perform the numerous simulations. Here, we present a 189-member ensemble of global river runoff and water resource stress simulations that adequately address this uncertainty. Following several adaptations and modifications, the ensemble creation time has been reduced from 750 h on a typical single-processor personal computer to 9 h of high-throughput computing on the University of Reading Campus Grid. Here, we outline the changes that had to be made to the hydrological impacts model and to the Campus Grid, and present the main results. We show that, although there is considerable uncertainty in both the magnitude and the sign of regional runoff changes across different GCMs with climate change, there is much less uncertainty in runoff changes for regions that experience large runoff increases (e.g. the high northern latitudes and Central Asia) and large runoff decreases (e.g. the Mediterranean). Furthermore, there is consensus that the percentage of the global population at risk to water resource stress will increase with climate change.

Towards an integrated script for risk and value management: insights from the dramaturgical metaphor

It is contended that the current conceptual distinction between risk management and value management is unsustainable. The origins of the two traditions are reviewed and critiqued from a postmodernist perspective. It is concluded that they differ primarily in terms of their rhetoric, rather than their substantive content. Insights into the current practice of risk and value management are provided by considering their enactment in terms of ‘performance’. The scripts for such performances are seen to be provided by the accepted methodologies which determine the language to be used and the roles to be acted out. A coherent integrated script for risk and value management can be provided by the methodology known as strategic choice, which replaces the language of ‘risk’ and ‘value’ with that of ‘uncertainty’. The benefits of adopting this alternative script are illustrated through six case studies.

A Tort for Risk and Endogenous Bankruptcy

Conventional tort law bars victims of exposure to a toxic substance from filing suit until they actually develop symptoms of illness. Practically speaking, this rule often bars recovery due to bankruptcy and causal uncertainty. One solution is to allow victims to file at exposure for expected damages (a tort for risk). The trade-off is that such a rule may trigger a race to file among exposure victims, thereby itself inducing bankruptcy. This paper characterizes the conditions under which such a race will occur in equilibrium and examines the implications for social welfare.

Science-policy in environmental and health risk assessment: if we cannot do without, can we do better?

How can empirical evidence of adverse effects from exposure to noxious agents, which is often incomplete and uncertain, be used most appropriately to protect human health? We examine several important questions on the best uses of empirical evidence in regulatory risk management decision-making raised by the US Environmental Protection Agency (EPA)'s science-policy concerning uncertainty and variability in human health risk assessment. In our view, the US EPA (and other agencies that have adopted similar views of risk management) can often improve decision-making by decreasing reliance on default values and assumptions, particularly when causation is uncertain. This can be achieved by more fully exploiting decision-theoretic methods and criteria that explicitly account for uncertain, possibly conflicting scientific beliefs and that can be fully studied by advocates and adversaries of a policy choice, in administrative decision-making involving risk assessment. The substitution of decision-theoretic frameworks for default assumption-driven policies also allows stakeholder attitudes toward risk to be incorporated into policy debates, so that the public and risk managers can more explicitly identify the roles of risk-aversion or other attitudes toward risk and uncertainty in policy recommendations. Decision theory provides a sound scientific way explicitly to account for new knowledge and its effects on eventual policy choices. Although these improvements can complicate regulatory analyses, simplifying default assumptions can create substantial costs to society and can prematurely cut off consideration of new scientific insights (e.g., possible beneficial health effects from exposure to sufficiently low 'hormetic' doses of some agents). In many cases, the administrative burden of applying decision-analytic methods is likely to be more than offset by improved effectiveness of regulations in achieving desired goals. Because many foreign jurisdictions adopt US EPA reasoning and methods of risk analysis, it may be especially valuable to incorporate decision-theoretic principles that transcend local differences among jurisdictions.

