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Graph We are sorry to report that Peter C. Fishburn died on June 10, 2021, in Racine, Wisconsin. In that paper, Fishburn gives a new proof of the theorem and explains issues in the literature with the correctness of the theorem. In paper (Fishburn & Gehrlein, [13]), Fishburn and Gehrlein discuss the propensities of different voting procedures to elect the simple majority candidate when one exists in a multicandidate election. For example, papers (Fishburn, [5]) and (Fishburn, [7]) deal with subjective expected utility theory. [Extracted from the article]

Professor Rapoport's work focused on clarifying the conditions under which the rationality assumption provides useful predictions of behavior. Call for papers - Special issue in memory of Amnon Rapoport: Experimental Economics It is our great pleasure to announce a special issue in memory of Professor Rapoport's contributions to the field of experimental economics. [Extracted from the article]

Product updates have become prevalent on online digital platforms, given the unique market, product, and technological characteristics that underlie digital platforms. Scholars have begun to explore the efficacy of updates on digital platforms, highlighting the merits of implementing frequent updates. Drawing on behavioral decision theory, this paper analyzes the trade-offs that consumers encounter between the benefits and costs associated with updates and challenges an implicit assumption held in extant studies with respect to the linear efficacy of updates. In the context of mobile platforms, this paper, using a multi-method study, identifies the existence of an inverted U-shaped relationship between product updates and complementor product performance. We also find that the moderating efficacy of platform endorsement is positive in that it serves as an amplifier of whatever a complementor offers, tightly intertwined with the complementor's offerings. Our paper expands the platform literature by investigating successful complementor product strategies that take into account unique platform characteristics with a strong grounding in interdependent owner–complementor dynamics. [ABSTRACT FROM AUTHOR]

With the development of renewable energy power systems, electric vehicles, as an important carrier of green transportation, are gradually having an impact on the power grid load curve due to their charging behavior. However, the significant influx of electric vehicles (EVs) and distributed power sources has led to multiple uncertainties, increasing the difficulty in making grid scheduling decisions. Traditional robust scheduling strategies tend to be overly conservative, resulting in poor economic performance. Therefore, this paper proposes a robust and economic dispatch strategy for park power grids based on the information gap decision theory (IGDT). Firstly, based on the probabilistic characteristics of the spatial and temporal distribution of EVs charging, the Monte Carlo method is used to generate typical electricity usage scenarios for EVs. Simultaneously, an economic dispatch model for the park power grid is established with the objective of minimizing operating costs. Taking into account the uncertainty of renewable energy output, simulation analysis is conducted through the IGDT model. Finally, through the verification of the improved IEEE-33 node test system and comparison with other methods, the proposed approach in this paper can reduce decision conservatism and effectively reconcile the contradiction. Through analysis, the proposed method in this paper can reduce the total operational cost of the system by up to 3.2%, with a computational efficiency of only 8.9% of the traditional stochastic optimization time. [ABSTRACT FROM AUTHOR]

This paper introduces the concept of rough fermatean neutrosophic sets and investigates their properties. Additionally, a cosine similarity measure between these sets is proposed. By applying this measure to a medical diagnosis example, the paper illustrates how the method can be used in practical situations, highlighting its effectiveness in complex decision-making scenarios. This innovation holds promise for improving decision-making processes, especially in critical areas like medical diagnosis, where making accurate assessments amidst uncertainty is crucial. [ABSTRACT FROM AUTHOR]

Purpose: Part building orientation (PBO) is an important factor affecting the quality of laser powder bed fusion (L-PBF), which can affect the surface quality and manufacturing cost. The purpose of this paper is to propose a PBO optimization method to optimize the surface roughness and molding time of parts at the same time on the premise of small calculation scale and arbitrary resolution. Design/methodology/approach: Efficient and accurate evaluation is an important index of PBO optimization method. In this paper, a PBO optimization method based on scaling enumeration method is proposed, and the surface roughness and molding time of L-PBF parts are modeled as the objective evaluation function of PBO optimization process. To realize multi-objective optimization, an expert system is established, and the fuzzy multiple-attribute group decision-making theory is used to provide weights for each objective evaluation function. Findings: Research shows that the scaling-enumeration method can optimize the surface roughness and molding time at the same time and get the best PBO. Compared with the traditional method, the surface roughness and molding time are reduced by 1.1% and 0.58%, respectively, and the operation scale of the scaling-enumeration method is reduced by 99% compared with the traditional method. PBO with arbitrary angular resolution can be achieved. Originality/value: This paper presents a new method to optimize the forming direction of L-PBF parts. This method has small operation scale and accurate results, so it is meaningful for industrial application. [ABSTRACT FROM AUTHOR]

