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,

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,

Impact of seismic image quality on fault interpretation uncertainty

Peer reviewed

Taking risks in the face of uncertainty:an exploratory analysis of green innovation

The relationship between uncertainty and firms’ risk-taking behaviour has been a focus of investigation since early discussion of the nature of enterprise activity. Here, we focus on how firms’ perceptions of environmental uncertainty and their perceptions of the risks involved impact on their willingness to undertake green innovation. Analysis is based on a cross-sectional survey of UK food companies undertaken in 2008. The results reinforce the relationship between perceived environmental uncertainty and perceived innovation risk and emphasise the importance of macro-uncertainty in shaping firms’ willingness to undertake green innovation. The perceived (market-related) riskiness of innovation also positively influences the probability of innovating, suggesting either a proactive approach to stimulating market disruption or an opportunistic approach to innovation leadership.

Modulating membership grades to gain consensus for fuzzy set uncertainty values in a clinical decision support system

This paper deals with a very important issue in any knowledge engineering discipline: the accurate representation and modelling of real life data and its processing by human experts. The work is applied to the GRiST Mental Health Risk Screening Tool for assessing risks associated with mental-health problems. The complexity of risk data and the wide variations in clinicians' expert opinions make it difficult to elicit representations of uncertainty that are an accurate and meaningful consensus. It requires integrating each expert's estimation of a continuous distribution of uncertainty across a range of values. This paper describes an algorithm that generates a consensual distribution at the same time as measuring the consistency of inputs. Hence it provides a measure of the confidence in the particular data item's risk contribution at the input stage and can help give an indication of the quality of subsequent risk predictions. © 2010 IEEE.

The framework of the virtual laser tracker - A systematic approach to the assessment of error sources and uncertainty in laser tracker measurement

Laser trackers have been widely used in many industries to meet increasingly high accuracy requirements. In laser tracker measurement, it is complex and difficult to perform an accurate error analysis and uncertainty evaluation. This paper firstly reviews the working principle of single beam laser trackers and state-of- The- Art of key technologies from both industrial and academic efforts, followed by a comprehensive analysis of uncertainty sources. A generic laser tracker modelling method is formulated and the framework of the virtual tracker is proposed. The VLS can be used for measurement planning, measurement accuracy optimization and uncertainty evaluation. The completed virtual laser tracking system should take all the uncertainty sources affecting coordinate measurement into consideration and establish an uncertainty model which will behave in an identical way to the real system. © Springer-Verlag Berlin Heidelberg 2010.

A Study of How Economic Attitudes Are Shaped by Environmental Shocks and Life Experiences

Social attitudes, attitudes toward financial risk and attitudes toward deferred gratification are thought to influence many important economic decisions over the life-course. In economic theory, these attitudes are key components in diverse models of behavior, including collective action, saving and investment decisions and occupational choice. The relevance of these attitudes have been confirmed empirically. Yet, the factors that influence them are not well understood. This research evaluates how these attitudes are affected by large disruptive events, namely, a natural disaster and a civil conflict, and also by an individual-specific life event, namely, having children.

By implementing rigorous empirical strategies drawing on rich longitudinal datasets, this research project advances our understanding of how life experiences shape these attitudes. Moreover, compelling evidence is provided that the observed changes in attitudes are likely to reflect changes in preferences given that they are not driven just by changes in financial circumstances. Therefore the findings of this research project also contribute to the discussion of whether preferences are really fixed, a usual assumption in economics.

In the first chapter, I study how altruistic and trusting attitudes are affected by exposure to the 2004 Indian Ocean tsunami as long as ten years after the disaster occurred. Establishing a causal relationship between natural disasters and attitudes presents several challenges as endogenous exposure and sample selection can confound the analysis. I take on these challenges by exploiting plausibly exogenous variation in exposure to the tsunami and by relying on a longitudinal dataset representative of the pre-tsunami population in two districts of Aceh, Indonesia. The sample is drawn from the Study of the Tsunami Aftermath and Recovery (STAR), a survey with data collected both before and after the disaster and especially designed to identify the impact of the tsunami. The altruistic and trusting attitudes of the respondents are measured by their behavior in the dictator and trust games. I find that witnessing closely the damage caused by the tsunami but without suffering severe economic damage oneself increases altruistic and trusting behavior, particularly towards individuals from tsunami affected communities. Having suffered severe economic damage has no impact on altruistic behavior but may have increased trusting behavior. These effects do not seem to be caused by the consequences of the tsunami on people’s financial situation. Instead they are consistent with how experiences of loss and solidarity may have shaped social attitudes by affecting empathy and perceptions of who is deserving of aid and trust.

