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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]

Most classification algorithms are “passive”, in that they assign a class label to each instance based only on the description given, even if that description is incomplete. By contrast, an active classifier can—at some cost—obtain the values of some unspecified attributes, before deciding upon a class label. This can be useful, for instance, when deciding whether to gather information relevant to a medical procedure or experiment. The expected utility of using an active classifier depends on both the cost required to obtain the values of additional attributes and the penalty incurred if the classifier outputs the wrong classification. This paper analyzes the problem of learning optimal active classifiers, using a variant of the probably-approximately-correct (PAC) model. After defining the framework, we show that this task can be achieved efficiently when the active classifier is allowed to perform only (at most) a constant number of tests. We then show that, in more general environments, this task of learning optimal active classifiers is often intractable. [Copyright &y& Elsevier]

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]

An introduction is presented in which the editor discusses various reports within the issue on topics including economic methodology, decision theory, and policy science.

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]