Water supply and consumption uncertainty: a conflict-equilibrium

This paper addresses the economic efficiency of water production and distribution in a vertical supply chain consisting of a water-provider and a consumer (municipality). The inherent conflicts over stocks and supply costs that emerge among the parties in the water supply chain are modeled as a zero-sum stochastic differential game. Consequently, the effects of collaboration and competition are contrasted as well as the application of block pricing and subsidies in overcoming potential conflicts between the water-provider and the municipality subject to political risks of not meeting demands for water. The effect of uncertainty is elaborated as well. In particular, we show that when the supply variance depends on the mean supply, the effect of uncertainty depends on the intensity of the conflict between the municipality and the water-provider.     

Greenhouse gas abatement — toward Pareto-optimal decisions under uncertainty

This paper examines the economic logic of integrated assessment — balancing the costs against the benefits of greenhouse gas abatement. Stylized facts are employed in a multiregion computable general equilibrium model with a public good. The percentage shares of global emissions are determined outside the model — based upon some form of international agreement — and emission rights are tradeable between regions. The analysis is confined to Pareto-optimal (cooperative) solutions. We focus on the sensitivity of initial decisions to low-probability, high-consequence scenarios associated with cumulative emissions. For simplicity, there are only two regions, two tradeable goods, two time periods, and two states-of-world. With the particular form of public good model adopted here (production rather than utility function impacts), it turns out that a Pareto-optimal hedging strategy is indepedent of the emission shares allocated to each region. Equity issues may be separated from those of economic efficiency. Similar results extend to cases in which there are additional regions, tradeable goods, time periods, and states-of-world.Presented at the Conference on the Economics of Global Environmental Change, Birmingham University, May 9–11, 1994. This research was funded by the Electric Power Research Institute (EPRI). The views presented here are solely those of the individual authors, and do not necessarily represent the views of EPRI or its members.     

A sharp Balian–Low uncertainty principle for shift-invariant spaces

A sharp version of the Balian–Low theorem is proven for the generators of finitely generated shift-invariant spaces. If generators ${\{f_k\}}_{k=1}^K?L^2({\mathbb{R}}^d)$ are translated along a lattice to form a frame or Riesz basis for a shift-invariant space V, and if V has extra invariance by a suitable finer lattice, then one of the generators $f_k$ must satisfy ${\int }_{{\mathbb{R}}^d}|x||f_k(x){|}^{2}dx=\infty$, namely, $\stackrel{?}{f_k}?H^{1/2}({\mathbb{R}}^d)$. Similar results are proven for frames of translates that are not Riesz bases without the assumption of extra lattice invariance. The best previously existing results in the literature give a notably weaker conclusion using the Sobolev space $H^{d/2+?}({\mathbb{R}}^d)$; our results provide an absolutely sharp improvement with $H^{1/2}({\mathbb{R}}^d)$. Our results are sharp in the sense that $H^{1/2}({\mathbb{R}}^d)$ cannot be replaced by $H^s({\mathbb{R}}^d)$ for any $s<1/2$.     

Basic modelling of uncertainty: young students’ mental models

This paper focuses on a research programme that aims to explore students?? mental models when acting within elementary situations of uncertainty before stochastics have been addressed in school. In order to frame our research, we begin by reporting briefly on the recent history of research in stochastics education. Then, we discuss the main aspects of our theoretical foundation concerning the research programme. Afterwards we restrict our discussion to an exploratory study aiming to further develop our theoretical framework, describing the development of tasks representing situations of uncertainty and the method of the exploratory study involving students of grade 4 and grade 6. Finally, from the perspective of theory development, we discuss the results of analysing students?? solutions gained through quantitative analysis of tests and qualitative analysis of written rationales and of interviews. We conclude our paper with a discussion of our research results and potential future research questions.     

