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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]

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 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]

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 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]

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]

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 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]

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 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]

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]

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]

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]

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]

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]

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]

In a targeted double‐scoring procedure for performance assessments that are used for licensure and certification purposes, a subset of responses receives an independent second rating if the first rating falls into a preidentified critical score range (CSR) where an additional rating would lead to considerably more reliable pass‐fail decisions. This study evaluates the CSRs using two approaches—one based on imputation of missing scores and the other based on statistical decision theory—using data from the Performance Assessment for School Leaders (PASL). Results from the evaluation indicate that the currently used CSRs are effective. Report Number: RR‐23‐01 [ABSTRACT FROM AUTHOR]

Ecological communities are increasingly subject to natural and human‐induced additions of species, as species shift their ranges under climate change, are introduced for conservation and are unintentionally moved by humans. As such, decisions about how to manage ecosystems subject to species introductions and considering multiple management objectives need to be made. However, the impacts of gaining new species on ecological communities are difficult to predict due to uncertainty in introduced species characteristics, the novel interactions that will be produced by that species, and the recipient ecosystem structure. Drawing on ecological and conservation decision theory, we synthesise literature into a conceptual framework for species introduction decision‐making based on ecological networks in high‐uncertainty contexts. We demonstrate the application of this framework to a theoretical decision surrounding assisted migration considering both biodiversity and ecosystem service objectives. We show that this framework can be used to evaluate trade‐offs between outcomes, predict worst‐case scenarios, suggest when one should collect additional data, and allow for improving knowledge of the system over time. [ABSTRACT FROM AUTHOR]

Many financing decisions continue to exacerbate global biodiversity loss, despite a growing understanding of the economic and financial risks of biodiversity loss. Applying Coase's financial decision‐making theory based on cost, revenues, and risks, the article analyzes literature and current developments of biodiversity finance and green finance to evaluate why financial decision‐making is incentivized to ignore biodiversity risks and mostly fails to maximize biodiversity conservation or nature‐positive finance. Based on the identified gaps, the article develops four principles to integrate biodiversity into financial decision‐making: (1) regulation must set limits on nature's exploitation, as finance only values limited aspects of nature as "bankable," while the destruction of other parts are without negative or positive value to the investor; (2) biodiversity‐related financial risk evaluation needs focus on local risks due to the local nature of biodiversity impacts and dependencies (in comparison, e.g., to climate finance's global risks); (3) scale biodiversity finance utilizing its secondary benefits, particularly in conjunction with climate finance; (4) biodiversity finance development needs to be co‐led and championed by financial decision‐makers. This article contributes to the emerging literature on biodiversity finance and green finance for sustainable development with implications for policy makers and standard setters. [ABSTRACT FROM AUTHOR]

Rationale, Aims and Objectives: The development of clinical practice guidelines (CPG) suffers from the lack of an explicit and transparent framework for synthesising the key elements necessary to formulate practice recommendations. We matched deliberations of the American Society of Haematology (ASH) CPG panel for the management of pulmonary embolism (PE) with the corresponding decision‐theoretical constructs to assess agreement of the panel recommendations with explicit decision modelling. Methods: Five constructs were identified of which three were used to reformulate the panel's recommendations: (1) standard, expected utility threshold (EUT) decision model; (2) acceptable regret threshold model (ARg) to determine the frequency of tolerable false negative (FN) or false positive (FP) recommendations, and (3) fast‐and‐frugal tree (FFT) decision trees to formulate the entire strategy for management of PE. We compared four management strategies: withhold testing versus d‐dimer → computerized pulmonary angiography (CTPA) ('ASH‐Low') versus CTPA→ d‐dimer ('ASH‐High') versus treat without testing. Results: Different models generated different recommendations. For example, according to EUT, testing should be withheld for prior probability PE < 0.13%, a clinically untenable threshold which is up to 15 times (2/0.13) below the ASH guidelines threshold of ruling out PE (at post probability of PE ≤ 2%). Three models only agreed that the 'ASH low' strategy should be used for the range of pretest probabilities of PE between 0.13% and 13.27% and that the 'ASH high' management should be employed in a narrow range of the prior PE probabilities between 90.85% and 93.07%. For all other prior probabilities of PE, choosing one model did not ensure coherence with other models. Conclusions: CPG panels rely on various decision‐theoretical strategies to develop its recommendations. Decomposing CPG panels' deliberation can provide insights if the panels' deliberation retains a necessary coherence in developing guidelines. CPG recommendations often do not agree with the EUT decision analysis, widely used in medical decision‐making modelling. [ABSTRACT FROM AUTHOR]

