Risk Preferences Of Decision Makers Research Articles

A Semi-Infinite Interval-Stochastic Risk Management Model for River Water Pollution Control under Uncertainty

In this study, a semi-infinite interval-stochastic risk management (SIRM) model is developed for river water pollution control, where various policy scenarios are explored in response to economic penalties due to randomness and functional intervals. SIRM can also control the variability of the recourse cost as well as capture the notion of risk in stochastic programming. Then, the SIRM model is applied to water pollution control of the Xiangxihe watershed. Tradeoffs between risks and benefits are evaluated, indicating any change in the targeted benefit and risk level would yield varied expected benefits. Results disclose that the uncertainty of system components and risk preference of decision makers have significant effects on the watershed's production generation pattern and pollutant control schemes as well as system benefit. Decision makers with risk-aversive attitude would accept a lower system benefit (with lower production level and pollutant discharge); a policy based on risk-neutral attitude would lead to a higher system benefit (with higher production level and pollutant discharge). The findings can facilitate the decision makers in identifying desired product generation plans in association with financial risk minimization and pollution mitigation.

Robust goal programming using different robustness echelons via norm-based and ellipsoidal uncertainty sets

The notion of robust goal programming (RGP) using cardinality-constrained robustness via interval-based uncertainty was first examined over a decade ago. Since then, the RGP methodology has not been widely researched, specifically when considering different uncertainty sets to implement. Within this context, this paper compares interval-based and norm-based uncertainty sets using cardinality-constrained robustness. Strict robustness using ellipsoidal uncertainty sets is also examined in the RGP realm. The aforementioned methods are demonstrated for a simple instance from the literature, and the results are summarized. Conclusions are made regarding the proposed RGP models when likened to a similar RGP model seen in the literature. Further, the suitability of each RGP model is offered when a decision maker's risk preference or computing availability are taken into consideration. Inferences are made regarding the effectiveness of each uncertainty set in the context of solutions that are relatively unaffected by data uncertainty – that is, robust solutions.

A Method for Multi-attribute Group Decision Making with Triangular Intuitionistic Fuzzy Numbers and Application to Trustworthy Service Selection

The trustworthy service selection is a typical multi-attribute group decision making (MAGDM) problem. The aim of this paper is to develop a novel method for MAGDMwithtriangular intuitionistic fuzzy numbers (TIFNs) and apply to the trustworthy service selectionproblem.Firstly, we define the mean-index, variance-index and standard deviation of TIFN. And a new distance measure of TIFNs is proposed and corresponding proofs are given. Based on these concepts of mean-index and standard deviation, a ranking method for TIFNs is developed considering the risk preferenceof decision maker (DM). Further, according to the crisp relative closeness coefficient matrix with respect to the normalized TIFNs decision matrix, we use entropy measure to obtain attribute weights. The DMs weights are calculated by the similarity between the individual decisions and the average decisions. Then, a decision procedure is described to solve the MAGDM under triangular intuitionistic fuzzy environment. Finally, a real trustworthy service selection example is analyzed to verify the practicality and effectiveness of the developed method.

Decision process in MCDM with large number of criteria and heterogeneous risk preferences

A new decision process is proposed to address the challenge that a large number criteria in the multi-criteria decision making (MCDM) problem and the decision makers with heterogeneous risk preferences. First, from the perspective of objective data, the effective criteria are extracted based on the similarity relations between criterion values and the criteria are weighted, respectively. Second, the corresponding types of theoretic model of risk preferences expectations will be built, based on the possibility and similarity between criterion values to solve the problem for different interval numbers with the same expectation. Then, the risk preferences (Risk-seeking, risk-neutral and risk-aversion) will be embedded in the decision process. Later, the optimal decision object is selected according to the risk preferences of decision makers based on the corresponding theoretic model. Finally, a new algorithm of information aggregation model is proposed based on fairness maximization of decision results for the group decision, considering the coexistence of decision makers with heterogeneous risk preferences. The scientific rationality verification of this new method is given through the analysis of real case.

