An uncertain future, deep uncertainty, scenarios, robustness and adaptation: How do they fit together?

A highly uncertain future due to changes in climate, technology and socio-economics has led to the realisation that identification of “best-guess” future conditions might no longer be appropriate. Instead, multiple plausible futures need to be considered, which requires (i) uncertainties to be described with the aid of scenarios that represent coherent future pathways based on different sets of assumptions, (ii) system performance to be represented by metrics that measure insensitivity (i.e. robustness) to changes in future conditions, and (iii) adaptive strategies to be considered alongside their more commonly used static counterparts. However, while these factors have been considered in isolation previously, there has been a lack of discussion of the way they are connected. In order to address this shortcoming, this paper presents a multidisciplinary perspective on how the above factors fit together to facilitate the devel- opment of strategies that are best suited to dealing with a deeply uncertain future

Indeterminacy-aware prediction model for authentication in IoT.

The Internet of Things (IoT) has opened a new chapter in data access. It has brought obvious opportunities as well as major security and privacy challenges. Access control is one of the challenges in IoT. This holds true as the existing, conventional access control paradigms do not fit into IoT, thus access control requires more investigation and remains an open issue. IoT has a number of inherent characteristics, including scalability, heterogeneity and dynamism, which hinder access control. While most of the impact of these characteristics have been well studied in the literature, we highlighted “indeterminacy” in authentication as a neglected research issue. This work stresses that an indeterminacy-resilient model for IoT authentication is missing from the literature. According to our findings, indeterminacy consists of at least two facets: “uncertainty” and “ambiguity”. As a result, various relevant theories were studied in this work. Our proposed framework is based on well-known machine learning models and Attribute-Based Access Control (ABAC). To implement and evaluate our framework, we first generate datasets, in which the location of the users is a main dataset attribute, with the aim to analyse the role of user mobility in the performance of the prediction models. Next, multiple classification algorithms were used with our datasets in order to build our best-fit prediction models. Our results suggest that our prediction models are able to determine the class of the authentication requests while considering both the uncertainty and ambiguity in the IoT system

Scenario Discovery via Rule Extraction

Scenario discovery is the process of finding areas of interest, commonly referred to as scenarios, in data spaces resulting from simulations. For instance, one might search for conditions - which are inputs of the simulation model - where the system under investigation is unstable. A commonly used algorithm for scenario discovery is PRIM. It yields scenarios in the form of hyper-rectangles which are human-comprehensible. When the simulation model has many inputs, and the simulations are computationally expensive, PRIM may not produce good results, given the affordable volume of data. So we propose a new procedure for scenario discovery - we train an intermediate statistical model which generalizes fast, and use it to label (a lot of) data for PRIM. We provide the statistical intuition behind our idea. Our experimental study shows that this method is much better than PRIM itself. Specifically, our method reduces the number of simulations runs necessary by 75% on average

Evaluating the Robustness of Project Performance under Deep Uncertainty of Climate Change: A Case Study of Irrigation Development in Kenya

While financing for climate adaptation projects is gaining prominence worldwide, the methods of performance evaluation of adaptation-related projects have not as yet been established. One reason for this is that future project effects are subject to deep uncertainty. As a case study of the evaluation of adaptation benefits under the uncertainty of climate change, we evaluate the robustness of the project performance of a Kenyan irrigation development project. Based on a simulation analysis carried out using the Robust Decision Making (RDM) approach, we assess the robustness of the positive expected outcomes of the project and find that the development of irrigation facilities, especially when combined with the soft adaptation measures of farming practices, could bring about an increase of household income in the future under a large variety of conditions. These beneficial effects are partly a reflection of the reduced damage from climate change achieved by the project. We conduct this study by utilizing the available resources and capacity of a development agency that has a scope of future applications to actual infrastructure projects. In this paper, we also discuss factors that could become relevant for the application of RDM-based project evaluation in the field of climate finance

Strategic adaptation pathway planning to manage sea-level rise and changing coastal flood risk

Communities around the world are already committed to future sea-level rise. Long-term adaptation planning to manage associated coastal flood impacts is, however, challenged by uncertainty and contested stakeholder priorities. This study provides a proof of concept for a combined robust decision making (RDM) and dynamic adaptive policy pathways (DAPP) approach in coastal flood risk management. The concept uses model-based support and largely open source tools to help local government plan coastal adaptation pathways. Key steps in the method are illustrated using a hypothetical case study in Australia. The study shows how scenario discovery can provide multi-dimensional descriptions of adaptation tipping points which may inform the development of technical signpost indicators. Transient scenarios uncovered limitations in seemingly robust adaptation policies, where historical path dependencies may constrain the rate of adaptation and the extent to which future coastal flood impacts can be successfully managed. Lived values have the potential to offer insights about non-material social trade-offs that residents may need to accept for the benefit of reduced flood risk, and could form a basis for defining socially-oriented signpost indicators. However, the nuances and subjectivity of lived values means that ongoing engagement with residents is essential as part of a combined RDM and DAPP approach to preserve the communities’ way of life. The learnings from this hypothetical case study suggest that testing in a real world participatory setting could be valuable to further develop a combined RDM and DAPP approach to plan adaptation pathways and manage future coastal flood risk

