Uncertainty Space Research Articles

En medio de la violencia: recursos, tácticas y violencia contra el sector ganadero

This article discusses the main results of a research study on the interactions between stockbreeders and armed groups in relation to the illegal collection of funds, as narrated in the stockbreeders’ testimonies and memories. The paper relies on both statistical and narrative analysis methodologies in order to explore the patterns and configurations in interactions with armed groups. It is argued here that the stockbreeders were not supporters or passive victims, rather they acted strategically when making decisions and relying on social and financial capitals actors to negotiate the terms of their own subjugation. Drawing upon studies on remembrance and historicization of the Colombian armed conflict, the paper stresses the need to understand local contexts, taken as spaces of uncertainty, agitation, and ongoing readjustment. The paper also cautions scholars against the tendency to reduce the interactions between civilians and armed groups to mere economic or political motivations. Rather, these interactions are considered to emerge mainly from individuals’ everyday concerns and the desire to protect their lives.

Efficient Uncertainty Quantification of Reservoir Properties for Parameter Estimation and Production Forecasting

Three levels of uncertainty and their impact on reservoir estimation and forecasting are examined: (1) definition of reservoir facies geometry derived from uncertain geologic and geophysical information; (2) non-uniqueness of identifying the permeability distribution through inverse parameter estimation using injection and production data; and (3) unknown, fine-scale spatial variation in the heterogeneous rock properties. Inverse parameter estimation with pilot points and kriging is used to create accurate estimates of the permeability field. Efficient sampling of the uncertainty space surrounding these estimates through both probability-field (p-field) simulation and sequential Gaussian simulation (sGs) is demonstrated using a test case reservoir with permeability dominated by a sand-shale, facies distribution. A geologic conceptual model, measured permeabilities at the wells and 2100 days of injection and production data are used to estimate the permeability distribution and create empirical prediction intervals for future production. Diversity of the estimated and sampled fields across all three levels of uncertainty is examined through multi-dimensional scaling. Sampled permeability fields provide precise and accurate parameter estimation from injection and production data. The sGs fields create a wider prediction interval and underestimate the true production rates relative to the p-field samples. For a given estimated variogram model, the p-field samples result in shorter ranges and lower nugget values relative to the sGs fields. Conditional sampling through p-field and sGs simulation provides greater diversity in the solution space than the parameter estimation alone for a fraction of the computational expense.

Pregnancy intentions in a group of remote-dwelling Australian Aboriginal women: a qualitative exploration of formation, expression and implications for clinical practice

BackgroundUnintended pregnancies are associated with poorer obstetric outcomes and are sometimes measured at a population level as a surrogate marker for reproductive autonomy and access to health services, including contraception. Aboriginal Australians face many disparities in health outcomes, including in reproductive health and antenatal care. We aimed to explore the formation and expression of pregnancy intentions in an Aboriginal population to inform health service improvements.MethodsSemi-structured interviews were conducted with 27 remote-dwelling Aboriginal women, aged 18–49 years. Content analysis was conducted; key themes were discussed with groups of women from participating communities to refine interpretation.ResultsMost (19/27) participants expressed pregnancy intentions congruent with reported contraceptive behaviour while eight expressed ambivalent or uncertain intentions. Intentions were shaped by traditional kinship practices, reproductive autonomy and desired family formation. Younger women tended to aspire to smaller family sizes than older women and support was expressed for the postponement of first pregnancy to achieve other life goals. Women in these communities hold strong traditional beliefs, including regarding conception, but did not use traditional methods of contraception in place of modern methods. Reproductive coercion, in the form of pressure to fall pregnant, was recognised as an important issue by women in the community.ConclusionConsultation strategies that promote rapport, allow space for uncertainty and are inclusive of important personal and cultural contexts are likely to improve shared understanding of pregnancy intention. Universal screening for reproductive coercion and broad counselling on contraceptive options (including discrete methods) may reduce unmet need for contraception. Community approaches supporting reproductive autonomy that is inclusive of men, and enhanced educational and occupational opportunities for young women are needed.

