Confidence Of Decision Makers Research Articles

Long- and Short-Term Memory Model of Cotton Price Index Volatility Risk Based on Explainable Artificial Intelligence.

Market uncertainty greatly interferes with the decisions and plans of market participants, thus increasing the risk of decision-making, leading to compromised interests of decision-makers. Cotton price index (hereinafter referred to as cotton price) volatility is highly noisy, nonlinear, and stochastic and is susceptible to supply and demand, climate, substitutes, and other policy factors, which are subject to large uncertainties. To reduce decision risk and provide decision support for policymakers, this article integrates 13 factors affecting cotton price index volatility based on existing research and further divides them into transaction data and interaction data. A long- and short-term memory (LSTM) model is constructed, and a comparison experiment is implemented to analyze the cotton price index volatility. To make the constructed model explainable, we use explainable artificial intelligence (XAI) techniques to perform statistical analysis of the input features. The experimental results show that the LSTM model can accurately analyze the cotton price index fluctuation trend but cannot accurately predict the actual price of cotton; the transaction data plus interaction data are more sensitive than the transaction data in analyzing the cotton price fluctuation trend and can have a positive effect on the cotton price fluctuation analysis. This study can accurately reflect the fluctuation trend of the cotton market, provide reference to the state, enterprises, and cotton farmers for decision-making, and reduce the risk caused by frequent fluctuation of cotton prices. The analysis of the model using XAI techniques builds the confidence of decision-makers in the model.

Conflict management-based consensus reaching process considering conflict relationship clustering in large-scale group decision-making problems

In large-scale group decision-making (LSGDM) events, conflicts among decision makers (DMs) usually occur, causing serious damage to the decision-making process. Accurate conflict detection and timely management in LSGDM can improve the efficiency of the consensus reaching process (CRP) and reduce the overall conflict degree. This paper presents a conflict management-based consensus reaching process (CM-CRP) to achieve centralized and efficient management of DMs. In CM-CRP, to further manage the conflict relationships among DMs, a new clustering algorithm is considered where DMs with conflicting opinions are clustered into a subgroup. To improve the accuracy of the management and precisely the portray behaviors of DMs, for a multi-attributes LSGDM event, the confidence of DMs at the attribute level is captured. Further, the dynamic opinion weight operator is proposed in CM-CRP combining the confidence level and conflict degree of DMs, which enables a more accurate measurement of DMs’ receptivity to others’ opinions. Comparative experiments prove that the proposed CM-CRP outperforms the existing CRP, and several simulations investigate the effect of different factors on the CM-CRP.

An experiment on the impact of predictive analytics on kidney offers acceptance decisions

Because of the breadth of factors that might affect kidney transplant decisions to accept an organ or wait for another, presumably “better” offer, a high degree of heterogeneity in decision making exists among transplant surgeons and hospitals. These decisions do not typically include objective predictions regarding the future availability of equivalent or better-quality organs or the likelihood of patient death while waiting for another organ. To investigate the impact of displaying such predictions on organ donation decision making, we conducted a statistically designed experiment involving 53 kidney transplant professionals, in which kidney offers were presented via an online application and systematically altered to observe the effects on decision making. We found that providing predictive analytics for time-to-better offers and patient mortality improved decision consensus and decision-maker confidence in their decisions. Providing a visual display of the patient’s mortality slope under accept/reject conditions shortened the time-to-decide but did not have an impact on the decision itself. Presenting the risk of death in a loss frame as opposed to a gain frame improved decision consensus and decision confidence. Patient-specific predictions surrounding future organ offers and mortality may improve decision quality, confidence, and expediency while improving organ utilization and patient outcomes.