It is well known that Peter prefers cumulative prospect theory (e.g., Diecidue and Wakker [27]; Fennema & Wakker, [32]). Many of Peter's later papers were inspired by Schmeidler ([60]) (e.g., Sarin & Wakker, [59]; Trautmann & Wakker, [62]; Wakker, [72], [75]). In the late 1980s and early 1990s, Peter wrote several clarifying works on how additive separability across mutually exclusive events can be derived (Chateauneuf & Wakker, [18]; Wakker, [71], [73], [74]). Hans and Peter collaborated on several projects in the 1980s and 1990s, particularly related to revealed preference and bargaining (e.g., de Koster et al., [24]; Peters & Wakker, [55], [54]; Wakker et al., [80]) with an occasional excursion to quality of life measurement (Miyamoto et al., [49]). [Extracted from the article]

With the rapid growth of the digital economy, data centers have emerged as significant consumers of electricity. This presents challenges due to their high energy demand but also brings opportunities for utilizing waste heat. This paper introduces an operation optimization method for multi-energy systems with data centers, leveraging the information gap decision theory (IGDT) to consider various uncertainties from data requests and the environment. First, a model is established for the operation of a multi-energy system within data centers, considering the integration of server waste heat recovery technology. Second, IGDT is employed to address uncertainties of photovoltaic output and data load requests, thereby formulating an optimal energy management strategy for the data center park. Case studies demonstrate that the electricity purchase cost increased by 5.3%, but the total cost decreased by 30.4%, amounting to 5.17 thousand USD after optimization. It indicates that the operational strategy effectively ensures both efficient and cost-effective power supply for the data center and the park. Moreover, it successfully mitigates the risks associated with fluctuations in data load, thus minimizing the possibility of data load abandonment during uncertain periods. [ABSTRACT FROM AUTHOR]

The traffic volume and maintenance demand on Taiwan's provincial highways have been steadily increasing. One of the most challenging issues in maintenance is determining the optimal timing and allocation of funds to avoid duplicative investments and maximize resource utilization. Currently, provincial highway maintenance units rely heavily on manual processes and paper-based records, using experiential methods to formulate maintenance strategies and conduct maintenance operations. This indicates a lack of objective maintenance strategies and pavement management systems in these units. This study aims to address this gap by integrating domestic and international literature on pavement maintenance decision-making. Existing approaches typically fall into two categories: "Pavement Indicator Rating" and "Pavement Maintenance Prioritization". However, there has not been research integrating these methods for decision-making. Therefore, this research integrates these two approaches to establish a comprehensive maintenance strategy for Taiwan's provincial highways. The Analytic Hierarchy Process (AHP) is employed as the decision-making theory, involving expert interviews to calculate maintenance weights for different pavement maintenance indicators. The results show that the pothole count, International Roughness Index (IRI), and Pavement Condition Index (PCI) are the three most critical maintenance indicators. The first phase of the maintenance strategy uses the "Pavement Indicator Rating" to directly assess the pothole count, IRI, and PCI to categorize pavement sections as "maintenance sections" or "observation sections". The second phase employs "Pavement Maintenance Prioritization", integrating maintenance weights for each indicator to calculate maintenance scores. This phase prioritizes maintenance activities based on the results of the first phase's rating for "maintenance sections". Additionally, a provincial highway pavement management system is proposed to implement these strategies, enhancing maintenance management efficiency and ensuring the overall quality and longevity of provincial highway maintenance efforts. [ABSTRACT FROM AUTHOR]

It has been widely suggested that a key goal of visualization systems is to assist decision making, but is this true? We conduct a critical investigation on whether the activity of decision making is indeed central to the visualization domain. By approaching decision making as a user task, we explore the degree to which decision tasks are evident in visualization research and user studies. Our analysis suggests that decision tasks are not commonly found in current visualization task taxonomies and that the visualization field has yet to leverage guidance from decision theory domains on how to study such tasks. We further found that the majority of visualizations addressing decision making were not evaluated based on their ability to assist decision tasks. Finally, to help expand the impact of visual analytics in organizational as well as casual decision making activities, we initiate a research agenda on how decision making assistance could be elevated throughout visualization research. [ABSTRACT FROM AUTHOR]