Efficient uncertainty analysis of the 3D electrical tomography inverse problem

We have evaluated the uncertainty analysis of the 3D electrical tomography inverse problem using model reduction via singular-value decomposition and performed sampling of the nonlinear equivalence region via an explorative member of the particle swarm optimization (PSO) family. The procedure begins with the local inversion of the observed data to find a good resistivity model located in the nonlinear equivalence region. Then, the dimensionality is reduced via the spectral decomposition of the 3D geophysical model. Finally, the exploration of the uncertainty space is performed via an exploratory version of PSO (RR-PSO). This sampling methodology does not prejudge where the initial model comes from as long as this model has a geologic meaning. The 3D subsurface conductivity distribution is arranged as a 2D matrix by ordering the conductivity values contained in a given earth section as a column array and stacking parallel sections as columns of the matrix. There are three basic modes of ordering: mode 1 and mode 2, by using vertical sections in two perpendicular directions, and mode 3, by using horizontal sections. The spectral decomposition is then performed using these three 2D modes. Using this approach, it is possible to sample the uncertainty space of the 3D electrical resistivity inverse problem very efficiently. This methodology is intrinsically parallelizable and could be run for different initial models simultaneously. We found the application to a synthetic data set that is well-known in the literature related to this subject, obtaining a set of surviving geophysical models located in the nonlinear equivalence region that can be used to approximate numerically the posterior distribution of the geophysical model parameters (frequentist approach). Based on these models, it is possible to perform the probabilistic segmentation of the inverse solution found, meanwhile answering geophysical questions with its corresponding uncertainty assessment. This methodology has a general character could be applied to any other 3D nonlinear inverse problems by implementing their corresponding forward model.

The Bhattacharyya distance: Enriching the P-box in stochastic sensitivity analysis

Abstract The tendency of uncertainty analysis has promoted the transformation of sensitivity analysis from the deterministic sense to the stochastic sense. This work proposes a stochastic sensitivity analysis framework using the Bhattacharyya distance as a novel uncertainty quantification metric. The Bhattacharyya distance is utilised to provide a quantitative description of the P-box in a two-level procedure for both aleatory and epistemic uncertainties. In the first level, the aleatory uncertainty is quantified by a Monte Carlo process within the probability space of the cumulative distribution function. For each sample of the Monte Carlo simulation, the second level is performed to propagate the epistemic uncertainty by solving an optimisation problem. Subsequently, three sensitivity indices are defined based on the Bhattacharyya distance, making it possible to rank the significance of the parameters according to the reduction and dispersion of the uncertainty space of the system outputs. A tutorial case study is provided in the first part of the example to give a clear understanding of the principle of the approach with reproducible results. The second case study is the NASA Langley challenge problem, which demonstrates the feasibility of the proposed approach, as well as the Bhattacharyya distance metric, in solving such a large-scale, strong-nonlinear, and complex problem.

The Forensic Eye and the Public Mind: The Bertillon System of Crime Scene Photography

In fin de siècle France, Alphonse Bertillon—best known for his widely adopted system of criminal identification—pursued “other applications” for judicial photography, suggesting that photography might be used to procure “an exact, complete, and impartial” view of “locales, things, and beings.” Photography, Bertillon was suggesting, could preserve a crime scene. In many ways, crime scene photography seems like the logical fulfillment of what Allan Sekula termed the “evidentiary promise” of photography. Understanding crime scene photography as a form of evidence places it in the realm of empirical science, with the photograph preserving proof of misdeeds and aiding the detective's forensic pursuit of truth. But, perhaps surprisingly, this was not the use that Bertillon foresaw for crime scene photography. Instead he suggested that crime scene photography was destined for the courtroom, and for the eyes of the jury. There it would not be a vehicle of objective proof, but rather an emotional catalyst for conviction. This paper examines the Bertillon system of crime scene photography as a rhetorical strategy calibrated for emotional impact, showing how it attempted to move viewers from the space of investigation and uncertainty to the space of conviction.

