Decision Costs Research Articles

Decision Rules for Renewable Energy Utilization Using Rough Set Theory

Rough Set (RS) theory is used for data analysis and decision making where decision-making rules can be derived through attribute reduction and feature selection. Energy shortage is an issue for governments, and solar energy systems have become an important source of renewable energy. Rough sets may be used to summarize and compare rule sets for different periods. In this study, the analysis of rules is an element of decision support that allows organizations to make better informed decisions. However, changes to decision rules require adjustment and analysis, and analysis is inhibited by changes in rules. With this consideration, a solution approach is proposed. The results show that not only can decision costs be reduced, but policymakers can also make it easier for the public to understand the incentives of green energy programs and the use of solar panels. The application process is simplified for the implementation of sustainable energy policies.

Constructing shadowed set based on game analysis of uncertainty and decision cost

As a three-way approximation scheme to deal with fuzzy set, shadowed set approximately divides fuzzy objects with uncertainty into elevated, reduced, and shadow areas by a pair of decision thresholds α,β, thus effectively reducing the uncertainty of information. However, the current construction of shadowed set is all based on a single principle, lacking the comprehensive consideration of multiple principles, and these construction principles focus on the rationality of semantic interpretation of model construction process and threshold determination, and lack of consideration of the validity of partition results. To resolve these issues, this paper proposes a new shadowed set model through the game analysis between uncertainty and decision cost (UC-GTSS). First, Combining shadowed set construction with game theory, UC-GTSS is proposed based on game competition mechanism. Through the game analysis of uncertainty and decision cost, the optimal α,β is found, and a game payoff fusion algorithm based on Topsis is proposed. Second, the definition of UC-GTSS model expression, game player, strategy, payoff function, equilibrium analysis, optimization α,β determination and the influence of model parameters are analyzed and discussed. Third, the UC-GTSS model is extended and discussed based on the validity of the partition results, such as the game between approximate classification accuracy and coverage, the game between the Gini coefficient of the partition areas. Finally, through the analysis of algorithms, instances and data experiments, the construction process, validity, and rationality of UC-GTSS and its extended model are demonstrated.

Can Artificial Intelligence help a clinical laboratory to draw useful information from limited data sets? Application to mixed connective tissue disease

<p>Diagnosis is a key step of patient management. During decades, refined decision algorithms and numerical scores based on conventional statistical methods were elaborated to ensure optimal reliability. Recently, a number of machine learning tools were developed and applied to process more and more extensive data sets, including up to millions of items and yielding sophisticated classification models. While this approach met with impressive efficiency in some cases, practical limitations stem from the high number of parameters that may be required by a model, resulting in increased cost and delay of decision making. Also, information relative to the specificity of local recruitment may be lost, hampering any simplification of universal models. Here, we explored the capacity of currently available artificial intelligence tools to classify patients found in a single health center on the basis of a limited number of parameters. As a model, the discrimination between systemic lupus erythematosus (SLE) and mixed connective tissue disease (MCTD) on the basis of thirteen biological parameters was studied with eight widely used classifiers (including logistic regression, support vector machine, nearest neighbor classifier, random forests and neural networks). A retrospective study including 44 patients (34 SLE, 10 MCTD) was conducted in Marseilles hospital organization. The best area under ROC curve yielded on test sets with classifiers using all 13 parameters was 0.83 ± 0.03 standard error and 0.86 ± 0.02 SE with 5 selected parameters. It is concluded that classification efficiency may be significantly improved by a knowledge-based selection of discriminating parameters.</p>

Achieving Extremely Low Latency: Incremental Coding for Real-Time Applications

Extremely low-latency communication has attracted considerable recent attention because it holds the promise of supporting emerging real-time applications such as autonomous driving, smart grids, and Industrial Internet of Things (IIoT). Owing to the limited bandwidth in wireless environments, the sub-packets or even bits have to be transmitted successively, thereby inducing non-negligible delay-induced cost for real-time remote monitoring, estimation, decision making, and control. In this paper, we present a unified incremental decoding framework for real-time applications, the costs of which are extremely sensitive to the latency of each individual sub-packet or bit. In contrast to conventional methods, in which a decision is made after fully decoding the entire packet, the incremental decoding strategy allows monitors or actors to make their decisions in real time based on partially received packet. By this means, there is no need to wait for the whole packet to be decoded, thereby reducing the delay-induced costs substantially. To minimize cumulative cost during the real-time monitoring and control, we design source coding and decision making algorithms jointly, in which a backward induction property is found. Furthermore, we conceive a dynamic programming algorithm for a given source codebook to significantly reduce the cumulative decision costs while maintaining low computational complexity.