Analysis of Heterogeneous Data Sources for Veterinary Syndromic Surveillance to Improve Public Health Response and Aid Decision Making

The standard technique of implementing veterinary syndromic surveillance (VSyS) is the detection of temporal or spatial anomalies in the occurrence of health incidents above a set threshold in an observed population using the Frequentist modelling approach. Most implementation of this technique also requires the removal of historical outbreaks from the datasets to construct baselines. Unfortunately, some challenges exist, such as data scarcity, delayed reporting of health incidents, and variable data availability from sources, which make the VSyS implementation and alarm interpretation difficult, particularly when quantifying surveillance risk with associated uncertainties. This problem indicates that alternate or improved techniques are required to interpret alarms when incorporating uncertainties and previous knowledge of health incidents into the model to inform decision-making. Such methods must be capable of retaining historical outbreaks to assess surveillance risk. In this research work, the Stochastic Quantitative Risk Assessment (SQRA) model was proposed and developed for detecting and quantifying the risk of disease outbreaks with associated uncertainties using the Bayesian probabilistic approach in PyMC3. A systematic and comparative evaluation of the available techniques was used to select the most appropriate method and software packages based on flexibility, efficiency, usability, ability to retain historical outbreaks, and the ease of developing a model in Python. The social media datasets (Twitter) were first applied to infer a possible disease outbreak incident with associated uncertainties. Then, the inferences were subsequently updated using datasets from the clinical and other healthcare sources to reduce uncertainties in the model and validate the outbreak. Therefore, the proposed SQRA model demonstrates an approach that uses the successive refinement of analysis of different data streams to define a changepoint signalling a disease outbreak. The SQRA model was tested and validated to show the method's effectiveness and reliability for differentiating and identifying risk regions with corresponding changepoints to interpret an ongoing disease outbreak incident. This demonstrates that a technique such as the SQRA method obtained through this research may aid in overcoming some of the difficulties identified in VSyS, such as data scarcity, delayed reporting, and variable availability of data from sources, ultimately contributing to science and practice

Describing adaptation tipping points in coastal flood risk management

Assessing changing coastal flood risk becomes increasingly uncertain across multi-decadal timeframes. This uncertainty is a fundamental complexity faced in vulnerability assessments and adaptation planning. Robust decision making (RDM) and dynamic adaptive policy pathways (DAPP) are two state-of-the-art decision support methods that are useful in such situations. In this study we use RDM to identify a small set of conditions that cause unacceptable impacts from coastal flooding, signifying that an adaptation tipping point is reached. Flexible adaptation pathways can then be designed using the DAPP framework. The methodology is illustrated using a case study in Australia and underpinned by a geographic information system model. The results suggest that conditions identified in scenario discovery direct the attention of decision-makers towards a small number of uncertainties most influential on the vulnerability of a community to changing flood patterns. This can facilitate targeted data collection and coastal monitoring activities when resources are scarce. Importantly, it can also be employed to illustrate more broadly how uncontrolled societal development, land use and historic building regulations might exacerbate flood impacts in low-lying urban areas. Notwithstanding the challenges that remain around simulation modelling and detection of environmental change, the results from our study suggest that RDM can be embedded within a DAPP framework to better plan for changing coastal flood risks

Proactive Coordination In Healthcare Service Systems Through Near Real-Time Analytics

