A Risk-Informed Decision-Making Framework for Climate Change Adaptation through Robust Land Use and Irrigation Planning

Uncertainty and variability are key challenges for climate change adaptation planning. In the face of uncertainty, decision-making can be addressed in two interdependent stages: make only partial ex ante anticipative actions to keep options open until new information is revealed, and adapt the first-stage decisions with respect to newly acquired information. This decision-making approach corresponds to the two-stage stochastic optimization (STO) incorporating both anticipative ex ante and adaptive ex post decisions within a single model. This paper develops a two-stage STO model for climate change adaptation through robust land use and irrigation planning under conditions of uncertain water supply. The model identifies the differences between decision-making in the cases of perfect information, full uncertainty, and two-stage STO from the perspective of learning about uncertainty. Two-stage anticipative and adaptive decision-making with safety constraints provides risk-informed decisions characterized by quantile-based Value-at-Risk and Conditional Value-at-Risk risk measures. The ratio between the ex ante and ex post costs and the shape of uncertainty determine the balance between the anticipative and adaptive decisions. Selected numerical results illustrate that the alteration of the ex ante agricultural production costs can affect crop production, management technologies, and natural resource utilization

A System-of-Systems Framework for Exploratory Analysis of Climate Change Impacts on Civil Infrastructure Resilience

Climate change has various chronic and acute impacts on civil infrastructure systems (CIS). A long-term assessment of resilience in CIS requires understanding the transformation of CIS caused by climate change stressors and adaptation decision-making behaviors of institutional agencies. In addition, resilience assessment for CIS includes significant uncertainty regarding future climate change scenarios and subsequent impacts. Thus, resilience analysis in CIS under climate change impacts need to capture complex adaptive behaviors and uncertainty in order to enable robust planning and decision making. This study presented a system-of-systems (SoS) framework for abstraction and integrated modeling of climate change stressors, physical infrastructure performance, and institutional actors’ decision making. The application of the proposed SoS framework was shown in an illustrative case study related to the impacts of sea level rise and subsequent saltwater intrusion on a water system. Through the use of the proposed SoS framework, various attributes, processes, and interactions related to physical infrastructure and actor’s decision making were abstracted and used in the creation of a computational simulation model. Then, the computational model was used to simulate various scenarios composed of sea level rise and adaptation approaches. Through an exploratory analysis approach, the simulated scenario landscape was used to identify robust adaptation pathways that lead to a greater system resilience under future uncertain sea level rise. The results of the illustrative case study highlight the various novel capabilities of the SoS framework: (i) abstraction of various attributes and processes that affect the long-term resilience of infrastructure under climate change; (ii) integrated modeling of CIS transformation based on simulating the adaptive decision-making processes, physical infrastructure performance, and climate change impacts; and (iii) exploratory analysis and identification of robust pathways for adaptation to climate change impacts

A System-of-Systems Framework for Exploratory Analysis of Climate Change Impacts on Civil Infrastructure Resilience

Climate change has various chronic and acute impacts on civil infrastructure systems (CIS). A long-term assessment of resilience in CIS requires understanding the transformation of CIS caused by climate change stressors and adaptation decision-making behaviors of institutional agencies. In addition, resilience assessment for CIS includes significant uncertainty regarding future climate change scenarios and subsequent impacts. Thus, resilience analysis in CIS under climate change impacts need to capture complex adaptive behaviors and uncertainty in order to enable robust planning and decision making. This study presented a system-of-systems (SoS) framework for abstraction and integrated modeling of climate change stressors, physical infrastructure performance, and institutional actors’ decision making. The application of the proposed SoS framework was shown in an illustrative case study related to the impacts of sea level rise and subsequent saltwater intrusion on a water system. Through the use of the proposed SoS framework, various attributes, processes, and interactions related to physical infrastructure and actor’s decision making were abstracted and used in the creation of a computational simulation model. Then, the computational model was used to simulate various scenarios composed of sea level rise and adaptation approaches. Through an exploratory analysis approach, the simulated scenario landscape was used to identify robust adaptation pathways that lead to a greater system resilience under future uncertain sea level rise. The results of the illustrative case study highlight the various novel capabilities of the SoS framework: (i) abstraction of various attributes and processes that affect the long-term resilience of infrastructure under climate change; (ii) integrated modeling of CIS transformation based on simulating the adaptive decision-making processes, physical infrastructure performance, and climate change impacts; and (iii) exploratory analysis and identification of robust pathways for adaptation to climate change impacts

A method for acquiring random range uncertainty probability distributions in proton therapy.

