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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]

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]

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]

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]

In this paper we consider a scenario where there are two wireless body area networks (WBANs) interfere with each other from a game theoretic perspective. In particular, we envision two WBANs playing a potential game to enhance their performance by decreasing interference to each other. Decreasing interference extends the sensors' batteries life time and reduces the number of re‐transmissions. We derive the required conditions for the game to be a potential game and its associated the Nash equilibrium (NE). Specifically, we formulate a game where each WBAN has three strategies. Depending on the payoff of each strategy, the game can be designed to achieve a desired NE. Furthermore, we employ a learning algorithm to achieve that NE. In particular, we employ the Fictitious play (FP) learning algorithm as a distributed algorithm that WBANs can use to approach the NE. The simulation results show that the NE is mainly a function of the power cost parameter and a reliability factor that we set depending on each WBAN setting (patient). However, the power cost factor is more dominant than the reliability factor according to the linear cost function formulation that we use throughout this work. [ABSTRACT FROM AUTHOR]

Since its publication in 2014, L. A. Paul's book Transformative Experience has sparked many discussions in philosophy and beyond. Her main argument is that experiences we have not had before can transform us epistemically (i.e., we learn something we could not learn without the experience) and personally (i.e., our point of view changes radically). This has implications for decision theory in particular, but also for other fields. The present paper provides an overview of how transformative experiences are thought to challenge rational choice and how authors have responded to this challenge (with a focus on the epistemic transformation). Furthermore, it also shows how the concept of transformative experience has been fruitful beyond the decision‐theoretical context, with a particular focus on illness, medical treatment, and the ethical issues involved. Finally, the paper presents open questions in the literature on transformative experiences. [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]

Energy communities are one of the vital puzzle pieces of future smart cities. This paper proposes a novel structure for a sustainable smart city by integrating energy communities in a multi‐carrier energy network. Each energy community has a manager; the so‐called Energy Community Managers (ECM), who trades energy with the upstream Multi‐Carrier Network Operator (MCNO). On the other hand, MCNO participates in the upstream energy markets to satisfy the demands of energy communities by maximizing its own profit. Therefore, ECMs and MCNO should solve a bilevel optimization problem associated with some common variables at both levels such as: energy carrier price and the amount of energy carrier exchange. In fact, MCNO is the leader and ECMs are the followers of such a bilevel optimization problem. Strong duality is employed to convert the bilevel optimization into a single level, while uncertainties are modelled by information gap decision theory and a scenario‐based approach. Sensitivity analysis shows that the thermal energy selling price and the gas buying price are the most crucial influencing on the profit of MCNO by 3.22% and −3.91%, respectively. Furthermore, the obtained results indicate that the risk attitude of the multi‐carrier energy network operator has a critical role in the total profit. [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]

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]

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]

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]

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]

This paper focuses on risk‐averse‐based optimal operation of a grid‐connected hybrid energy system (HES) composed of photovoltaic (PV), diesel generator, and battery storage system (BSS). For this goal, information gap decision theory (IGDT) is used to model load demand uncertainty. The aim of the optimal operation is to minimize cost of PV/diesel/BSS by optimal determination of the power purchased from the electricity grid. Since in the risk‐averse strategy, load demand has an undesirable impact on the objective function, the decision maker attempts to maximize the uncertainty radius in a way that any deviation of the uncertain parameter leads to an objective function value which is not worse than the critical value. Over the case studies (considering different radiations), simulation results indicate that in the risk‐neutral strategy, at high, medium, and low radiations, the operation cost is 28.88, 36.10, and 42.63$, respectively. In the risk‐averse strategy, when the radiation is high, by increase of the deviation factor from 0.1 to 0.25, the optimal uncertainty radius increases from 6.98% to 15.72% (increase of around 125%) and the operation cost increases from 31.768 to 36.101$. When the radiation is low, by increase of the deviation factor from 0.1 to 0.25, the uncertainty radius increases from 8.64% to 16.9% (increase of around 96%) and the operation cost increases from 46.895 to 53.291$. [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]

This paper presents a new algorithm named combination search strategy (CSS)‐based improved particle swarm optimisation (IPSO) to address the resource allocation problem of multiple jammers. First, the authors propose a CSS to effectively broaden the limited search range of the two‐step solving framework. This method not only simplifies the solution framework but also considers the combined relationship between beam pointing and transmit power to determine the global solution to the original problem directly. Second, the authors propose the IPSO because the complexity of the decision variables is increased by CSS. This method can change the focus of searching the optimal solution at different stages, correct the direction of particle evolution over time and avoid the interference between the variables. Finally, this study simulates the problem of resource allocation of multiple jammers based on the CSS‐IPSO. Based on the simulation results, the combined search strategy can obtain better resource allocation results in a short time, and the IPSO algorithm can further improve the accuracy and stability of the resource allocation results. [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]