A hesitant fuzzy extension of VIKOR method for evaluation and selection problems under uncertainty

In complex real-world problems, hesitant fuzzy sets could help experts or decision makers (DMs) when they are hesitant among several performance values and can express their judgments as a set of possible values for an element between zero and one. In this respect, the main purpose of this paper is to present hesitant fuzzy information to develop the vlsekriterijumska optimizacija i kompromisno resenje in serbian (VIKOR) method, namely HF-VIKOR by utilising the weights of decision makers and distance measure along with proposing a new index for the ranking process of compromise solution procedure. The hesitant fuzzy hamming distance measure is applied to compute the utility measure and regret measure; also, hesitant fuzzy Euclidean distance measure is used to compute the proposed new index for the evaluation of alternatives by considering risk preferences of DMs. Moreover, two application examples about new product selection and energy policy selection problems under uncertainty are presented to indicate the suitability of the proposed HF-VIKOR method, and then it is compared with the recent methods from the literature. Finally, sensitivity analysis is considered to indicate effects of the DMs' weights on preference order ranking.

A Portfolio Theory Approach to Identify Risk-efficient Enablers of Change in Global Production Networks

In recent years, due to the shift of markets and the global competitive environment companies have attempted to increase efficiency by operating in globally distributed production networks. But these networks have often grown historically neglecting a future-oriented strategy. Nowadays, the consideration of investments into these complex, hierarchical and heterogeneous network structures is in companies’ focus. This so-called migration planning has become a complex decision-making problem. Here, changeability is increasingly referred to as a factor of success. Adequate methods are needed to operationalize and quantify the costs but also the benefits and consequently the profitability of change enabling measures. But the determination of the optimal degree of changeability is crucial to enable flexible and fast migrations of the network configuration. However, currently there is no approach in the literature which values the reduction potential with regard to migration costs by investments in enablers of change or even accelerators of change under uncertain future developments of key drivers of change. By developing a method to monetarize occurring time expenses of planning, implementation and ramp-up activities for the migration of the network configuration the presented approach describes how these migration costs can be estimated and major cost drivers be influenced through investments in enablers or accelerators of change. Furthermore, the resulting cost savings are contrasted with the investment expenditures to determine the return on investment of the respective enablers and accelerators. Based on the principals of modern portfolio theory in a last step bundles of enablers and accelerators are built to minimize the risk of misinvestments and to establish an efficient and tailor-made implementation plan based on the decision maker's risk preferences.

TRIANGULAR INTUITIONISTIC FUZZY TRIPLE BONFERRONI HARMONIC MEAN OPERATORS AND APPLICATION TO MULTI-ATTRIBUTE GROUP DECISION MAKING

As an special intuitionistic fuzzy set defined on the real number set, triangular intuitionistic fuzzy number (TIFN) is a fundamental tool for quantifying an ill-known quantity. In order to model the decision maker's overall preference with mandatory requirements, it is necessary to develop some Bonferroni harmonic mean operators for TIFNs which can be used to effectively intergrate the information of attribute values for multi-attribute group decision making (MAGDM) with TIFNs. The purpose of this paper is to develop some Bonferroni harmonic operators of TIFNs and apply to the MAGDM problems with TIFNs. The weighted possibility means of TIFN are firstly defined. Hereby, a new lexicographic approach is presented to rank TIFNs sufficiently considering the risk preference of decision maker. The sensitivity analysis on the risk preference parameter is made. Then, three kinds of triangular intuitionistic fuzzy Bonferroni harmonic aggregation operators are defined, including a triangular intuitionistic fuzzy triple weighted Bonferroni harmonic mean operator (TIFTWBHM) operator, a triangular intuitionistic fuzzy triple ordered weighted Bonferroni harmonic mean (TIFTOWBHM) operator and a triangular intuitionistic fuzzy triple hybrid Bonferroni harmonic mean (TIFTHBHM) operator. Some desirable properties for these operators are discussed in detail. By using the TIFTWBHM operator, we can obtain the individual overall attribute values of alternatives, which are further integrated into the collective ones by the TIFTHBHM operator. The ranking order of alternatives is generated according to the collective overall attribute values of alternatives. A real investment selection case study verifies the validity and applicability of the proposed method.