Introduction: With the development of the energy market and the gradual rise of emerging market players, the linkage of interests between energy sources and loads in the Integrated Energy System (IES) has become increasingly complex. Additionally, the reliability of the system has been impacted by the growing proportion of renewable energy output. Methods: To address the challenges posed by the above issues. This paper first proposes an operational strategy for an integrated energy system that incorporates the uncertainty of wind and solar output using a master-slave game approach. To enhance system robustness and cost-effectiveness, the paper introduces the information gap decision theory (IGDT). Second, building on this foundation, the system operator is considered as the leader, adding a tiered carbon trading mechanism and cloud energy storage system, and building a system revenue maximization model. Then, the user is regarded as the follower, and an optimization model is developed based on integrated demand response (IDR). Finally, the two-layer model is converted into a mixed-integer linear programming problem (MILP) to be solved by the Karush-Kuhn-Tucker conditions (KKT) combined with the big M method. Results: The analysis of the example shows that according to the difference of the decision maker's attitude towards risk, different scheduling schemes can be obtained through the two perspectives of risk-seeking and risk-avoiding, which can provide guidance for the dynamic operation of the system, and at the same time, the users can be guided by the energy differentials to reasonably use the energy under this strategy. Discussion: Therefore, the proposed strategy in this paper can balance the economy and robustness of the system. [ABSTRACT FROM AUTHOR]

In this paper, I reply to Ruth Chang's 'What is so Hard about Hard Choices'. [ABSTRACT FROM AUTHOR]

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]

The efficiency and safety of civil aviation heavily depend on the robust performance of avionic systems, which provide essential communication, navigation, and surveillance capabilities for flight operations. To sustain the requisite level of reliability in these complex systems, a methodological foundation for their design and modernization is critical. This paper presents a comprehensive framework for the Avionics Management System, emphasizing systematic component analysis and developmental methodologies. This framework includes a multidisciplinary approach that integrates advanced principles, theories, models, and methods drawn from current achievements in information technology, artificial intelligence, mathematical statistics, and decision-making theory. It also considers societal needs, economic trends, and future developmental prospects. Proposed approach ensures that AMS not only addresses immediate operational requirements but is also adaptable to future technological evolutions, thus supporting the life cycle of avionic systems from conception through to utilization. The proposed framework is poised to guide the industry in achieving higher levels of system reliability and maintenance efficiency, with potential implications for the broader realm of civil aviation and its everadvancing technological landscape. [ABSTRACT FROM AUTHOR]

This document discusses the importance of decision-making theories and methodologies for water, energy, and food security. It highlights nine selected papers that contribute to this field of research. The papers cover various topics such as the efficiency of the water-energy-food system, hydrological modeling, nutrient pollution in water, river regulation decisions, water governance, water allocation, water pollution management, integrated management of water resources, and water conservation behavior. These papers provide valuable insights and recommendations for environmental scientists, water resource managers, industry experts, and national authorities. [Extracted from the article]

Questionnaires are among the most basic and widespread tools to assess the mental health of a population in epidemiological and public health studies. Their most obvious advantage (firsthand self-report) is also the source of their main problems: the raw data requires interpretation, and are a snapshot of the specific sample's status at a given time. Efforts to deal with both issues created a bi-dimensional space defined by two orthogonal axes, in which most of the quantitative mental health research can be located. Methods aimed to assure that mental health diagnoses are solidly grounded on existing raw data are part of the individual validity axis. Tools allowing the generalization of the results across the entire population compose the collective validity axis. This paper raises a different question. Since one goal of mental health assessments is to obtain results that can be generalized to some extent, an important question is how robust is a questionnaire result when applied to a different population or to the same population at a different time. In this case, there is deep uncertainty, without any a priori probabilistic information. The main claim of this paper is that this task requires the development of a new robustness to deep uncertainty axis, defining a three-dimensional research space. We demonstrate the analysis of deep uncertainty using the concept of robustness in info-gap decision theory. Based on data from questionnaires collected before and during the Covid-19 pandemic, we first locate a mental health assessment in the space defined by the individual validity axis and the collective validity axis. Then we develop a model of info-gap robustness to uncertainty in mental health assessment, showing how the robustness to deep uncertainty axis interacts with the other two axes, highlighting the contributions and the limitations of this approach. The ability to measure robustness to deep uncertainty in the mental health realm is important particularly in troubled and changing times. In this paper, we provide the basic methodological building blocks of the suggested approach using the outbreak of Covid-19 as a recent example. [ABSTRACT FROM AUTHOR]