Managing Risk in Uncertain Times

ABSTRACTRecent clearing of squatter housing along rivers in Jakarta demonstrates a government focused on eliminating the uncertainty and risk of flooding, characterised by Zeiderman, Austin [(2015). Spaces of Uncertainty: Governing Urban Environmental Hazards. In Modes of Uncertainty, edited by Paul Rabinow and Limor Samimian-Darash, 182–200. Chicago: University of Chicago Press] as a strategy of securitization. Such government actions optimise technologies of state power rather than local ways of engaging with risk. A study of riverbank populations in Yogyakarta demonstrates the capacity of local citizens to calculate and manage the risks they face. Frequently government action in the name of risk reduction is not based on adequate communication with these riverbank populations and ignores this community capacity to handle risk. This article examines the strategies adopted by those most at risk, the urban poor, based on their understanding of major ‘natural’ disasters of floods and earthquakes as a combination of both spiritual and natural forces.

A generalized many‐objective optimization approach for scenario discovery

AbstractScenario discovery is a model‐based approach for scenario development, aimed at finding one or more subspaces within the uncertainty space associated with a model that is decision‐relevant. These identified subspaces can subsequently be translated into narratives or shared in other ways a broader participatory process. Finding such as subspace involves solving a three‐objective optimization problem. A subspace should cover many of the decision relevant model runs, while containing as few as possible nondecision relevant model runs, and being easy to interpret. Existing techniques for scenario discovery, however, focus only on finding a subspace that minimizes the number of nondecision relevant model runs. Adopting a single objective optimization approach for a many‐objective optimization problem implies that the full trade‐off space is not identified. In this paper, we introduce a many‐objective optimization approach for scenario discovery. We compare this with an improved usage of Patient Rule Induction Method (PRIM) for identifying the multidimensional trade‐offs amongst coverage, density, and interpretability. We find that the many‐objective optimization approach produces results which slightly dominate those of the improved version of PRIM on all three objectives. Qualitatively, however, both approaches identify essentially the same subspaces. The prime benefits of the many‐objective optimization approach are its potential in bringing additional scenario relevant concerns such as consistency or diversity into the scenario discovery framework, as well as its ability to avoid overfitting. Potentially more important, it also paves the way for future work on using more sophisticated metaheuristic optimization approaches for scenario discovery.

Conditional Expectation in an Uncertainty Space

In the classical (probability) setting, the Radon-Nikodym Theorem is used to prove the existence of conditional expectations. This paper states the Radon-Nikodym Theorem for uncertain measures which were introduced by Liu (2007) to model belief degrees. This version of the theorem is adapted from Graf (1980) who established the theorem for non-additive measures called capacities. This paper then uses the theorem to define the conditional expectation of an uncertain variable with respect to a σ-algebra. Properties of the conditional expectation are established.

Data kit inversion and uncertainty analysis

Linear and nonlinear discrete inverse problems are by genesis ill-posed, that is, several solutions exist compatible with the prior information and fit the observed data within the same error bounds (equivalent models). This fact and the presence of noise and modelling errors cause their solution to be uncertain. Uncertainty analysis and solution appraisal is a crucial step in inverse modelling, and it is typically accomplished via random sampling methodologies and Bayesian approaches (provided a good knowledge of the probabilistic distributions of the model and data spaces is at disposal) or via exploratory global optimization algorithms that perform an approximate posterior sampling of the nonlinear equivalence region. This paper demonstrates that a simple method to sample the uncertainty space in linear and nonlinear problems consists in performing different inversions of random data bags. This novel methodology is called data kit inversion and it is inspired in the bootstrapping technique in statistical inference, switching in this case to the existence of other equivalent models, instead of using in estimating the properties of a given estimator. We present its application to different synthetic linear and nonlinear inverse problems and also to the inversion of a real dataset of gravimetric inversion in the Atacama Desert and also to 1D seismic and AVO inversion. We show that using partial information (random data bags) it is possible to sample different models of the equivalence region (uncertainty space) in order to explore the existence of different plausible geophysical scenarios according to the geophysical model that it is adopted. This algorithm has a general character and can be used in different fields of science and technology.

