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Planning maintenance of medium voltage overhead lines is one of the most important issues in the studies on power distribution network, which will prevent and reduce unwanted interruption. In this paper, long‐term maintenance of medium voltage networks was planned by multi‐objective function, including an extended mixed‐integer linear model to optimize costs, energy not supplied (ENS), and average interruption duration index (SAIDI). In addition, the uncertainty about the annual growth rate of the load, the increase in the cost of goods and services and the increase in the selling price of energy as well as various constraints are all included in the desired objective function, which is one of the main innovations of this paper compared to other published studies. To apply the uncertainties, information gap decision theory (IGDT) has been used, and to solve the objective functions, LP‐Metric method has been used. The proposed method was implemented on the standard 11‐bus RBTS network by MATLAB and GAMS. The results showed that three different long‐term maintenance plans proposed here lead to the optimization of the annual maintenance costs of network, reduction in ENS and interruption, and increase in the reliability of the network based on the uncertainty of each feeder. [ABSTRACT FROM AUTHOR]

I respond to a dilemma for Causal Decision Theory (CDT) under determinism, posed in Adam Elga's paper "Confessions of a Causal Decision Theorist". The treatment I present highlights (i) the status of laws as predictors, and (ii) the consequences of decision dependence, which arises natively out of Jeffrey Conditioning and CDT's characteristic equation. My argument leverages decision dependence to work around a key assumption of Elga's proof: to wit, that in the two problems he presents, the CDTer must employ subjunctive‐suppositional (rather than evidential) transformations of a shared prior. [ABSTRACT FROM AUTHOR]

Hot dry rock (HDR) is regarded as a promising resource of geothermal energy and becomes an important field for future geothermal development due to its advantages of high temperature, wide distribution and huge reserves. At present, HDR research is mainly focused on the modeling and efficiency evaluation of power generation cycle, but its relationship with the source side of the system has not been considered in the field of integrated energy systems. Therefore, this paper proposes a day‐ahead scheduling method for regional integrated energy systems (RIES) with HDR based on information gap decision theory (IGDT). First, the heat transfer system model of HDR is established according to the energy flow model and basic structure of the HDR enhanced geothermal system (EGS). Second, a comprehensive geothermal energy system scheduling model is established from HDR based on the energy hub modeling structure. Then, the IGDT is introduced to analyze the renewable energy output uncertainty in the model. Finally, through a real RIES analysis, the simulation results verified the correctness and effectiveness of the proposed model. The scheduling cost was ¥47,073 when EGS participated in the scheduling. Access to EGS reduced the system's total 24‐h energy purchase by 8305 kW, natural gas consumption by 3051.9 m3, and total carbon emissions by 742.28 kg. The latter emphasized that the proposed model achieves the purpose of reducing the system cost, saving energy and reducing emissions. [ABSTRACT FROM AUTHOR]

Last decade demonstrate the massive growth in organizational data which keeps on increasing multi‐fold as millions of records get updated every second. Handling such vast and continuous data is challenging which further opens up many research areas. The continuously flowing data from various sources and in real‐time is termed as streaming data. While deriving valuable statistics from data streams, the variation that occurs in data distribution is called concept drift. These drifts play a significant role in a variety of disciplines, including data mining, machine learning, ubiquitous knowledge discovery, quantitative decision theory, and so forth. As a result, a substantial amount of research is carried out for studying methodologies and approaches for dealing with drifts. However, the available material is scattered and lacks guidelines for selecting an effective technique for a particular application. The primary novel objective of this survey is to present an understanding of concept drift challenges and allied studies. Further, it assists researchers from diverse domains to accommodate detection and adaptation algorithms for concept drifts in their applications. Overall, this study aims to contribute to deeper insights into the classification of various types of drifts and methods for detection and adaptation along with their key features and limitations. Furthermore, this study also highlights performance metrics used to evaluate the concept drift detection methods for streaming data. This paper presents the future research scope by highlighting gaps in the existing literature for the development of techniques to handle concept drifts. This article is categorized under:Algorithmic Development > Ensemble MethodsApplication Areas > Data Mining Software ToolsFundamental Concepts of Data and Knowledge > Big Data Mining [ABSTRACT FROM AUTHOR]

In the hydrogen‐based integrated energy system (HIES), there exists a hydrogen trading market where hydrogen producers and consumers are distinct stakeholders. Current research in hydrogen trading predominantly focuses on high‐cost green hydrogen (GH), which is not aligned with the current trend of utilizing hydrogen from multiple sources. To address this, this paper proposes a hydrogen trading strategy between the virtual hydrogen plant (VHP) and electro‐hydrogen energy system (EHES) based on a bi‐level model, considering the synergy of GH produced from electrolyzers and blue hydrogen (BH) derived from natural gas in the HIES. In the VHP level, the objective is to maximize profit from hydrogen sales, allowing for the determination of hydrogen prices. In the EHES level, the goal is to minimize the cost of energy supply, leading to the formulation of GH and BH purchasing plans based on hydrogen prices. Additionally, this paper incorporates a risk‐averse model from the information gap decision theory (IGDT) to account for the impact of wind power output uncertainties in the VHP level. Subsequently, leveraging the Karush–Kuhn–Tucker (KKT) conditions of the EHES level, the bi‐level problem is transformed into a solvable single‐level mathematical program with equilibrium constraints (MPEC), with the non‐linear equilibrium constraints linearized. The proposed bi‐level optimization model is validated through case studies encompassing industrial and residential hydrogen utilization within the HIES. The outcomes confirm the rationality of the proposed model, demonstrating that, in comparison to exclusively trading GH, the coordinated GH and BH trading can increase the profit of the VHP by 2.7% and reduce the costs of the EHES by 8.5%. [ABSTRACT FROM AUTHOR]