The United States (U.S.) healthcare system is the most expensive in the world. To improve the quality and safety of care, health information technology (HIT) is broadly adopted in hospitals. While EHR systems form a critical data backbone for the facility, we need improved \u27work-flow\u27 coordination tools and platforms that can enhance real-time situational awareness and facilitate effective management of resources for enhanced and efficient care. Especially, these IT systems are mostly applied for reactive management of care services and are lacking when they come to improving the real-time operational intelligence of service networks that promote efficiency and quality of operations in a proactive manner. In particular, we leverage operations research and predictive analytics techniques to develop proactive coordination mechanisms and decision methods to improve the operational efficiency of bed management service in the network spanning the emergency department (ED) to inpatient units (IUs) in a hospital, a key component of healthcare in most hospitals. The purpose of this study is to deepen our knowledge on proactive coordination empowered by predictive analytics in dynamic healthcare environments populated by clinically heterogeneous patients with individual information changing throughout ED caregiving processes. To enable proactive coordination for improved resource allocation and patient flow in the ED-IU network, we address two components of modeling/analysis tasks, i.e., the design of coordination mechanisms and the generation of future state information for ED patients. First, we explore the benefits of early task initiation for the service network spanning the emergency department (ED) and inpatient units (IUs) within a hospital. In particular, we investigate the value of proactive inpatient bed request signals from the ED to reduce ED patient boarding. Using data from a major healthcare system, we show that the EDs suffer from severe crowding and boarding not necessarily due to high IU bed occupancy but due to poor coordination of IU bed management activity. The proposed proactive IU bed allocation scheme addresses this coordination requirement without requiring additional staff resources. While the modeling framework is designed based on the inclusion of two analytical requirements, i.e., ED disposition decision prediction and remaining ED length of stay (LoS) estimation, the framework also accounts for imperfect patient disposition predictions and multiple patient sources (besides ED) to IUs. The ED-IU network setting is modeled as a fork-join queueing system. Unlike typical fork-join queue structures that respond identically to a transition, the proposed system exhibits state-dependent transition behaviors as a function of the types of entities being processed in servers. We characterize the state sets and sequences to facilitate analytical tractability. The proposed proactive bed allocation strategy can lead to significant reductions in bed allocation delay for ED patients (up to ~50%), while not increasing delays for other IU admission sources. We also demonstrate that benefits of proactive coordination can be attained even in the absence of highly accurate models for predicting ED patient dispositions. The insights from our models should give confidence to hospital managers in embracing proactive coordination and adaptive work flow technologies enabled by modern health IT systems. Second, we investigate the quantitative modeling that analyzes the patterns of decreasing uncertainty in ED patient disposition decision making throughout the course of ED caregiving processes. The classification task of ED disposition decision prediction can be evaluated as a hierarchical classification problem, while dealing with temporal evolution and buildup of clinical information throughout the ED caregiving processes. Four different time stages within the ED course (registration, triage, first lab/imaging orders, and first lab/imaging results) are identified as the main milestone care stages. The study took place at an academic urban level 1 trauma center with an annual census of 100,000. Data for the modeling was extracted from all ED visits between May 2014 and April 2016. Both a hierarchical disposition class structure and a progressive prediction modeling approach are introduced and combined to fully facilitate the operationalization of prediction results. Multinomial logistic regression models are built for carrying out the predictions under three different classification group structures: (1) discharge vs. admission, (2) discharge vs. observation unit vs. inpatient unit, and (3) discharge vs. observation unit vs. general practice unit vs. telemetry unit vs. intensive care unit. We characterize how the accumulation of clinical information for ED patients throughout the ED caregiving processes can help improve prediction results for the three-different class groups. Each class group can enable and contribute to unique proactive coordination strategies according to the obtained future state information and prediction quality, to enhance the quality of care and operational efficiency around the ED. We also reveal that for different disposition classes, the prediction quality evolution behaves in its own unique way according to the gain of relevant information. Therefore, prediction and resource allocation and task assignment strategies can be tailored to suit the unique behavior of the progressive information accumulation for the different classes of patients as a function of their destination beyond the ED

Supply chain resilience and risk management strategies and methods

Abstract. The changing global market due to Industry 4.0 and the recent pandemic effect has created a need for more responsiveness in an organization’s supply chain. Supply chain resilience offers the firm not only to avoid disruptions but also to withstand the losses due to a disruption. The objective of this research is to find out how resilience is defined so far in other literature and find out the strategies available to gain the resilience fit for an organization. First, in the literature review, the previous studies on resilience were studied to understand what supply chain resilience means. Then, the key results and findings are discussed and conclusions are presented. The research found some interesting strategies for gaining the resilience fit. The benefits and the stakeholders for each strategy are also pointed out. These strategies can be used according to the organization’s business strategy. These strategies aligned with the business strategy can make a huge difference to withstand potential disruption and gaining a competitive advantage against the market competitors

Model of scenario development in defense planning: Integration of scenario and decision: Making methods

It is almost impossible to predict the future environment today, so instead of using prediction different methods of scenario development are used. Scenario as a hypothetical situation for future environment enables exploration of different possibilities of environment development in the future. Many methods based on a morphological analysis are used to develop scenarios. Numerous configurations of morphological fields prevent further use of scenarios and a practical description of the various scenarios. It is possible to reduce the number of configuration of morphological fields, classify configuration according to the different scenario classes and determine the representative configuration for the worst and best scenario case for each class of scenarios by integrating the method for scenario development with the decision-making methods. In this way further processing and description of scenarios is enabled, thus fulfilling a practical role of scenarios in the planning process