Risk Preferences Of Decision Makers Research Articles

Interval-valued intuitionistic fuzzy multi-attribute group decision-making method considering risk preference of decision-makers and its application

An improved interval-valued intuitionistic fuzzy multi-attribute group decision-making method considering the risk preference of decision-makers is proposed to solve the multi-attribute group decision-making problem with interval-valued intuitionistic fuzzy numbers and the condition that the attribute weight information is completely unknown. Firstly, the decision-maker weight of each attribute is determined by combining similarity and proximity. In order to consider the influence of the decision-maker's risk preference on the decision result and avoid the asymptotic behavior of interval-valued intuitionistic fuzzy matrix, the risk aversion coefficient of the decision-maker is introduced and combined with the determined decision-maker's weight aggregation to form a group decision matrix. Then, the information of group decision matrix is mined, and the interval-valued intuitionistic fuzzy entropy is used to determine the attribute weight and relative weight. Based on the interval-valued intuitionistic fuzzy distance measure formula and the TODIM method, the overall superiority of each scheme relative to other schemes is obtained by calculating the superiority between schemes, and the optimal scheme is determined by comparing and sequencing. Finally, the rationality and effectiveness of the proposed method are verified by an example of mechanical assembly supplier selection decision.

Dual-Source Procurement Strategy of Cross-Border E-Commerce Supply Chain considering Members’ Risk Attitude

The risk attitude of decision-makers will significantly affect the decision-making of enterprise risk management. Specifically, high risk represents the potential premise of high return for risk preference decision-makers, and for risk-averse decision-makers, the increase of risk degree will stimulate decision-makers’ aversion to uncertainty and turn to seek safer business strategies. Although there are many pieces of literature on the risk preference of decision-makers, they usually only assume the risk attitude of one party and rarely consider the risk attitude of suppliers and retailers in the scenario of cross-border e-commerce at the same time. Therefore, under the background of supply disruption, for the cross-border e-commerce supply chain composed of cross-border suppliers, overseas suppliers, overseas retailers, and consumers, combined with the risk attitude preference of enterprise subjects, this paper constructs the mean-variance model dominated by overseas retailers and reversely solves the risk-aversion attitude of a single cross-border supplier. When a single overseas retailer maintains a risk-aversion attitude and both cross-border suppliers and overseas retailers hold a risk-neutral or risk-aversion attitude, the pricing of products in different channels is analyzed. Finally, an example is given to analyze the impact of supply disruption probability, risk-aversion coefficient, channel distribution coefficient, and other parameters on purchase price, market demand, target profit, and utility. It is of great practical significance for improving the stability of cross-border e-commerce supply chain system and reducing revenue loss to study how different degrees of risk-aversion attitudes of cross-border suppliers and overseas retailers affect enterprise procurement pricing strategy, target profit, and utility in case of supply disruption.

Grey Multiattribute Emergency Decision-Making Method for Public Health Emergencies Based on Cumulative Prospect Theory

In view of the different risk preferences of decision-makers and the information gap in the timing of the subjects under evaluation, a multiattribute dynamic decision-making model that considers decision risk in addition to the priority of time is proposed. First, to account for the uncertainty of the evaluation information, the interval grey number method was applied to perform numerical abstraction and key data feature extraction on the scene of public health emergencies and to obtain the information after attributes were unified. Second, acknowledging the bounded rational behavior of actual decision-makers, the decision-makers’ risk preference was introduced into emergency decision-making. In combination with the time degree, the multiattribute unified decision matrix was transformed into an expected dynamic cumulative prospect decision matrix. Furthermore, based on cumulative prospect theory (CPT), the prospect value function and the weight function are constructed; a new optimization model is proposed to obtain the attribute weight. And according to the comprehensive prospect value of each alternative, the priority of the alternatives was determined. Finally, with the COVID-19 epidemic serving as an example, the results confirm that the emergency decision-making method proposed in this paper is feasible and effective.