Integrated sensitivity analysis, calibration, and uncertainty propagation analysis approaches for supporting hydrological modeling

The successful performance of a hydrological model is usually challenged by the quality of the sensitivity analysis, calibration and uncertainty analysis carried out in the modeling exercise and subsequent simulation results. This is especially important under changing climatic conditions where there are more uncertainties associated with climate models and downscaling processes that increase the complexities of the hydrological modeling system. In response to these challenges and to improve the performance of the hydrological models under changing climatic conditions, this research proposed five new methods for supporting hydrological modeling. First, a design of experiment aided sensitivity analysis and parameterization (DOE-SAP) method was proposed to investigate the significant parameters and provide more reliable sensitivity analysis for improving parameterization during hydrological modeling. The better calibration results along with the advanced sensitivity analysis for significant parameters and their interactions were achieved in the case study. Second, a comprehensive uncertainty evaluation scheme was developed to evaluate three uncertainty analysis methods, the sequential uncertainty fitting version 2 (SUFI-2), generalized likelihood uncertainty estimation (GLUE) and Parameter solution (ParaSol) methods. The results showed that the SUFI-2 performed better than the other two methods based on calibration and uncertainty analysis results. The proposed evaluation scheme demonstrated that it is capable of selecting the most suitable uncertainty method for case studies. Third, a novel sequential multi-criteria based calibration and uncertainty analysis (SMC-CUA) method was proposed to improve the efficiency of calibration and uncertainty analysis and control the phenomenon of equifinality. The results showed that the SMC-CUA method was able to provide better uncertainty analysis results with high computational efficiency compared to the SUFI-2 and GLUE methods and control parameter uncertainty and the equifinality effect without sacrificing simulation performance. Fourth, an innovative response based statistical evaluation method (RESEM) was proposed for estimating the uncertainty propagated effects and providing long-term prediction for hydrological responses under changing climatic conditions. By using RESEM, the uncertainty propagated from statistical downscaling to hydrological modeling can be evaluated. Fifth, an integrated simulation-based evaluation system for uncertainty propagation analysis (ISES-UPA) was proposed for investigating the effects and contributions of different uncertainty components to the total propagated uncertainty from statistical downscaling. Using ISES-UPA, the uncertainty from statistical downscaling, uncertainty from hydrological modeling, and the total uncertainty from two uncertainty sources can be compared and quantified. The feasibility of all the methods has been tested using hypothetical and real-world case studies. The proposed methods can also be integrated as a hydrological modeling system to better support hydrological studies under changing climatic conditions. The results from the proposed integrated hydrological modeling system can be used as scientific references for decision makers to reduce the potential risk of damages caused by extreme events for long-term water resource management and planning.

The legitimation of risk and Bt cotton: a case study of Bantala village in Warangal, Andhra Pradesh, India

This article explores Ulrich Beck’s theorisation of risk society through focusing on the way in which the risk of Bt cotton is legitimated by six cultivators in Bantala, a village in Warangal, Andhra Pradesh, in India. The fieldwork for this study was conducted between June 2010 and March 2011, a duration chosen to coincide with a cotton season. The study explores the experience of the cultivators using the ‘categories of legitimation’ defined by Van Leeuwen. These are authorisation, moral evaluation, rationalisation and mythopoesis. As well as permitting an exploration of the legitimation of Bt cotton by cultivators themselves within the high-risk context of the Indian agrarian crisis, the categories also serve as an analytical framework with which to structure a discourse analysis of participant perspectives. The study examines the complex trade-off, which Renn argues the legitimation of ambiguous risk, such as that associated with Bt technology, entails. The research explores the way in which legitimation of the technology is informed by wider normative conceptualisations of development. This highlights that, in a context where indebtedness is strongly linked to farmer suicides, the potential of Bt cotton for poverty alleviation is traded against the uncertainty associated with the technology’s risks, which include its purported links to animal deaths. The study highlights the way in which the wider legitimation of a neoliberal approach to development in Andhra Pradesh serves to reinforce the choice of Bt cotton, and results in a depoliticisation of risk in Bantala. The research indicates, however, that this trade-off is subject to change over time, as economic benefits wane and risks accumulate. It also highlights the need for caution in relation to the proposed extension of Bt technology to food crops, such as Bt brinjal (aubergine).