Decisions In Uncertain Environment Research Articles

Climate policy uncertainty, media coverage of climate change, and energy markets: New evidence from time-varying causality analysis

Climate change is one of the most critical issues in the last decade, making investigating climate change risks' effects on the economy vital. Employing a novel time-varying Granger causality approach, we test causality from climate policy uncertainty (CPU) to the index returns of clean energy and nonrenewable energy sectors between November 2009 and December 2021. Our analysis generally reveals a significant causality from CPU to S&P clean energy sector index return throughout the sample period. In contrast, very limited significant causality is observed running from the CPU to the S&P nonrenewable energy index return. This result implies that investments in clean energy firms are affected by the CPU due to the cost of reversing the decisions in uncertain environments. On the other hand, the U.S. newspaper media coverage of climate change has a significant impact not only on the clean energy index returns but also on non-renewable energy index returns, implying an increase in the media coverage regarding climate change influences the awareness of investors on climate change, which affects their trading strategies.

Bi-objective optimization of an EDM process with Cu-MWCNT composite tool using single-valued neutrosophic grey relational analysis

Purpose In many of today’s manufacturing industries, such as automobile, aerospace, defence, die and mould making, medical and electrical discharge machining (EDM) has emerged as an effective material removal process. In this process, a series of discontinuous electric discharges is used for removing material from the workpiece in the form of craters generating a replica of the tool into the workpiece in a dielectric environment. Appropriate selection of the tool electrode material and combination of input parameters is an important requirement for performance enhancement of an EDM process. This paper aims to optimize an EDM process using single-valued neutrosophic grey relational analysis using Cu-multi-walled carbon nanotube (Cu-MWCNT) composite tool electrode. Design/methodology/approach This paper proposes the application of grey relational analysis (GRA) in a single-valued neutrosophic fuzzy environment to identify the optimal parametric intermix of an EDM process while considering Cu-MWCNT composite as the tool electrode material. Based on Taguchi’s L9 orthogonal array, nine experiments are conducted at varying combinations of four EDM parameters, i.e. pulse-on time, duty factor, discharge current and gap voltage, with subsequent measurement of two responses, i.e. material removal rate (MRR) and tool wear rate (TWR). The electrodeposition process is used to fabricate the Cu-MWCNT composite tool. Findings It is noticed that both the responses would be simultaneously optimized at higher levels of pulse-on time (38 µs) and duty factor (8), moderate level of discharge current (5 A) and lower level of gap voltage (30 V). During bi-objective optimization (maximization of MRR and minimization of TWR) of the said EDM process, the achieved values of MRR and TWR are 243.74 mm3/min and 0.001034 g/min, respectively. Originality/value Keeping in mind the type of response under consideration, their measured values for each of the EDM experiments are expressed in terms of linguistic variables which are subsequently converted into single-valued neutrosophic numbers. Integration of GRA with single-valued neutrosophic sets would help in optimizing the said EDM process with the Cu-MWCNT composite tool while simultaneously considering truth-membership, indeterminacy membership and falsity-membership degrees in a human-centric uncertain decision-making environment.

Relaxed Rank Order Centroid Weighting MCDM Method With Improved Grey Relational Analysis for Subcontractor Selection: Photothermal Power Station Construction

This article addresses the problem of subcontractor selection in an uncertain decision-making environment by proposing a new hybrid method and improving a classical multicriteria decision-making (MCDM) method. First, the proposed hybrid method combines subjective and objective weighting methods to estimate the weights of criteria that are represented as grey numbers. The subjective weighting method is the PA weighting method. The objective weighting method is the rank order centroid with slacks (ROCS) weighting method used to compensate for the limitation of the rank order centroid weighting method. Second, grey relational analysis (GRA) is improved by introducing both positive and negative reference (PNR) alternatives instead of a single reference alternative as in the classical GRA. Subsequently, the proposed hybrid grey-point allocation-ROCS weighting method and the GRA-PNR evaluation method are applied to select the most suitable subcontractor for the supply of heliostats for photothermal power station construction. Sensitivity analysis is conducted to verify the robustness of the results. Finally, the technique for order preferences by similarity to an ideal solution with grey values, simple additive weighting with grey relations, and additive ratio assessment with grey criteria scores is applied to validate the participation of the selected subcontractor in the project.