We build an econometric learning model based on case‐based decision theory to analyse tax avoidance by Chinese manufacturing firms. In our model, firms forecast the consequences of tax avoidance by judging the similarity between present and remembered circumstances. We allow firms to consider not only their own experiences but also those of neighbouring firms. This is the first empirically fitted model of case‐based individual and social learning. Our measure of tax avoidance is based on underreported profits, which we measure using difference‐in‐difference (DID) and propensity score matching (PSM) with a case‐based similarity function between non‐state and state firms. We find that firms learn from their past and neighbours' experiences weighted as about 65% as important as their own. We also find that the average tax audit rate for non‐state firms is less than 2% and that more than half of non‐state firms practice tax avoidance. Our government policy simulations suggest that increasing the tax audit rate or fine would significantly deter tax avoidance. [ABSTRACT FROM AUTHOR]

Normative decision theory proves inadequate for modeling human responses to the social‐engineering campaigns of advanced persistent threat (APT) attacks. Behavioral decision theory fares better, but still falls short of capturing social‐engineering attack vectors which operate through emotions and peripheral‐route persuasion. We introduce a generalized decision theory, under which any decision will be made according to one of multiple coexisting choice criteria. We denote the set of possible choice criteria by C$\mathcal {C}$. Thus, the proposed model reduces to conventional Expected Utility theory when |CEU|=1$|\mathcal {C}_{\text{EU}}|=1$, while Dual‐Process (thinking fast vs. thinking slow) decision making corresponds to a model with |CDP|=2$|\mathcal {C}_{\text{DP}}|=2$. We consider a more general case with |C|≥2$|\mathcal {C}|\ge 2$, which necessitates careful consideration of how, for a particular choice‐task instance, one criterion comes to prevail over others. We operationalize this with a probability distribution that is conditional upon traits of the decisionmaker as well as upon the context and the framing of choice options. Whereas existing signal detection theory (SDT) models of phishing detection commingle the different peripheral‐route persuasion pathways, in the present descriptive generalization the different pathways are explicitly identified and represented. A number of implications follow immediately from this formulation, ranging from the conditional nature of security‐breach risk to delineation of the prerequisites for valid tests of security training. Moreover, the model explains the "stepping‐stone" penetration pattern of APT attacks, which has confounded modeling approaches based on normative rationality. [ABSTRACT FROM AUTHOR]

There are two distinct definitions of "P‐value" for evaluating a proposed hypothesis or model for the process generating an observed dataset. The original definition starts with a measure of the divergence of the dataset from what was expected under the model, such as a sum of squares or a deviance statistic. A P‐value is then the ordinal location of the measure in a reference distribution computed from the model and the data, and is treated as a unit‐scaled index of compatibility between the data and the model. In the other definition, a P‐value is a random variable on the unit interval whose realizations can be compared to a cutoff α to generate a decision rule with known error rates under the model and specific alternatives. It is commonly assumed that realizations of such decision P‐values always correspond to divergence P‐values. But this need not be so: Decision P‐values can violate intuitive single‐sample coherence criteria where divergence P‐values do not. It is thus argued that divergence and decision P‐values should be carefully distinguished in teaching, and that divergence P‐values are the relevant choice when the analysis goal is to summarize evidence rather than implement a decision rule. [ABSTRACT FROM AUTHOR]

