Context Of Decision Support Research Articles

Employing Fuzzy AHP in Modeling a Decision Support System for Determining Scholarship Recipients within the University Context

In the realm of university scholarship programs, the process of selecting deserving recipients presents a complex decision-making challenge. This study explores the integration of Fuzzy Analytical Hierarchy Process (AHP) into the modeling of a Decision Support System (DSS) aimed at facilitating the determination of scholarship awardees. The utilization of Fuzzy AHP enables a more comprehensive and nuanced evaluation of candidates by accommodating uncertainties and imprecisions inherent in the decision-making process. This research investigates the application of Fuzzy AHP within the specific context of university scholarship recipient selection. The proposed DSS framework not only enhances the objectivity and transparency of the decision-making process but also contributes to the optimization of resource allocation and the identification of candidates best aligned with the scholarship's objectives. By employing Fuzzy AHP in this decision-support context, universities can effectively address the intricate considerations involved in awarding scholarships, thereby promoting fairness and increasing the likelihood of rewarding the most deserving individuals.

Satellite Altimetry for Ocean and Coastal Applications: A Review

More than 30 years of observations from an international suite of satellite altimeter missions continue to provide key data enabling research discoveries and a broad spectrum of operational and user-driven applications. These missions were designed to advance technologies and to answer scientific questions about ocean circulation, ocean heat content, and the impact of climate change on these Earth systems. They are also a valuable resource for the operational needs of oceanographic and weather forecasting agencies that provide information to shipping and fishing vessels and offshore operations for route optimization and safety, as well as for other decision makers in coastal, water resources, and disaster management fields. This time series of precise measurements of ocean surface topography (OST)—the “hills and valleys” of the ocean surface—reveals changes in ocean dynamic topography, tracks sea level variations at global to regional scales, and provides key information about ocean trends reflecting climate change in our warming world. Advancing technologies in new satellite systems allows measurements at higher spatial resolution ever closer to coastlines, where the impacts of storms, waves, and sea level rise on coastal communities and infrastructure are manifest. We review some collaborative efforts of international space agencies, including NASA, CNES, NOAA, ESA, and EUMETSAT, which have contributed to a collection of use cases of satellite altimetry in operational and decision-support contexts. The extended time series of ocean surface topography measurements obtained from these satellite altimeter missions, along with advances in satellite technology that have allowed for higher resolution measurements nearer to coasts, has enabled a range of such applications. The resulting body of knowledge and data enables better assessments of storms, waves, and sea level rise impacts on coastal communities and infrastructure amongst other key contributions for societal benefit. Although not exhaustive, this review provides a broad overview with specific examples of the important role of satellite altimetry in ocean and coastal applications, thus justifying the significant resource contributions made by international space agencies in the development of these missions.

Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model

Abstract. It is incumbent on decision-support hydrological modelling to make predictions of uncertain quantities in a decision-support context. In implementing decision-support modelling, data assimilation and uncertainty quantification are often the most difficult and time-consuming tasks. This is because the imposition of history-matching constraints on model parameters usually requires a large number of model runs. Data space inversion (DSI) provides a highly model-run-efficient method for predictive uncertainty quantification. It does this by evaluating covariances between model outputs used for history matching (e.g. hydraulic heads) and model predictions based on model runs that sample the prior parameter probability distribution. By directly focusing on the relationship between model outputs under historical conditions and predictions of system behaviour under future conditions, DSI avoids the need to estimate or adjust model parameters. This is advantageous when using integrated surface and sub-surface hydrologic models (ISSHMs) because these models are associated with long run times, numerical instability and ideally complex parameterization schemes that are designed to respect geological realism. This paper demonstrates that DSI provides a robust and efficient means of quantifying the uncertainties of complex model predictions. At the same time, DSI provides a basis for complementary linear analysis that allows the worth of available observations to be explored, as well as of observations which are yet to be acquired. This allows for the design of highly efficient, future data acquisition campaigns. DSI is applied in conjunction with an ISSHM representing a synthetic but realistic river–aquifer system. Predictions of interest are fast travel times and surface water infiltration. Linear and non-linear estimates of predictive uncertainty based on DSI are validated against a more traditional uncertainty quantification which requires the adjustment of a large number of parameters. A DSI-generated surrogate model is then used to investigate the effectiveness and efficiency of existing and possible future monitoring networks. The example demonstrates the benefits of using DSI in conjunction with a complex numerical model to quantify predictive uncertainty and support data worth analysis in complex hydrogeological environments.