Blinded by Attachment: Examining the Overconfidence Bias of Sports Fans’ Intertemporal Ticket Purchase Decisions

Optimally deciding on the best deal for sport event tickets requires the ability to evaluate risk and make informed decisions in uncertain environments. This study examines how individual trait factors, such as experience, expertise, and involvement, influence consumers' decision-making process when purchasing tickets online for sporting events. To examine and test the study hypotheses, 640 respondents from a Qualtrics survey panel were recruited from geographically confined subjects of New York City sports fans over a ten-day data collection period. The research subjects were surveyed to assess their perception of the expected likelihood of obtaining event tickets at a lower rate (ELR) and the expected likelihood that tickets would remain available (ETA) as the event day approached. MANOVA showed that there was a significant effect of the time period on the participants' ETA and ELR risk assessments [Λ = 0.954, F (18, 1262) = 1.653, p < 0.05]. The ETA was highest ten days before the event and lowest the day before the event, with a similar pattern observed for the ELR. The mediation path analysis showed that fan involvement had a strong positive correlation with confidence (B = 0.496, p < 0.001). Confidence, in turn, was a significant predictor of the ELR (B = 5.729, p < 0.05) but not for the ETA (B = 1.516, p = 0.504). The positive mediation of confidence between fan involvement and the ELR indicates that consumers with higher fan involvement tend to have overconfidence in their ability to evaluate the uncertain purchase environment, which ultimately impacts their risk perception and decision-making. The study highlights the importance of considering both temporal and psychological factors when assessing the likelihood of ticket purchases and provides behavioral insights for sports marketers and ticket distributors.

A literature review of a cognitive heuristic: The anchoring effect

The anchoring effect has gained popularity as a cognitive heuristic that aims to explain judgment errors people may make in their decision-making process. This article reviews and documents the literature in this area and finds that there is ample and extensive research available on this topic. By summarizing the results of previous research, it is found that the anchoring effect is a pervasive judgment bias when people are in an uncertain decision-making environment, taking different forms and having varying degrees of influence on decision outcomes. Therefore, this paper aims to summarize the existing findings and build on them for further exploration. To achieve this, the paper takes the type of anchor as an entry point and analyzes how externally provided anchors and decision makers' self-generated anchors respectively influence the decision-making process. Afterward, the article further explores the underlying logic of the anchoring effect through its two explanatory models. Since the anchoring effect has long been considered one of the most robust and common cognitive heuristics, this article then demonstrates the breadth of its application by documenting the various domains in which it has been shown to occur. Finally, this review article suggests several methods in which each of the two types of anchors can be weakened to some extent.

A global optimization feedback model with PSO for large scale group decision making in hesitant fuzzy linguistic environments

In an increasingly complex and uncertain decision-making environment, large-scale group decision-making (LSGDM) can offer a more efficient method, allowing a large number of decision-makers (DMs) to truly participate in the decision-making process. The consensus-reaching process (CRP) is an effective method for resolving conflicting opinions among large-scale DMs. However, in the existing CRP of LSGDM, the new consensus state and the adjustment cost borne by inconsistent DMs after implementing feedback suggestions are not taken into consideration. To address this issue, this paper proposes a global optimization feedback model with particle swarm optimization (PSO) for LSGDM in hesitant fuzzy linguistic environments. An improved density-based spatial clustering of applications with noise (DBSCAN) on hesitant fuzzy linguistic term sets (HFLTSs) is introduced to classify large-scale DMs into several clusters, and a weight determination method that combines cluster size and intra-cluster tightness is also presented. The consensus degree of clusters is calculated at two levels: intra-consensus and inter-consensus. To improve the global consensus level with minimum cost, a global optimization feedback model is established to generate recommendation advice for inconsistent DMs, and the model is solved by PSO. A numerical example related to “COVID-19” and some comparisons are provided to verify the feasibility and advantages of the proposed method.

Smart experience-oriented customer requirement analysis for smart product service system: A novel hesitant fuzzy linguistic cloud DEMATEL method