Safety evaluation of urban rail transit operation considering uncertainty and risk preference: A case study in China

Safety evaluation of urban rail transit (URT) operation can not only have a great impact on passenger travel options, but is also related to planning of urban development. The evaluation results of URT operational safety are important for transit operators and passengers. In this study, we develop an integrated multi-stage evaluation framework to assess URT operation from the perspective of safety, which considers information uncertainty of key influence factors (KIFs), and risk preference of decision-makers (DMs). Firstly, to identify and select KIFs, this study makes systematic analysis of impacts on URT operational safety from five aspects: passengers, management, equipment, environment, and disaster respectively. Vensim software is used to build a stock flow model based on system dynamics method, and a two-level index system for assessing URT operational safety is established. Secondly, DMs’ weights are specified based on interval valued triangular fuzzy (IVTF)-TOPSIS method, and IVTF-AHP-entropy is introduced to determine the combined weight of each indicator. Thirdly, an S-shaped utility function under IVTF environment is developed to get the ranking order of research objects, and operational safety levels are determined based on cloud model. Finally, Changsha subway network is selected as a case study to test our proposed evaluation framework, and some related suggestions for operators and managers have been put forward in the future.

Multiple-Attribute Decision Making Based on Interval-Valued Intuitionistic Fuzzy Generalized Weighted Heronian Mean

Due to the complexity and uncertainty of objective things, interval-valued intuitionistic fuzzy (I-VIF) numbers are often used to describe the attribute values in multiple-attribute decision making (MADM). Sometimes, there are correlations between the attributes. In order to make the decision-making result more objective and reasonable, it is often necessary to take the correlation factors into account. Therefore, the study of MADM based on the correlations between attributes in the I-VIF environment has important theoretical and practical significance. Thus, in this paper, we propose new operators (AOs) for I-VIF information that are able to reflect the completeness of the information, attribute relevance, and the risk preference of decision makers (DMs). Firstly, we propose some new AOs for I-VIF information, including I-VIF generalized Heronian mean (I-VIFGHM), I-VIF generalized weighted Heronian mean (I-VIFGWHM), and I-VIF three-parameter generalized weighted Heronian mean (I-VIFTPGWHM). The properties of the obtained operators, including their idempotency, monotonicity, and boundedness are studied. Furthermore, an MADM method based on the I-VIFGWHM operator is provided. Finally, an example is provided to explain the rationality and feasibility of the proposed method.

Public health emergency decision-making and management system sound research using rough set attribute reduction and blockchain

Public health emergency decisions are explored to ensure the emergency response measures in an environment where various emergencies occur frequently. An emergency decision is essentially a multi-criteria risk decision-making problem. The feasibility of applying prospect theory to emergency decisions is analyzed, and how psychological behaviors of decision-makers impact decision-making results are quantified. On this basis, the cognitive process of public health emergencies is investigated based on the rough set theory. A Decision Rule Extraction Algorithm (denoted as A-DRE) that considers attribute costs is proposed, which is then applied for attribute reduction and rule extraction on emergency datasets. In this way, decision-makers can obtain reduced decision table attributes quickly. Considering that emergency decisions require the participation of multiple departments, a framework is constructed to solve multi-department emergency decisions. The technical characteristics of the blockchain are in line with the requirements of decentralization and multi-party participation in emergency management. The core framework of the public health emergency management system-plan, legal system, mechanism, and system can play an important role. When delta { = }0.10, the classification accuracy under the K-Nearest Neighbor (KNN) classifier reaches 73.5%. When delta { = }0.15, the classification accuracy under the Support Vector Machines (SVM) classifier reaches 86.4%. It can effectively improve China’s public health emergency management system and improve the efficiency of emergency management. By taking Coronavirus Disease 2019 (COVID-19) as an example, the weight and prospect value functions of different decision-maker attributes are constructed based on prospect theory. The optimal rescue plan is finally determined. A-DRE can consider the cost of each attribute in the decision table and the ability to classify it correctly; moreover, it can reduce the attributes and extract the rules on the COVID-19 dataset, suitable for decision-makers' situation face once an emergency occurs. The emergency decision approach based on rough set attribute reduction and prospect theory can acquire practical decision-making rules while considering the different risk preferences of decision-makers facing different decision-making results, which is significant for the rapid development of public health emergency assistance and disaster relief.