In treatment planning we depend upon accurate knowledge of geometric and range uncertainties. If the uncertainty model is inaccurate then the plan will produce under-dosing of the target and/or overdosing of OAR. We aim to provide a method for which centre and site-specific population range uncertainty due to inter-fraction motion can be quantified to improve the uncertainty model in proton treatment planning. Daily volumetric MVCT data from previously treated radiotherapy patients has been used to investigate inter-fraction changes to water equivalent path-length (WEPL). Daily image-guidance scans were carried out for each patient and corrected for changes in CTV position (using rigid transformations). An effective depth algorithm was used to determine residual range changes, after corrections had been applied, throughout the treatment by comparing WEPL within the CTV at each fraction for several beam angles. As a proof of principle this method was used to quantify uncertainties for inter-fraction range changes for a sample of head and neck patients of [Formula: see text] mm, [Formula: see text] mm and overall [Formula: see text] mm. For prostate [Formula: see text] mm, [Formula: see text] mm and overall [Formula: see text] mm. The choice of beam angle for head and neck did not affect the inter-fraction range error significantly; however this was not the same for prostate. Greater range changes were seen using a lateral beam compared to an anterior beam for prostate due to relative motion of the prostate and femoral heads. A method has been developed to quantify population range changes due to inter-fraction motion that can be adapted for the clinic. The results of this work highlight the importance of robust planning and analysis in proton therapy. Such information could be used in robust optimisation algorithms or treatment plan robustness analysis. Such knowledge will aid in establishing beam start conditions at planning and for establishing adaptive planning protocols.This work was funded by a Medical Research Council Studentship to the University of Cambridge (G1000384). Dr S. Holloway is currently supported by a Cancer Research UK Centres Network Accelerator Award Grant (A21993) to the ART-NET consortium

Structuring Decisions Under Deep Uncertainty

Innovative research on decision making under ‘deep uncertainty’ is underway in applied fields such as engineering and operational research, largely outside the view of normative theorists grounded in decision theory. Applied methods and tools for decision support under deep uncertainty go beyond standard decision theory in the attention that they give to the structuring of decisions. Decision structuring is an important part of a broader philosophy of managing uncertainty in decision making, and normative decision theorists can both learn from, and contribute to, the growing deep uncertainty decision support literature

Response-adaptive dose-finding under model uncertainty

Dose-finding studies are frequently conducted to evaluate the effect of different doses or concentration levels of a compound on a response of interest. Applications include the investigation of a new medicinal drug, a herbicide or fertilizer, a molecular entity, an environmental toxin, or an industrial chemical. In pharmaceutical drug development, dose-finding studies are of critical importance because of regulatory requirements that marketed doses are safe and provide clinically relevant efficacy. Motivated by a dose-finding study in moderate persistent asthma, we propose response-adaptive designs addressing two major challenges in dose-finding studies: uncertainty about the dose-response models and large variability in parameter estimates. To allocate new cohorts of patients in an ongoing study, we use optimal designs that are robust under model uncertainty. In addition, we use a Bayesian shrinkage approach to stabilize the parameter estimates over the successive interim analyses used in the adaptations. This approach allows us to calculate updated parameter estimates and model probabilities that can then be used to calculate the optimal design for subsequent cohorts. The resulting designs are hence robust with respect to model misspecification and additionally can efficiently adapt to the information accrued in an ongoing study. We focus on adaptive designs for estimating the minimum effective dose, although alternative optimality criteria or mixtures thereof could be used, enabling the design to address multiple objectives.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS445 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Planning with Information-Processing Constraints and Model Uncertainty in Markov Decision Processes

Information-theoretic principles for learning and acting have been proposed to solve particular classes of Markov Decision Problems. Mathematically, such approaches are governed by a variational free energy principle and allow solving MDP planning problems with information-processing constraints expressed in terms of a Kullback-Leibler divergence with respect to a reference distribution. Here we consider a generalization of such MDP planners by taking model uncertainty into account. As model uncertainty can also be formalized as an information-processing constraint, we can derive a unified solution from a single generalized variational principle. We provide a generalized value iteration scheme together with a convergence proof. As limit cases, this generalized scheme includes standard value iteration with a known model, Bayesian MDP planning, and robust planning. We demonstrate the benefits of this approach in a grid world simulation.Comment: 16 pages, 3 figure