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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]

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]

Galletta and Lederer concluded that the short-form measure of user information satisfaction (UIS) lacks sufficient test-retest reliability. We question the validity of this conclusion since the method used by the authors for assessing test-retest reliability is not grounded in classical reliability theory. Analysis of their data suggests that the UIS instrument has adequate test-retest reliability. [ABSTRACT FROM AUTHOR]

This article focuses on Professor I.I. Cummings' timely and thought-provoking mapping of the current state of organizational behavior. It is suggested that an important challenge facing organizational behavior is to reconcile the approaches to research and to capitalize on the contributions made in developing the field. There should be integration in organizational behavior and behavioral decision-making literature. Alternate models of choice in organizational contexts should be developed. Research methods used in behavioral decision making should be re-evaluated. Emphasis should be given on the strategic aspects of behavioral decision making.

The article comments on several research articles contributed to the "Decision Sciences" periodical in 1974 by behavioral scientists who are producing scholarship contributory to effective decision making and implementation. The research articles offer a range of theory, new empirical data and critical review from multiple behavioral disciplines. The article discusses each research paper and examines its key contributions to studies on decision making and decision theory.

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

This paper 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]

In many finance courses at schools of business throughout this country, students are often encouraged, if not required, to participate in a portfolio selection project. The project often takes the form of a "winner-take-all" contest, in which the objective is to select at the beginning of the semester the portfolio of securities which will realize the highest rate of return during the semester. The winner gets a prize; everyone else gets nothing. One unique feature of this kind of contest is that a near-miss counts exactly the same as missing by a mile; so that if, for example, the highest rate of return turns out to be 30% then a gain of 29.99% is worth no more than a 100% loss. One might expect that the rules for optimal portfolio selection in this "all or nothing" situation would differ considerably from the conventional rules. This paper confirms that expectation by proving three propositions regarding optimal portfolio selection in a winner-take-all environment. A major conclusion which emerges from the paper is that studies based upon the results of such contests may not have any relevance for testing propositions about investor behavior in a conventional setting. Similarly, teachers of finance seeking to give their students practice in applying the principles of conventional portfolio selection should avoid contests of the winner-take-all type. [ABSTRACT FROM AUTHOR]

The article analyzes economist James Ohlson's paper "Analysis of the Usefulness of Accounting Data for the Portfolio Decision: A Decision-Theory Approach," in order to determine if he's making a model comparison or a model choice. Ohlson's thesis is that he used methods of comparing models, but the author demonstrates the inaccuracies in that thesis. The author tests Ohlson's findings and also considers the portfolio-choice problem and whether the findings are conditional upon the comparison of the forecasting models used.

It is timely and appropriate to examine both philosophical and pragmatical issues associated with formalizing the adoption of artificial intelligence as a reference discipline for decision support systems research. This paper reflects on where we were when the first special issue of Decision Sciences on expert systems and decision support systems was published, addresses the dynamics of what has taken place subsequent to the publication of that first special issue, sets forth a proposition to stimulate ongoing dialog with respect to synergies between the decision support system research agenda and the research agenda of the artificial intelligence discipline, and demonstrates how the papers appearing in this follow-up special issue of Decision Sciences are representative of an emerging, challenging, and exciting new decision support systems era. [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]

The paper introduces a textual analysis methodology which utilizes a systemic concept and a modified version of the systems modelling language from soft systems methodology (SSM). For ease of reference, the methodology is referred to as the systemic textual analysis methodology (STAM). Following trends in hermeneutics and linguistics, STAM balances objective and subjective aspects of the process of textual analysis by using the notion of a ‘recoverable’ textual analysis. STAM can be used in a wide range of Management Science and qualitative research projects, but is presented in this paper in a form suitable for the analysis of texts which constitute formal descriptions of processes or methodologies. Such analysis supports critical evaluation of texts in terms of characteristics such as content, descriptive quality, logical structure, overall coherence and completeness. The basic tenet of STAM is that textual data can be represented as a set of activities, and therefore conceptualized as an activity system. STAM opetationalizes this approach in the form of a six-step process. The paper includes a simplistic exemplification of STAM where it is used to analyze a short piece of text. Copyright © 2007 John Wiley & Sons, Ltd. [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]