A robust model for aggregated bidding of energy storages and wind resources in the joint energy and reserve markets

The high reliability and flexibility of Battery Energy Storage (BES) resources in comparison with other renewable technologies promote the development of this technology in smart grids. The fast response of BES to load variations could help the power system operators to maintain the balance of generation and consumption in real-time, and improve the flexibility of the smart grid, effectively. In this work, a new model is presented that determines the aggregated scheduling of BES and Wind Power Resource (WPR) in the joint energy and reserve markets. To evaluate the performance of BES in different markets, the proposed model is divided into day-ahead and real-time planning horizons. According to market prices, ramp rates, marginal costs, and technical constraints of units, the optimal participation levels in different markets are determined. The deployed power in real-time and wind power are considered as the uncertain parameters and the Robust Optimization (RO) framework is proposed to manage the related financial risk based on the worst-case realizations of uncertain parameters. The robust strategy is formulated based on the Mixed Integer Linear Programming (MILP) technique, which can be solved via the branch-and-bound method. Finally, the performance and effectiveness of the model are analyzed via different case studies. Simulation results show that the day-ahead and real-time markets are the best options for buying and selling the energy of BESs, and participation in the reserve market and regulation service increases their profit, significantly. Furthermore, the expected profit greatly depends on the risk preferences of decision-makers, and reducing the variation interval of wind generation by 40 % leads to an increase of 74.65 % in revenues.

Study on the early warning and forecasting of flash floods in small watersheds based on the rainfall pattern of risk probability combination

Flash floods cause great harm to people’s lives and property safety. Rainfall is one of the main causes of flash floods in small watersheds. The uncertainty of rainfall events results in inconsistency between the traditional single rainfall pattern and the actual rainfall process, which poses a great challenge for the early warning and forecasting of flash floods. To carry out the effective flash flood early warning and forecasting, this paper proposes a novel rainfall pattern by coupling total rainfall and peak rainfall intensity based on copula functions, i.e., the rainfall pattern of risk probability combination (RPRPC). On this basis, the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) hydrological model is used to simulate the rainfall-runoff process, the trial algorithm is used to calculate the critical rainfall (CR), and the optimistic-general-pessimistic (O–G-P) early warning mode considering the decision maker's risk preference is proposed. The small watershed of Xinxian in Henan province, China, is taken as a case study for calculation. The results show that the RPRPC is feasible and closer to the actual rainfall process than the traditional rainfall pattern, Frank copula function is the best for determining the joint distribution function of total rainfall and peak rainfall intensity, and the HEC-HMS model can be applied to small watersheds in hilly areas. Additionally, both RPRPC and antecedent soil moisture condition (ASMC) have influence on CR, and the variation of RPRPC will change the influence of ASMC on CR. Finally, the effectiveness of O–G-P early warning mode is verified.

A Novel Extension of Best-Worst Method With Intuitionistic Fuzzy Reference Comparisons

Best-worst method (BWM) has attracted increasing attention. It has been generalized to different fuzzy environments and applied to various real-life decision problems. This article develops a new intuitionistic fuzzy (IF) BWM (IFBWM) for multicriteria decision-making. When a decision maker (DM) makes comparisons, there may be some hesitancies. Thus, the reference comparisons are represented as intuitionistic fuzzy values (IFVs), the Best-to-Others vector and the Others-to-Worst vector are IF vectors. According to the multiplicative consistency of intuitionistic fuzzy preference relation, this article gives the consistency equations and views them as IF equations. The derivation of optimal IF weights of criteria is formulated as an IF decision-making problem. Thereby, a mathematical programming model is constructed to assure that the derived optimal IF weights of criteria is a normalized IF weight vector. Depending on the risk preference of DM, four linear programming models are presented to obtain the optimal IF weights based on the constructed mathematical programming model for the optimistic DM, the pessimistic DM, and the neutral DM, respectively. Furthermore, this article investigates the process of improving the consistency. Several examples are demonstrated to show the application and effectiveness of the proposed IFBWM.