The precautionary principle is often argued to be irrational because it cannot adequately explain how resources should be distributed across multiple possible catastrophes or between catastrophic and noncatastrophic risks. We address this problem of trade‐offs by extending a recently proposed formal interpretation of the precautionary principle (PP) within a lexical utility framework and using it to prove results about which distribution of resources maximizes lexical utility when several catastrophic risks exist, given different assumptions. We also explain how our lexical utility interpretation of PP can recommend balanced distributions of resources between disaster prevention and other concerns. [ABSTRACT FROM AUTHOR]

Suppose, for example, that for each I i i , we observe HT ht , for some parametric family of distributions HT ht . Assume independence between units I i i , and let HT ht be the maximum likelihood estimator based on HT ht . Asymptotic Approximations Gu and Koenker regard the normality assumption on HT ht as an approximation. [Extracted from the article]

We provide a novel Bayesian decision-theoretic motivation for Stein-type estimates, producing them as an adaptive choice between standard point estimation and estimation that rewards proximity to the origin. Unlike conventional approaches, our arguments provide shrunken estimates under any sampling model or prior. They also lead naturally to a form of credible interval, describing uncertainty about the underlying parameters yet focusing on the shrunken estimate. One specific method for constructing intervals provides a close Bayesian analogue of Samworth (2005)'s approximate confidence intervals around Stein estimates. Several examples are given, showing how shrinkage's focus on regions remote to the high-support areas of the posterior can lead to substantially larger credible sets. [ABSTRACT FROM AUTHOR]

It would be good to have a Bayesian decision theory that assesses our decisions and thinking according to everyday standards of rationality—standards that do not require logical omniscience (Garber, 1983; Hacking, 1967). To that end we develop a "fragmented" decision theory in which a single state of mind is represented by a family of credence functions, each associated with a distinct choice condition (Lewis, 1982; Stalnaker, 1984). The theory imposes a local coherence assumption guaranteeing that as an agent's attention shifts, successive batches of "obvious" logical information become available to her. A rule of expected utility maximization can then be applied to the decision of what to attend to next during a train of thought. On the resulting theory, rationality requires ordinary agents to be logically competent and to often engage in trains of thought that increase the unification of their states of mind. But rationality does not require ordinary agents to be logically omniscient. [ABSTRACT FROM AUTHOR]

Sometimes we make a decision about an action we will undertake later and form an intention, but our judgment of what it is best to do undergoes a temporary shift when the time for action comes round. What makes it rational not to give in to temptation? Many contemporary solutions privilege diachronic rationality; in some "rational non‐reconsideration" (RNR) accounts once the agent forms an intention, it is rational not to reconsider. This leads to other puzzles: how can someone be motivated to follow a plan that is contrary to their current judgment? How can it be rational to form a plan to resist if we can predict that our judgment will shift? I show how these puzzles can be solved in a framework where there are multiple units of agency, distinguishing between the judgments of the timeslice and those of the person over time, and allowing that the timeslice can "self identify", taking the person over time as the relevant unit of agency and doing intrapersonal team reasoning (with a different causal role for intentions than RNR accounts). On my account, resisting temptation is compatible with synchronic rationality, so synchronic and diachronic rationality are aligned. However, either resisting or succumbing to temptation can be instrumentally rational, depending on the unit of agency that is identified with. In order to show why we ought to resist temptation, we need to draw on a non‐instrumental rationale. I sketch possible routes for doing this. [ABSTRACT FROM AUTHOR]