Often, data in multi-criteria decision making (MCDM) problems are imprecise and changeable. Therefore, an important step in many applications of MCDM is to perform a sensitivity analysis on the input data. This paper presents a methodology for performing a sensitivity analysis on the weights of the decision criteria and the performance values of the alternatives expressed in terms of the decision criteria. The proposed methodology is demonstrated on three widely used decision methods. These methods are the weighted sum model (WSM), the weighted product model (WPM), and the analytic hierarchy process (AHP). This paper formalizes a number of important issues on sensitivity analysis and derives some critical theoretical results. Also, a number of illustrative examples and computational experiments further illustrate the application of the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper pertains to goal programming with fuzzy goals and fuzzy priorities, Hannan [1], in his paper on fuzzy goal programming, alludes to the difficulty of handling fuzzy priorities and further notes that a method that this author proposed [2] may lead to incorrect results. In this note, the general problem of goal programming with fuzzy priorities is reexamined, along with the solution to the specific example presented in my original paper[2]. It is shown that the method for handling fuzzy priorities originally proposed by this author does indeed capture the relative importance of goals. [ABSTRACT FROM AUTHOR]

The article presents the author's commentary on the report "Decision Theory Aspects of Internal Control System Design/Compliance and Substantive Tests," by William Kinney, included within the issue, criticizing his decision-theory paradigm to the American Institute of Certified Public Accountant's Statement on Auditing Standards (SAS) number 1, section 320. It is suggested that Kinney's mathematical models are oversimplified in many elements of practice and implication.

Similarity judgments are an important and distinct aspect of strategy formulation. They are manifest in strategic decisions and errors, and in the construction of analytic concepts such as strategic groups and relatedness in diversification. However, existing models of strategy formulation either assume away the process of making similarity judgments or regard it as unproblematic. This paper highlights the role of similarity judgments in strategy formulation, and discusses cognitive findings showing that decision makers' assessment of similarity is not free from bias. The cognitive findings help construct a new process explanation for a wide range of apparently isolated strategic errors. The process explanation is contrasted and integrated with traditional explanations based on imperfections in decision inputs. Finally, in light of the cognitive findings, the methods and assumptions of existing approaches in strategy formulation research to the construction of analytic concepts are reexamined. [ABSTRACT FROM AUTHOR]

Despite widespread incorporation of risk measures in strategy research, there is little consensus regarding the meaning and measurement of risk. In contrast to the variability measures widely used in strategy studies, this paper draws from behavioral decision theory, finance, and management theory to present an alternative perspective on organizational risk--downside risk. The paper explains three categories of organizational downside risk measures based on the concept of lower partial moments. The latter returns of the paper present considerations involved in specifying operational measures of downside risk and an empirical comparison of alternative downside risk measures. [ABSTRACT FROM AUTHOR]

Researchers have previously found that different materiality thresholds explain some of the difference between auditors' materiality judgments. By treating materiality as an ordinally scaled variable rather than as a nominally scaled variable, and by using the conjoint measurement methodology, the study reported in this paper was able to focus on two other possible sources of differences-the form of the individual auditor's decision model and the scaling of levels of contributing variables. The study revealed that there were some differences in the form of auditors' decision models. The study also provided evidence that auditors scaled the contributing variables differently. Thus future efforts to explain auditors materiality decisions must give consideration to these two factors (rather than assuming additive models and linear part-worth functions for all auditors). The research methodology presented in this paper provides the researcher with a tool which will allow investigation of these two factors. [ABSTRACT FROM AUTHOR]

Strategic decision-making can be viewed as a special kind of decision-making under uncertainty. Such decision-making involves the activities of goal formulation, problem identification, alternatives generation, and evaluation selection. Researchers in cognitive psychology and behavioural decision theory have identified a number of cognitive simplification processes which may affect the way decision-makers perform these tasks. Within this paper, the research on these processes is summarized and their possible effects on strategic decision-making are discussed. Implications for future research in this area are also drawn. [ABSTRACT FROM AUTHOR]

A great deal of attention has focused on the potential benefits of the dialectic during strategic decision making. Some field studies have shown favourable perceptions of the dialectic. Exclusive use of field studies, however, is inadequate for a comprehensive evaluation of the dialectic. Controlled studies in the laboratory and the field are needed. Operationalizing the dialectic and measuring its effectiveness in Controlled settings has been the subject of debate. This paper reviews three controlled approaches lens model studies, creative task experiments, and case studies. The applicability of these approaches to dialectical research is discussed. Basic assumptions underlying controlled research are that the dialectic can be created in controlled settings and its effectiveness can be measured in terms of better decisions. [ABSTRACT FROM AUTHOR]