An almost robust model for minimizing disruption exposures in supply systems

This paper studies two-stage disruption exposure minimization problems, motivated by the supply disruption issues in the energy and water supply systems. In particular, we address the ambiguity in both the probability distribution and risk preference of decision-makers towards disruption exposures. First, we propose a two-stage distributionally robust model with adjustable uncertainty sets, which solves a supply system solution with the least possible disruption exposures. We show that this two-stage robust disruption exposure model can be reduced to a computationally attractive single-stage mixed-integer linear program. We then propose an extended almost-robust disruption guarantee model to account for the ambiguity in the risk preference of decision-makers. We demonstrate that this almost-robust guarantee model can reveal clear preferences of most decision-makers under limited distribution information, which however does not resort to any particular disutility function specification and can be solved efficiently using a binary search algorithm. A decision support framework is also developed to guide users on how to apply the proposed disruption exposure models. Finally, we apply the proposed models to a distributed energy supply system design problem. Numerical results show that our models significantly outperform a risk-neutral model in hedging against a broad set of supply distributions. Moreover, the almost-robust guarantee model exhibits its advantages in hedging against high disruption levels, and performs the best under the vast majority of distributions regarding all tested statistical criteria.

Evaluation tool for assessing the influence of structural health monitoring on decision-maker risk preferences

Different rationally behaving individuals can make different decisions even under same conditions. Concept of risk preference is one way of describing this discrepancy. Risk preference refers to the way decision-makers weight different risky options. Expected utility theory is a popular way to treat this weighting mathematically. Previous research in the field of structural health monitoring has shown that in a multi-stakeholder decision-making, discrepancies in risk preferences between the stakeholders can have large effects on the feasibility of structural health monitoring implementation. Results from that previous research obtained using expected utility theory showed that under certain conditions, differences in stakeholder risk attitudes can make the value of structural health monitoring system even negative. As the overall aim of structural health monitoring is to enable systematic data-based management of infrastructure, understanding of risk preferences and their effect on the structural health monitoring decision-making is of paramount importance. In this work, we created a risk preference assessment tool that is derived from Domain-Specific Risk-Taking scale. We propose the tool and explain its development and validation. The validation results indicate that the proposed assessment tool is technically sound and has high potential in the future valuation of human factors influencing decision-making based on structural health monitoring.

Robust parameter design of inventory policy considering the risk preference of decision makers

Aiming at reducing the risk in inventory management of supply chain system suffering from the influence of uncertainties, we propose a strategy to analyse the optimal inventory policy considering different risk preference of decision makers. The proposed method is a combination of the mean-conditional value-at-risk and the response surface model. From the perspective of robust optimization, the proposed method is adopted to solve the inventory control problems in a multi-echelon supply chain system, the effectiveness of the proposed method is verified in the simulation model. Furthermore, the comparison analysis between the proposed method and the dual-response surface method is conducted in this paper, and the comparison results shows the superiority of the proposed method in improving the robustness of supply chain system.

Risk Analysis and Utility Function-Based Decision-Making Model for Spinning Reserve Allocations

Spinning reserve (SR) is an essential resource for system operators to compensate for unpredictable imbalances between load and generation caused by sudden unit outages and unforeseen fluctuations of load demand and wind power. System operators could face many alternative SR allocations determined by different methods and decide the optimal one based on the experience and consideration of risks in the power system. Considering the stochastic nature of power system components, this paper proposes a utility function-based decision-making model for SR allocations, which incorporates the whole distribution of social benefits (SBs) of the SR allocation and the risk preferences of decision-makers together. Three different utility functions are established that relate the SBs of SR allocations to risk-seeking, risk-neutral, and risk-averse preferences, respectively. Besides, the risk preference degree is represented by a shape parameter. The risk of different SR allocations is analyzed and the expected utilities, which reflect the relative satisfaction, are used to determine the optimal SR allocation for the operators with various risk preferences. Incorporating both the distribution of SBs and decision-makers’ risk preferences makes the final decision for optimal SR allocation more flexible than the decision strategies based only on the expected social benefit or a probabilistic reliability index (such as loss of load probability and expected load not served). The effectiveness is examined using the IEEE-RTS.