In distribution networks, among the planning problems, optimal placement of medium voltage to low voltage (MV/LV) transformers is a vital and challenging issue. Electrical load uncertainty is an important factor that affects the result of this planning problem. This paper investigates optimal allocation of service transformers with respect to the load uncertainty modelled by information gap decision theory (IGDT). For this aim, the planning problem is solved in risk‐neutral (RN) and risk‐averse (RA) frameworks. In RN strategy, objective function is defined to minimize the cost of service transformers and low voltage feeders as well as the cost of power losses. On the other hand, in RA strategy, objective function is defined to maximize the radius of the uncertainty in such a way that any deviation of the uncertain parameter results in an objective function value that is not worse than the critical limit. The optimization problem is solved by crow search algorithm (CSA) and particle swarm optimization (PSO) and the results are compared. In mid‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 5.89%, 13.64%, 21.37%, 28.97%, 34.39% and 43.46%, respectively. In long‐term planning, with respect to the deviation factors of 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, optimal values of the uncertainty radius are 6.92%, 13.33%, 20.39%, 27.03%, 34% and 40.46%, respectively. Moreover, on average, CSA finds more promising results than PSO. [ABSTRACT FROM AUTHOR]

Considering the importance of the renewable energy sector in the distribution systems, energy operation, and management which are connected to the distribution network (DN) in the form of multiple microgrids (MMGs) is crucial in reducing cost and pollution. Hence, this paper aims to propose optimal energy management for MMGs in the DN. Different objective functions have been taken into account in this optimization, including network cost, pollution reduction, and distribution network power losses. To design the multi‐objective optimization problem, a fuzzy method has been adopted for simultaneous multi‐objective calculations. Furthermore, the effect of the placement of distributed generations (DGs) and microgrids (MGs) is considered to reduce the distribution network power losses. Information gap decision theory (IGDT) has formulated uncertainties about renewable sources and consumers. To solve this optimization problem, a new method of the modified Harris Hawk optimization (MHHO) algorithm has been implemented, compared with the original HHO and genetic algorithm (GA). Finally, the proposed method has been analysed under the IEEE 33‐bus distribution network for a 24‐hour time horizon, including three MGs considering different renewable energy sources (RESs). The simulation results have demonstrated the high performance of the allocated network with the MHHO algorithm compared to the other scenarios. [ABSTRACT FROM AUTHOR]

In the power system, optimal sizing of hybrid energy systems (HESs) is a vital and challenging issue. Optimal sizing leads to designing a cost‐effective and reliable power generation system. In such a problem, modelling the load uncertainty helps the planner to make appropriate decisions for optimizing the performance of HESs against possible changes of the electrical load. In this paper, techno‐economic and optimal sizing of an off‐grid photovoltaic‐diesel generator‐fuel cell (PV‐diesel‐FC) HES is investigated in two frameworks, risk‐neutral strategy and risk‐averse strategy, where the load uncertainty is modelled by information gap decision theory. To size the HES, objective function is defined as the total net present cost and with respect to a desirable loss of power supply probability, size of the system components is optimally determined. In the risk‐averse framework, the sizing problem is solved with different critical tolerance levels of the objective function and the results are evaluated. Over the case study, simulation results show that at risk‐averse framework, optimal combination of PV, diesel generator and FC leads to a cost‐effective and reliable HES. [ABSTRACT FROM AUTHOR]

This paper proposes a novel energy and reserve scheduling model for power systems with high penetration of wind turbines (WTs). The objective of the proposed model is to minimize the total operation cost of the system while static and dynamic security is guaranteed by preserving the frequency nadir, RoCoF, and quasi‐steady‐state frequency in the predefined range. Likewise, a supervisory, control, and data acquisition (SCADA) system is developed which allows Vanadium Redox Flow Batteries (VRFBs) to continuously communicate and participate in the primary frequency response. To cope with the uncertainties, adaptive information gap decision theory is used that ensures a target operating cost for the risk‐averse operator of the power system. The proposed scheduling model is applied on a modified IEEE 39 bus test system to verify the impacts of the fast reserve provided by the VRFBs in the dynamic frequency security enhancement of the power system with high penetration of WTs. [ABSTRACT FROM AUTHOR]

This paper deals with the real‐time optimal operation of active distribution networks (ADNs) hosting hybrid hydrogen‐electricity refuelling stations by benefiting from renewable energy sources (RESs), conversion facilities, and energy storage systems. The hybrid refuelling stations, which are controlled by ADN operator, supply electricity and hydrogen for electric vehicles (EVs) and hydrogen vehicles (HVs), respectively. In addition, the deployment of water equipment technologies in the ADNs, is considered by utilizing water well pumps in the hybrid stations to serve water demand. The principal aim is to minimize the expected operation cost, including the cost of purchasing power from the upstream grid and maintenance and operation costs of each hybrid refuelling station. Various technical and physical constraints are considered to ensure the reliable operation and realistic scheduling of ADNs in the presence of hybrid refuelling stations. This study employs a hybrid information gap decision theory (IGDT)‐stochastic approach to address the uncertain behaviour of wholesale market price, electricity demand of EVs in refuelling stations, RESs output power and nodal demand of ADN to reach a risk‐averse strategy. The developed approach is coded under GAMS software and the effectiveness of the approach is validated by testing on the modified IEEE 33‐bus test system. [ABSTRACT FROM AUTHOR]

As an important medium of science communication, academic journals promote the flow and growth of scientific knowledge. To examine the influence of factors on authors' choice of a journal, this paper reviews the literature on journal selection. A systematic review and critical interpretive synthesis methods were used in this study. A total of 132 articles were included and the content characteristics were extracted. Then, based on behavioural decision theory, the extracted data on journal selection factors were synthesized based on critical interpretive synthesis principles. Four synthetic constructs emerged: factors related to information acquisition, factors related to journal evaluation, factors related to submission outcome feedback, and factors related to the authors' backgrounds. The articles revealed that factors related to journal information acquisition and journal evaluation directly influenced authors' submission behaviour, while factors related to authors' backgrounds were moderating variables. Future research should focus on the processes of manuscript‐submission behaviour, to examine the relationships between the factors and identify the mechanisms. [ABSTRACT FROM AUTHOR]