Smart product service system (PSS) has become an essential strategy to transform towards digital servitization for manufacturing companies. By leveraging smart capabilities, smart PSS aims to create superior user experience in a smart context. To develop a successful smart PSS, customer requirement management from smart experience perspective is necessary. However, it is a challenging task to identify and evaluate diverse, implicit and interrelated smart experience-oriented customer requirement (SEO-CR) in smart PSS context. Hence, this paper proposes an effective methodology to elicit and analyze SEO-CRs. At first, a generic, two-dimensional SEO-CR system is presented as a basis to derive the tailored SEO-CRs for various smart PSS applications. Second, a novel HFLC-DEMATEL (hesitant fuzzy linguistic cloud-based Decision-making and trial evaluation laboratory) method is proposed to accurately evaluate the priority and complicated interaction of SEO-CRs, considering the hesitancy, fuzziness and randomness under uncertain decision environment. Some new operations (e.g., cloud total-relation matrix and weight determination method) and a cloud influence relation map are developed to fully take advantage of cloud model in DEMATEL implementation. Finally, a real case of smart vehicle service system (SVSS) is presented. The 18 SEO-CRs of the SVSS are derived based on the generalized SEO-CRs. By using HFLC-DEMATEL, some important SEO-CRs in context of SVSS are identified, such as autonomous and convenience. The finding of results can help designers make proper decisions in design and development of SVSS with a superior smart experience. The effectiveness and reliability of the proposed method are validated by conducting some comparative analyses.

A survey of collaborative decision-making: Bibliometrics, preliminaries, methodologies, applications and future directions

Complex and uncertain decision-making environment increasingly requires the collaborations of participants, the exchange and interaction of knowledge of different actors can effectively promote and improve the decision-making process. Collaborative decision-making (CDM), a prevailing decision-making method, is extensively studied by scholars, in which different actors can communicate mutually and then reach a common goal. Hence, in view of the advantages and usefulness of CDM, an overview is conducted for showing the state of the art of CDM, pointing out the current issues and indicating the future research directions. To achieve those goals, firstly, the bibliometric analysis is implemented to briefly review the previous research topics. Then, the definitions, comparisons, weights and consensus concerning CDM are recalled, which are followed by the summarization of methodologies used for solving CDM problems. Subsequently, the current research directions and popular application areas are highlighted. Finally, the challenges and potential research directions are concluded from five perspectives.

Choice of mixed strategy in matrix game with nature by Hurwitz criterion

The article solves the problem of choosing an optimal, by the Hurwitz criterion, mixed strategy for arbitrary matrix game against nature. We reduce the problem to solving $n$ linear programming problems (where $n$ is the number of scenarios). As far as we know, this is a new result. It can be used to make decisions in uncertain environments, if the game situation is repeated many times, or physical mixture of pure strategies is realizable.

Applying Game Theory Models for Risk Management in Supply Chain Networks

Supply chain networks are complex systems that involve multiple entities and activities, making them vulnerable to various risks that can negatively impact their performance. Game theory models have been used in various fields to analyze strategic interactions among agents and to make decisions in uncertain environments. This study investigates the application of game theory models for risk management in supply chain networks. Then, we present a framework for applying game theory models for risk management in supply chain networks. Our framework consists of three stages: risk identification, risk analysis, and risk mitigation. We validate the application of the proposed framework using a case study of a supply chain network for a fictional company. The results of the case study demonstrate that game theory models can provide valuable insights into the behavior of supply chain entities in different risk scenarios. The models can also help in identifying optimal strategies for mitigating risks and improving the performance of the supply chain network. The finding imply that the proposed framework can be used as a guide for practitioners to apply game theory models in their supply chain risk management practices.

Narratives, probabilities, and the currency of thought

Abstract Whereas most commentators agree about the centrality of narratives in decision-making, the commentaries revealed little consensus about the nature of radical uncertainty. Here we consider thirteen objections to our views, including our characterization of the uncertain decision environment and associated cognitive, affective, and social processes. We conclude that under radical uncertainty, narratives rather than probabilities are the currency of thought.

Three-way decision approach for water ecological security evaluation and regulation coupled with VIKOR: A case study in Beijing-Tianjin-Hebei region

Owing to the great correlation between human survival and water ecological security (WES), the research on the evaluation and regulation of WES is of great significance. However, the evaluation thought of WES is not clear enough and most of the evaluation methods can only get ranking results subjectively, which are not suit to judge the grade, especially in the complex and uncertain decision environment. Thus, under the guidance of the resilience idea, it is proposed to build an evaluation indicator system of WES from three aspects: resistance, restorative and adaptability. Then the data were transformed into Pythagorean fuzzy number (PFN), and the resilience index was obtained based on VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) method to realize ranking; After that, the comprehensive thresholds were estimated by the resilience index, and a new three-way decision (3WD) model for the WES evaluation was constructed for grade evaluation; Finally, the model was applied to the evaluation of WES in Beijing-Tianjin-Hebei region (BTH) of China from 2006 to 2020. The results show that the WES in BTH has significant spatial-temporal differentiation; From 2006 to 2020, except Hebei, the WES level has not been significantly improved; Beijing, Tianjin and Hebei were respectively in grade III, V and I in 2016–2020; The resistance of WES is an urgent direction to be optimized in Beijing and Tianjin, while Tianjin and Hebei should strengthen the restorative of WES. The model framework constructed in this study is effective and has good generalizability, which can provide a new way for WES evaluation.