In the second chapter, co-authored with Ryan Brown, Duncan Thomas and Andrea Velasquez, we investigate how attitudes toward financial risk are affected by elevated levels of insecurity and uncertainty brought on by the Mexican Drug War. To conduct our analysis, we pair the Mexican Family Life Survey (MxFLS), a rich longitudinal dataset ideally suited for our purposes, with a dataset on homicide rates at the month and municipality-level. The homicide rates capture well the overall crime environment created by the drug war. The MxFLS elicits risk attitudes by asking respondents to choose between hypothetical gambles with different payoffs. Our strategy to identify a causal effect has two key components. First, we implement an individual fixed effects strategy which allows us to control for all time-invariant heterogeneity. The remaining time variant heterogeneity is unlikely to be correlated with changes in the local crime environment given the well-documented political origins of the Mexican Drug War. We also show supporting evidence in this regard. The second component of our identification strategy is to use an intent-to-treat approach to shield our estimates from endogenous migration. Our findings indicate that exposure to greater local-area violent crime results in increased risk aversion. This effect is not driven by changes in financial circumstances, but may be explained instead by heightened fear of victimization. Nonetheless, we find that having greater economic resources mitigate the impact. This may be due to individuals with greater economic resources being able to avoid crime by affording better transportation or security at work.

The third chapter, co-authored with Duncan Thomas, evaluates whether attitudes toward deferred gratification change after having children. For this study we also exploit the MxFLS, which elicits attitudes toward deferred gratification (commonly known as time discounting) by asking individuals to choose between hypothetical payments at different points in time. We implement a difference-in-difference estimator to control for all time-invariant heterogeneity and show that our results are robust to the inclusion of time varying characteristics likely correlated with child birth. We find that becoming a mother increases time discounting especially in the first two years after childbirth and in particular for those women without a spouse at home. Having additional children does not have an effect and the effect for men seems to go in the opposite direction. These heterogeneous effects suggest that child rearing may affect time discounting due to generated stress or not fully anticipated spending needs.

The Political Economy of Climate Adaptation and Environmental Health: The Case of Ethiopia

The environment affects our health, livelihoods, and the social and political institutions within which we interact. Indeed, nearly a quarter of the global disease burden is attributed to environmental factors, and many of these factors are exacerbated by global climate change. Thus, the central research question of this dissertation is: How do people cope with and adapt to uncertainty, complexity, and change of environmental and health conditions? Specifically, I ask how institutional factors, risk aversion, and behaviors affect environmental health outcomes. I further assess the role of social capital in climate adaptation, and specifically compare individual and collective adaptation. I then analyze how policy develops accounting for both adaptation to the effects of climate and mitigation of climate-changing emissions. In order to empirically test the relationships between these variables at multiple levels, I combine multiple methods, including semi-structured interviews, surveys, and field experiments, along with health and water quality data. This dissertation uses the case of Ethiopia, Africa’s second-most populous nation, which has a large rural population and is considered very vulnerable to climate change. My fieldwork included interviews and institutional data collection at the national level, and a three-year study (2012-2014) of approximately 400 households in 20 villages in the Ethiopian Rift Valley. I evaluate the theoretical relationships between households, communities, and government in the process of adaptation to environmental stresses. Through my analyses, I demonstrate that water source choice varies by individual risk aversion and institutional context, which ultimately has implications for environmental health outcomes. I show that qualitative measures of trust predict cooperation in adaptation, consistent with social capital theory, but that measures of trust are negatively related with private adaptation by the individual. Finally, I describe how Ethiopia had some unique characteristics, significantly reinforced by international actors, that led to the development of an extensive climate policy, and yet with some challenges remaining for implementation. These results suggest a potential for adaptation through the interactions among individuals, communities, and government in the search for transformative processes when confronting environmental threats and climate change.

Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output

Uncertainty quantification (UQ) is both an old and new concept. The current novelty lies in the interactions and synthesis of mathematical models, computer experiments, statistics, field/real experiments, and probability theory, with a particular emphasize on the large-scale simulations by computer models. The challenges not only come from the complication of scientific questions, but also from the size of the information. It is the focus in this thesis to provide statistical models that are scalable to massive data produced in computer experiments and real experiments, through fast and robust statistical inference.

Chapter 2 provides a practical approach for simultaneously emulating/approximating massive number of functions, with the application on hazard quantification of Soufri\`{e}re Hills volcano in Montserrate island. Chapter 3 discusses another problem with massive data, in which the number of observations of a function is large. An exact algorithm that is linear in time is developed for the problem of interpolation of Methylation levels. Chapter 4 and Chapter 5 are both about the robust inference of the models. Chapter 4 provides a new criteria robustness parameter estimation criteria and several ways of inference have been shown to satisfy such criteria. Chapter 5 develops a new prior that satisfies some more criteria and is thus proposed to use in practice.

Parallel nonconvex generalized Benders decomposition for natural gas production network planning under uncertainty

A scenario-based two-stage stochastic programming model for gas production network planning under uncertainty is usually a large-scale nonconvex mixed-integer nonlinear programme (MINLP), which can be efficiently solved to global optimality with nonconvex generalized Benders decomposition (NGBD). This paper is concerned with the parallelization of NGBD to exploit multiple available computing resources. Three parallelization strategies are proposed, namely, naive scenario parallelization, adaptive scenario parallelization, and adaptive scenario and bounding parallelization. Case study of two industrial natural gas production network planning problems shows that, while the NGBD without parallelization is already faster than a state-of-the-art global optimization solver by an order of magnitude, the parallelization can improve the efficiency by several times on computers with multicore processors. The adaptive scenario and bounding parallelization achieves the best overall performance among the three proposed parallelization strategies.