Agricultural entrepreneurship fostering is an important part of the EU and US rural policies. Despite the fact that literature points attention to the issue of limited number of new entrants in agriculture, existing policies are mostly focused on support of existing entrepreneurs. Recent research describes such behavioral reasons of limited number of new entrants as low attractiveness and not appealing image of the agricultural sector. This paper considers the issue of attracting new entrepreneurs to agriculture from behavioral decision theory perspective and suggests celebrity branding as a tool of agricultural entrepreneurship fostering. Through the application of multi-criteria decision analysis, the paper proves that celebrity branding has a significant positive effect on attractiveness of entrepreneurship in agriculture and on expected financial and non-financial benefits from entrepreneurial career in the sector. Also, the paper reveals that financial motivation plays the most important role for individuals attracted to urban spheres of entrepreneurship and is the second most important factor (after self-realization) for individuals attracted to the agricultural sphere of entrepreneurship. The paper also demonstrates a debiasing effect of celebrity branding on the perception of entrepreneurial career in agriculture. [ABSTRACT FROM AUTHOR]

Featured Application: Optimal operation for renewable energy integrated smart microgrid under uncertainty. Multi-microgrid collaborative scheduling can promote the local consumption of renewable energy in the smart grid and reduce the operating costs of the power grid park. At the same time, the access of the distributed energy storage (ES) system provides an opportunity to further enhance the park's peak shaving and valley filling capacity, thereby reducing costs. However, the uncertainty of photovoltaic (PV) power generation and load demand seriously affects the profit maximization of the microgrid in the park. To address this challenge, this paper proposes a stochastic optimal scheduling strategy for industrial park smart microgrids with multiple transformers based on the information gap decision theory (IGDT). We first introduce a revenue maximization model for industrial parks, incorporating a two-part tariff system and distributed ES. Subsequently, we employ an envelope constraint model to accurately represent the uncertainty associated with PV generation and load demand. By integrating these components, we establish the IGDT stochastic optimization scheduling model for industrial parks with multiple transformers. Finally, we simulate and analyze the performance of the proposed IGDT model under various cost deviation factors during typical spring and summer days. The simulation results demonstrate the effectiveness of the proposed control strategy in mitigating the impact of PV generation and load uncertainty on industrial parks. The IGDT-based scheduling approach provides an efficient solution for maximizing revenue and enhancing the operational stability of industrial park microgrids. [ABSTRACT FROM AUTHOR]

The evaluation of risk and uncertainty in the context of software project management is the subject of this paper. This paper discusses the difficulties faced by project managers in handling uncertainty brought on by the complex nature of software projects and the ever evolving requirements of technology. A review of the literature, data production, visualization, statistical analysis, and mathematical modeling are included in this study. The goal of this study is to create a methodical approach to assist project managers in making decisions by considering the inherent uncertainty in software development and to find approaches and procedures that may successfully reduce risks, improve decision-making, and eventually result in the implementation of successful projects. The following tasks were carried out: to evaluate risk and uncertainty by examining the state-of-theart in decision theory and its applications in software project management; to develop an integrated strategy that blends Monte Carlo Simulation with Decision Trees to assess risk and uncertainty in software project management; to generate data, visualize it, and perform statistical analysis to comprehend how project outcomes, costs, and time are affected; to identify important variables affecting project results and decisionmaking using decision trees; to use Monte Carlo simulation to create project scenarios and weigh the likelihood of each; and to supply project managers with knowledge and suggestions to help them make informed decisions and successfully manage risks. Methods. To evaluate risk and uncertainty in software project management, this paper analyzes the decision theory approaches currently used as well as Decision Trees and Monte Carlo Simulation techniques. Results. This study offers thorough insights into how project results, costs, and duration vary among various techniques. The critical factors that have a substantial influence on project success are shown through decision trees. According to the study’s findings, combining decision theory and statistical analysis equips project managers to make wise decisions despite uncertainty. Conclusions. Project managers may improve decision making, risk reduction, and overall project success by applying these cutting-edge approaches. To adapt these techniques to unique software project management contexts and real-world situations, further study and implementation in practice are necessary. With the use of such techniques, the software development sector would be better able to manage the complexity of projects and provide good results within set financial and time parameters. [ABSTRACT FROM AUTHOR]