Demand response programs (DRPs) have paved a meaningful role in the power supply–demand balance in a smart grid. Also, a residential community with the presence of renewable energy sources (RESs) and electric vehicles (EVs) provides a new way to tackle growing concerns about energy efficiency and environmental pollution. The inherent uncertainty of RESs generation and EVs behaviour leads to difficulty in the economic scheduling of the demand side. Different types of uncertainty modelling have been investigated, such as Monte Carlo (MC) simulation, fuzzy method, and robust optimization. They are faced with many scenarios and computational complexity. This paper uses the information gap decision theory (IGDT) method to study variations of uncertainty radius on residential community electricity costs. Therefore, to achieve an optimal strategy for scheduling the appliances considering the deep uncertainties of RESs and EVs, a novel IGDT‐based demand response scheduling for a residential community is proposed. Impacts of different levels of uncertainties are studied. The simulation results depict the privileges of the proposed method when confronting deep uncertainties. By increasing the radius of the uncertainty of RES and the initial charge of EVs, energy consumption costs grew 20% and 2%, respectively, which indicates the system operator can manage the costs effectively. [ABSTRACT FROM AUTHOR]

Our aim in this paper is to explore two possible directions of interaction between normative folk psychology and decision theory. In one direction, folk psychology plays a regulative role that constrains practical decision‐making. In the other direction, decision theory provides novel tools and norms that shape folk psychology. We argue that these interactions could lead to the emergence of an iterative "decision theoretic spiral," where folk psychology influences decision‐making, decision‐making is studied by decision theory, and decision theory influences folk psychology. Understanding these interactions is important both for the theoretical study of social cognition and decision theory, and also for thinking about how to implement practical interventions into real‐world decision‐making. [ABSTRACT FROM AUTHOR]

In this paper, I diagnose the problems facing causal decision theory as a special case of the general phenomenon of decision‐dependence–the possibility that some input into our evaluation of options can be affected by the decision we end up making. I go on to develop a new decision theory, which treats each choice as a tournament between options competing in pairs, and show how it can capture attractive features of the causal view while avoiding the problems generated by decision‐dependence. [ABSTRACT FROM AUTHOR]

In this paper, I argue for a new normative theory of rational choice under risk, namely expected comparative utility (ECU) theory. I first show that for any choice option, a, and for any state of the world, G, the measure of the choiceworthiness of a in G is the comparative utility (CU) of a in G—that is, the difference in utility, in G, between a and whichever alternative to a carries the greatest utility in G. On the basis of this principle, I then argue that for any agent, S, faced with any decision under risk, S should rank his or her decision options (in terms of how choiceworthy they are) according to their comparative expected comparative utility (CECU) and should choose whichever option carries the greatest CECU. For any option, a, a's CECU is the difference between its ECU and that of whichever alternative to a carries the greatest ECU, where a's ECU is a probability‐weighted sum of a's CUs across the various possible states of the world. I lastly demonstrate that in some ordinary decisions under risk, ECU theory delivers different verdicts from those of standard decision theory. [ABSTRACT FROM AUTHOR]

This paper introduces a new methodology that mitigates the impact of interference produced by full‐duplexing transmission in the device‐to‐device (D2D) environment. One of the most significant issues is the approach with which the D2Ds' receivers recognise and decode the outcomes detected from the multi‐codewords sent by the peer D2Ds' transmitters. To address this issue, a novel proposed framework exchanged by the D2D users is applied and guided by two policies. Initially, all the D2Ds' transmitters expressly characterise and interchange the transmission policy regarding the modulation index, number of streams, and the order of the transmitted codewords. Specifically, this work introduces a novel 3GPP‐compliant binary integer optimisation model subject to several practical constraints for defining the transmission policy at the transmitters. Second, a new classification method is proposed, where the Bayesian decision theory is utilised at the D2Ds' receivers for acquiring the decision parameters from the exchanged transmission policy. Hence, this classification offers a determination for each codeword through enabling symbols' estimation and recognition in terms of posterior probability statements. A series of numerical and comprehensive simulations shows that using the proposed approach leads to several improvements on the conventional full‐duplex D2D schemes. [ABSTRACT FROM AUTHOR]

Conservation decisions are challenging, not only because they often involve difficult conflicts among outcomes that people value, but because our understanding of the natural world and our effects on it is fraught with uncertainty. Value of Information (VoI) methods provide an approach for understanding and managing uncertainty from the standpoint of the decision maker. These methods are commonly used in other fields (e.g. economics, public health) and are increasingly used in biodiversity conservation. This decision‐analytical approach can identify the best management alternative to select where the effectiveness of interventions is uncertain, and can help to decide when to act and when to delay action until after further research. We review the use of VoI in the environmental domain, reflect on the need for greater uptake of VoI, particularly for strategic conservation planning, and suggest promising areas for new research. We also suggest common reporting standards as a means of increasing the leverage of this powerful tool. The environmental science, ecology and biodiversity categories of the Web of Knowledge were searched using the terms 'Value of Information,' 'Expected Value of Perfect Information,' and the abbreviation 'EVPI.' Google Scholar was searched with the same terms, and additionally the terms decision and biology, biodiversity conservation, fish, or ecology. We identified 1225 papers from these searches. Included studies were limited to those that showed an application of VoI in biodiversity conservation rather than simply describing the method. All examples of use of VOI were summarised regarding the application of VoI, the management objectives, the uncertainties, the models used, how the objectives were measured, and the type of VoI. While the use of VoI appears to be on the increase in biodiversity conservation, the reporting of results is highly variable, which can make it difficult to understand the decision context and which uncertainties were considered. Moreover, it was unclear if, and how, the papers informed management and policy interventions, which is why we suggest a range of reporting standards that would aid the use of VoI. The use of VoI in conservation settings is at an early stage. There are opportunities for broader applications, not only for species‐focussed management problems, but also for setting local or global research priorities for biodiversity conservation, making funding decisions, or designing or improving protected area networks and management. The long‐term benefits of applying VoI methods to biodiversity conservation include a more structured and decision‐focused allocation of resources to research. [ABSTRACT FROM AUTHOR]