Little guidance has been given to the management of action research and this paper suggests that ideas from the actor-network theory (ANT) may be helpful. Although ANT has been widely used as a tool for interpretation and description of the interplay of the social and technical aspects of organizations, this paper presents a different use of the ideas. It advocates a proactive use, to bring about changes in a situation rather than merely observing and describing that situation disinterestedly from the outside. The case is described of a long-term action research study carried out within a corporate bank, where the ideas of ANT were employed in this way, as a device for understanding the turbulent social and political context of the research, to plan and manage the intervention process, and where ideas such as networks of interest, translation of interests and black boxes were used to practical effect. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The paper draws significantly on the proposition that the stakeholder concept is an appropriate way of acknowledging socio-political aspects of organization decision making. Stakeholder analysis is proposed as a means of reconciling organization concern for control of system outputs with potentially conflicting expectations of other stakeholder groups inside and outside the organization. The paper's hypothesis is that the incorporation of stakeholder perspectives in decision-making processes will enhance effectiveness, employee motivation and organizational justice as system outputs. The viability of the method is demonstrated by its application within a stakeholder analysis of performance management in knowledge-based organizations. Evidence from the case study, together with recent developments in systems theory and OD, is used to develop a viable systems stakeholder model of performance management that can achieve an appropriate balance between effective management, good governance and organizational justice in knowledge-based organizations of the twenty-first century. Copyright © 2005 John Wiley & Sons, Ltd. [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]

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

Recently, many claims have been made that new models of organization are emerging in the public and voluntary sector. These are partnerships, alliances, fora and consortia, to mention but a few, and they encompass both active participation of their members and/or users and the governance of their activities. The patterns of governance emerging, in my view, offer a different set of problems for the management sciences. Policy, strategy and accountability are now being developed within a complex network of different organizations drawn from the public and voluntary sector, aiming to govern or regulate the public sphere. This paper will explore how management scientists can address these patterns of governance. It will be argued that, if governance has supplanted management as an issue for the management sciences in this context, a new or different language is needed to ‘re-present’ the problems. In particular, it will be shown that management science can be employed to find a clear balance between good governance and effective management. This paper will also propose a framework that may be used to help the new organizational arrangements achieve effective governance. Copyright © 2001 John Wiley & Sons, Ltd. [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]

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]

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]

Presents lists of research papers on risk and insurance. "Effects of Quality and Quantity of Information on Decision Effectiveness," by Kevin Lane Keller; "Efficient Awards and Standards of Proof in Judicial Proceedings," by Daniel L. Rubinfeld and David E. M. Sappington; "Decision Theory As a Branch of Evolutionary Theory: A Biological Derivation of the Savage Axioms," by William S. Cooper; "Managerial Perspectives on Risk and Risk Taking," by James G. March and Zur Shapira; Others.

This paper considers the effects of multicollinearity on the multiple coefficient of determination and on the standard errors of the estimated regression coefficients. Limiting results are well known for these problems, and some particular cases have been investigated. However, the limiting cases do not explain the nonmonotonic relationships that have been observed in practice. This paper explores these relationships and attempts to provide a unifying graphical explanation that includes the previous results as special cases. Implications for stepwise regression and for the interpretation of any multiple regression involving multicollinear independent variables are discussed. [ABSTRACT FROM AUTHOR]

The topic of behavioral objectives has recently received considerable attention in the academic community. This paper contains a comprehensive overview of issues and explores the relevance of using the behavioral objective framework for teaching quantitative methods (QM) to undergraduate business administration students. Included in the paper are (1) a discussion of the nature and meaning of behavioral objectives, (2) arguments for and against their use, (3) a rationale for their use in a QM course, and (4) a series of examples demonstrating the use of behavioral objectives in a quantitative course. The evidence—consisting of expert opinion from professionals who have successfully used behavioral objectives for teaching quantitative material, the logic of the arguments for behavioral objectives, consideration of arguments against objectives along with rebuttal to these arguments, and the available empirical studies—indicates that behavioral objectives do appear to offer considerable potential for improving QM teaching. The use of behavioral objectives is worthy of thoughtful consideration by QM teachers. [ABSTRACT FROM AUTHOR]