Designing lawful machine behaviour: Roboticists’ legal concerns

Regulatory requirements weighing on roboticists are becoming heavier, and at the same time, the activity of producing robots is theorized as creating new types of legal risks. Roboticists become responsible not only for compliance to a set of regulatory objectives, but they also become responsible for giving machines an ability to make decisions in uncertain environments. All of this gives roboticists’ legal literacy a newfound importance, and reveals the lack of data on that population from a socio-legal angle. While we have some knowledge of lawyers’ and policy makers’ concerns for robots, we do not know how roboticists understand their relationship to law and regulation, or the legal value they attribute to various engineering processes. This article offers a preliminary exploration of that population and of their legal concerns, through interviews of ten engineers from Danish robotics companies. The analysis shows that they perceive legal concerns as dependant and integrated into their concerns for safety, which are themselves inferior to functionality and economy concerns. The article further highlights differences in understanding and perception of various legal matters by our informants, and from our practitioner perspective. Hopefully, highlighting these differences will by itself help close the gap between the two professions.

Multi-stage humanitarian emergency logistics: robust decisions in uncertain environment

Multi-stage humanitarian emergency logistics: robust decisions in uncertain environment

Probabilistic Linguistic PROMETHEE I and II Methods for Evaluation of the Reform Scheme of Postgraduate Innovation and Entrepreneurship Education Talent Training Mode under the Big Data Environment

Talent training quality is an important field within higher education research. Innovating the talent training mode and deepening educational reform programs are both of great significance for enhancing the quality of postgraduate innovation and entrepreneurship education in universities. In this study, Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) I and II methods are extended with the probability linguistic term set (PLTS) to accurately express and quantitatively evaluate the reform scheme of postgraduate innovation and entrepreneurship education talent training mode under the big data environment. First, probabilistic linguistic PROMETHEE I and II methods are presented for quantitatively evaluating the reform scheme of postgraduate innovation and entrepreneurship education talent training, which have the advantages of good effectiveness and feasibility. Second, the PLTS is imported into the evaluation methods and applied to accurately depict qualitative information about the index data of the reform scheme effect by the degree of probability. Third, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) with PLTS is proposed to perform a comparative study and conduct visual analysis to verify the effectiveness of the extended probabilistic linguistic PROMETHEE I and II methods. Fourth, an empirical example illustrates the specific evaluation process, verifies the feasibility of the extended methods, and explains the effectiveness of the results. The research findings indicate that the proposed method to reform scheme evaluation can lead to better decision quality, especially in a complex fuzzy and uncertain decision-making environment.

THE FUZZY OPTIMISTIC-REASONABLE-PESSIMISTIC INVENTORY MODEL

In inventory and production decision problems, decision makers are interested to identify the optimal inventory and production level. In a certain decision environment, the optimal inventory level could be determined through traditional inventory methods and the optimal ptoduction level could be determined through linear programming algorithms. In an uncertain decision environment, the traditional methods and algorithms can not provide efficient and relevant solutions for these levels, due to the vague and changing parameters. In this case it is neccesary to develop new methods and models that can deal with vague variables and provide optimal levels. In this paper, the optimal inventory and production levels are determined through a single model that uses fuzzy linear programming. This new model is Fuzzy Optimistic-Reasonable-Pessimistic Inventory Model. It has three scenario: optimistic, reasonable and pessimistic, that are defined through triangular fuzzy numbers. In this way, decision makers can deal with vague parameters. These scenarios help managers to divide the Fuzzy ORP Model into three sub-models, that can be easily solved through traditional Simplex Algorithms. Each sub-model provides a crisp solution for each scenario. The solutions forms the final fuzzy optimal solution. The Fuzzy PRO Inventory Model helps managers to identify three optimal levels and to rank them according to their evaluations. This is useful, also, in predictions, where the decision makers should predict different scenarios for the production process. The limit of this model is the definition of the variables and scenarios. This model consider that all values for all variables and coefficients have the same definition: the inferior limit is related to the optimistic sceanrio, the peak is represents the reasonable limit and the superior limit is related to the pessimistic scenario. In real problem, the decision variables could have different definition than coefficients. The inferior limit of the cost is related to the optimistic scenario, but the superior limit of the production level can be related to the optimistic scenario. There are different representations for the scenarios.