Robust scenario formulations for strategic supply chain optimization under uncertainty

Strategic supply chain optimization (SCO) problems are often modelled as a two-stage optimization problem, in which the first-stage variables represent decisions on the development of the supply chain and the second-stage variables represent decisions on the operations of the supply chain. When uncertainty is explicitly considered, the problem becomes an intractable infinite-dimensional optimization problem, which is usually solved approximately via a scenario or a robust approach. This paper proposes a novel synergy of the scenario and robust approaches for strategic SCO under uncertainty. Two formulations are developed, namely, naïve robust scenario formulation and affinely adjustable robust scenario formulation. It is shown that both formulations can be reformulated into tractable deterministic optimization problems if the uncertainty is bounded with the infinity-norm, and the uncertain equality constraints can be reformulated into deterministic constraints without assumption of the uncertainty region. Case studies of a classical farm planning problem and an energy and bioproduct SCO problem demonstrate the advantages of the proposed formulations over the classical scenario formulation. The proposed formulations not only can generate solutions with guaranteed feasibility or indicate infeasibility of a problem, but also can achieve optimal expected economic performance with smaller numbers of scenarios.

Novel Formulation and Efficient Solution Strategy for Strategic Optimization of an Industrial Chemical Supply Chain Under Demand Uncertainty

This paper is concerned with strategic optimization of a typical industrial chemical supply chain, which involves a material purchase and transportation network, several manufacturing plants with on-site material and product inventories, a product transportation network and several regional markets. In order to address large uncertainties in customer demands at the different regional markets, a novel robust scenario formulation, which has been developed by the authors recently, is tailored and applied for the strategic optimization. Case study results show that the robust scenario formulation works well for this real industrial supply chain system, and it outperforms the deterministic formulation and the classical scenario-based stochastic programming formulation by generating better expected economic performance and solutions that are guaranteed to be feasible for all uncertainty realizations. The robust scenario problem exhibits a decomposable structure that can be taken advantage of by Benders decomposition for efficient solution, so the application of Benders decomposition to the solution of the strategic optimization is also discussed. The case study results show that Benders decomposition can reduce the solution time by almost an order of magnitude when the number of scenarios in the problem is large.

Climate change adaptation planning under uncertainty in Ho Chi Minh City, Vietnam: a case study on institutional vulnerability, adaptive capacity and climate change governance

Climate change is expected to have wide-ranging impacts on urban areas and creates additional challenges for sustainable development. Urban areas are inextricably linked with climate change, as they are major contributors to it, while also being particularly vulnerable to its impacts. Climate change presents a new challenge to urban areas, not only because of the expected rises in temperature and sea-level, but also the current context of failure to fully address the institutional barriers preventing action to prepare for climate change, or feedbacks between urban systems and agents. Despite the importance of climate change, there are few cities in developing countries that are attempting to address these issues systematically as part of their governance and planning processes. While there is a growing literature on the risks and vulnerabilities related to climate change, as yet there is limited research on the development of institutional responses, the dissemination of relevant knowledge and evaluation of tools for practical planning responses by decision makers at the city level. This thesis questions the dominant assumptions about the capacity of institutions and potential of adaptive planning. It argues that achieving a balance between climate change impacts and local government decision-making capacity is a vital for successful adaptation to the impacts of climate change. Urban spatial planning and wider environmental planning not only play a major role in reducing/mitigating risks but also have a key role in adapting to uncertainty in over future risk. The research focuses on a single province - the biggest city in Vietnam - Ho Chi Minh City - as the principal case study to explore this argument, by examining the linkages between urban planning systems, the structures of governance, and climate change adaptation planning. In conclusion it proposes a specific framework to offer insights into some of the more practical considerations, and the approach emphasises the importance of vertical and horizontal coordination in governance and urban planning.

Resolving taxonomic uncertainty in vulnerable elasmobranchs: are the Madeira skate (Raja maderensis) and the thornback ray (Raja clavata) distinct species?

Skates and rays constitute the most speciose group of chondrichthyan fishes, yet are characterised by remarkable levels of morphological and ecological conservatism. They can be challenging to identify, which makes monitoring species compositions for fisheries management purposes problematic. Owing to their slow growth and low fecundity, skates are vulnerable to exploitation and species exhibiting endemism or limited ranges are considered to be the most at risk. The Madeira skate Raja maderensis is endemic and classified as ‘Data Deficient’ by the IUCN, yet its taxonomic distinctiveness from the morphologically similar and more wide-ranging thornback ray Raja clavata is unresolved. This study evaluated the sequence divergence of both the variable control region and cytochrome oxidase I ‘DNA barcode’ gene of the mitochondrial genome to elucidate the genetic differentiation of specimens identified as R. maderensis and R. clavata collected across much of their geographic ranges. Genetic evidence was insufficient to support the different species designations. However regardless of putative species identification, individuals occupying waters around the Azores and North African Seamounts represent an evolutionarily significant unit worthy of special consideration for conservation management.