Development of a fuzzy-stochastic programming with Green Z-score criterion method for planning water resources systems with a trading mechanism.

This study developed a fuzzy-stochastic programming with Green Z-score criterion (FSGZ) method for water resources allocation and water quality management with a trading-mechanism (WAQT) under uncertainties. FSGZ can handle uncertainties expressed as probability distributions, and it can also quantify objective/subjective fuzziness in the decision-making process. Risk-averse attitudes and robustness coefficient are joined to express the relationship between the expected target and outcome under various risk preferences of decision makers and systemic robustness. The developed method is applied to a real-world case of WAQT in the Kaidu-Kongque River Basin in northwest China, where an effective mechanism (e.g., market trading) to simultaneously confront severely diminished water availability and degraded water quality is required. Results of water transaction amounts, water allocation patterns, pollution mitigation schemes, and system benefits under various scenarios are analyzed, which indicate that a trading-mechanism is a more sustainable method to manage water-environment crisis in the study region. Additionally, consideration of anthropogenic (e.g., a risk-averse attitude) and systemic factors (e.g., the robustness coefficient) can support the generation of a robust plan associated with risk control for WAQT when uncertainty is present. These findings assist local policy and decision makers to gain insights into water-environment capacity planning to balance the basin's social and economic growth with protecting the region's ecosystems.

An application of normative decision theory to the valuation of energy efficiency investments under uncertainty

Abstract Previous market analyses have projected a huge potential for reduction of building energy consumption through retrofits. Large-scale application of currently available technologies could save at least 20% of the total energy consumed by buildings in the next decade. It is expected that such savings will not be realized unless involved parties, and in particular the clients and investors, trust the predicted savings and can engage in a transparent contracting process. The authors argue that this requires a comprehensive and innovative investment valuation method. Conventional investment decisions in energy efficiency rely on deterministic analysis of net present value and internal rate of return, both of which suffer from two critical drawbacks. First, the assumption that decisions are made with certain, accurate information is unrealistic. The other drawback is that traditional investment valuation does not take the risk preferences of each decision maker into account, hence it does not recognize that a certain decision maker will weigh the risk of future loss more heavily than a potential gain, or vice versa. This paper aims to address these drawbacks by applying axiomatic utility theory as a normative basis for decision-making. We analyze optimal decisions involved in drawing up a performance contract that accounts for uncertainty and uses explicit risk preferences of the decision maker. How this can work in practice is demonstrated in a real-life case study. It is discussed how the proposed methodology manages uncertainty in energy saving predictions while incorporating a decision maker's risk preferences. Results show that the methodology results in a decision that will rationally maximize the decision maker's value. As energy performance becomes an important factor in awarding construction projects, the availability of risk-transparent information will increase the willingness of all parties to follow through with a contract and agree on the budget. Moreover, the downstream automation of the decision process that maximizes all decision makers' values will lead to a higher realization rate, thus benefitting the construction market as a whole.