Do we have enough data? Robust reliability via uncertainty quantification

A generalised probabilistic framework is proposed for reliability assessment and uncertainty quantification under a lack of data. The developed computational tool allows the effect of epistemic uncertainty to be quantified and has been applied to assess the reliability of an electronic circuit and a power transmission network. The strength and weakness of the proposed approach are illustrated by comparison to traditional probabilistic approaches. In the presence of both aleatory and epistemic uncertainty, classic probabilistic approaches may lead to misleading conclusions and a false sense of confidence which may not fully represent the quality of the available information. In contrast, generalised probabilistic approaches are versatile and powerful when linked to a computational tool that permits their applicability to realistic engineering problems.     

Naor’s model with heterogeneous customers and arrival rate uncertainty

This paper examines an extension of Naor’s observable queueing model in which the arrival rate is not known with certainty by either customers or the system manager. Further, customers are heterogeneous in terms of their service valuation and waiting time cost. We analyze the stability of the system and properties of the revenue maximizing fee and socially optimal fee.     

On Hardy’s uncertainty principle for connected nilpotent Lie groups

In (Kaniuth and Kumar in Math. Proc. Camb. Phil. Soc. 131, 487–494, 2001) Hardy’s uncertainty principle for was generalized to connected and simply connected nilpotent Lie groups. In this paper, we extend it further to connected nilpotent Lie groups with non-compact centre. Concerning the converse, we show that Hardy’s theorem fails for a connected nilpotent Lie group G which admits a square integrable irreducible representation and that this condition is necessary if the simply connected covering group of G satisfies the flat orbit condition.     

Students’ emergent articulations of uncertainty while making informal statistical inferences

Research on informal statistical inference has so far paid little attention to the development of students?? expressions of uncertainty in reasoning from samples. This paper studies students?? articulations of uncertainty when engaged in informal inferential reasoning. Using data from a design experiment in Israeli Grade 5 (aged 10?C11) inquiry-based classrooms, we focus on two groups of students working with TinkerPlots on investigations with growing sample size. From our analysis, it appears that this design, especially prediction tasks, helped in promoting the students?? probabilistic language. Initially, the students oscillated between certainty-only (deterministic) and uncertainty-only (relativistic) statements. As they engaged further in their inquiries, they came to talk in more sophisticated ways with increasing awareness of what is at stake, using what can be seen as buds of probabilistic language. Attending to students?? emerging articulations of uncertainty in making judgments about patterns and trends in data may provide an opportunity to develop more sophisticated understandings of statistical inference.     

“Value” of additional information in multicriterion analysis under uncertainty

The expected value of information in classical (monocriterion) decision analysis has been well covered in the literature. One cannot say the same thing about the multicriterion analysis, particularly when one is in the presence of multicriterion aggregation procedures based on outranking relations for a ranking problematic. The objective of this paper is to try to extend the Bayesian approach to a multicriterion analysis in the context of uncertainty. After illustrating the a posteriori analysis, we shall mention some difficulties associated with the pre a posteriori analysis and the concepts of the “expected value” of perfect or imperfect information.     

Sure things — Dominance and independence rules for choice under uncertainty

The primary purpose of this paper is to show that differences between models of choice under uncertainty may be derived primarily from different assumptions about the appropriate ways in which states of the world may be compared and combined. It considers different concepts of stochastic dominance arising from different permitted transformations on the ordering of prizes during a comparison of two lotteries. These concepts imply various forms of the Independence axiom and correspond to various non-expected utility theories.     

Promoting uncertainty to support preservice teachers’ reasoning about the tangent relationship

Including opportunities for students to experience uncertainty in solving mathematical tasks can prompt learners to resolve the uncertainty, leading to mathematical understanding. In this article, we examine how preservice secondary mathematics teachers’ thinking about a trigonometric relationship was impacted by a series of tasks that prompted uncertainty. Using dynamic geometry software, we asked preservice teachers to compare angle measures of lines on a coordinate grid to their slope values, beginning by investigating lines whose angle measures were in a near-linear relationship to their slopes. After encountering and resolving the uncertainty of the exact relationship between the values, preservice teachers connected what they learned to the tangent relationship and demonstrated new ways of thinking that entail quantitative and covariational reasoning about this trigonometric relationship. We argue that strategically using uncertainty can be an effective way of promoting preservice teachers’ reasoning about the tangent relationship.     