Risk analysis of impacts of climate change on changes in municipal wastewater production in wastewater reuse plans

ABSTRACT Long-term changes in municipal wastewater production may disrupt wastewater reuse plans. However, no model/method has been developed to quantify the risks of climate change impacts on changes in wastewater production under relevant uncertainties. Accordingly, this research proposed a hybrid algorithm to analyze these risks under greenhouse gas concentration scenarios. This algorithm included a fuzzy risk assessment model based on six risk components and a framework for quantification of these components, which increased the relative confidence of decision-makers about risk values by 10%. The results showed that ‘considering’ and ‘ignoring’ the adaptive capacity to climate change under Representative Concentration Pathway scenarios in the risk analysis process lead to two completely different interpretations of risk. The risk difference based on these two approaches for Tehran was 50%, which was due to the occurrence of 11%–22% of untreated excess wastewater beyond capacity of wastewater reuse system under the temperature increase of 1.29°C.

Farmers’ Credit Risk Assessment Based on Sustainable Supply Chain Finance for Green Agriculture

With the development of green agriculture, the demand of farmers for operation loans is increasing. Supply chain finance is becoming a new way to solve the problem of difficult credit in agricultural development. As the importance of sustainability issues continues to rise, there are growing numbers of practical examples of combining agricultural supply chain finance (ASCF) with sustainability, and the attendant risks are emerging. The objectives of this study are first to construct a risk indicator system for sustainable ASCF, then to propose a fuzzy decision method that considers the confidence of decision-makers, and finally to perform a risk assessment of a credit case in the coffee bean supply chain. A combination of the neutrosophic enhanced best–worst method (NE-BWM) and combined compromise solution (COCOSO) is used to evaluate risk problems. The practicality and effectiveness of this research method is verified by a numerical simulation and a comparison with the method. The results show that the credit rating of core companies is the most important indicator. In the context of green and sustainable development, this indicator system is more suitable for the current green transformation development of agriculture and can help decision-makers scientifically and reasonably assess the risk level of ASCF. When loans are needed to transform green agriculture, this study provides new ideas for credit models for various actors in the agricultural supply chain and offers a new entry point to the issue of sustainable agricultural development.

The application of applied category theory to quantify mission success

Mission engineering is the quantification of the effects applied by a system of systems to achieve measurable desired results. The execution of the mission is defined by a mission thread; that is the sequence of actions/processes executed by elemental systems. The domain of complex missions has been described as “wicked” because traditional military and space program-based systems engineering practices fail due to a lack of discrete phases, a dependence on context, and the non-uniqueness of a “good-enough” mission thread. Wicked problems also tend to be unstructured (non-hierarchical) with no centralized control and do not lend themselves to linear step-by-step processes. Wicked problems are inherently uncertain leading to the broader issue of trust across a mission knowledge base, and any mission level analyses. Wicked problems are also characterized and challenged by combinatoric complexity. Mission success is primarily driven by the interrelationships between systems and not just by the individual systems themselves. The success of mission engineering will require an iterative approach of modeling, simulation, and analysis resulting in a continuous reduction in uncertainty and refinement in the topology of the mission thread. OODA-based decomposition of mission threads focused on Boyd’s Orient function provides focus on system interrelationships. Trust provides decision-maker confidence in the results that has proved elusive to traditional validation approaches. The approach described in this paper is based upon using applied category theory as a basis for building mission threads, mission models, data stores, and integrating simulation ensembles.