Summary: The paper introduces the concept of higher order summative cofactors (HOSCs) to the circuit analysis. Although the concept is not new, it is not well known. In the paper, some mathematical background of HOSCs is presented. The further development of the concept of HOSC will yield computer implementation arithmetic of HOSC. A cancellation‐free symbolic analysis technique, which is based on HOSC arithmetic, is presented. This technique allows results to be created directly from a netlist in the form of a binary decision diagram, which is called a parameter decision diagram. Additionally, HOSC arithmetic allows the calculation to be started in many places (sometimes distant) simultaneously. The techniques of rolling up the already analyzed parts of a circuit, which is built into HOSC arithmetic, result in a novel multilevel hierarchical analysis method that is called hierarchical parameter decision diagram (HPDD). Unlike in most hierarchical methods, the results that are obtained based on the subcircuit representation in HPDD always maintain a cancellation‐free form. The HPDD always represents the sum of the product form, which is heavily compressed due to the self‐similarities of the actual circuit. The time that is required for any recalculation of the transfer functions is greatly reduced. Analysis of models that are based on pathological components is also a natural consequence of using HOSC arithmetic. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we analysed classification rules under Bayesian decision theory. The setup we considered here is fairly general, which can represent all possible parametric models. The Bayes classification rule we investigated minimises the Bayes risk under general loss functions. Among the existing literatures, the 0‐1 loss function appears most frequently, under which the Bayes classification rule is determined by the posterior predictive densities. Theoretically, we extended the Bernstein–von Mises theorem to the multiple‐sample case. On this basis, the oracle property of Bayes classification rule has been discussed in detail, which refers to the convergence of the Bayes classification rule to the one built from the true distributions, as the sample size tends to infinity. Simulations show that the Bayes classification rules do have some advantages over the traditional classifiers, especially when the number of features approaches the sample size. The Bayes classification rule minimising the Bayes risk under general loss function, for data from any parametric model. [ABSTRACT FROM AUTHOR]

The recent benefit–risk framework (BRF) developed by the Food and Drug Administration (FDA) is intended to improve the clarity and consistency in communicating the reasoning behind the FDA's decisions, acting as an important advancement in US drug regulation. In the PDUFA VI implementation plan, the FDA states that it will continue to explore more structured or quantitative decision analysis approaches; however, it restricts their use within the current BRF that is purely qualitative. By contrast, European regulators and researchers have been long exploring the use of quantitative decision analysis approaches for evaluating drug benefit–risk balance. In this paper, we show how quantitative modelling, backed by decision theory, could complement and extend the FDA's BRF to better support the appraisal of evidence and improve decision outcomes. After providing relevant scientific definitions for benefit–risk assessment and describing the FDA and European Medicines Agency (EMA) frameworks, we explain the components of and differences between qualitative and quantitative approaches. We present lessons learned from the EMA experience with the use of quantitative modelling and we provide evidence of its benefits, illustrated by a real case study that helped to resolve differences of judgements among EMA regulators. [ABSTRACT FROM AUTHOR]

Abstract: This paper focuses on the Requirements Engineering process's vital role during systems development as the preparatory point and critical foundation of Systems Engineering. This paper proposes a framework for structuring decisions, based upon the INCOSE Decision Management Process that improves decisions early in the System Life Cycle. This paper introduces and applies the Nexus Engineering Execution Generation (NEX GEN) framework, a novel and unique capability to improve requirements by enabling a prediction of their impact on expected systems’ performance to gain efficiencies in budget and acquisition. Using this framework defines requirements in a quantitative manner to determine documented requirement issues. Regression analysis indicated a statistically significant relationship between the predictor variable of ambiguity (linguistic quality attribute) and the response variable of system performance (usability metric). This study provides a framework and metrics as a complementary method for the development of requirements in a theoretical manner for the reflective practitioner. This framework is demonstrated through multiple federal system program case studies. [ABSTRACT FROM AUTHOR]

Summary: Small area estimation is concerned with methodology for estimating population parameters associated with a geographic area defined by a cross‐classification that may also include non‐geographic dimensions. In this paper, we develop constrained estimation methods for small area problems: those requiring smoothness with respect to similarity across areas, such as geographic proximity or clustering by covariates, and benchmarking constraints, requiring weighted means of estimates to agree across levels of aggregation. We develop methods for constrained estimation decision theoretically and discuss their geometric interpretation. The constrained estimators are the solutions to tractable optimisation problems and have closed‐form solutions. Mean squared errors of the constrained estimators are calculated via bootstrapping. Our approach assumes the Bayes estimator exists and is applicable to any proposed model. In addition, we give special cases of our techniques under certain distributional assumptions. We illustrate the proposed methodology using web‐scraped data on Berlin rents aggregated over areas to ensure privacy. [ABSTRACT FROM AUTHOR]