On the optimality of threshold control in queues with model uncertainty

We consider a single-stage queuing system where arrivals and departures are modeled by point processes with stochastic intensities. An arrival incurs a cost, while a departure earns a revenue. The objective is to maximize the profit by controlling the intensities subject to capacity limits and holding costs. When the stochastic model for arrival and departure processes are completely known, then a threshold policy is known to be optimal. Many times arrival and departure processes can not be accurately modeled and controlled due to lack of sufficient calibration data or inaccurate assumptions. We prove that a threshold policy is optimal under a max–min robust model when the uncertainty in the processes is characterized by relative entropy. Our model generalizes the standard notion of relative entropy to account for different levels of model uncertainty in arrival and departure processes. We also study the impact of uncertainty levels on the optimal threshold control.     

A likelihood-based method for haplotype association studies of case-control data with genotyping uncertainty

This paper discusses the associations between traits and haplotypes based on Fl (fluorescent intensity) data sets. We consider a clustering algorithm based on mixtures of t distributions to obtain all possible genotypes of each individual (i.e. "GenoSpec-trum"). We then propose a likelihood-based approach that incorporates the genotyping uncertainty to assessing the associations between traits and haplotypes through a haplotype-based logistic regression model. Simulation studies show that our likelihood-based method can reduce the impact induced by genotyping errors.     

Anti-optimization technique – a generalization of interval analysis for nonprobabilistic treatment of uncertainty

Anti-optimization technique, on the one hand, represents an alternative and complement to traditional probabilistic methods, and on the other hand, it is a generalization of the mathematical theory of interval analysis. In this study, in terms of interval analysis or interval mathematics, the arithmetic operations and the partial order relation of anti-optimization technique can be defined, and the convex model variables and the convex model extension function of convex models can also be introduced. The comparison of the Lagrange multiplier method with the convex model extension method for evaluating the region of static displacements of structures with uncertain-but-bounded parameters shows that the width of the upper and lower bounds on the static displacement yielded by the Lagrange multiplier method of convex models is tighter than those produced by the convex model extension.     

When are static and adjustable robust optimization problems with constraint-wise uncertainty equivalent?

Adjustable robust optimization (ARO) generally produces better worst-case solutions than static robust optimization (RO). However, ARO is computationally more difficult than RO. In this paper, we provide conditions under which the worst-case objective values of ARO and RO problems are equal. We prove that when the uncertainty is constraint-wise, the problem is convex with respect to the adjustable variables and concave with respect to the uncertain parameters, the adjustable variables lie in a convex and compact set and the uncertainty set is convex and compact, then robust solutions are also optimal for the corresponding ARO problem. Furthermore, we prove that if some of the uncertain parameters are constraint-wise and the rest are not, then under a similar set of assumptions there is an optimal decision rule for the ARO problem that does not depend on the constraint-wise uncertain parameters. Also, we show for a class of problems that using affine decision rules that depend on all of the uncertain parameters yields the same optimal objective value as when the rules depend solely on the non-constraint-wise uncertain parameters. Finally, we illustrate the usefulness of these results by applying them to convex quadratic and conic quadratic problems.     

A derivative algorithm for inexact quadratic program – application to environmental decision-making under uncertainty

Inexact quadratic programming (IQP) is an extension of conventional quadratic programming for handling both nonlinearities in cost objectives and uncertainties with modeling parameters. It has been a useful tool for environmental systems analysis. However, inefficiency in its solution method has existed, leading to difficulties in its practical application. In this study, a derivative algorithm (DAM) is proposed for solving the IQP. It improves upon the existing method through provision of a quantitative expression for uncertain relationships between the quadratic objective function and the decision variables. The DAM requires much lower computational efforts than the existing algorithm, which is especially meaningful for the IQP's application to large-scale problems. The developed DAM is applied to a hypothetical problem of municipal solid waste management and planning. Detailed solution steps are provided to clearly demonstrate the method's advantages.     

Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty

Reducing the energy consumption of virtualized datacenters and the Cloud is very important in order to lower CO\( _2 \) footprint and operational cost of a Cloud operator. However, there is a trade-off between energy consumption and perceived application performance. In order to save energy, Cloud operators want to consolidate as many Virtual Machines (VM) on the fewest possible physical servers, possibly involving overbooking of resources. However, that may involve SLA violations when many VMs run on peak load. Such consolidation is typically done using VM migration techniques, which stress the network. As a consequence, it is important to find the right balance between the energy consumption and the number of migrations to perform. Unfortunately, the resources that a VM requires are not precisely known in advance, which makes it very difficult to optimise the VM migration schedule. In this paper, we therefore propose a novel approach based on the theory of robust optimisation. We model the VM consolidation problem as a robust Mixed Integer Linear Program and allow to specify bounds for e.g. resource requirements of the VMs. We show that, by using our model, Cloud operators can effectively trade-off uncertainty of resource requirements with total energy consumption. Also, our model allows us to quantify the price of the robustness in terms of energy saving against resource requirement violations.     

Robust and reliable forward–reverse logistics network design under demand uncertainty and facility disruptions

There are two broad categories of risk, which influence the supply chain design and management. The first category is concerned with uncertainty embedded in the model parameters, which affects the problem of balancing supply and demand. The second category of risks may arise from natural disasters, strikes and economic disruptions, terroristic acts, and etc. Most of the existing studies surveyed these types of risk, separately. This paper proposes a robust and reliable model for an integrated forward–reverse logistics network design, which simultaneously takes uncertain parameters and facility disruptions into account. The proposed model is formulated based on a recent robust optimization approach to protect the network against uncertainty. Furthermore, a mixed integer linear programing model with augmented p-robust constraints is proposed to control the reliability of the network among disruption scenarios. The objective function of the proposed model is minimizing the nominal cost, while reducing disruption risk using the p-robustness criterion. To study the behavior of the robustness and reliability of the concerned network, several numerical examples are considered. Finally, a comparative analysis is carried out to study the performance of the augmented p-robust criterion and other conventional robust criteria.     

Justification shift and uncertainty: why are low-probability near misses underrated against organizational routines?

Knowledge of near misses is helpful in preventing accidents, but it does not always lead to changes in organizational routines or contribute to accident prevention. In this article, the authors argue that low-probability near misses reinforce beliefs of experts and professionals in existing routines, proposing a concept of justification shift. Justification shift is underestimation of risks of known near misses vis-à-vis overestimation of reliabilities of existing routines. Consequently, signals of “close calls” tend to be disregarded, and accidents become more likely. When justification shift occurs, experts and professionals who want changes in routines face burden of proof about risks of those routines. Uncertainty in requirements and data may increase the burden and make justification shift more likely. To explore how justification shift occurs and how uncertainty influences the shift, the authors have developed an agent-based model based on theories in organizational studies and the case of the space shuttle Challenger accident in 1986. From the simulation using the model, it becomes clear that uncertainty does not make a difference in frequencies and degrees of justification shift. The authors also discuss implications of this finding to efforts to utilize knowledge of near misses and to further research on the efforts and organizational decisions.     

A generalizaton of Hardy’s uncertainty principle on compact extensions of \mathbb R ^n

Let \(K\) be a compact subgroup of automorphisms of \(\mathbb R ^n\) . We prove in this paper a generalization of Hardy’s uncertainty principle on the semi-direct product \(K\ltimes \mathbb R ^n\) , and we solve the sharpness problem. As a consequence, a complete analogue of classical Hardy’s theorem is obtained. The representation theory and the Plancherel formula play an important role in the proofs.