A Fuzzy Approach for Maintenance Management of Urban Roadway Bridges

A successful bridge management system needs to utilise an efficient decision-making model for prioritising the bridges for repair and maintenance operations to deal with the limited allocated funds. Models based on certain mathematics and divalent logic that need accurate data are not flexible with the uncertainty space of project management procedure and lead to imprecise outputs. Unlike classical logic, fuzzy logic represents the propositions with degrees of truthfulness and falsehood. In this paper, a fuzzy decision-making model was developed to prioritise a large number of urban roadway bridges and put them on the agenda for repair and maintenance operations. The proposed model considers experts' feelings, knowledge, and judgment expressed by linguistic variables, vague data or uncertain values in the modelling. The introduced model uses a fuzzy multi-attribute decision-making matrix to evaluate a large number of bridges to a large number of effective factors in the bridge maintenance area and determines the fuzzy desirability priority of each bridge as well as the preference value of every bridge to another one. This capability makes the model adaptable for a particular region or condition and helps managers make quick and more accurate strategic decisions.

Liminal Identity: Reconstruction of Syrian Identity in Trump's America

Abstract During November of 2016, the Electoral College elected Donald Trump as President of the United States of America. The following spring, I conducted research with Syrian refugees in Austin, Texas. Using liminality, or the space of uncertainty, I examined identity loss and change with Syrian refugees and within myself. As an immigrant from Syria, my identity was always an issue growing up in the United States, especially as my family struggled for years to attain citizenship. Trump's election evoked my past feelings of uncertainty regarding personhood. Understanding the political context and the challenges of resettlement, I conducted my thesis research in Austin, Texas, with Syrian refugees in order to examine changes in their identities. Their continued feelings of identity loss and change fortified their existence in a space I am very familiar with—liminality.

A new bi-matrix game model with fuzzy payoffs in credibility space

Uncertain theory based on expert's evaluation and non-additive measure is introduced to explore the bi-matrix game with the uncertain payoffs. The uncertainty space based on the axiom of the uncertain measures is presented. Some basic characteristics of uncertain events are described and the expected value of the uncertain variables is given in uncertainty space. A new model of bi-matrix game with uncertain payoffs is established and its equivalent strategy is given. Then, we develop an expected model of uncertain bi-matrix games and define the uncertain equilibrium strategy of uncertain bi-matrix games. By using the expected value of uncertain variable, we transform the model into a linear programming, the expected equilibrium strategy of the uncertain bi-matrix games is identified through solving linear equations.

Several Inequalities on Moment of Uncertain Variables

An uncertain variable is a Borel measurable function whose domain is uncertainty space and range is the set of real numbers. However, for many reasons, like the difficult of collecting data, the value of an uncertain variable is usually not easy to measure accurately. Hence many scholars study the estimation range of the value of an uncertain variable, and usually to estimate the upper or lower bounds of the moment of an uncertain variable is the primary idea. Many inequalities are established to estimate the above bounds, but there are still some problems on the estimation of the moment of uncertain variables. For instance, the even-order moment of an uncertain variable cannot be uniquely calculated at present. So the aim of this paper is to estimate the upper or power bounds of the moment of uncertain variables or the uncertain measure of an event by establishing several new inequalities. Firstly, we extend the Lyapunov inequality on uncertain variable and this inequality gives the upper bound of the even-order moment of an uncertain variable, and as a corollary, the lower bound of the above even-order moment is given. Then the inequality of arithmetic-geometry is proved, which estimates the lower bound of the expected value of an uncertain variable. After that, two equivalent inequalities are given, which can be used to judge the existence of the expected value of a function of an uncertain variable. Finally, as for two independent and identically distributed uncertain variables, the weakly symmetric inequalities are investigated to estimate the upper and lower bounds of the uncertainty distributions of the difference of these uncertain variables which implies the uncertain measures of several events. The above inequalities extend the application range of uncertain variable.