ANALYSIS OF INDEPENDENT ASSURANCE STATEMENTS: BIST SUSTAINABILITY INDEX

Purpose- Assurance engagements are conducted to increase the confidence of decision makers in this information by auditing the non-financial information about companies by an independent institution. Companies disclose their non-financial information in their sustainability reports or integrated reports, and if they have received assurances from independent audit firms for this information, they also include independent assurance statements in these reports. The purpose of this study is to determine whether companies included in the BIST Sustainability Index (XUSRD) have an independent assurance statement regarding non-financial performance indicators, and to examine the content of these statements, if any. Methodology- For this purpose, the year 2020 sustainability reports and integrated reports of 65 companies included in the BIST Sustainability Index were examined, and the independent assurance statements included in these reports were analyzed using the content analysis technique, one of the qualitative analysis methods. Findings- As a result of the research, 22 (34%) of the 65 companies included in the BIST Sustainability Index have an independent assurance statement from independent audit firms. It has been determined that the companies received an independent assurance statement 36.4% are manufacturing, 36.4% are financial institutions, the remaining 27.2% is in wholesale and retail trade, electricity, gas and water and construction and public works sectors. All 22 companies with independent assurance statements received limited assurance. It is among the findings that 2 (9%) of these companies received assurance only for environmental indicators, 9 (41%) for environmental and social indicators, 11 (50%) for environmental, social and economic indicators. In addition, all of the independent audit firms that give an independent assurance statement consist of three big audit firms within the big four. It has been found that there are 2 statements in which only the ISAE 3000 standard is used in the assurance engagement, 17 statements that the ISAE 3000 standard is used with the ISAE 3410 standard, 2 statements that the ISAE 3000 standard is used with the ISO 14064-3 standard, and 1 statement that the ISAE 3000 standard is used with the AA1000 standard. Conclusion- It has been seen that even in the BIST Sustainability Index, which includes companies traded on Borsa Istanbul with a high level of sustainability performance, companies do not demand much assurance. Assurance statements that guide decision makers about the accuracy of non-financial information are an indicator of the transparency of companies. The assurance of this information by the companies will increase the reliability and reputation of the companies. Keywords: Non-financial indicators, independent assurance statements, BIST Sustainability Index JEL Codes: M40, M42.

Gaining decision-maker confidence through community consensus: developing environmental DNA standards for data display on the USGS Nonindigenous Aquatic Species database

Gaining decision-maker confidence through community consensus: developing environmental DNA standards for data display on the USGS Nonindigenous Aquatic Species database

Generalized fuzzy number and graph theory matrix approach in fuzzy decision making of smartphone selection

A number of studies have proposed a method of ranking a smartphone due to the fact that newly smartphones in the market are offering more features which could give more benefit to the user and thus become increasingly important as a tool in helping them to make everyday decisions. But most of the studies have exhibited shortcoming associated to level of confidence of decision makers’ in giving a judgement, which significantly influences final ranking result. This study proposes Graph Theory and Matrix Approach (GTMA) on ranking smartphone alternatives that ensures consideration of uncertainty and confidence level upon their judgement towards smartphone selection. A Generalized Triangular Fuzzy Number (GTFN) is used in the calculation of interrelation matrix which leads to give more meaningful value of importance weights of the attributes. Selection index is then calculated to rank the smartphone through fuzzy determinant. A numerical example is provided to illustrate the proposed approach whereby five well-known smartphones brand are considered which are apple, Samsung, Huawei, OPPO and Asus. The result shows that Huawei is the most preferred brand followed by OPPO and Apple in selecting the best smartphone.

Dynamic Cooperative M odel for R anking Construction R isks using Monte Carlo Simulation

Construction projects suffer from diverse uncertainties that hinder the key objectives’ achievement. These uncertainties represent risks that may appear through the project life cycle. This paper introduces a quantitative model to estimate and rank risks dynamically during the risk planning phase. Such ranking would help decision-makers appropriately respond to and/or control construction risks. The model provides proper risk contingency reserves for both project time and cost that meet decision-makers' selected confidence levels using Monte Carlo Simulation (MCS). In order to quantify the project uncertainty, severities of residual risks are determined and allocated at the project's activities-level using a planning/scheduling spreadsheet model and a MCS tool suitable for spreadsheets. The model is able to calculate the contribution of each risk from the determined contingency at both the project level for both the time and cost at the decision-maker confidence level.The model represents a direct implementation for a Risk Planning Contingency Model (RPCM); which involves four modules as follows: (1) Risk Register (RR), (2) Risk Allocator (RA), (3) Risk Simulator (RS), and (4) Contingency Calculator (CC). These modules are hosted in a critical path model scheduling spreadsheet to facilitate risk management. In addition, a simulation engine add-in is used for analyzing the probability distribution for the project time and cost outcomes. In order to verify the proposed model, the process and analysis have been applied to a case study project. The results show that the RPCM is capable to rank and estimate the residual risks in an easy, fast, and effective way.