We seek to understand rational decision making and if it exists whether finite (bounded) agents may be able to achieve its principles. This aim has been a singular objective throughout much of human science and philosophy, with early discussions identified since antiquity. More recently, there has been a thriving debate based on differing perspectives on rationality, including adaptive heuristics, Bayesian theory, quantum theory, resource rationality, and probabilistic language of thought. Are these perspectives on rationality mutually exclusive? Are they all needed? Do they undermine an aim to have rational standards in decision situations like politics, medicine, legal proceedings, and others, where there is an expectation and need for decision making as close to "optimal" as possible? This special issue brings together representative contributions from the currently predominant views on rationality, with a view to evaluate progress on these and related questions. Whether it is possible to define a rational standard in decision making and, if yes, whether such a standard is achievable by finite agents (such as humans) has been a hotly debated issue. This special issue offers an overview of some promising modern approaches to these questions, taking advantage of the latest developments in decision theory. [ABSTRACT FROM AUTHOR]

Business continuity planning (BCP), which achieves a sustainable power supply for essential appliances under large‐scale disasters, is an important concept for hospitals. Similarly, life continuity planning (LCP) was developed in the housing field. It assists medically vulnerable people who require continuous use of medical equipment in their homes because a failure of the LCP during power outages causes serious problems. If PV systems are used, storage batteries would be most fitting to economically implement LCP during normal situations. This can be achieved by effectively utilizing PV power generation. Here, the installation of an appropriate battery capacity is crucial. Considering the uncertainty of PV power generation owing to weather conditions, appropriate sizing of the battery capacity maximizes the expected economic benefits while leaving a margin LCP implementation. We discuss uncertainty of PV power generation at a residential site by applying the envelope‐bound model (EBM), which is popular in information gap decision theory, and proposed a sizing method for a battery storage system that economically achieves LCP. We performed a case study to prove that the proposed method, which considers the uncertainty in the expected PV output based on EBM, can be an efficient decision‐making tool to select an appropriate battery size. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

It is more and more important to analyse and process complex data for gaining more valuable knowledge and making more accurate decisions. The multigranulation decision theory based on conditional probability and cost loss has the advantage of processing decision‐making problems from multi‐levels and multi‐angles, and the neighbourhood rough set model (NRS) can facilitate the analysis and processing of numerical or mixed type data, and can address the limitation of multigranulation decision‐theoretic rough sets (MG‐DTRS), which is not easy to cope with complex data. Based on the in‐depth study of hybrid‐valued decision systems and MG‐DTRS models, this study analysed neighbourhood MG‐DTRS (NMG‐DTRS) deeply by fusing MG‐DTRS and NRS; a matrix‐based approach for approximation sets of NMG‐DTRS model was proposed on the basis of the matrix representations of concepts; the positive, boundary and negative domains were constructed from the matrix perspective, and the concept of positive decision recognition rate was introduced. Furthermore, the authors explored the related properties of NMG‐DTRS model, and designed and described the corresponding solving algorithms in detail. Finally, some experimental results that were employed not only verified the effectiveness and feasibility of the proposed algorithm, but also showed the relationship between the decision recognition rate and the granularity and threshold. [ABSTRACT FROM AUTHOR]

The use of tiered warnings and multicategorical forecasts are ubiquitous in meteorological operations. Here, a flexible family of scoring functions is presented for evaluating the performance of ordered multicategorical forecasts. Each score has a risk parameter α$$ \alpha $$, selected for the specific use case, so that it is consistent with a forecast directive based on the fixed threshold probability 1−α$$ 1-\alpha $$ (equivalently, a fixed α$$ \alpha $$‐quantile mapping). Each score also has use‐case specific weights so that forecasters who accurately discriminate between categorical thresholds are rewarded in proportion to the weight for that threshold. A variation is presented where the penalty assigned to near misses or close false alarms is discounted, which again is consistent with directives based on fixed risk measures. The scores presented provide an alternative to many performance measures currently in use, whose optimal threshold probabilities for forecasting an event typically vary with each forecast case, and in the case of equitable scores are based around sample base rates rather than risk measures suitable for users. [ABSTRACT FROM AUTHOR]