Formalizing a Two-Step Decision-Making Process in Land Use: Evidence from Controlling Forest Clearcutting Using Spatial Information

In this paper, we examine a particular case of land use pattern: forest management activities facing an uncertainty related to spatial information signals received. We investigate the combination of two well-known theoretical approaches, the Blackwell theorem and entropy analysis, in providing a decision support framework for decision makers. We examine the uncertainty related to the information signals received within a decision support context and compute the optimal actions. Drawing on satellite imagery as an additional source of information provided by French spatial data infrastructure (SDI), we illustrate our approach through a clear-cutting control case study. The control of clear-cutting is a central issue in forest management. In order to perform an efficient control operation, uncertainty regarding the decisions to be taken needs to be minimized. Reducing uncertainty in a decision-making context related to forest management provides greater opportunities for improving productivity and for saving time and money. The results show that the information structure through the SDI signals has the most significant information power. Moreover, a maximum of two information structures can be compared when applying the Blackwell theorem. However, while using the entropy approach, a comparison of several information structures can be performed.

Decision Support Tools in Adult Long-term Care Facilities: Scoping Review.

BackgroundDigital innovations are yet to make real impacts in the care home sector despite the considerable potential of digital health approaches to help with continued staff shortages and to improve quality of care. To understand the current landscape of digital innovation in long-term care facilities such as nursing and care homes, it is important to find out which clinical decision support tools are currently used in long-term care facilities, what their purpose is, how they were developed, and what types of data they use.ObjectiveThe aim of this review was to analyze studies that evaluated clinical decision support tools in long-term care facilities based on the purpose and intended users of the tools, the evidence base used to develop the tools, how the tools are used and their effectiveness, and the types of data the tools use to contribute to the existing scientific evidence to inform a roadmap for digital innovation, specifically for clinical decision support tools, in long-term care facilities.MethodsA review of the literature published between January 1, 2010, and July 21, 2021, was conducted, using key search terms in 3 scientific journal databases: PubMed, Cochrane Library, and the British Nursing Index. Only studies evaluating clinical decision support tools in long-term care facilities were included in the review.ResultsIn total, 17 papers were included in the final review. The clinical decision support tools described in these papers were evaluated for medication management, pressure ulcer prevention, dementia management, falls prevention, hospitalization, malnutrition prevention, urinary tract infection, and COVID-19 infection. In general, the included studies show that decision support tools can show improvements in delivery of care and in health outcomes.ConclusionsAlthough the studies demonstrate the potential of positive impact of clinical decision support tools, there is variability in results, in part because of the diversity of types of decision support tools, users, and contexts as well as limited validation of the tools in use and in part because of the lack of clarity in defining the whole intervention.