This paper proposes a risk‐averse strategy for resilience improvement multiyear planning for distribution networks against hurricanes, considering the uncertainty of load demand and wind power generation. For resilience enhancement, DG placement and line hardening are regarded as planning measures, and DG rescheduling, topology reconfiguration, microgrid forming, and priority‐based load curtailment are considered as operational measures. The most conservative scenario is considered for modelling the high‐impact/low‐probability (HILP) event's impacts. Besides, the information‐gap decision theory (IGDT) is utilized to acquire a robust solution protected against the different realization of uncertain variables. As the uncertain loads and wind power generation competitively tend to maximize their robust regions for a particular value of the budget of uncertainty, the ε‐constraint method is applied to solve the proposed multi‐objective IGDT‐based resilience‐oriented distribution network planning (RODNP) model. Afterwards, fuzzy decision‐making is exploited to find the best solution among Pareto optimal solutions. The proposed risk‐averse strategy is implemented on IEEE 33‐bus test system, and various case studies are analyzed to demonstrate the proficiency of the approach. The results indicate that the proposed risk‐averse strategy can optimally find the hardening schemes to achieve a resilient distribution network in a medium‐term planning horizon with a yearly limited monetary budget. [ABSTRACT FROM AUTHOR]

In this paper, a condition‐based maintenance model for a multi‐unit production system is proposed and analyzed using Markov renewal theory. The units of the system are subject to gradual deterioration, and the gradual deterioration process of each unit is described by a three‐state continuous time homogeneous Markov chain with two working states and a failure state. The production rate of the system is influenced by the deterioration process and the demand is constant. The states of the units are observable through regular inspections and the decision to perform maintenance depends on the number of units in each state. The objective is to obtain the steady‐state characteristics and the formula for the long‐run average cost for the controlled system. The optimal policy is obtained using a dynamic programming algorithm. The result is validated using a semi‐Markov decision process formulation and the policy iteration algorithm. Moreover, an analytical expression is obtained for the calculation of the mean time to initiate maintenance using the first passage time theory. [ABSTRACT FROM AUTHOR]

The contributions of this paper are as follows: (a) proposing a two‐stage model to study whether an event has temporary or permanent effects on the probability of choosing a good within a market basket that traditional decision theory cannot explain and (b) studying the effects of COVID‐19 on consumers' decisions in Colombia. Findings suggest that the pandemic has transitory effects on preferences in the short run. If it lasts longer, it could induce to permanent changes. Thus, this model can be used to analyze the temporary or permanent effects of any event, regardless of its nature or geographical region, on consumer's decision. [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]

It is an important subject to mine valuable knowledge from complex and massive data in the era of big data. Rough set theory is a new mathematical tool for dealing with uncertain and inaccurate data, decision-theoretic rough set model (DTRS), as an extension of classical rough set model, is used to analyze decision information systems and multi-granulation decision-theoretic rough set model (MG-DTRS) can analyze and process target concepts from different angles and levels. However, the classical DTRS model exists some limitations in dealing numerical or hybrid-valued data. Considering the different influence of numerical features and symbolic features on decision-making, the paper proposes an incomplete neighborhood multi-granulation decision-theoretic rough set model in hybrid-valued decision system through integrating MG-DTRS with neighbourhood rough sets, and two types of neighborhood multi-granulation decision-theoretic set models are emphatically analysed. Furthermore, taking pessimistic and optimistic neighborhood multi-granulation decision-theoretic rough sets as examples, the implementation algorithms and related properties of the two type of models are studied. Finally, the relationship between the proposed model and other models is analyzed through formula derivation. The model proposed in this paper can effectively solve the decision-making problem of hybrid-valued incomplete information system through multi-angle and multi-level analysis. [ABSTRACT FROM AUTHOR]

Decision theory can be used to test the logic of decision making—one may ask whether a given set of decisions can be justified by a decision‐theoretic model. Indeed, in principal–agent settings, such justifications may be required—a manager of an investment fund may be asked what beliefs she used when valuing assets and a government may be asked whether a portfolio of rules and regulations is coherent. In this paper we ask which collections of uncertain‐act evaluations can be simultaneously justified under the maxmin expected utility criterion by a single set of probabilities. We draw connections to the fundamental theorem of finance (for the special case of a Bayesian agent) and revealed‐preference results. [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]

I investigate the existence of epistemic models of complete information games that satisfy the following properties: players do not rule out their opponent's use of rational ex ante strategies for deriving their choices; they do not rule out, ex ante, that they can come to know the action profile that is ultimately played; and they do not rule out strategic uncertainty. In this paper I show that for a large class of games there are no epistemic models that satisfy these three properties. [ABSTRACT FROM AUTHOR]