Dealing with uncertainty in famine predictions: How complex events affect food security early warning skill in the Greater Horn of Africa

Early warning systems are essential tool for humanitarian preparedness and response. The diversity of inputs required, ranging from agricultural production estimates to market price variability and weather forecasts, means that interpreting food security signals is not an easy task. Each of these inputs is fraught with uncertainty which analysts need to assess when making projections about future food security. Understanding the accuracy rates of early warning systems is therefore of paramount importance to enable improvements to food security prediction. However, to date, limited analyses of early warning accuracy have been conducted. Here we analyze Famine Early Warning System Network (FEWS NET) early warning data for the Greater Horn of Africa and show that, despite accuracy in projections, there remain important challenges for food security projections. The two major sources of uncertainty are associated with complex weather phenomena and conflict – with uncertainty in weather forecasts being twice as important as conflict in overall FEWS NET accuracy. Indeed, the least accurate projections are recorded in seasons with particularly complex weather events such as the 2015/2016 El Niño Southern Oscillation as well as in zones that are affected by internal conflict (e.g. South Sudan). With respect to predicting crisis transitions, areas with more frequent transitions tend to be more accurate, possibly because predicting the drivers behind these transitions are better understood. Our novel analysis provides a framework to invest resources in specific aspects of early warning. We also hope that by measuring the reliability of these systems, we can increase the confidence of decision makers to act early to mitigate the growing risks posed by hunger and famine.

Evaluating social sustainable development factors using multi-experts Z-fuzzy AHP

This study presents a multi-experts multiple criteria decision making approach for quantitatively evaluating social sustainable development factors. The proposed model which integrates Z-fuzzy numbers and fuzzy AHP enables to weight and rank social sustainable development factors, which may give guidance to many sustainable development researches. In addition to the first usage of the Z-fuzzy numbers for the weighting decision of social sustainable development factors, another contribution of the study is presenting the Z-fuzzy numbers integrated AHP method with multi-experts which can be useful in many problems and applications containing uncertainty. The most important advantage of the Z-fuzzy numbers integrated AHP method is that it allows the degree of confidence of decision makers to be included to the calculations. An application of the proposed approach is also presented for prioritizing the social sustainable development factors based on the experts’ evaluations together with a sensitivity analysis.

A fuzzy goal programming approach to solving decentralized bi-level multi-objective linear fractional programming problems

This paper presents a new approach for solving decentralized bi-level multi-objective linear fractional programming problems. The main goal was to find a simple algorithm with high confidence of decision-makers in the results. First, all the linear fractional programming models on the given set of constraints were solved separately. Next, all the linear fractional objective functions were linearized, membership functions of objective functions and decision variables controlled by decision-makers at the highest level calculated, and a fuzzy multi-objective linear programming model formed and solved as linear goal programming problem by using simplex algorithm. The efficiency of the proposed algorithm was investigated using an economic example, and the obtained results compared with those obtained using an existing method.

SuDS & Sponge Cities: A Comparative Analysis of the Implementation of Pluvial Flood Management in the UK and China

In recent decades, rapid urbanization has resulted in a growing urban population, transformed into regions of exceptional socio-economic value. By removing vegetation and soil, grading the land surface and saturating soil air content, urban developments are more likely to be flooded, which will be further exacerbated by an anticipated increase in the number of intense rainfall events, due to climate change. To date, data collected show that urban pluvial flood events are on the rise for both the UK and China. This paper presents a critical review of existing sustainable approaches to urban flood management, by comparing UK practice with that in China and critically assessing whether lessons can be learnt from the Sponge City initiative. The authors have identified a strategic research plan to ensure that the sponge city initiative can successfully respond to extreme climatic events and tackle pluvial flooding. Hence, this review suggests that future research should focus on (1) the development of a more localized rainfall model for the Chinese climate; (2) the role of retrofit SuDS (Sustainable Drainage Systems) in challenging water environments; (3) the development of a robust SuDS selection tool, ensuring that the most effective devices are installed, based on local factors; and (4) dissemination of current information, and increased understanding of maintenance and whole life-costing, alongside monitoring the success of sponge cities to increase the confidence of decision makers (5) the community engagement and education about sponge cities.