Many sample size criteria exist. These include power calculations and methods based on confidence interval widths from a frequentist viewpoint, and Bayesian methods based on credible interval widths or decision theory. Bayesian methods account for the inherent uncertainty of inputs to sample size calculations through the use of prior information rather than the point estimates typically used by frequentist methods. However, the choice of prior density can be problematic because there will almost always be different appreciations of the past evidence. Such differences can be accommodated a priori by robust methods for Bayesian design, for example, using mixtures or ϵ-contaminated priors. This would then ensure that the prior class includes divergent opinions. However, one may prefer to report several posterior densities arising from a "community of priors," which cover the range of plausible prior densities, rather than forming a single class of priors. To date, however, there are no corresponding sample size methods that specifically account for a community of prior densities in the sense of ensuring a large-enough sample size for the data to sufficiently overwhelm the priors to ensure consensus across widely divergent prior views. In this paper, we develop methods that account for the variability in prior opinions by providing the sample size required to induce posterior agreement to a prespecified degree. Prototypic examples to one- and two-sample binomial outcomes are included. We compare sample sizes from criteria that consider a family of priors to those that would result from previous interval-based Bayesian criteria. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper discusses a general design pattern that can provide systems engineering guidance for building adaptive systems. The Neocortex Adaptive System Pattern (NASP) architecture is an adaptive decision-making architecture. It is derived from the physical architecture observed within the neocortex. It allows different adaptive system components with diverse methodologies and techniques to coexist and cooperate within a single system. A literature review of relevant neuroscience literature is provided. An architectural view, along with a taxonomy of architecture features, is explained in detail. Experiment results that compare an implemented NASP system, a non-NASP adaptive system, and rule-based systems within a combative simulation are presented. The NASP design pattern unifies the diverse set of algorithms and technologies found across the spectrum of adaptive systems. Evidence from this study indicates that multiple methods and time windows for adaptation can result in superior performance of the overall system. The implications of this result infer a clear preference for Bayesian-based adaptive systems over neural network-based adaptive systems when the system faces time-sensitive problems. This paper supports the claim of a design pattern by providing an example system that shares the same architectural features as NASP. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper suggests several improvements over the original Time-expanded Decision Network (TDN; a powerful and generic methodology introduced by Silver and de Weck for designing evolvable complex systems) in order to leverage its ease, flexibility, and scope of use. A more accurate model of the switching process, a simpler network representation, and the ability to address systems with unknown lifetime are some of the improvements. However, the most important added feature is the ability to account for other design requirements within the process, which enables the designers to embed the TDN methodology into their general concept exploration phase. As a result, the enhanced TDN (ETDN) mitigates the need for iterations over several requirement domains and makes achieving the optimal design easier. It is also discussed in the paper that all of the improvements are attained without adding to the original method's complexity and only by a marginal increment of computational costs. As an example, the design of a sports car system is described and all of the new features are applied and the results are thoroughly discussed-although the example's focus is solely on the new improvements and not on the features that are directly inherited from the original method. [ABSTRACT FROM AUTHOR]

In this paper, we study the Bühlmann credibility model with constant interest rate and equal dependence structure over risks and time under balanced loss function. By means of orthogonal projection, the inhomogeneous and homogeneous credibility premium estimators are derived, which extend those for the existing models to slightly more general versions. Finally, we investigate the estimation of the structure parameters and present a numerical example to show the effectiveness of the inhomogeneous estimator. [ABSTRACT FROM AUTHOR]

Many decisions are made in interactive situations in which decision makers interact and may also affect each other's decision outcomes. In order to model decision makings in such interactive situations, this paper incorporates game theory into influence diagrams and presents a new approach, called game theory-based influence diagrams (GIDs). GIDs consider an extra factor, that of the choice of strategies made by other decision makers, to the list of determinants that influence the decision-making process of each decision maker, so that decision makers can make more rational decisions. As a result of integrating influence diagrams and game theory, GIDs benefit from the simplicity and efficiency of influence diagrams for modelling complex decision problems as well as the rationality and suitability of applying game theory for making decisions in dynamic interactive scenarios. This paper also introduces genetic algorithm-based methods to evaluate GIDs. Experimental studies have been performed for validation and evaluation. [ABSTRACT FROM AUTHOR]

A knowledge-based decision theory faces what has been called the prodigality problem (Greco, 2013): given that many propositions are assigned probability 1, agents will be inclined to risk everything when betting on propositions which are known. In order to undo probability 1 assignments in high risk situations, the paper develops a theory which systematically connects higher level goods with higher-order knowledge. [ABSTRACT FROM AUTHOR]

AIM: This paper is a report of a review in which decision theory from economics and psychology was applied to understand why some women with access to care do not seek cancer screening. BACKGROUND: Mammography and cervical smear testing are effective modes of cancer screening, yet many women choose not to be screened. Nurses need to understand the reasons behind women's choices to improve adherence. DATA SOURCES: Research papers published between January 1994 and November 2008 were identified using the Cumulative Index to Nursing and Allied Health Literature, MEDLINE and PsycINFO data bases. The search was performed using the following terms: cervical cancer screening, breast cancer screening, decision, choice, adherence and framing. Forty-seven papers were identified and reviewed for relevance to the search criteria. METHODS: Nineteen papers met the search criteria. For each paper, reasons for obtaining or not obtaining cancer screening were recorded, and organized into four relevant decision theory principles: emotions, Prospect Theory, optimism bias and framing. FINDINGS: All women have fears and uncertainty, but the sources of their fears differ, producing two main decision scenarios. Non-adherence results when women fear medical examinations, providers, tests and procedures, do not have/seek knowledge about risk and frame their current health as the status quo. Adherence is achieved when women fear cancer, but trust care providers, seek knowledge, understand risk and frame routine care as the status quo. CONCLUSION: Nurses need to address proactively women's perceptions and knowledge about screening by openly and uniformly discussing the importance and benefits. [ABSTRACT FROM AUTHOR]

1. Many studies of adaptive harvest management already exist in the literature, but most (if not all) use long, sometimes infinite, time horizons. Such long-term objectives provide an opportunity to manage experimentally, so that poorly understood dynamics are learned and any returns sacrificed for experimentation are repaid by improved management over the remaining time horizon. 2. However, a manager is unlikely to weight outcomes in the distant future equally against outcomes in the present. Furthermore, the most appropriate model of system dynamics may not remain constant over the time-frame required to experiment, learn and improve management. In these cases the use of discounting and/or a finite time horizon fit the manager's assumptions and goals more effectively, and the value of experimentation is likely to be diminished. 3. In this paper we construct a simple model of a hypothetical population and compare optimal passive and active adaptive harvest strategies over a range of time horizons. This allows us to determine the optimal level of experimentation for short-, medium- and long-term goals. 4. We discover that the optimal active adaptive harvest strategy may be precautionary over short to medium time horizons, rather than experimental. That is, an action with known moderate benefits is preferred over an action with uncertain but marginally larger expected benefits. This runs counter to the widespread assumption in the adaptive management literature that incorporating learning into an optimization of management will encourage experimentation. 5. Synthesis and applications. The general results of this paper have potential application to any environmental management problem where adaptive management might be applied; for example, conservation, pest control, harvesting and management of water flows. We examine adaptive management over a range of finite time horizons to reflect a variety of possible management goals and assumptions. Our simple example demonstrates that in the face of model uncertainty, the management strategy that maximizes benefits does not necessarily include deliberate experimentation and learning. Optimal active adaptive management weighs experimentation against all its potential consequences, and this can yield a precautionary approach. [ABSTRACT FROM AUTHOR]