Carbon-subsidized inter-regional electric power system planning under cost-risk tradeoff and uncertainty: A case study of Inner Mongolia, China

Inter-regional electricity transmission inevitably brings about unbalanced carbon emission and air pollution among different regions. It is vital to compensate for the economic and environmental loss of power output region by taking the carbon subsidy strategy into account. In this study, a risk explicit interval multistage stochastic programming model considering inter-regional carbon subsidy is developed to support an optimal energy system planning in Inner Mongolia, China. This model can not only deal with uncertainties embedded in the complex energy system described as interval values and random variables but also reflect dynamic linkage between multiple stages over a time series. It advanced the existing optimization methods by introducing the cost-risk tradeoff information based on the risk preferences of decision-makers. The obtained crisp solutions are more feasible, effective and optimum for the practical policymaking process. Different scenarios associated with carbon subsidy policy implementation, renewable energy share adjustment and carbon emission reduction strategy are employed to project the optimal power generation scheme, net system cost, installed electricity capacity, carbon intensity and pollution emission in the future. The results indicate that the amount of exported electricity will keep increasing, and the carbon subsidy strategy will prominently promote the economic growth of the electric power sector by 3.05%. The further power supply pattern is going to transform from fossil fuel to renewable energy, by which wind and solar power will occupy around 44% of total power generation capacity in West Inner Mongolia. Raising the share of renewable energy will be the most effective approach to adjust the power generation scheme; meanwhile, reducing carbon intensity will significantly contribute to enlarge green energy capacity, enhance low-carbon emission and mitigate air pollution. These findings will help decision-makers gain insights into a more scientific energy system planning under various uncertainties.

Research on the Method of Transforming Vague to Fuzzy Value

In the study of the transformation of Vague sets to Fuzzy sets, the key is to judge the tendency of the Vague degree. To determine the tendency of Vague degree, it is necessary to make decision on the information of the uncertainty. Therefore, the transformation of the Vague set to the Fuzzy set can be regarded as the decision maker’s risk decision under the uncertain conditions. Risk preference of decision-makers is a key decision parameter when decision makers make risk decision. In this paper, the weight of risk - income weighed weight is introduced into the transformation model. Risk preference of decision-makers is well reflected in the transformation results. The membership degree of the Fuzzy set is decomposed into two parts: the true membership value of the Vague set, the value of the risk and the value of the tendencies. The results show that it is necessary and meaningful to consider the risk preference of the decision-makers. The thinking mode of dealing with this kind of problem can be changed from “point” thinking to “face” thinking. The fuzziness and risk preference of decision-makers are presented to the decision-makers in the form of a two-dimensional decision map. The transformation model is to help decision makers to fully understand the facts. The role of the transformation model is to assist decision makers in making scientific decisions rather than making decisions for decision makers.

Optimal Allocation of Water Reuse Using Modified TODIM-GP Approach with Considering the Leopold Matrix Outputs

Allocation of water use is one of the most important challenges of water resources management that needs to evaluate water reuse options, especially in areas with limited water resources, arid climate, increasing water demand due to population growth. In this study, the modified TODIM method (Multi-Criteria Decision-Making in Portuguese with considering the risk preferences of decision makers) that can be used with goal programming (GP) by considering Leopold matrix outputs in order to preform Environmental Impact Assessment (EIA). The present study is aimed at optimally allocate water reuse to multiple stakeholders in Najafabad, Isfahan, Iran. The results showed that the numerical model developed with an integration of optimization and quantitative evaluation can be considered as a useful tool to develop feasible guidelines for adapting the requirements of different stakeholders for optimized allocation of recycled water.