Renovating old neighborhoods for the benefit of people has become increasingly important in urban renewal. Nevertheless, old neighborhood renovations are currently considered a group decision-making issue under public participation, involving diverse decision-making subjects. Conflicts within a group are a common problem during group decision-making. In this paper, conflict is examined in the decision-making process for renovating old neighborhoods and novel ideas are provided for quantifying conflict. Public participation in old neighborhood renovations is assessed using conflict degree calculations in group decision-making. Based on the preferences of decision-making experts, a MIP DEA–DA (Mixed Integer Programming Data Envelopment Analysis–Discriminant Analysis) based partial binary tree cyclic clustering model is constructed for clustering experts, and an aggregated group conflict indicator and an aggregated conflict vector are computed, allowing for the quantification of conflict during the renovation process of the old neighborhood based on actual situations. Results indicate that there is primarily a conflict between the benefits of decision-making subject interests and the professionalism of decision-making renovations. This paper contributes to improving public participation, promoting the application of group decision-making theory in old neighborhood renovation, reducing conflict between decision-makers, and speeding up urban renewal. [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]

Over the second half of the twentieth century, the moral economy of insurance has shifted from solidarity and mutual support to individual responsibility. In this context, the French regime for the protection against natural catastrophes that took shape in the 1980s exemplifies a strong and almost anachronical political will to foster solidarity at the national level, thus questioning the moral economy of responsibility. This paper offers a textual analysis of the official debates that led to the launch of the regime. It shows how the representatives chose to separate compensation, financed by equal individual participation, from state prevention. This contrasts with other schemes worldwide that rely on rational decision theory to situate the responsibility for prevention at the individual level. In this alternative, risk-based premiums play a theoretically crucial role in risk signals. In practice, however, they lead to affordability issues while failing to govern prevention as theoretically expected. With the climate crisis exacerbating this phenomenon, the examination of the French regime thus allows to fruitfully revive other moral economies of insurance. [ABSTRACT FROM AUTHOR]

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document. [ABSTRACT FROM AUTHOR]

In this paper, an optimisation framework is presented for planning a stand-alone microgrid for supplying EV charging (EVC) stations as a design and modelling approach for the FEVER (future electric vehicle energy networks supporting renewables) project. The main problem of the microgrid capacity sizing is making a compromise between the planning cost and providing the EV charging load with a renewable generation-based system. Hence, obtaining the optimal capacity for the microgrid components in order to acquire the desired level of reliability at minimum cost can be challenging. The proposed planning scheme specifies the size of the renewable generation and battery energy storage systems not only to maintain the generation–load balance but also to minimise the capital cost (CAPEX) and operational expenditures (OPEX). To study the impact of renewable generation and EV charging uncertainties, the information gap decision theory (IGDT) is used to include risk-averse (RA) and opportunity-seeking (OS) strategies in the planning optimisation framework. The simulations indicate that the planning scheme can acquire the global optimal solution for the capacity of each element and for a certain level of reliability or obtain the global optimal level of reliability in addition to the capacities to maximise the net present value (NPV) of the system. The total planning cost changes in the range of GBP 79,773 to GBP 131,428 when the expected energy not supplied (EENS) changes in the interval of 10 to 1%. The optimiser plans PV generation systems in the interval of 50 to 63 kW and battery energy storage system in the interval of 130 to 280 kWh and with trivial capacities of wind turbine generation. The results also show that by increasing the total cost according to an uncertainty budget, the uncertainties caused by EV charging load and PV generation can be managed according to a robustness radius. Furthermore, by adopting an opportunity-seeking strategy, the total planning cost can be decreased proportional to the variations in these uncertain parameters within an opportuneness radius. [ABSTRACT FROM AUTHOR]

This paper explores the consequences of applying two natural ideas from epistemology to decision theory: (1) that knowledge should guide our actions, and (2) that we know a lot of non-trivial things. In particular, we explore the consequences of these ideas as they are applied to standard decision theoretic puzzles such as the St. Petersburg Paradox. In doing so, we develop a "knowledge-first" decision theory and we will see how it can help us avoid fanaticism with regard to the St. Petersburg puzzle and related puzzles. The result will be a decision theory that gives a novel, but well-motivated, reason for discounting small probabilities when making decisions. We examine the merits and demerits of such a decision theory. [ABSTRACT FROM AUTHOR]

A de minimis risk is defined as a risk that is so small that it may be legitimately ignored when making a decision. While ignoring small risks is common in our day-to-day decision making, attempts to introduce the notion of a de minimis risk into the framework of decision theory have run up against a series of well-known difficulties. In this paper, I will develop an enriched decision theoretic framework that is capable of overcoming two major obstacles to the modelling of de minimis risk. The key move is to introduce, into decision theory, a non-probabilistic conception of risk known as normic risk. [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]