Evaluating fuzzy risk based on a new method of ranking fuzzy numbers using centroid of centroids

This paper describes a method to rank fuzzy numbers based on centroid of centroids of fuzzy numbers and emphasises the use of the subjectivity of the decision maker's view point, such as optimistic or pessimistic. Using the decision maker's view point and index of modality, a ranking index of each fuzzy number is calculated which serves as a criterion for ranking fuzzy numbers. The proposed fuzzy ranking method is used to analyse the fuzzy risk involved in manufacturing products by different companies, where the probability of failure of a product is represented by a fuzzy number. The proposed method is more flexible than the existing methods as it takes into consideration the degrees of confidence of decision maker's opinion in both the stages.

Seasonal Forecasts of the Summer 2016 Yangtze River Basin Rainfall

The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage flood waters, and are important sources of electricity for the region. Being able to forecast high-impact events at long lead times therefore has enormous potential benefit. Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services. The teleconnection from El Ni\~no to Yangtze River basin rainfall meant that the strong El Ni\~no in winter 2015/2016 provided a valuable opportunity to test the application of a dynamical forecast system. This paper therefore presents a case study of a real time seasonal forecast for the Yangtze River basin, building on previous work demonstrating the retrospective skill of such a forecast. A simple forecasting methodology is presented, in which the forecast probabilities are derived from the historical relationship between hindcast and observations. Its performance for 2016 is discussed. The heavy rainfall in the May-June-July period was correctly forecast well in advance. August saw anomalously low rainfall, and the forecasts for the June-July-August period correctly showed closer to average levels. The forecasts contributed to the confidence of decision-makers across the Yangtze River basin. Trials of climate services such as this help to promote appropriate use of seasonal forecasts, and highlight areas for future improvements.

A mixed-model multi-objective analysis of strategic supply chain decision support in the Thai silk industry

This paper presents a methodology for combined usage of data envelopment analysis (DEA), analytical hierarchy process (AHP) and extended goal programming (EGP) in order to provide managerial decision support. The methodology allows the three techniques to be used in a coordinated manner to give an enhanced level of holistic decision support. DEA is first used in a descriptive sense in order to provide information regarding the efficiency of a set of units. The AHP is then used in order to determine the importance of criteria arising from decision problem(s) related to the improvement of unit efficiency. Finally, EGP is used in a prescriptive sense in order to select a set of specific actions for improving unit efficiency. Two specific multi-objective situations arising from the Thai Silk industry are used as case studies for the proposed methodology. These involve supplier selection and inventory management system management in the presence of multiple conflicting goals and objectives. In the case studies, DEA is used to provide efficiency estimates of current suppliers and processes. AHP is then used in order to determine the relative importance of criteria for supply chain efficiency improvement. Adaptations are made to an automated inconsistency reduction algorithm in order to resolve high levels of inconsistency found. The relation between decision maker confidence and consistency is investigated. Finally, an EGP model is built in order to suggest improvement actions to the supply chain processes. Results are given for a set of eight Thai silk manufacturers and conclusions are drawn.