This paper examines some approaches to decision problems under uncertainty. Starr's domain criterion is presented and modified to take into account different philosophies concerning the desirability of winning versus the importance of avoiding losses. The concept of expected value of distribution information is defined and its computation is illustrated with a numerical example. Target values are then introduced into the model and a parametric procedure is used to maximize the chances of achieving a certain level of the given objective. Finally, we show how the concepts developed in this paper might provide further insight into some decision, situations reported in the literature. [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]

This paper axiomatizes the robust control criterion of multiplier preferences introduced by Hansen and Sargent (2001). The axiomatization relates multiplier preferences to other classes of preferences studied in decision theory, in particular, the variational preferences recently introduced by Maccheroni, Marinacci, and Rustichini (2006a). This paper also establishes a link between the parameters of the multiplier criterion and the observable behavior of the agent. This link enables measurement of the parameters on the basis of observable choice data and provides a useful tool for applications. [ABSTRACT FROM AUTHOR]

This paper argues that true ‘systemic’ epistemology should reveal multiple perspectives, conflicting realities and various other contexts due to its inherent pluralistic nature. Therefore, generalization in systems research occurs not across one linear conceptual framework but across the variety of nonlinear, conflicting, dialectal ‘realities’. It is therefore a misunderstanding of systems research to search for linear, singular and non-conflicting versions of events as though there is one overarching ‘mega concept’ that will explain world events. The paper presents the hallmarks of a systemic epistemology. Using an example the nature of a systems epistemology is demonstrated and explained. The paper concludes that a ‘systemic’ epistemology is one that should reflect a ‘systemic’ reality. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper investigates two different yet related research questions about stock management in feedback environments. The first purpose is to analyse the effects of selected experimental factors on the performances of subjects (players) in a stock management simulation game. In light of these results, our second objective is to evaluate the adequacy of standard decision rules typically used in dynamic stock management models and to seek improvement formulations. To carry out the research, the generic stock management problem is chosen as the interactive gaming platform. In the first part, gaming experiments are designed to test the effects of three factors on decision-making behaviour: different patterns of customer demand, minimum possible order decision (‘review’) interval and, finally, the type of receiving delay. ANOVA results of these three-factor, two-level experiments show which factors have significant effects on 10 different measures of behaviour (such as max–min range of orders, inventory amplitudes, periods of oscillations and backlog durations). In the second phase of research, the performances of subjects are compared against some selected ordering heuristics (formulations). First, the patterns of ordering behaviour of subjects are classified into three basic types. Comparing these three pattern types with simulation results using different decision rules, we observe that the common linear ‘anchoring and adjustment rule’ can represent the smooth and gradually damping type of behaviour, but cannot generate the non-linear and/or discrete ordering dynamics. Thus, several alternative non-linear rules are formulated and tested against subjects' behaviour patterns. Some standard discrete inventory control rules (such as (s, S)) common in the inventory management literature are also formulated and tested. These non-linear and/or discrete rules, compared with the linear stock adjustment rule, are found to be more representative of the subjects' ordering behaviour in many cases, in the sense that these rules can generate non-linear and/or discrete ordering behaviours. Another major finding is the fact that the well-documented oscillatory dynamic behaviour of the inventory is a quite general result, not just an artifact of the linear anchor and adjust rule. When the supply line is ignored or underestimated, large inventory oscillations result also with the non-linear rules, as well as the standard inventory management rules. Furthermore, depending on parameter values, non-linear ordering rules are more prone to yield unstable oscillations—even if the supply line is taken into account. Further methodological and experimental research questions are suggested. Copyright © 2004 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper proposes a decision rule to rank actions under strict uncertainty, the available information being limited to the states of nature, the set of alternative rows, and the consequence of choosing every row if a given state occurs. This rule is suitable to moderately pessimistic individuals and social groups, these agents being neither maximax nor maximin decision makers but people who assume that the best outcome from the action will not occur. For these decision makers the paper shows the existence of a consistent weight system in which one and only one weight is attached to each state of the world under plausible conditions of domination. Most of the traditional axioms are satisfied by the proposed ranking approach. In the frame of disappointment (measured by ranges of column dispersion), the meaning of some controversial postulates used in the literature is explained. The proposed criterion is a departure from Laplace's (1825) rule and from the remaining standard criteria. Only in the special case of equal column dispersion do both Laplace's rule and the proposed weights lead to the same solution. [ABSTRACT FROM AUTHOR]

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