On robust risk-based active-learning algorithms for enhanced decision support

Classification models are a fundamental component of physical-asset management technologies such as structural health monitoring (SHM) systems and digital twins. Previous work introduced risk-based active learning, an online approach for the development of statistical classifiers that takes into account the decision-support context in which they are applied. Decision-making is considered by preferentially querying data labels according to expected value of perfect information (EVPI). Although several benefits are gained by adopting a risk-based active learning approach, including improved decision-making performance, the algorithms suffer from issues relating to sampling bias as a result of the guided querying process. This sampling bias ultimately manifests as a decline in decision-making performance during the later stages of active learning, which in turn corresponds to lost resource/utility.The current paper proposes two novel approaches to counteract the effects of sampling bias: semi-supervised learning, and discriminative classification models. These approaches are first visualised using a synthetic dataset, then subsequently applied to an experimental case study, specifically, the Z24 Bridge dataset. The semi-supervised learning approach is shown to have variable performance; with robustness to sampling bias dependent on the suitability of the generative distributions selected for the model with respect to each dataset. In contrast, the discriminative classifiers are shown to have excellent robustness to the effects of sampling bias. Moreover, it was found that the number of inspections made during a monitoring campaign, and therefore resource expenditure, could be reduced with the careful selection of the statistical classifiers used within a decision-supporting monitoring system.

Decision-based scenario clustering for decision-making under uncertainty

In order to make sense of future uncertainty, managers have long resorted to creating scenarios that are then used to evaluate how uncertainty affects decision-making. The large number of scenarios that are required to faithfully represent several sources of uncertainty leads to major computational challenges in using these scenarios in a decision-support context. Moreover, the complexity induced by the large number of scenarios can stop decision makers from reasoning about the interplay between the uncertainty modelled by the data and the decision-making processes (i.e., how uncertainty affects the decisions to be made). To meet this challenge, we propose a new approach to group scenarios based on the decisions associated to them. We introduce a graph structure on the scenarios based on the opportunity cost of predicting the wrong scenario by the decision maker. This allows us to apply graph clustering methods and to obtain groups of scenarios with mutually acceptable decisions (i.e., decisions that remain efficient for all scenarios within the group). In the present paper, we test our approach by applying it in the context of stochastic optimization. Specifically, we use it as a means to derive both lower and upper bounds for stochastic network design models and fleet planning problems under uncertainty. Our numerical results indicate that our approach is particularly effective to derive high-quality bounds when dealing with complex problems under time limitations.

On the assimilation of environmental tracer observations for model-based decision support

Abstract. It has been advocated that history matching numerical models to a diverse range of observation data types, particularly including environmental tracer concentrations and their interpretations and derivatives (e.g., mean age), constitutes an effective and appropriate means to improve model forecast reliability. This study presents two regional-scale modeling case studies that directly and rigorously assess the value of discrete tritium concentration observations and tritium-derived mean residence time (MRT) estimates in two decision-support contexts; “value” is measured herein as both the improvement (or otherwise) in the reliability of forecasts through uncertainty variance reduction and bias minimization as a result of assimilating tritium or tritium-derived MRT observations. The first case study (Heretaunga Plains, New Zealand) utilizes a suite of steady-state and transient flow models and an advection-only particle-tracking model to evaluate the worth of tritium-derived MRT estimates relative to hydraulic potential, spring discharge and river–aquifer exchange flux observations. The worth of MRT observations is quantified in terms of the change in the uncertainty surrounding ecologically sensitive spring discharge forecasts via first-order second-moment (FOSM) analyses. The second case study (Hauraki Plains, New Zealand) employs paired simple–complex transient flow and transport models to evaluate the potential for assimilation-induced bias in simulated surface-water nitrate discharge to an ecologically sensitive estuary system; formal data assimilation of tritium observations is undertaken using an iterative ensemble smoother. The results of these case studies indicate that, for the decision-relevant forecasts considered, tritium observations are of variable benefit and may induce damaging bias in forecasts; these biases are a result of an imperfect model's inability to properly and directly assimilate the rich information content of the tritium observations. The findings of this study challenge the advocacy of the increasing use of tracers, and of diverse data types more generally, whenever environmental model data assimilation is undertaken with imperfect models. This study also highlights the need for improved imperfect-model data assimilation strategies. While these strategies will likely require increased model complexity (including advanced discretization, processes and parameterization) to allow for appropriate assimilation of rich and diverse data types that operate across a range of spatial and temporal scales commensurate with a forecast of management interest, it is critical that increased model complexity does not preclude the application of formal data assimilation and uncertainty quantification techniques due to model instability and excessive run times.