Six Sigma as a quality improvement framework cannot remain static if it is to sustain its value for businesses beyond the first waves of applications. This paper explores the possibilities of enhancing the usefulness and effectiveness of Six Sigma by the integration of established Operations Research/Management Science (OR/MS) techniques. In this paper, we elucidate the needs for OR/MS techniques to enhance Six Sigma deployment in operational and transactional environments and propose a new training roadmap for core Six Sigma professionals (Six Sigma Black Belts) which incorporates these techniques. A matrix relating the components of the proposed training curriculum to the actual deliverables during implementation for a hybrid of operational and transactional environments is also presented. A practical case study is also presented to demonstrate the usefulness of the OR/MS tools in a typical transactional environment. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Intelligence analysts commonly associate cases on the basis of similarities found in compared characteristics of scientific evidence. The present paper studies some of the inferential difficulties associated with such operations. An analysis is proposed that breaks down the reasoning process into inference to common source, and inference to case linkage. The former requires an approach to the difficulty associated with evaluating the similarities of items of evidence from different cases with no putative source being available. The latter requires consideration to be given to the relevance of evidence. Throughout the paper, probability theory is used to describe the nature of the proposed inferences. Graphical models are also introduced with the aim of providing further insight into the dependence and independence relationships assumed to hold among the various propositions considered. Notions from decision theory are used to discuss ways in which intelligence analysts may assist investigators in deciding whether or not cases should be considered as linked. [ABSTRACT FROM AUTHOR]

As the Internet continues to spread and equipment performance improves, the typical operation has been to assign multiple Internet Protocol (IP) addresses to one node. At this type of node, whether the node is the destination of the received packet must be efficiently determined. Conventionally, a Berkeley Software Distribution (BSD) system used an algorithm that performs a linear search for the address of the node. As described in this paper, the Widely Integrated Distributed Environment (WIDE) Project conducted R&D on an improved method using a routing table algorithm. Later an original improvement using a hash table was introduced into Internet Protocol Version 4 (IPv4) implementation in NetBSD and BSD/OS. In this paper, we explain these three methods and evaluate their performances using actual equipment. From the evaluation results, a decision method based on the routing table or the hash table is clearly an improvement over the conventional technique based on a linear search. In addition, the performance of the decision method using the routing table algorithm does not degrade even if the number of addresses increases similar to a conventional routing lookup. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 86(9): 1–8, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.10110 [ABSTRACT FROM AUTHOR]

The paper investigates the impact of trust on market efficiency and bilateral contracts. We prove that a market in which agents are trusted to the degree they deserve to be trusted is as efficient as a market with complete trustworthiness. In other words, complete trustworthiness is not a necessary condition for market efficiency. We prove that distrust could significantly reduce market efficiency, and we show how to solve the problem by using appropriately designed multiagent contracts. The problem of trust is studied in the context of a bilateral negotiation game between a buyer and a seller. It is shown that if the seller’s trust equals the buyer’s trustworthiness, then the social welfare, the amount of trade, and the agents’ utility functions are maximized. The paper also studies the efficiency of advance payment contracts as a tool for improving trustworthiness. It is proved that advance payment contracts maximize the social welfare and the amount of trade. Finally, the paper studies the problem of how to make agents truthfully reveal their level of trustworthiness. An incentive–compatible contract is defined, in which agents do not benefit from lying about their trustworthiness. The analysis and the solutions proposed in this paper could help agent designers avoid many market failures and produce efficient interaction mechanisms. [ABSTRACT FROM AUTHOR]

Examines the debate between evidential and causal decision theory. Reactions to papers by Clark Glymour and Christopher Meek (1994) and by Christopher Hitchcock (1996); Argument that the papers are seduced by a plausible line of thought into a mistaken sympathy for evidentialism; Account of non-causal probabilistic rationality developed earlier with Helen Beebee to deal with causal contexts as well.

ABSTRACT This paper introduces a conceptual, yet quantifiable, architecture framework by extending the notion of system modularity in its broadest sense. Acknowledging that modularity is not a binary feature and comes in various types and levels, the proposed framework introduces higher levels of modularity that naturally incorporate decentralized architecture on the one hand and autonomy in agents and subsystems on the other. This makes the framework suitable for modularity decisions in Systems of Systems and for analyzing the impact of modularity on broader surrounding ecosystems. The stages of modularity in the proposed framework are naturally aligned with the level of variations and uncertainty in the system and its environment, a relationship that is central to the benefits of modularity. The conceptual framework is complemented with a decision layer that makes it suitable to be used as a computational architecture decision tool to determine the appropriate stage and level of modularity of a system, for a given profile of variations and uncertainties in its environment. We further argue that the fundamental systemic driving forces and trade-offs of moving from monolithic to distributed architecture are essentially similar to those for moving from integral to modular architectures. The spectrum, in conjunction with the decision layer, could guide system architects when selecting appropriate parameters and building a system-specific computational tool from a combination of existing tools and techniques. To demonstrate the applicability of the framework, a case for fractionated satellite systems based on a simplified demo of the DARPA F6 program is presented where the value of transition from a monolithic architecture to a fractionated architecture, as two consecutive levels of modularity in the proposed spectrum, is calculated and ranges of parameters where fractionation increases systems value are determined. [ABSTRACT FROM AUTHOR]