In the Internet of Vehicles driven by mobile edge computing, the service requests are offloaded to the roadside units (RSUs) via wireless network, reducing the service latency and enhancing the utilization of resources of RSUs. However, the high mobility of vehicles leads to the frequent switching of services, decreasing the quality of service, which fails to meet the requirements of latency-sensitive vehicular services. In this paper, we proposed a latency-aware service migration method with decision theory, named LSMD. Specifically, we first model the network architecture and introduce the transmission and computation of the service requests in detail. Then, considering the high mobility of vehicles, we analyze the dynamic change of vehicle locations and transform the service migration problem into an uncertain decision optimization problem. Afterward, we find the optimal service migration strategy with the objectives of minimizing the service latency and balancing the workload on RSUs. Finally, numerical experiment results on real-world datasets demonstrate that our method outperforms the other two baselines. [ABSTRACT FROM AUTHOR]

With the increasing uncertainty in information, fuzzy theory has become a crucial approach for solving Multiple Attribute Group Decision Making (MAGDM) problems. This paper proposes two methods for addressing MAGDM problems with interval-valued Fermatean fuzzy information. Firstly, a series of Hamacher operators on the interval-valued Fermatean fuzzy numbers (IVFFNs) are derived, based on the fundamental operational norms of IVFFNs and the definitions of Hamacher operators. Secondly, two decision-making methods are proposed in this paper, based on the newly introduced operators, and they are effectively applied to address the enterprise recruitment problem. Finally, we investigate the influence of parameters on the final decision and observe that variations in parameters have small impact on the outcome. This feature simplifies the decision-making process by eliminating the need for extensive parameter tuning. Furthermore, a comparative analysis is conducted between the proposed methods and the research of other scholars, demonstrating the effectiveness of our approach and validating its reliability. [ABSTRACT FROM AUTHOR]

This paper proposes a linear consensus protocol QuickBFT based on Vague sets and multi-attribute decision-making methods. QuickBFT simplifies the communication process based on the HotStuff protocol, reduces the four-stage communication to three-stage communication, and reduces the consensus delay. Furthermore, we introduce the Vague set and multi-attribute decision-making theory into the consensus protocol and propose a new leader node selection algorithm, which can prevent Byzantine nodes from becoming leader nodes, thereby improving the protocol performance when the leader node is attacked. Experimental results show that the throughput of QuickBFT is slightly higher than that of the HotStuff protocol without Byzantine nodes, and the consensus delay is reduced by 20%. In the presence of Byzantine nodes, the throughput of QuickBFT is increased by 80% compared with the HotStuff protocol, and the consensus delay is reduced by 60%. [ABSTRACT FROM AUTHOR]

In order to improve the scientific quality of the public, the Chinese Association for Science and Technology has put forward a call to combine popular science education with leisure and entertainment. In view of the fact that online shopping involves a wide range of areas, and the people pay more attention to it, the paper completed the innovation of online shopping risk warning science knowledge, the design of popular science mechanism and the dissemination of popular science knowledge. The paper used complex network's knowledge discovery methods and decision theory to design online shopping risk warning science knowledge; Using the complex network public opinion dissemination trust analysis realize the dissemination of popular science knowledge and promote the improvement of the public's quality of popular science. The spread of risk early warning science knowledge in the network shows that the risk early warning mechanism designed can achieve the purpose of improving public science knowledge when the reward and punishment measures are appropriate. [ABSTRACT FROM AUTHOR]

Neutrosophic theory can effectively and reasonably express indeterminate, inconsistent, and incomplete information. Since Smarandache proposed the neutrosophic theory in 1998, neutrosophic theory and related research have been developed and applied to many important fields. Indeterminacy and fuzziness are one of the main research issues in the field of civil engineering. Therefore, the neutrosophic theory is very suitable for modeling and applications of civil engineering fields. This review paper mainly describes the recent developments and applications of neutrosophic theory in four important research areas of civil engineering: the neutrosophic decision-making theory and applied methods, the neutrosophic evaluation methods and applications of slope stability, the neutrosophic expressions and analyses of rock joint roughness coefficient, and the neutrosophic structural optimization methods and applications. In terms of these research achievements in the four areas of civil engineering, the neutrosophic theory demonstrates its advantages in dealing with the indeterminate and inconsistent issues in civil engineering and the effectiveness and practicability of existing applied methods. In the future work, the existing research results will be further improved and extended in civil engineering problems. In addition, the neutrosophic theory will also have better application prospects in other fields of civil engineering. [ABSTRACT FROM AUTHOR]