They can be vitally important to the decision, and it is necessary to consider them alongside more easily quantified objectives, such as species abundance and cost.Scarce resourcesResources (e.g., time, staff capacity, money, space) available for conservation are often limited, requiring consideration of how to best allocate resources to achieve objectives.Complex alternativesIn complex ecological decisions, the range of possible alternative actions is often very large and multifaceted.Irreversible consequences and tipping pointsConservation decisions sometimes involve tipping points between different system states or irreversible outcomes to be avoided. In our experience, decision analysis and the broader field of decision science provide a useful lens through which to address conservation problems, a greater awareness of the role of values and science in these decisions, and a process for identifying alternatives that are more likely to achieve multiple important values in a timely manner. APPLYING DECISION ANALYSIS Improving the chance of good outcomes for difficult conservation decisions (challenges listed in Table 1) arises from first knowing how to think through decisions with the foundational concepts of decision theory (Keeney, 2004; Raiffa, 2002; Smith, 2020a). Very few, typically the most complex decisions (~50 [0.5%]), will require a full decision analysis and would benefit from more time and resources gl Decisions for questions like these are difficult because they involve multiple value judgments, considerable uncertainty, potentially irreversible consequences, and other challenging characteristics common to conservation decisions (Table 1). [Extracted from the article]

Adversarial risk analysis (ARA) is a relatively new area of research that informs decision‐making when facing intelligent opponents and uncertain outcomes. It is a decision‐theoretic alternative to game theory. ARA enables an analyst to express her Bayesian beliefs about an opponent's utilities, capabilities, probabilities, and the type of strategic calculations that the opponent is using to make his decision. Within that framework, the analyst then solves the problem from the perspective of the opponent. This calculation produces a distribution over the actions of the opponent that permits the analyst to maximize her expected utility. This review covers conceptual, modeling, computational, and applied issues in ARA as well as interesting open research issues. This article is categorized under:Statistical and Graphical Methods of Data Analysis > Bayesian Methods and TheoryApplications of Computational Statistics > Defense and National Security [ABSTRACT FROM AUTHOR]

People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the size and structure of semantic representations, as well as individual and age differences in cognitive performance. We conclude that the extent to which experience‐dependent changes in semantic representations contribute to individual differences in cognitive aging remains unclear. To help fill this gap, we outline an empirical agenda that utilizes network analysis and involves the concurrent assessment of large‐scale semantic networks and cognitive performance in younger and older adults. We present preliminary data to establish the feasibility and limitations of such empirical, network‐analytical approaches. Whether it is possible to define a rational standard in decision making and, if yes, whether such a standard is achievable by finite agents (such as humans) has been a hotly debated issue. This special issue offers an overview of some promising modern approaches to these questions, taking advantage of the latest developments in decision theory. We review evidence that suggests links between individual experiences, semantic representations, and age differences in cognitive performance, and present an empirical agenda and pilot data involving the assessment of large‐scale, individual semantic networks. [ABSTRACT FROM AUTHOR]

Identifying critical uncertainties about ecological systems can help prioritize research efforts intended to inform management decisions. However, exclusively focusing on the ecological system neglects the objectives of natural resource managers and the associated social values tied to risks and rewards of actions.I demonstrate how to prioritize research efforts for a harvested population by applying expected value of perfect information (EVPI) to harvest decisions made with a density‐independent matrix population model. Research priorities identified by EVPI diverge from priorities identified by matrix elasticity analyses that ignore social utility.Using a density‐dependent harvest model, the value of information about the intrinsic productivity of a population is shown to be sensitive to the socially determined penalty for implementing a harvest rate that deviates from the goal because of imperfection in estimation.Synthesis and applications. The effect of including social values into harvest decision‐making depends on the assumed population model, uncertainty in population vital rates, and the particular form of the utility function used to represent risk/reward of harvest. EVPI analyses that include perceived utility of different outcomes can be used by managers seeking to optimize monitoring and research spending. Collaboration between applied ecologists and social scientists that quantitatively measure peoples' values is needed in many structured decision‐making processes. [ABSTRACT FROM AUTHOR]