Human Rights, Regulation and the Right to Restrictions on Algorithmic Police Intelligence Analysis Tools in the UK

The deployment of machine-learning technologies around decision-making and decision-support contexts in UK policing raises some new, and in other ways some very familiar, human rights issues. The discussion in this paper is centred around key considerations of the right to restriction of law enforcement processing (of data points in an algorithm) where the data processing may lead to 'inaccuracy' that is misleading - and which in turn might lead to errors in the treatment of a data subject in the justice system that may be a breach, not just of data protection rights, but of fundamental human rights, as a result. This paper also gives a short case study, from a human rights perspective, on the trials of facial recognition technology by a police force in Wales (UK).

Economic Valuation for Cost\u2013Benefit Analysis of Health Risk Reduction in Drinking Water Systems

Microbial risk mitigation measures in drinking water systems aiming at preventing gastrointestinal disease can provide substantial societal health benefits if implemented properly. However, the procedure of including and monetising the health benefits in cost–benefit analysis (CBA) has been somewhat scattered and inconsistent in the literature, and there is a need for a comparison of available methods. First, through a literature review, we identified the methods to include health benefits in decision support and to monetise these benefits in CBA. Second, we applied the identified health valuation methods in a case study. In the case study, we investigated if changing the health valuation method could change the rank order of the decision alternatives’ net present values. In the case study a risk-based decision model that combined quantitative microbial risk assessment and CBA was used. Seven health valuation methods were identified, each of them including different aspects of health benefits. The results of the case study showed that the choice of the health valuation method can change the rank order of decision alternatives with respect to their net present values. These results highlight the importance of choosing an appropriate health valuation method for the specific application. Although this study focused on the drinking water context, the identified health valuation methods can be applied in any decision support context, provided that input in terms of the health risk reduction is available.

The Practical Approach to Care Kit (PACK) guide: developing a clinical decision support tool to simplify, standardise and strengthen primary healthcare delivery

For the primary health worker in a low/middle-income country (LMIC) setting, delivering quality primary care is challenging. This is often complicated by clinical guidance that is out of date, inconsistent...

Flexible decision support for sustainable development: the SUSTAIN framework model

Sustainable transport planning necessitates a rethinking of traditional decision making. This is conventionally supported by cost-benefit analysis (CBA) that systematically quantifies and compares the various benefits and costs generated by a transportation project or policy. Generally, CBA has been found less useful for the handling and assessment of multiple, often conflicting objectives or criteria like environmental or social issues intrinsically difficult to quantify. Therefore, it is necessary to broaden the decision making process beyond merely economic factors. The Danish research project on Sustainable National Transport Planning (SUSTAIN 2012-2016) seeks, among other things, to develop a flexible decision support model (tool) to include and assess sustainability planning criteria in a socio-economic framework, which makes up the SUSTAIN Framework Model (SFM). The SFM comprises two parts, namely a process part consisting of stakeholder involvement and an analytical tools part consisting of an Excel-based software model. The latter employs the use of CBA, multi-criteria decision analysis and risk analysis techniques enabling the assessment of non-quantifiable impacts within a decision support context. The concept of a planning workshop is introduced as relevant for dealing with the various strategic elements not included in the CBA. Moreover, SUSTAIN is rooted in cross-disciplinary sustainability research that recognises that a transition towards sustainability must involve normative, analytical and strategic considerations to be successful. The paper concludes that the SFM can contribute to the analytical dimension. Thus, the framework model allows for the appraisal of planning criteria (indicator sets) in a socio-economic appraisal setting for national sustainable transport planning which enhances both the concept and principles of sustainable development while at the same time it provides a flexible decisionsupport tool for policy-makers.