Multi-criteria Decision Support System for Prioritizing Challenges of Internet of Things (IoT)

Abstract: The adoption and utilization of Internet of Things (IoT) technologies such as IoT Platforms, 5G, connected sensors, WAN, etc mostly come with numerous, seemingly complex and expensive challenges for institutions and organisations, particularly in developing countries as they work to fill their fair share of the global IoT market. Multi-criteria decision support systems (MCDSS) can be indispensable tools for evaluating and prioritizing these complex competing difficulties by multistakeholder decision makers. While eliminating conventional decision problems such as, human biases, time consumption, cost of decision, etc, MCDSS make it possible for companies to concentrate on solving challenges which their limited resources can cater for. This study presents an approach for design and implementation of a prototype MCDSS for prioritizing challenges that institutions and organizations face while adopting and utilizing IoT technologies using Fuzzy Analytic Hierarchy Process (FAHP) methodology. ASP.Net model View controller (MVC) framework and C Sharp programming Language was used for the GUI and the Logic development. The system's default IoT challenges and dataset were adapted from the works of A.K. Mohammadzadeh (Baseline dataset). The system usability test result obtained shows that the system is friendly and usable. The output weights and rating of the IoT technology adoption challenges/sub-challenges exhibit over 80% similarity when compared to the Baseline dataset. Further studies are recommended to make the system a

Sensor data reduction with novel local neighborhood information granularity and rough set approach

Data description and data reduction are important issues in sensors data acquisition and rough sets based models can be applied in sensors data acquisition. Data description by rough set theory relies on information granularity, approximation methods and attribute reduction. The distribution of actual data is complex and changeable. The current model lacks the ability to distinguish different data areas leading to decision-making errors. Based on the above, this paper proposes a neighborhood decision rough set based on justifiable granularity. Firstly, the rough affiliation of the data points in different cases is given separately according to the samples in the neighborhood. Secondly, the original labels are rectified using pseudo-labels obtained from the label noise data that has been found. The new judgment criteria are proposed based on justifiable granularity, and the optimal neighborhood radius is optimized by the particle swarm algorithm. Finally, attribute reduction is performed on the basis of risky decision cost. Complex data can be effectively handled by the method, as evidenced by the experimental results.

Multi-granularity active learning based on the three-way decision

The reliance on data and the high cost of data labelling are the main problems facing deep learning today. Active learning aims to make the best model with as few training samples as possible. Previous query strategies for active learning have mainly used the uncertainty and diversity criteria, and have not considered the data distribution's multi-granularity. To extract more valid information from the samples, we use three-way decisions to select uncertain samples and propose a multi-granularity active learning method (MGAL). The model divides the unlabeled samples into three parts: positive, negative and boundary region. Through active iterative training samples, the decision delay of the boundary domain can reduce the decision cost. We validated the model on five UCI datasets and the CIFAR10 dataset. The experimental results show that the cost of three-way decisions is lower than that of two-way decisions. The multi-granularity active learning achieves good classification results, which validates the model. In this case study, the reader can learn about the ideas and methods of the three-way decision theory applied to deep learning.

Management of Local Community-Based Tourism in West Sekotong Village – West Lombok Regency

The concept of CBT (community base tourism) in the management of a tourism object is a rational choice and answer to various weaknesses with the G + B (government and business world) approach. In CBT's view, management is also carried out by the local community/local community, so the pattern becomes G+B+C. This study aims to describe forms of tourism management by local communities, and their relationship with G+B. Besides that, the research seeks to explore the operationalization of one aspect of Sapta Pesona into programs/activities that have been carried out by the local community. The research location is in West Sekotong Village, West Lombok Regency, with key informants as respondents. The survey was also conducted on domestic tourists to get their opinion in the form of an assessment of the implementation of the Cleanliness Program, and as feedback (feed back) on the CBT management model. Full data analysis using qualitative data. The participation of the private sector. Important indicators in the CBT model are decision-making costs (DMC) and financing of the use of local human resources (utilization of human resource cost, UHRC) in building community participation. Based on a survey of domestic tourists on the Cleaning Program (from Sapta Pesona) in Sekotong Barat Village, it was shown that in general they thought the program was 2839relatively good, with a score of 4.104 from the range 1 (=very bad) – 5 (=very good).