A projection-based TODIM method under multi-valued neutrosophic environments and its application in personnel selection

The personnel selection is a vital activity for companies, and multi-valued neutrosophic sets (MVNSs) can denote the fuzziness and hesitancy in the processes of the personnel selection. The extant fuzzy TODIM (an acronym in Portuguese of interactive and multi-criteria decision-making) methods take advantage of distance to denote the difference between two fuzzy sets (FSs). Nevertheless, the distance measurement, which ignores the included angle between two FSs, cannot comprehensively reflect the difference between two FSs. To cover this defect, a projection-based TODIM method with MVNSs for personnel selection is established to consider the risk preference of decision-makers and overcome the defect of the extant fuzzy TODIM methods. The proposed TODIM method makes use of an improved comparison method which overcomes the deficiency of extant comparison method. Furthermore, a projection-based difference measurement is defined and utilized in the projection-based TODIM method. We conduct a numerical example of the personnel selection to explain the application of the projection-based TODIM method and discuss the influence of the parameter. Finally, the proposed method is compared with several extant methods to verify its feasibility.

A Generalized PROMETHEE III with Risk Preferences on Losses and Gains

This study aims to generalize the Preference Ranking Organization METHod for Enrichment Evaluations (PROMETHEE) III model by introducing risk preferences of decision makers. The risk preferences are expressed by an S-shaped value function with gain and loss parts. This study then illustrates an environmental evaluation of waste treatment plants for waste electrical and electronic equipment (WEEE) in Taiwan. Sensitivity analysis and the rank test demonstrate that the proposed model is rather stable. The PROMETHEE methods have been involved in various applications, especially in environmental management. One core process of PROMETHEE is to establish a preference difference function with two types of thresholds. The range of the slope lines of the linear preference is within the interval of [0, 1]. Working from the concept of the prospect theory, we extend its S-shaped function to the interval range of [-1, 1] so as to express risk preferences that occur in two quadrants. This research assesses a project on 15 local WEEE treatment plants to promote their recycling capability and technology competitiveness. According to the five aspects, the performance measures of the plants are obtained from a field study. The proposed model has an advantage on rank invariance by changing the thresholds in our case with sensitivity analysis demonstrating the robustness of the model. The generalized PROMETHEE III with risk preferences indeed provides an extension for making a decision in an uncertain environment.

Simplified Neutrosophic Linguistic Multi-criteria Group Decision-Making Approach to Green Product Development

For many companies, green product development has become a key strategic consideration due to regulatory requirements and market trends. In this paper, the life cycle assessment technique is used to develop an innovative multi-criteria group decision-making approach that incorporates power aggregation operators and a TOPSIS-based QUALIFLEX method in order to solve green product design selection problems using neutrosophic linguistic information. Differences in semantics as well as the risk preferences of decision-makers are considered in the proposed method. The practicality and effectiveness of the proposed approach are then demonstrated through an illustrative example, in which the proposed method is used to select the optimum green product design, followed by sensitivity and comparative analyses.

Biodiesel supply chain optimization via a hybrid system dynamics-mathematical programming approach

Development of biofuels causes reduction of environmental pollution, but certain limitations affect their production. In this research, a hybrid system dynamics-mathematical programming approach is developed to design and plan a biodiesel supply chain from biomass fields to consumption markets. The supply chain faces limitations in biodiesel production. Water resource limitations for biodiesel production, land limitations for biomass procurement, and technological issues are the most important limitations considered in the system dynamics model. In addition, competition between fossil fuels and biodiesel is taken into account. The proposed methodology, firstly, estimates the most important parameters in biodiesel supply chain in a given planning horizon. Then, estimated parameters are used as inputs of the mathematical model and the optimal supply chain decisions are made by means of a stochastic mixed-integer programming model. Besides, a scenario-based approach is used to model the disruption risks for links and biomass fields. Finally, a numerical experiment is presented to show the applicability of the methodology according to some interviews with experts in Iran. Results demonstrate the potential appropriate market of biodiesel in Iran while several resource and technology limitations and environmental pollution avoid growth of biodiesel market. Moreover, a sensitivity analysis is performed on risk preferences of decision makers and government policies adopted to improve the biodiesel market.