Stories of becoming: implications of future‐oriented theories for a vital literacy education

AbstractDrawing on a multi‐year qualitative study of immigrant and non‐immigrant youth storytelling, I describe youth's imaginative labour as they constructed and moved into one another's worlds. I argue that although multicultural literature is vital for affirming and expanding youth perspectives on their own and others' worlds, the publishing industry lags behind the immediate needs of immigrant youth to be understood and heard as fully human. Storytelling, in the form of co‐narration, creates an imaginative space of uncertainty and playfulness for inviting possible futures into mutual awareness and interpretation. Using two storytelling episodes as illustrations and drawing on contemporary sociocultural and decolonizing theories, I outline four ways future‐oriented teaching and research may be realized: (1) rename the world, by considering ‘what if’, (2) reframe narratives of damage so that narratives of desire may be foregrounded, (3) contribute to a more just and equal world rather than merely participate in existing practices and (4) reclaim childhoods for freedom and exploration, especially in our relations with Black and Brown youth who are perceived in adult terms much earlier than their White peers. I argue, overall, that we must understand and develop a contemporary theory of imagination in order to meet the challenges of future‐oriented literacy teaching and research.

Developing a workflow to represent fractured carbonate reservoirs for simulation models under uncertainties based on flow unit concept

Description of fractured reservoir rock under uncertainties in a 3D model and integration with reservoir simulation is still a challenging topic. In particular, mapping the potential zones with a reservoir quality can be very useful for making decisions and support development planning. This mapping can be done through the concept of flow units. In this paper, an integrated approach including a Hierarchical Cluster Analysis (HCA), geostatistical modeling and uncertainty analysis is developed and applied to a fractured carbonate in order to integrate on numerical simulation. The workflow begins with different HCA methods, performed to well-logs in three wells, to identify flow units and rock types. Geostatistical techniques are then applied to extend the flow units, petrophysical properties and fractures into the inter-well area. Finally, uncertainty analysis is applied to combine different types of uncertainties for generating ensemble reservoir simulation models. The obtained clusters from different HCA methods are evaluated by the cophenetic coefficient, correlation coefficient, and variation coefficient, and the most appropriate clustering method is used to identify flow units for geostatistical modeling. We subsequently define uncertainties for static and dynamic properties such as permeability, porosity, net-to-gross, fracture, water-relative permeability, fluid properties, and rock compressibility. Discretized Latin Hypercube with Geostatistical (DLHG) method is applied to combine the defined uncertainties and create an ensemble of 200 simulation models which can span the uncertainty space. Eventually, a base production strategy is defined under operational conditions to check the consistency and reliability of the models created with UNISIM-II-R (reference model) as a real reservoir with known results. Results represent the compatibility of the methodology to characterize fractured reservoirs since those models are consistent with the reference model (used to generate the simulation models). The proposed workflow provides an efficient and useful means of supporting development planning under uncertainty.

Informing the Delineation of Input Uncertainty Space in Exploratory Modelling Using a Heuristic Approach

Exploratory modelling is an emerging approach which can address the challenge of model-based decision making in dealing with input model uncertainties. Exploratory modelling samples from an input uncertainty space and generates extensive computational experiments to analyse possible model behaviours in an output solution space. The way that the input uncertainty space is delineated influences the results of exploratory modelling and its computational cost. In this article, we show the statistical significance of the implication of the size of an input uncertainty space on the resulted output solution space. We also propose a heuristic approach which informs the delineation of input uncertainties by screening the relevant model behaviour in the solution space. An illustrative example of an aircraft fleet management system is used to demonstrate the implementation of our approach in practice. We conclude that the delineation of input uncertainty space can be a way to control simulations in exploratory modelling and to enhance the efficiency of the exploration process and the confidence of the final results.

An agent-monitored framework for the output-oriented design of experiments in exploratory modelling

Exploratory modelling is an approach for modelling under uncertainty based on the generation and analysis of computational experiments. The results of exploratory modelling are sensitive to the way that experiments are designed, such as the way that the uncertainty space is delineated. This article introduces an agent-monitored framework—i.e. a design metaphor of the interactions among modellers and stakeholders and the simulation process—for controlling the design of experiments based on monitoring model behaviour in the output space. To demonstrate the benefits of the suggested framework in the exploratory modelling process, the article shows how the use of the framework with an output-oriented approach informs the delineation of an appropriate uncertainty space with an illustrative example in the decision-making context. The article concludes that the design of experiments based on feedback from the output space can be a useful approach: to control simulations in exploratory modelling; to build more confidence in final results; and to inform the design of other aspects of experiments, such as selecting policy levers and sampling method.