The relationship between decision theory and the theory of natural selection in evolutionary biology offers a fertile ground for philosophical inquiry. A topic that has recently been addressed in the philosophical literature is the connection between decision-theoretic and biological discussions of risk. The paper adds to this literature by drawing attention to a distinction between two different notions of risk originating in the economic literature and by exploring their relationship in a biological context. More specifically, the paper shows that the two notions of risk can part ways in models of risk-sensitive foraging theory. The paper also draws attention to an important difference in contemporary explanations of the apparent lack of empirical success of rational choice theory and risk-sensitive foraging theory. [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]

Simple Summary: Cells continually sense and receive signals from the environment and respond accordingly. Due to biological noise, however, the response is not always as expected. Such a response can induce a different cell fate and may disrupt some cellular functions. In the presence of noise, cells may either mistakenly perceive non-existent signals and act accordingly, or may ignore the actual signals and do nothing. We label these two as false alarm and signal miss events, respectively. In this paper, we consider an important signaling system with one input and two outputs to show how the likelihood of false alarm and signal miss events can be computed, using the experimentally measured joint response of the two outputs of the signaling system. The two system outputs are the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2), whereas the system input is the tumor necrosis factor (TNF). These molecules are highly involved in essential processes such as cell survival, cell death, and viral replication. The introduced methodology and the measured false alarm and miss probabilities using experimental data can model complex cellular decision-making processes and provide insight into how they may contribute to the development of some pathological conditions. A cell constantly receives signals and takes different fates accordingly. Given the uncertainty rendered by signal transduction noise, a cell may incorrectly perceive these signals. It may mistakenly behave as if there is a signal, although there is none, or may miss the presence of a signal that actually exists. In this paper, we consider a signaling system with two outputs, and introduce and develop methods to model and compute key cell decision-making parameters based on the two outputs and in response to the input signal. In the considered system, the tumor necrosis factor (TNF) regulates the two transcription factors, the nuclear factor κB (NFκB) and the activating transcription factor-2 (ATF-2). These two system outputs are involved in important physiological functions such as cell death and survival, viral replication, and pathological conditions, such as autoimmune diseases and different types of cancer. Using the introduced methods, we compute and show what the decision thresholds are, based on the single-cell measured concentration levels of NFκB and ATF-2. We also define and compute the decision error probabilities, i.e., false alarm and miss probabilities, based on the concentration levels of the two outputs. By considering the joint response of the two outputs of the signaling system, one can learn more about complex cellular decision-making processes, the corresponding decision error rates, and their possible involvement in the development of some pathological conditions. [ABSTRACT FROM AUTHOR]

The degrees of belief of rational agents should be guided by the evidence available to them. This paper takes as a starting point the view—argued elsewhere—that the formal model best able to capture this idea is one that represents degrees of belief using Dempster–Shafer belief functions. However, degrees of belief should not only respect evidence; they also guide decision and action. Whatever formal model of degrees of belief we adopt, we need a decision theory that works with it: that takes as input degrees of belief so represented. The task of this paper is to develop such a decision theory for the belief function model of degrees of belief. This is not the first paper to attempt that task, but compared to the existing literature it takes a more abstract route to its destination, via a consideration of the very idea of rational decision making in light of one's beliefs and desires. After presenting the new decision theory and comparing it to existing views, the paper goes on to consider diachronic decision situations. [ABSTRACT FROM AUTHOR]

In this paper I propose and motivate a logic of the interdefined concepts of making true and control, understood as intensional propositional operators to be indexed to an agent. While bearing a resemblance to earlier logics in the tradition, the motivations, semantics, and object language theory differ on crucial points. Applying this logic to widespread formal theories of agency, I use it as a framework to argue against the ubiquitous assumption that the strongest actions or options available to a given agent must always be pairwise incompatible. The conclusion is that this assumption conflicts with failures of higher order control of agents over their degree or precision of control, failures exhibited by such imperfect agents as ourselves. I discuss models in this setting for understanding such imperfectly self-controlling agents. In an appendix, I prove several relevant results about the logic described, including soundness and completeness both for it and for certain natural extensions. [ABSTRACT FROM AUTHOR]