(Dis) integrated valuation – Assessing the information gaps in ecosystem service appraisals for governance support

The operational challenges of integrated ecosystem service (ES) appraisals are determined by study purpose, system complexity and uncertainty, decision-makers’ requirements for reliability and accuracy of methods, and approaches to stakeholder–science interaction in different decision contexts. To explore these factors we defined an information gap hypothesis, based on a theory of cumulative uncertainty in ES appraisals. When decision context requirements for accuracy and reliability increase, and the expected uncertainty of the ES appraisal methods also increases, the likelihood of methods being used is expected to drop, creating a potential information gap in governance. In order to test this information gap hypothesis, we evaluate 26 case studies and 80 ecosystem services appraisals in a large integrated EU research project. We find some support for a decreasing likelihood of ES appraisal methods coinciding with increasing accuracy and reliability requirements of the decision-support context, and with increasing uncertainty. We do not find that information costs are the explanation for this information gap, but rather that the research project interacted mostly with stakeholders outside the most decision-relevant contexts. The paper discusses how alternative definitions of integrated valuation can lead to different interpretations of decision-support information, and different governance approaches to dealing with uncertainty.

Big data dashboards as smart decision support tools for i-cities – An experiment on stockholm

This study addresses the strategic governance challenges of modern smart cities from the new viewpoint of big data management. It seeks to develop and highlight a systematic methodological framework for handling multivariate big data in a smart urban decision support context in the ‘New Urban World’, so as to enhance the cities’ competitive performance through the design and development of operational urban management principles and strategies. The specific aim of this paper is to provide the critical and basic cornerstones for an applicable interactive dashboard architecture as a supporting tool in a structured process of innovative city strategies and consequent enhanced socio-economic performance. A core element in the present paper is formed by a smart urban dashboard system that acts as an interactive navigation tool supporting operational choices of all stakeholders involved. This dashboard is able to integrate complex and ever-changing big data bases serving as ‘signposts’ of city intelligence (or i-city smartness) for daily or strategic decisions of all urban stakeholders. This study thus outlines successively the concept of smart i-cities in our ‘urban century’, the great potential of digital technology for managing big data in governing i-cities, and the foundations of an urban dashboard on the basis of the so-called Pentagon model as a policy strategy vehicle. Starting from extensive data on a broad set of global cities, the potential of this approach is exemplified by means of an illustrative application of a smart urban dashboard for the city of Stockholm.

On how to manage uncertainty when considering regulatory HSE interventions

Regulatory health, safety, and environment (HSE) interventions have an impact on both costs and benefits for the industry. It is common for the regulators to evaluate such interventions by providing a comparison of costs and benefits as a basis for decision-making. Fulfilling an assignment for the Norwegian government, two consulting companies proposed a methodology for regulatory evaluation in the petroleum industry. This methodology acknowledges that uncertainty must have a higher weight than given through traditional cost–benefit analyses, but it is still to a great extent based on the use of expected values. We question this use of modified cost–benefit analyses for providing decision support in contexts where uncertainty is the dominating attribute. Furthermore, we argue that the decision-makers should be able to take a dynamic approach, where the chosen method should fit its context. As an example, we present a framework in line with such a dynamic approach. The article is an extended version of an ESREL conference article.

Development of a spatially explicit approach for mapping ecosystem services in the Brazilian Savanna – MapES