EVALUATION OF GOVERNMENT INVESTMENT USING NESTED PROBABILISTIC LINGUISTIC PREFERENCE RELATIONS BASED ON GRAPH THEORY

Government investment, as a major government function, is closely related to national development and economic growth. It plays a key role to maximize the benefits of this fund, which requires the government to choose the optimal investment plan. Considering the complex and uncertain decision-making environment, we propose the nested probabilistic linguistic preference relation (NPLPR) based on the nested probabilistic linguistic term sets (NPLTSs), to express preference information from the qualitative and quantitative angle. According to graph theory, we define a consistency index and an acceptable consistency of NPLPR to measure the additive consistency. Based on which, we establish a novel algorithm for unacceptable consistent NPLPR to meet the acceptable consistency. Finally, projects in government investment are evaluated by the proposed decision-making method, and some comparative analyses, discussions, and implications are provided from three angles. This study provides a new perspective for scholars to make scientific and rational decisions with the help of technological and economic development in various fields.

Vague multi-attribute group decision-making method based on evidence theory with a new aspect to solve weights

In order to solve the problems of weight solving and information aggregation in the Vague multi-attribute group decision-making, this paper first solves the weight of Vague evaluation value, and then fuses the information of Vague sets through evidence theory, and obtains an information aggregation algorithm for Vague multi-attribute group decision-making. Firstly, The algorithm draws on the idea of solving the weight of evidence in the improved evidence theory algorithm, and calculates the weight of Vague evaluation value, and revises the original evaluation information after obtaining the weight of each Vague evaluation value. Secondly, this algorithm analyzes the mathematical relationship between the Vague sets and the evidence theory, and uses the evidence theory to fuse the evaluation information to obtain the final Vague evaluation value of each alternative. Finally, this algorithm uses a score function to calculate the score of each alternative to determine the best alternative. The algorithm given in the paper enables decision-makers to make rational decisions in uncertain environments, and then select the best alternative.

Maximization of Strategic Crops Production in Iraq with Fuzzy Goal Programming

The need to increase agricultural production has become a challenging task for most countries. Generally, many resource factors affect the deterioration of production level, such as low water level, desertification, soil salinity, low on capital, lack of equipment, the impact of export and import of crops, lack of fertilizers, pesticide, and the ineffective role of agricultural extension services which are significant in this sector. The main objective of this research is to develop fuzzy goal programming (GP) model to improve agricultural crop production, leading to increased agricultural benefits (more tons of produce per acre) based on the minimization of the main resources (water, fertilizer, and pesticide) to determine the weight in the objectives function subject to different constraints (land area, irrigation, labor, fertilizer, pesticide, equipment, and seed). Fuzzy GP (FGP) and GP were utilized to solve multi-objective decision-making (MODM) problems. From the results, this research has successfully presented a new alternative method that introduced multi-interval weights in solving a multi-objective FGP and GP model problem in a fuzzy manner, in the current uncertain decision-making environment for the agricultural sector. The significance of this research lies in the fact that some of the farming zones have resource limitations while others adversely impact their environment due to misuse of resources.

Farmer’s Perception of Climate Change and the Impacts on Livelihood in South Sulawesi

Climate change and variability have affected people, especially in developing countries whose livelihood depends upon agriculture. Smallholder farmers in South Sulawesi are identified as particularly vulnerable with climate change impact and have limited knowledge on how to make production decisions in uncertain environment. The farmer’s perception and the impact of climate change are crucial factors that affect farm productivity and adaptation strategy. This study investigates the perceptions of farmers towards climate change and the impacts. Therefore, a cross-sectional survey was conducted with a random sample of 120 smallholders’ farmers that were distributed into 4 districts in South Sulawesi. It was discovered that the majority of the respondents believed that climate change is real and about 83 percent of them acclaimed this phenomenon had caused negative effect on their livelihood. Some indications of the impact of climate change include increasing temperatures, and long dry seasons that had reduced rain-fed rice production. The results also indicate that pests and plant diseases had elevated in several places due to uncertain weather. In addition, climate change had influenced the availability of water sources. Hence, policy implications are that appropriate strategies are crucial to help farmers make adaptation choices in this situation.