Haavelmo (1944) proposed a probabilistic structure for econometric modeling, aiming to make econometrics useful for decision making. His fundamental contribution has become thoroughly embedded in econometric research, yet it could not answer all the deep issues that the author raised. Notably, Haavelmo struggled to formalize the implications for decision making of the fact that models can at most approximate actuality. In the same period, Wald (1939, 1945) initiated his own seminal development of statistical decision theory. Haavelmo favorably cited Wald, but econometrics did not embrace statistical decision theory. Instead, it focused on study of identification, estimation, and statistical inference. This paper proposes use of statistical decision theory to evaluate the performance of models in decision making. I consider the common practice of as‐if optimization: specification of a model, point estimation of its parameters, and use of the point estimate to make a decision that would be optimal if the estimate were accurate. A central theme is that one should evaluate as‐if optimization or any other model‐based decision rule by its performance across the state space, listing all states of nature that one believes feasible, not across the model space. I apply the theme to prediction and treatment choice. Statistical decision theory is conceptually simple, but application is often challenging. Advancing computation is the primary task to complete the foundations sketched by Haavelmo and Wald. [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]

This paper discusses the state estimation and optimal control problem of a class of partially-observable stochastic hybrid systems (POSHS). The POSHS has interacting continuous and discrete dynamics with uncertainties. The continuous dynamics are given by a Markov-jump linear system and the discrete dynamics are defined by a Markov chain whose transition probabilities are dependent on the continuous state via guard conditions. The only information available to the controller are noisy measurements of the continuous state. To solve the optimal control problem, a separable control scheme is applied: the controller estimates the continuous and discrete states of the POSHS using noisy measurements and computes the optimal control input from the state estimates. Since computing both optimal state estimates and optimal control inputs are intractable, this paper proposes computationally efficient algorithms to solve this problem numerically. The proposed hybrid estimation algorithm is able to handle state-dependent Markov transitions and compute Gaussian- mixture distributions as the state estimates. With the computed state estimates, a reinforcement learning algorithm defined on a function space is proposed. This approach is based on Monte Carlo sampling and integration on a function space containing all the probability distributions of the hybrid state estimates. Finally, the proposed algorithm is tested via numerical simulations. [ABSTRACT FROM AUTHOR]

Peer‐to‐peer (P2P) energy trading is expected to be emerged as a new energy management paradigm that allows the transaction of energy from one prosumer to another prosumer without any dependency on a central controller. In Malaysia, first pilot test of P2P trading has been conducted recently, which exclusively participated by industrial prosumers and commercial consumers only. In order to investigate the impact of P2P energy trading to Malaysia market, this paper proposes an auction‐based two‐stage P2P trading market‐clearing strategy, for multi‐cities and intra‐city in Malaysia. A motivational game theory‐based price scheme is presented to ensure an unbiased market operation, to maximize the saving and payoff for each prosumer, at the same time benefits the power utility company. The proposed two‐stage market clearing model is solved by Linear Programming optimization approach. A realistic representation of P2P trading in Malaysia is constructed and tested out under multi‐cities and intra‐city energy trading test cases. Comparison between the proposed P2P energy trading with existing solar generation net metering scheme is also presented. The numerical results indicate the viability and potential of motivational game theory based P2P trading in future Malaysia transactive energy market. [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]

The aim of this paper is to highlight the role of the Decision Support System within the field of multi-criteria decision aid (MCDA). The MCDA tools have been incorporated into systems to create Multi-Criteria Decision Support Systems (MCDSSs). In our literature review, we noticed that more than 100 papers have been written over a 20-year period in which MCDSS was used as a decision-making tool. The present paper describes some real applications of MCDSS in different fields, harmoniously combined with decision-making methods such as analytic hierarchy process, Utility Additive, and Goal Programming. The present study proposes an integrative MCDSS evaluation through guidance on the tools most useful for a specific user with a particular decision problem. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Measurements of ambiguity attitudes have so far focused on artificial events, where (subjective) beliefs can be derived from symmetry of events and can be then controlled for. For natural events as relevant in applications, such a symmetry and corresponding control are usually absent, precluding traditional measurement methods. This paper introduces two indexes of ambiguity attitudes, one for aversion and the other for insensitivity/perception, for which we can control for likelihood beliefs even if these are unknown. Hence, we can now measure ambiguity attitudes for natural events. Our indexes are valid under many ambiguity theories, do not require expected utility for risk, and are easy to elicit in practice. We use our indexes to investigate time pressure under ambiguity. People do not become more ambiguity averse under time pressure but become more insensitive (perceive more ambiguity). These findings are plausible and, hence, support the validity of our indexes. [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]

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]

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]

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]