Bayesian experimental design is a fast growing area of research with many real-world applications. As computational power has increased over the years, so has the development of simulation-based design methods, which involve a number of algorithms, such as Markov chain Monte Carlo, sequential Monte Carlo and approximate Bayes methods, facilitating more complex design problems to be solved. The Bayesian framework provides a unified approach for incorporating prior information and/or uncertainties regarding the statistical model with a utility function which describes the experimental aims. In this paper, we provide a general overview on the concepts involved in Bayesian experimental design, and focus on describing some of the more commonly used Bayesian utility functions and methods for their estimation, as well as a number of algorithms that are used to search over the design space to find the Bayesian optimal design. We also discuss other computational strategies for further research in Bayesian optimal design. [ABSTRACT FROM AUTHOR]

Sensitivity or importance analysis has been widely used for identifying system weaknesses and supporting system improvement and maintenance activities. Despite the rich literature on the sensitivity analysis of many mission-critical and safety-critical systems, no existing work has been devoted to wireless sensor networks (WSN). In this paper, we first analyze link and node importance with respect to the infrastructure communication reliability of WSN systems. The binary decision diagrams based algorithms are implemented to evaluate and compare three importance measures: structural importance measure, Birnbaum's measure, and criticality importance measure. The effects of node degree, choice of the destination node, data delivery models, as well as mission time on the importance analysis results are investigated through examples. Results from this work can facilitate the design, deployment, and maintenance of reliable WSN for critical applications. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A proper maintenance plan is directly related to the definition of critical indexes for ensuring a high level of safety and high level in service quality for all equipments in the plants. The traditional approach, according to risk-based inspection and maintenance (RBI&M), requires that each parameter considered in the definition of critical indexes shall be divided into intervals in order to assign it a score. By the elaboration of these scores, the critical indexes are calculated. However, what are the rules that allow the company the definition of the range and the assignment of the relative score? Are these rules subjective or objectives? Literature in the field highlights that these decisions are often carried out by maintenance managers. In order to overcome this approach, in this paper, a method based on Fuzzy Cognitive Maps (FCMs) is presented. FCMs have been used for structuring and supporting decisional processes. The criticality of equipments is described in terms of concepts affecting its functioning. No ranges or scores are defined, but only structural and functional features are considered in order to define a criticality index. The resulting fuzzy model can be used to analyse, simulate, test the influence of concepts and predict the behaviour of the system. The RBI&M model, proposed in this work, has been analysed through a case study of an Italian refinery Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes the median filter based on the fuzzy rules, as a filter that effectively eliminates the impulsive noise without greatly distorting the signal such as image. It is already known that the median filter is effective in eliminating the impulsive noise, but the median filter also distorts the signal waveform itself. To cope with this situation, it is desirable that the filter should operate as the median filter when there exists the impulsive noise and, if not, should only pass the input to the output. In this approach, however, the rule to decide on the existence/nonexistence of the impulsive noise generally is ambiguous. From such as viewpoint, this paper proposes a new type of a median filter where then above decision rule is represented by the fuzzy rules and the output is controlled by the result of the decision. The filter is designed optimally by training. The effectiveness of the filter is demonstrated by actually eliminating the noise from the image. [ABSTRACT FROM AUTHOR]

When we engage in practical deliberation, we sometimes engage in careful probabilistic reasoning. At other times, we simply make flat out assumptions about how the world is or will be. A question thus arises: when, if ever, is it rationally permissible to engage in the latter, less sophisticated kind of practical deliberation? Recently, a number of authors have argued that the answer concerns whether one knows that p. Others have argued that the answer concerns whether one is justified in believing that one knows that p. Against both of these, this paper argues that the answer concerns whether p is 'practically certain'-that is, whether the actual epistemic probability that p differs from epistemic certainty that p only in ways that are irrelevant to the decision one currently faces. [ABSTRACT FROM AUTHOR]

Following integration of the observed diffraction spots, the process of `data reduction' initially aims to determine the point-group symmetry of the data and the likely space group. This can be performed with the program POINTLESS. The scaling program then puts all the measurements on a common scale, averages measurements of symmetry-related reflections (using the symmetry determined previously) and produces many statistics that provide the first important measures of data quality. A new scaling program, AIMLESS, implements scaling models similar to those in SCALA but adds some additional analyses. From the analyses, a number of decisions can be made about the quality of the data and whether some measurements should be discarded. The effective `resolution' of a data set is a difficult and possibly contentious question (particularly with referees of papers) and this is discussed in the light of tests comparing the data-processing statistics with trials of refinement against observed and simulated data, and automated model-building and comparison of maps calculated with different resolution limits. These trials show that adding weak high-resolution data beyond the commonly used limits may make some improvement and does no harm. [ABSTRACT FROM AUTHOR]

Aim Geographic mapping of risks is a useful analytical step in ecological risk assessments and in particular, in analyses aimed to estimate risks associated with introductions of invasive organisms. In this paper, we approach invasive species risk mapping as a portfolio allocation problem and apply techniques from decision theory to build an invasion risk map that combines risk and uncertainty in a single map product. Location Canada. Methods We divide the study area into a set of spatial domains and treat each domain as an individual 'portfolio' with a unique distribution of the expected impacts of invasion. The risk of invasion is then mapped by finding nested 'efficient' portfolio sets that identify the geographic areas exhibiting the worst combinations of the estimated risk of invasion and the uncertainty in that estimate. For Canadian municipalities, we apply the approach to quantify the risk that a given location will receive invasive forest pests with commercial freight transported via the North American road network. We compare risk allocation techniques that employ the concepts of nested mean-variance ( M-V) frontiers and second-degree stochastic dominance. Results While both methods based on M-V and the stochastic dominance principles identified similar areas of highest risk, they differed in how they demarcated moderate-risk areas. Furthermore, they address uncertainty in different ways, treating it as a risk premium (in the case of nested M-V frontiers) or producing risk-averse delineations (in the case of stochastic dominance). Main conclusions The portfolio-based approach offers a viable strategy for dealing with the typically wide variability in risk estimates caused by a lack of knowledge about a new invader. The methodology also provides a tractable way of incorporating decision-making preferences into the final risk estimates and thus better aligns risk assessments with particular decision-making scenarios about the organism of concern. [ABSTRACT FROM AUTHOR]