Research on Utility Evaluation of Grid Investment considering Risk Preference of Decision-Makers

Effectiveness evaluations are one of the important ways to guide grid investment and to improve investment efficiency. Improving the effectiveness of grid investment evaluations is studied based on the optimization of the investment evaluation index system and the utility evaluation model. The index system is optimized by establishing an evaluation index system of grid investment effectiveness, considering the redundancy between the indices, and constructing an ISM-DEA model. The utility function model was introduced to fully consider the different risk appetites of decision-makers, and a utility evaluation model that takes risk appetite into account was established. An improved weight integration model based on multiobjective optimization was established by considering the minimum deviation and the trend-optimal objective function when setting the index weights. The calculation results show that the feasibility of the index system optimization model and utility evaluation model constructed in this study is verified under the premise of satisfying the assumptions. By adjusting the risk preference coefficient of decision-makers, the dynamic optimization of the grid investment utility evaluation results can be realized.

A novel three-way decision method in a hybrid information system with images and its application in medical diagnosis

Three-way decisions are effective and heuristic methods in information processing, moreover, it provides a trisecting-and-acting framework for complex problem solving. In this paper, combining with the practical application scenario, we propose a novel three-way decisions approach and apply it to medical diagnosis. First, we build an information system which is called a hybrid information system with images by considering the characteristics of the examination items of nephritis, including urinary color, urinary tuberculosis, pH, red blood cell count, urinary irritation, computed tomography, white blood cell count and so on. Second, to describe two objects of the conditional attribute set in a hybrid information system with images, we propose the hybrid distance based on Euclidean distance. Then, the tolerance relation induced by this system is constructed. In addition, considering that missing values exist in a hybrid information system with images, interval-valued numbers are used to obtain the loss function. Given different types of parameters can respond the level of the tolerance relation and the risk preference of decision makers, and the decision rules are shown in tabular forms. Finally, an illustration is showed to verify the feasibility and reasonability of the proposed method.

Spatial conservation planning under uncertainty using modern portfolio theory and Nash bargaining solution

In recent years, researchers from interdisciplinary teams involving ecologists, economists and operations researchers collaborated to provide decision support tools to address the challenges of preserving biodiversity by optimizing the design of reserves. The goal of this paper is to further advance this area of research and provide new solutions to solve complex Spatial Conservation Planning (SCP) problems under uncertainty that consider risk preferences of decision makers. Our approach employs modern portfolio theory to address uncertainties in SCP problems, and involves two conflicting objectives: maximizing return and minimizing risk. We apply concepts from game theory such as the Nash bargaining solution to directly compute a desirable Pareto-optimal solution for the proposed bi-objective optimization formulation in natural resource management problems. We demonstrate with numerical examples that by directly computing a Nash bargaining solution, a Binary Quadratically Constrained Quadratic Program (BQCQP) can be solved. We show that our approach (implementable with commercial solvers such as CPLEX) can effectively solve the proposed BQCQP for much larger problems than previous approaches published in the ecological literature. Optimal solutions for problems with less than 400 parcels can be computed within a minute. Near optimal solutions (within at most 0.2% gap from an optimal solution) for high-dimensional problems involving up to 800 parcels can be computed within 8 h on a standard computer. We have presented a new approach to solve SCP optimization problems while considering uncertainty and risk tolerance of decision makers. Our new approach expands considerably the applicability of such SCP optimization methods to address real conservation problems.

Interval based transmission contingency-constrained unit commitment for integrated energy systems with high renewable penetration

As the reliance on natural gas to meet electric generation requirements increases, additional operational measures and risks must be considered to better understand the implications of the complex interdependency between the natural gas system and the power system. This paper develops a novel interval optimization framework of contingency-constrained unit commitment for integrated energy systems. Uncertainties in contingencies are captured by interval numbers, which reduces the computational burden compared with other uncertainty representations. Moreover, we incorporate the risk preferences of decision makers in the framework to alleviate the conservativeness of solutions. It is observed that power-to-gas technology can reduce renewable curtailment and offer auxiliary services. Numerical results demonstrate the economic value of power-to-gas in integrated energy systems.