Integration of Fuzzy Analytic Hierarchy Process into multi-objective Computer Aided Molecular Design

In this paper, a novel Computer Aided Molecular Design (CAMD) framework is developed to solve multi-objective molecular design problems. CAMD can be formulated as a multi-objective optimisation problem when there are multiple target properties to be optimised simultaneously. A major obstacle faced by multi-objective CAMD problems is the difficulty in assigning weighting factors to the target properties, since the relative importance of these factors is not always defined. It is particularly difficult to compare target properties which belong to different categories, such as physicochemical, safety, health and environmental properties, on a common scale. This paper presents a systematic CAMD algorithm built on Fuzzy Analytic Hierarchy Process (FAHP) to deal with the ambiguity involved in evaluating the weights of target properties in multi-objective CAMD problem. Instead of using exact numerical values, FAHP approach expresses the pairwise comparison of target properties through triangular fuzzy numbers, which allow the degree of confidence of decision maker to be quantified. Hence, the proposed approach can address the uncertainties arising from ambiguity involved during value judgement elicitation in multi-objective CAMD problems. The solutions generated provide a better balance of performance for a set of identified target properties. The proposed methodology is illustrated through a case study on designing a better solvent for extracting residual oil from palm pressed fibre.

A Comparison Contrast of Keynes's 1933-1935 IS-LM Model Incorporating Degree of Confidence (The State of the News) with Hicks' 1937 IS-LM Model without Degree of Confidence: D. Champernowne Got It Right in 1936

Between 1933 and 1935, Keynes used his own original version of the IS-LM model in his lectures concerning his new approach to economic theory, the General Theory. This model explicitly incorporated the degree of confidence that decision makers have in their calculated expectations, which is a probabilistic concept, in both the IS curve and LM curve in the variable termed the “State of the News”. Keynes did not explicitly emphasize this model in the General Theory because he could not explicitly make use of his interval valued approach to probabilities, which occurs if there is any state less then complete confidence. Instead, he substituted a generalized version, his D-Z model, of AC Pigou’s special theory of an aggregate demand function and an aggregate supply function, for his original IS-LM model. Keynes carried out this analysis in chapter 3, the appendix to chapter 19, chapter 20, and chapter 21 of the General Theory. This allowed him to specify his Aggregate Supply Curve, which is a locus of all expected, possible D-Z intersections that satisfy the necessary and sufficient first and second order conditions for maximizing economic profits in a purely competitive world in the short run with one variable input, labor, and one fixed input, capital. Keynes was able to do this because he replaced Pigou’s realized prices and profits with a set of expected prices and expected profits, expectations, that incorporated the idea of definite, numerical, as well as indefinite non-numerical (intervals) probability. Champernowne explicitly incorporated the State of the News into both of his IS-LM functions as independent variables and explicitly showed that his variable, which would shift both curves, was missing from the classical and neoclassical models where expectations are strictly based only on a concept of precise probability. Hicks read Champernowne’s paper before he submitted his own paper for publication in Econometrica in 1937. He deliberately left this variable out because he knew that it did not fit into the classical model he had created for comparison with Keynes’s model and did not understand what Keynes was talking about because his concept of probability interpreted the probability estimate as being the confidence expressed by a decision maker. Nevertheless, Hicks then attempted to rectify this situation by specifying a completely, infinitely elastic left tail for his LM curve that did reflect, but in an ad hoc manner, the lack of confidence of decision makers in the state of the news.

Development of an algorithm for risk-based management of wastewater reuse alternatives

Abstract Due to water resources limitations, special attention has been paid to wastewater reuse in recent years. The risks associated with wastewater reuse alternatives should be considered in decision-making. Even when selecting the alternative with the least risk, risk management issues are of high importance. This study aims to develop an algorithm for risk-based management of wastewater reuse alternatives. This algorithm uses a three-step risk assessment and management approach. Risks are identified, then risks of alternatives are assessed, and, finally, risk management measures are proposed for risk reduction in the selected alternative. In risk identification, economic, social, health, and environmental aspects are taken into account. In risk assessment, its three components of likelihood, severity, and vulnerability are considered through a fuzzy inference system. Alternatives are prioritized based on calculated risks using a fuzzy VIKOR method. A case study is presented in which the proposed algorithm is used to select the best alternative for reuse of treated wastewater from Ekbatan Town, located in the western part Tehran in Iran. The results showed that the proposed approach provides the users with an easier understanding of risks and increases the relative confidence of decision-makers about the selection of the best alternatives for wastewater reuse and their risk control methods.