A flexible and simple Bayesian decision-theoretic design for dose-finding trials is proposed in this paper. In order to reduce the computational burden, we adopt a working model with conjugate priors, which is flexible to fit all monotonic dose-toxicity curves and produces analytic posterior distributions. We also discuss how to use a proper utility function to reflect the interest of the trial. Patients are allocated based on not only the utility function but also the chosen dose selection rule. The most popular dose selection rule is the one-step-look-ahead (OSLA), which selects the best-so-far dose. A more complicated rule, such as the two-step-look-ahead, is theoretically more efficient than the OSLA only when the required distributional assumptions are met, which is, however, often not the case in practice. We carried out extensive simulation studies to evaluate these two dose selection rules and found that OSLA was often more efficient than two-step-look-ahead under the proposed Bayesian structure. Moreover, our simulation results show that the proposed Bayesian method's performance is superior to several popular Bayesian methods and that the negative impact of prior misspecification can be managed in the design stage. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we consider two-stage designs with failure-time endpoints in single-arm phase II trials. We propose designs in which stopping rules are constructed by comparing the Bayes risk of stopping at stage I with the expected Bayes risk of continuing to stage II using both the observed data in stage I and the predicted survival data in stage II. Terminal decision rules are constructed by comparing the posterior expected loss of a rejection decision versus an acceptance decision. Simple threshold loss functions are applied to time-to-event data modeled either parametrically or nonparametrically, and the cost parameters in the loss structure are calibrated to obtain desired type I error and power. We ran simulation studies to evaluate design properties including types I and II errors, probability of early stopping, expected sample size, and expected trial duration and compared them with the Simon two-stage designs and a design, which is an extension of the Simon's designs with time-to-event endpoints. An example based on a recently conducted phase II sarcoma trial illustrates the method. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper we explore the connections between ethics and decision theory. In particular, we consider the question of whether decision theory carries with it a bias towards consequentialist ethical theories. We argue that there are plausible versions of the other ethical theories that can be accommodated by “standard” decision theory, but there are also variations of these ethical theories that are less easily accommodated. So while “standard” decision theory is not exclusively consequentialist, it is not necessarily ethically neutral. Moreover, even if our decision-theoretic models get the right answers vis-à-vis morally correct action, the question remains as to whether the motivation for the non-consequentialist theories and the psychological processes of the agents who subscribe to those ethical theories are lost or poorly represented in the resulting models. [ABSTRACT FROM AUTHOR]

Traditional approaches toward modeling the availability of a system often do not formally take into account uncertainty over the parameter values of the model. Such models are then frequently criticized because the observed reliability of a system does not match that predicted by the model. This paper extends a recently published segregated failures model so that, rather than providing a single figure for the availability of a system, uncertainty over model parameter values is incorporated and a predictive probability distribution is given. This predictive distribution is generated in a practical way by displaying the uncertainties and dependencies of the parameters of the model through a Bayesian network (BN). Permitting uncertainty in the reliability model then allows the user to determine whether the predicted reliability was incorrect due to inherent variability in the system under study, or due to the use of an inappropriate model. Furthermore, it is demonstrated how the predictive distribution can be used when reliability predictions are employed within a formal decision-theoretic framework. Use of the model is illustrated with the example of a high-availability computer system with multiple recovery procedures. An BN is produced to display the relations between parameters of the model in this case and to generate a predictive probability distribution of the system's availability. This predictive distribution is then used to make two decisions under uncertainty concerning the offered warranty policies on the system: a qualitative decision and an optimization over a continuous decision space. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In many forms of severe acute brain injury there is an early phase when prognosis is uncertain, followed later by physiological recovery and the possibility of more certain predictions of future impairment. There may be a window of opportunity for withdrawal of life support early, but if decisions are delayed there is the risk that the patient will survive with severe impairment. In this paper I focus on the example of neonatal encephalopathy and the question of the timing of prognostic tests and decisions to continue or to withdraw life-sustaining treatment. Should testing be performed early or later; and how should parents decide what to do given the conflicting values at stake? I apply decision theory to the problem, using sensitivity analysis to assess how different features of the tests or different values would affect a decision to perform early or late prognostic testing. I draw some general conclusions from this model for decisions about the timing of testing in neonatal encephalopathy. Finally I consider possible solutions to the problem posed by the window of opportunity. Decision theory highlights the costs of uncertainty. This may prompt further research into improving prognostic tests. But it may also prompt us to reconsider our current attitudes towards the palliative care of newborn infants predicted to be severely impaired. [ABSTRACT FROM AUTHOR]

In Lewis Carroll's Alice in Wonderland, the Cheshire Cat provides to Alice the underlying tenets of modern normative decision theory. These tenets are clear and simple. Yet few engineers have grasped their significance, and many design methods in common practice violate them with potentially devastating consequences. This paper reviews the advice given to Alice by the Cheshire Cat and places it in the context of engineering design. It goes on to discuss the contributions of von Neumann and Morgenstern as they add to the Cat's advice. Published in 2009 by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper shows how decision theory can be used to determine optimal cut-off scores on mental health screeners. The procedure uses (a) the costs and benefits of correct and erroneous decisions, and (b) the rates of correct and erroneous decisions as a function of the cut-off score. Using this information, for each cut-off point expected costs are calculated. The cut-off point with the lowest expected costs is the optimal cut-off score. An illustration is given in which the General Health Questionnaire is employed as a major depression screener. Optimal cut-off points are determined for four different contexts: patients, health service providers, society, and mental health researchers. As in these four situations different costs are encountered, different optimal cut-off points were found. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]