The objective of the presented study is the development of a spatially explicit approach for mapping ecosystem services (MapES) by using specific knowledge about the Cerrado biome (Brazilian Savanna). This biome covers an area of about 2 million km2, i.e. nearly 24% of the total area of Brazil, and has come under substantial pressure during the last 50 years caused by strong land-use/land-cover change, mostly due to agricultural expansion and urbanization. Because of its fast transformation rate, there is an enormous demand for knowledge, and its application, about the effects of land-use/land-cover on the capacity of providing or maintaining ecosystem services. The MapES approach was developed using a vast existing knowledge base. After analyzing and structuring this knowledge the relationships between land-use/land-cover and the potential to provide or maintain eight ecosystem services (Erosion Control, Runoff Control, Water Supply, Water Quality Maintenance, Soil Quality Maintenance, Biodiversity Maintenance, Food Production and Energy Production) were parametrized. In addition, the approach was developed as spatially explicit by including landscape properties (soil, slope and distance to river network) in the cell based system. A reference map of potential natural vegetation and a land use map for 2013 for a meso-scale experimental catchment (32.7km2) were produced. The catchment was used as an example to apply the approach, i.e. assessing and visualizing changes from before human interference to the current land use situation. Finally, a procedure for assessing the potential impacts of land-use/land-cover on ecosystem services considering the methodological limitations of the respective monitoring. The presented approach is easy to understand, to modify and to adapt to other situations and might be therefore used in other context of decision support. It might also help to fill the gap between land use planning and numeric modeling using very complex tools.

Options for integrating ecological, economic, and social objectives in evaluation and management of fisheries

AbstractThere has been growing international attention in recent years to the Ecosystem Approach to Fisheries, Ecologically Sustainable Development, and similar initiatives that demand a comprehensive evaluation of the social, economic, and ecological performance of fisheries. However, the practical integration and application of these aspects continue to present a significant challenge for management. Progress to date has been limited by gaps in governance, objectives, disciplinary breadth, and methods. In this study, we develop an inventory of the methods that have been proposed to be able to incorporate ecological, economic, and social objectives and to provide a more comprehensive evaluation of fisheries and management. Our inventory includes both a description of the range of methods, and an evaluation against a set of criteria related to their utility in an applied, decision support context.

Chance and service level constraints for stochastic generation expansion planning

We consider the problem of generation expansion planning (GEP) under uncertainty. Existing and planned deployment of demand response mechanisms necessitates extensions of GEP optimization models to minimize planned investment portfolio cost. We propose a combination of service level threshold and chance constraints to abstractly capture the impact of demand response mechanisms in generation expansion planning. We examine the computational properties of solutions from the proposed optimization model using real-world data from South Korea. Our results indicate that the proposed models can be solved to optimality in tractable run-times, allowing for their use in decision support contexts and avoiding the need for heuristic solution procedures.

Preference inference with general additive value models and holistic pair-wise statements

Additive multi-attribute value models and additive utility models with discrete outcome sets are widely applied in both descriptive and normative decision analysis. Their non-parametric application allows preference inference by analyzing sets of general additive value functions compatible with the observed or elicited holistic pair-wise preference statements. In this paper, we provide necessary and sufficient conditions for the preference inference based on a single preference statement, and sufficient conditions for the inference based on multiple preference statements. In our computational experiments all inferences could be made with these conditions. Moreover, our analysis suggests that the non-parametric analyses of general additive value models are unlikely to be useful by themselves for decision support in contexts where the decision maker preferences are elicited in the form of holistic pair-wise statements.

TOWARD INTERNATIONAL GOOD PRACTICES IN HEALTH TECHNOLOGY ASSESSMENT

Since the origin of technology assessment (TA) in the 1960s, including TAs of certain medical interventions, and formal recognition and institutionalization of health technology assessment (HTA) in the 1970s, the field of HTA has evolved in programs large and small in the public and private sectors. It has diffused into an increasing number of nations in a range of policy-making and decision-support contexts. Since its inception, the development and diffusion of HTA have been strongly characterized by international collaboration involving sharing of expertise and practical experience, and joint efforts to advance the state of the art. Also during that time, there have been periodic collaborations to identify or develop standard frameworks, good practices, guiding principles, checklists, and other normative, “how-to” documentation for the field. Recognizing the diversity and ongoing evolution of HTA, these efforts have generally sought to balance the appeal of standardization with that of flexibility to accommodate the ranges of HTA program remits and contexts.