Leveraging preclinical animal data for a phase I oncology trial is appealing yet challenging. In this paper, we use animal data to improve decision‐making in a model‐based dose‐escalation procedure. We make a proposal for how to measure and address a prior‐data conflict in a sequential study with a small sample size. Animal data are incorporated via a robust two‐component mixture prior for the parameters of the human dose–toxicity relationship. The weights placed on each component of the prior are chosen empirically and updated dynamically as the trial progresses and more data accrue. After completion of each cohort, we use a Bayesian decision‐theoretic approach to evaluate the predictive utility of the animal data for the observed human toxicity outcomes, reflecting the degree of agreement between dose–toxicity relationships in animals and humans. The proposed methodology is illustrated through several data examples and an extensive simulation study. [ABSTRACT FROM AUTHOR]

The risk of a financial position is usually summarized by a risk measure. As this risk measure has to be estimated from historical data, it is important to be able to verify and compare competing estimation procedures. In statistical decision theory, risk measures for which such verification and comparison is possible, are called elicitable. It is known that quantile-based risk measures such as value at risk are elicitable. In this paper, the existing result of the nonelicitability of expected shortfall is extended to all law-invariant spectral risk measures unless they reduce to minus the expected value. Hence, it is unclear how to perform forecast verification or comparison. However, the class of elicitable law-invariant coherent risk measures does not reduce to minus the expected value. We show that it consists of certain expectiles. [ABSTRACT FROM AUTHOR]

It is well known, in structural break problems, that it is much easier to detect the existence of a break in a data set than to determine the location of such a break in the sample span. This paper investigates why, in the context of Gaussian linear regressions, using a decision theory framework. The nub of the problem, even for moderately sized breaks, is that the posterior probability distribution of the possible break points is usually not very informative about the true break location. The information content is measured here by a proper scoring rule. Hence, even a locally optimal break location procedure, as introduced here, is ineffective. In the regression context, it turns out to be quite common, indeed the norm, for break location procedures to misidentify the true break position up to 100 per cent of the time. Unfortunately too, the magnitude of the difference between the misidentified and true break locations is usually not small. [ABSTRACT FROM AUTHOR]

This paper considers passive fund selection from an individual investor's perspective. The growth of the passive fund market over the past decade is staggering. Individual investors who wish to buy these funds for their retirement and brokerage accounts have many options and are faced with a difficult selection problem. Which funds do they invest in, and in what proportions? We develop a novel statistical methodology to address this problem by adapting recent advances in posterior summarization. A Bayesian decision‐theoretic approach is presented to construct optimal sparse portfolios for individual investors over time. [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 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]

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]

Decision science emphasizes necessary elements required for robust decision‐making. By incorporating decision science principles, frameworks, and tools, it has been demonstrated that decision‐makers can increase the chances of achieving conservation aims. Setting measurable objectives, clearly documenting assumptions about the impact of available actions on a specific threat or problem, explicitly considering constraints, exploring and characterizing uncertainty, and structured deliberation on trade‐offs have been identified as key elements of successful decision‐making. We quantify the extent to which these five elements were utilized in published examples of decision making in conservation in both academic and conservation practice between 2009 and 2018. We found that less than 50% of identified examples included all five elements, with differences in the degree of decision science applied across five commonly used decision support approaches: adaptive management (AM), systematic conservation planning (SCP), structured decision making (SDM), multi‐criteria decision analysis, and cost‐effectiveness analysis. Example applications that utilized the SDM framework were limited in numbers but used on average more than 50% of the five key elements we considered. Although SCP and AM constituted the majority of examples, they were more prevalent in academic studies rather than management applications. SCP and AM examples were widespread in protected area planning, threat abatement, and restoration. Strong geographic bias exists in documented conservation activities that deploy all five decision science elements. [ABSTRACT FROM AUTHOR]

Serious concerns have been raised that false positive findings are widespread in empirical research in business disciplines. This is largely because researchers almost exclusively adopt the 'p‐value less than 0.05' criterion for statistical significance; and they are often not fully aware of large‐sample biases which can potentially mislead their research outcomes. This paper proposes that a statistical toolbox (rather than a single hammer) be used in empirical research, which offers researchers a range of statistical instruments, including a range of alternatives to the p‐value criterion such as the Bayesian methods, optimal significance level, sample size selection, equivalence testing and exploratory data analyses. It is found that the positive results obtained under the p‐value criterion cannot stand, when the toolbox is applied to three notable studies in finance. [ABSTRACT FROM AUTHOR]

This paper provides nonparametric identification results for a class of latent utility models with additively separable unobservable heterogeneity. These results apply to existing models of discrete choice, bundles, decisions under uncertainty, and matching. Under an independence assumption, such models admit a representative agent. As a result, we can identify how regressors alter the desirability of goods using only average demands. Moreover, average indirect utility ("welfare") is identified without needing to specify or identify the distribution of unobservable heterogeneity. [ABSTRACT FROM AUTHOR]