Bolker's representation theorem for Jeffrey's decision theory Let's look in detail, then, at a famous axiomatisation of a famous orthodox decision theory: Bolker's axiomatisation of Jeffrey's (1966) decision theory. She replies: "in the end this debate should not take place at the level of which preferences seem reasonable anyway: it should proceed via a discussion of whether the axioms that underlie REU theory are rational requirements" (2013, 80) - the axioms that figure in her representation theorem for the theory. She notes that an REU maximizer cannot have different risk attitudes at different stakes, and she suggests: the axiom that the proponent of this objection is likely to challenge is Axiom (A1), the Ordering Axiom, which entails that all gambles are comparable. When I ask aficionados of decision theory to give me an argument for this, I mainly get two replies, each based on powerful mathematics: an argument from representation theorems, and one from convergence theorems. [Extracted from the article]

Decision makers often cite data quality as a limitation in environmental management. Value of information approaches evaluate the benefit of new data collection for management outcomes. Pesticide exposure risk assessment for endangered species is one context where data limitations may affect decisions and a value of information type approach could be useful for identifying optimal data quality and resolution. Under the U.S. Federal Insecticide, Fungicide and Rodenticide Act, the U.S. Environmental Protection Agency (EPA) is responsible for registering pesticides before they can be sold and regularly reviewing pesticides. Section 7 of the Endangered Species Act requires that the EPA consider potential impacts of pesticides to listed endangered species and critical habitats in this process, and for the Services—U.S. Fish and Wildlife Service and National Marine Fisheries Service—to complete a formal Section 7 consultation if the EPA deems it necessary. The current process is time‐intensive, lacks transparency and confidence among stakeholders, and leaves hundreds of unreviewed pesticides on the market. Increasing the resolution of pesticide usage data could address these concerns by improving estimated overlaps between species ranges and pesticide usage. Thus, we evaluated the relative importance of different resolutions of pesticide usage data for assessing expected carbaryl exposure to endangered plant species endemic to California. We found that spatially explicit, township resolution usage data (~36 mile2) excluded 33% of terrestrial plants (55/168) and 51% their critical habitats (27/53) from requiring a Section 7 consultation, while coarser resolution data excluded none. In contrast, the EPA's biological evaluation for carbaryl only excludes 4% of terrestrial plants (nationally) from requiring formal Section 7 consultation. This suggests high‐resolution data could increase pesticide review efficiency and decrease the amount of time pesticides remain on the market without a formal evaluation. [ABSTRACT FROM AUTHOR]

Regulations, governance quality, and political structure are important factors that may alter the cost and difficulties associated with doing business in foreign markets. Emerging‐market multinationals (EMNEs), with an imperative motivation to go abroad and to be competitive in a global landscape, should consider the impact of administrative distance, namely, the regulatory institutional differences between home and host countries. We suggest that the study of administrative distance is particularly important when addressing investment flows from emerging economies to developed ones, because of the differences between them. Drawing on transaction cost theory and institutional perspective, we analyze the moderating effect of administrative distance on the relationship between other cross‐national distance factors—cultural, geographic and economic—and establishment mode choice of EMNEs. From a sample of 357 outward foreign direct investments carried out by Indian firms, our results show that administrative distance moderates the relationship between cultural, geographic and economic distance, and establishment mode decisions. [ABSTRACT FROM AUTHOR]

We propose a positive model of empirical science in which an analyst makes a report to an audience after observing some data. Agents in the audience may differ in their beliefs or objectives, and may therefore update or act differently following a given report. We contrast the proposed model with a classical model of statistics in which the report directly determines the payoff. We identify settings in which the predictions of the proposed model differ from those of the classical model, and seem to better match practice. [ABSTRACT FROM AUTHOR]