A new method for prioritized multi-criteria group decision making with triangular intuitionistic fuzzy numbers

The triangular intuitionistic fuzzy number (TIFN) is a special intuitionistic fuzzy set defined on the real number set. The multi-criteria group decision making (MCGDM) with the prioritization among the criteria is called the prioritized MCGDM. The aim of this paper is to develop some prioritized geometric operators for TIFNs and apply to the prioritized MCGDM problems with TIFNs. Firstly, the weighted possibility means of TIFNs are defined and hereby a new lexicographic approach is given to rank the TIFNs sufficiently considering the risk preference of decision maker. Then, three kinds of prioritized geometric operators for TIFNs are developed including the prioritized weighted geometric (TIFPWG) operator, prioritized ordered weighted geometric (TIFPOWG) operator and prioritized hybrid weighted geometric (TIFPHWG) operator of TIFNs. Utilized the TIFPWG operator, the criteria values of alternatives are aggregated into the individual overall values of alternatives, which are further integrated into the collective ones by the TIFPHWG operator. The ranking order of alternatives is generated according to the collective overall values of alternatives. Finally, the system analyst selection example is analyzed to demonstrate the applicability and validity of the proposed method.

Robust global sensitivity analysis under deep uncertainty via scenario analysis

Complex social-ecological systems models typically need to consider deeply uncertain long run future conditions. The influence of this deep (i.e. incalculable, uncontrollable) uncertainty on model parameter sensitivities needs to be understood and robustly quantified to reliably inform investment in data collection and model refinement. Using a variance-based global sensitivity analysis method (eFAST), we produced comprehensive model diagnostics of a complex social-ecological systems model under deep uncertainty characterised by four global change scenarios. The uncertainty of the outputs, and the influence of input parameters differed substantially between scenarios. We then developed sensitivity indicators that were robust to this deep uncertainty using four criteria from decision theory. The proposed methods can increase our understanding of the effects of deep uncertainty on output uncertainty and parameter sensitivity, and incorporate the decision maker's risk preference into modelling-related activities to obtain greater resilience of decisions to surprise.

The Likelihood Ranking Methods for Interval Type-2 Fuzzy Sets Considering Risk Preferences

This paper proposes a ranking method that considers the risk preferences of decision makers for multiple-attribute decision-making problems in a multiple-interval type-2 trapezoidal fuzzy set environment. First, decision makers are classified according to the risk preferences and a measurement method of risk preferences is proposed. Second, a risk preference decision matrix is obtained and a new calculation formula of likelihood is defined. Finally, we obtain the ranking results of alternatives by calculating the signed distance. Our example analysis shows that the proposed method is scientific and reasonable, and different risk preferences influence the results of decision making. Comparison with previous methods shows that the proposed algorithm is more feasible; it is applicable for decision making on both risk preferences and risk conservation.

Facilitating risky project negotiation: An integrated approach using fuzzy real options, multicriteria analysis, and conflict analysis

This paper proposes a risky project negotiation framework, comprising fuzzy real options, ordered weighted averaging (OWA), and the graph model of conflict resolution (GMCR). Fuzzy real options analysis is employed to evaluate risky projects where the value of managerial flexibility cannot be overlooked. For civil engineering project evaluation, OWA is used to estimate the parameters of the fuzzy real options model, such as initial values and volatilities, to reflect the risk preference of decision makers (DMs). Because it can take into account DMs’ values and preferences for a risky project, GMCR is utilized to identify equilibria, or potential resolutions of the conflict, among all possible states, or combinations of DMs’ choices. While an option pricing model is employed to estimate the values of decision makers (DMs) toward a risky project, all parts are integrated via fuzzy functions in this framework. A brownfield redevelopment case, the Ralgreen Community in Kitchener, Ontario, Canada, is used as a typical risky project to demonstrate how to apply this integrated approach. The main contribution of this paper is to demonstrate the comprehensive use of fuzzy methods to assess brownfield projects. For this purpose, stochastic methods face difficulties because risks cannot be fully reflected in market prices. Expert knowledge that is descriptive, multi-attribute, or subjective in nature can be effectively quantified using fuzzy representations.