Geological and production uncertainty assessments of the cyclic text{CO}_2-assisted gravity drainage EOR process: a case study from South Rumaila oil field

Risk or uncertainty assessment in the reservoir flow modeling, especially in real field-scale evaluation, is essential to make a trustful decision regarding the future development plans. This paper presents an efficient uncertainty assessment workflow of geological and production data through the cyclic text{CO}_2-assisted gravity drainage (GAGD) process in South Rumaila oil field in Southern Iraq. First, the sequential Gaussian simulation created a large number of reservoir stochastic realizations that capture the entire geological uncertainty space. Second, ranking was applied to select the quartiles (text{P}10, text{P}20,ldots ,text{P}90) of reservoir permeability and anisotropy ratio to quantify the geological uncertainty. Next, the equation of state-compositional flow model was constructed to evaluate these realizations by calculating the reservoir flow response. Then, 81 designed simulations were created by factorial design considering the combined realizations of permeability and anisotropy ratio. In a successive step, the most-likely model was considered for the uncertainty quantification of the operational decision parameters through the cyclic GAGD process to restrict the uncertainty space, which leads to obtain the true optimal scenario. The cyclic GAGD operational parameters include durations of injection, soaking, and production and the minimum bottom hole pressure in production wells. The compositional reservoir flow model was again used to evaluate the multiple simulations created by the proxy-based Box–Behnken design and Monte Carlo simulation. The combined geological and production uncertainty workflow gave an idea about the uncertainty or risk space of the predicted reservoir flow response in the future cyclic GAGD process performance.

New Geosteering Work Flow Integrates Real-Time Measurements With Geomodels

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 191337, “An Interactive Decision Support System for Geosteering Operations,” by Sergey Alyaev, IRIS; Reidar Brumer Bratvold, SPE, University of Stavanger; and Xiaodong Luo, SPE, Erich Suter, SPE, and Erlend H. Vefring, SPE, IRIS, prepared for the 2018 SPE Norway One Day Seminar, Bergen, Norway, 18 April. The paper has not been peer reviewed. To place a well in the best possible reservoir zone, operators use geosteering to support real-time well-trajectory adjustments. Geosteering refers to the process of making directional well adjustments on the basis of real-time information acquired while drilling. This work presents a systematic geosteering work flow that automatically integrates a priori information and real-time measurements to update geomodels with uncertainties and uses the latest model predictions in a decision-support system (DSS). The DSS supports geosteering decisions by evaluating production potential against drilling and completion risks. Introduction This paper presents a consistent, systematic, and transparent work flow for geosteering. The starting point is a priori information, for example a probabilistic geomodel representing a geological interpretation based on surface seismic and logs from offset wells, including relevant interpretation uncertainties. Multiple geomodel realizations of the possible geological scenarios span the space of interpretation uncertainties. The real-time measurements obtained while drilling are continually integrated by updating the realizations automatically using an ensemble-based filtering method. The real-time update of the realizations leads to a reduction in interpretation uncertainty, providing up-to-date predictions of the geology ahead of the bit consistently. The update work flow is linked to a DSS. The DSS applies the probabilistic up-to-date geomodel to support geosteering decisions under uncertainty by evaluating the chosen value function of the well. The value function commonly includes multiple objectives, including production potential, costs for drilling and completion, and risks associated with the operation. The DSS presented here is optimized specifically for use with ensemble-based update work flows that are increasingly popular in the oil industry. The focus of this paper is the DSS. The DSS suggests steering correction or stopping, optimizing well trajectories over the ensemble of up-to-date geomodel realizations. A graphical user interface (GUI) enables geosteering experts to control the input to the DSS by means of interactive selection and adjustment of value functions and constraints (e.g., dogleg severity). The adjustments are applied in a matter of seconds using advanced dynamic programming algorithms that yield consistently updated decisions. The proposed steering decision is communicated through the GUI, which contains a visual representation of the current uncertainty and trajectory possibilities that give the best value for each realization.