Clinical curative effect is the core value and fundamental pursuit of medicine. The data-driven clinical quantitative evaluation model is an important research issue in clinical efficacy evaluation. The existing clinical efficacy evaluation methods have made a lot of contributions to the structured information of static attributes, but less attention is paid to the heterogeneous information with dynamic and random characteristics. This paper discusses a class of clinical efficacy evaluation decision-making problems that contain both time-series and dynamic characteristics and uses the three-way decision principle to describe these characteristics. The group decision-making theory is introduced to aggregate the evaluation information of time series dynamic features generated in the decision-making process. A time series dynamic three-way group decision model for clinical efficacy evaluation is constructed, the optimal decision rules with minimum expected loss objective are calculated and proofed in the proposed model, and then the optimal treatment alternative is obtained. Finally, taking the decision-making problem of clinical efficacy evaluation of traditional Chinese medicine (TCM) treatment of atopic dermatitis as the research object, based on the actual clinical data of Guangdong Provincial Hospital of TCM, the decision-making process and steps of the theoretical model constructed in this paper are verified and applied. The data-driven quantitative clinical evaluation model has achieved better application effects than the actual clinical scenarios. The research results in this paper provide new ideas and theoretical methods for real-world clinical efficacy evaluation research. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of the colour optimal matching model in art design through the method of Bayesian decision-making and the colour optimal matching model designed and applied to practice. Although Bayesian conditional theory constructs a representation theorem for causal decision theory, eliminating the formal differences between evidence decision theory and causal decision theory and reflecting their common intrinsic form. However, it still does not resolve the conflict between causal and evidential decision theories, but only translates it into a different interpretation of state parsing, and the choice of interpretation still comes from people's different intuitive understanding of rationality. To address the problem that the colour information of the target image in the traditional recolouring method easily interferes with the recolouring process and affects the colour effect of the resulting image, a new method of recolouring based on the centralization constraint is proposed in the paper. A new method of colour transfer based on random walk image segmentation is proposed. First, an improved random walk image segmentation method is introduced to segment the reference image and the content image to obtain a more reasonable segmentation region, which can enhance the hierarchy of the resulting image. Second, the proposed colour transfer strategy performs feature matching in the corresponding region to achieve colour transfer. Finally, the structure-preserving filter is introduced to further optimize the resulting image to effectively improve the visual effect of the resulting image. Extensive experimental results show that the proposed method can achieve significantly better-quality results than the colour clustering-based colour transfer method. The experimental analysis shows that the newly designed comprehensive objective evaluation index of colour transfer in the paper can effectively solve the problem of the one-sidedness of a single evaluation index and can achieve highly consistent evaluation results with subjective evaluation. [ABSTRACT FROM AUTHOR]

Among all the elements of the marketing mix, price is the only element that brings revenues to a firm. Pricing-related research is broadly carried out in two different disciplines, namely economics and marketing. The research in economics domain is primarily theoretical. The research in marketing is multi-disciplinary and uses principles of game theory, behavioral decision theory, psychology, and social dimensions to address various pricing-related managerial decision problems. Scholars have proposed multiple theories explaining the pricing-related decision-making processes of firms. However, most of the work is descriptive in nature and does not give adequate directions to operationalize the decisions related to pricing strategy. This paper is normative in nature and provides useful insides to practitioners on how pricing strategy decisions can be made. This paper proposes a decision hierarchy that can be used to operationalize decisions related to the pricing strategy of a firm. Since the inherent components of the problem structure closely resemble the axiomatic requirements of the Analytic Hierarchy Process (AHP) which is a popular multi-criteria decision-making method, this paper also demonstrates how the AHP method can be used to solve a strategic pricing decision problem. [ABSTRACT FROM AUTHOR]

The article focuses on publishing papers in various fields of logic, including philosophical and non-classical logics, algebraic logic, applications in computer science, linguistics, while providing detailed guidelines for manuscript submissions, formatting, references, and artwork.

Cloud computing is a model for allowing suitable, on-demand network access to a shared store of resources such as servers, networks, storage, apps, and services, modified according to specific needs or requirements. The main goal of cloud technology development is to increase the use of resources that work together to achieve reliability at the lowest cost. Cloud service providers (CSPs) have gained popularity in recent years due to their accessibility and availability, as well as the growing quantity of cloud service providers (CSPs) that appear. Choosing (CSPs) has grown to be a challenging decision for many companies. The paper aims to rank a set of cloud service providers based on the multi-criteria decision-making (MCDM) method. The suggested method's applicability is verified by comparing the outcomes with two established methodologies: SWARA and MARCOS methods under the type-2 neutrosophic number set (T2NNS) environment to calculate the importance of evaluation criteria and ranking the alternatives of cloud providers. A sensitivity analysis was executed to check the robustness of this model by examining the effect of criteria weights on the ranking of the alternatives. [ABSTRACT FROM AUTHOR]