Numerical Methods to Calculate Fuzzy Boundaries for Brownfield Redevelopment Negotiations

A numerical method is proposed to represent the likelihood of contamination of a brownfield using fuzzy boundaries, and then to estimate the parameters in a fuzzy real options model for brownfield evaluation from different decision maker perspectives. These different values can be used to facilitate negotiations on redevelopment projects. Linguistic quantifiers and ordered weighted averaging (OWA) techniques are utilized to determine the pollution likelihood at sample locations based on multiple environmental indicators. Risk preferences of decision makers are expressed as different “orness” levels of OWA operators, which affect likelihood estimates. When the fuzzy boundary of a brownfield is generated by interpolation of sample points, the parameters of fuzzy real options, drift rate and volatility, can be calculated as fuzzy numbers. Hence, this proposed method can act as an intermediate between decision makers and the fuzzy real options models, making this model much easier to apply. A potential negotiation support system (NSS) implementing these numerical methods is discussed in the context of negotiating brownfield redevelopment projects. A public–private-partnership will be enhanced through information sharing, scenario generation, and conflict analysis provided by the NSS, encouraging more efficient brownfield redevelopment and leading to greater regional sustainability.

Two-stage credibility-constrained programming with Hurwicz criterion (TCP-CH) for planning water resources management

In water-resources management problems, uncertainties exist in a number of impact factors and water-allocation processes, which can bring about enormous difficulties and challenges in generating desired decision alternatives under such complexities. In this study, a two-stage credibility-constrained programming with Hurwicz criterion (TCP-CH) approach is developed for water resources management and planning under uncertainty. TCP-CH can tackle uncertainties presented as probability distributions and fuzzy sets that exist in left- and right-hand sides of constraints and in objective function; it can also permit in-depth analyses of various policy scenarios based on confidence degrees that are associated with different levels of economic penalties when the promised targets are violated. The TCP-CH method is applied to a real case of planning water resources in Kaidu-qongque River basin, which is one of the aridest regions of China. Support-vector-regression (SVR) technique is introduced into TCP-CH for predicting the water demand in the future, which takes advantage of uncertainty reflection and dynamic analysis. Results of water allocation, water shortage, system benefit, and economic penalty are obtained. The results discover that water deficit has brought negative effects on regional economic development, particularly for agricultural production activities. Moreover, tradeoffs between economic benefit and system-failure risk are also examined under different risk preferences of decision makers (i.e., optimistic and pessimistic criteria), which support generating an increased robustness in risk control for water resources allocation under uncertainties. These findings can facilitate the local decision makers in adjusting the current water-allocation pattern based on risk preference option (Hurwicz criterion) to satisfy the increasing water demand.

Minimizing the Cost of Keeping Options Open for Conservation in a Changing Climate

Policy documents advocate that managers should keep their options open while planning to protect coastal ecosystems from climate-change impacts. However, the actual costs and benefits of maintaining flexibility remain largely unexplored, and alternative approaches for decision making under uncertainty may lead to better joint outcomes for conservation and other societal goals. For example, keeping options open for coastal ecosystems incurs opportunity costs for developers. We devised a decision framework that integrates these costs and benefits with probabilistic forecasts for the extent of sea-level rise to find a balance between coastal ecosystem protection and moderate coastal development. Here, we suggest that instead of keeping their options open managers should incorporate uncertain sea-level rise predictions into a decision-making framework that evaluates the benefits and costs of conservation and development. In our example, based on plausible scenarios for sea-level rise and assuming a risk-neutral decision maker, we found that substantial development could be accommodated with negligible loss of environmental assets. Characterization of the Pareto efficiency of conservation and development outcomes provides valuable insight into the intensity of trade-offs between development and conservation. However, additional work is required to improve understanding of the consequences of alternative spatial plans and the value judgments and risk preferences of decision makers and stakeholders.