Optimizing Management Decisions: Special Order Pricing in MSMEs

This study employs a descriptive quantitative research approach to analyze and test the application of the Analysis of Differential Costs in management decision making regarding the acceptance or rejection of special order prices in MSMEs. Data was collected through interviews and questionnaires, and the analysis focused on the differential costs incurred by Kedai Vivi, a representative MSME, in relation to alternative products. The results of the analysis indicated that accepting special orders in November 2021 yielded a profit of Rp. 1,119,800 for Kedai Vivi, while rejecting the special order would result in a profit of only Rp. 915,790. The findings highlight a positive differential profit, indicating that the differential cost was lower than the selling price set by MSME Kedai Vivi. This study provides valuable insights into the effective utilization of differential cost analysis in supporting management decision making within MSMEs, with implications for enhancing profitability and competitiveness in the global market. 
 Highlights:
 
 Differential cost analysis: Examining the differences in costs associated with alternative products or decisions, enabling informed management decision making.
 Special order pricing: Investigating the impact of accepting or rejecting special orders on profitability, considering factors such as selling price and differential costs.
 MSMEs and profitability: Exploring the implications of effective cost analysis and decision making for small and medium-sized enterprises, enhancing their profitability and competitiveness.
 
 Keywords: Differential costs, Management decision making, Special order pricing, MSMEs, Profitability analysis

‘But what if you miss something …?’: factors that influence medical student consideration of cost in decision making

ContextCost-conscious care is critical for healthcare sustainability but evidence suggests that most doctors do not consider cost in their clinical decision making. A critical step in changing this is understanding the barriers to encouraging behaviours and attitudes related to cost-conscious care. We therefore conducted a qualitative study to address the research question: what factors influence consideration of cost in emergency medicine (ED) clinical decision making?MethodsThis was a qualitative focus group study using patient vignettes to explore attitudes towards cost-conscious clinical decision making. Participants were Year 4 and Year 5 medical students from Singapore, a country with a fee-for-service healthcare system. After a data-driven initial data analysis, and to make sense of a multitude of factors impacting on cost conscious care, we selected Fishbein’s integrative model of behavioural prediction to underpin secondary data analysis.ResultsVia four focus groups with 21 participants, we identified five main themes relevant to the integrative model of behavioural prediction. These were: attitudes towards considering cost when managing a patient (e.g., “better safe than sorry”); normative beliefs (e.g., doing what others do, perceptions of patient wishes); efficacy beliefs (e.g., no authority to take decisions or challenge); skills and knowledge (e.g., little knowledge of costs), and environmental constraints (e.g., the nature of the healthcare system).DiscussionMedical students do not consider cost in their clinical decision making due to numerous factors, of which lack of knowledge of costs is but one. While some of the factors identified reflect those found in previous studies with residents and fully-trained staff, and in other contexts, theory driven analysis added value in that it facilitated a richer exploration of why students do not consider cost in clinical decision making. Our findings provide insight to inform how best to engage and empower educators and learners in teaching and learning about cost-conscious care.

#3143 MEASURING CLINICAL JUDGEMENT IN PROSPECTIVE KIDNEY TRANSPLANT RECIPIENTS: A FEASIBILITY STUDY

Abstract Background and Aims Exercising clinical judgement is becoming more challenging in the context of ageing populations, increasing prevalence of multimorbidity and frailty, and insufficient evidence regarding how best to manage increasing complexity in clinical practice. Measures of clinical judgement are surprisingly absent from health information systems despite their clear importance and the potential costs of poor decisions. Suitability for kidney transplantation is a high-stake clinical judgement, requiring consideration of complex biopsychosocial patient and organ-utilisation factors. This study aimed to test if it is feasible to assess clinical judgement quantitatively from routine clinical documentation in kidney transplant recipients. Method Participants were identified from a regional register of patients on haemodialysis, who underwent deceased donor kidney transplantation (KTR) and had at least 1 year of follow-up. We identified 15 KTR who had poor outcomes, defined as more than 6 hospital admissions or death within one-year of transplantation. Fifteen KTR controls were matched using the United Kingdom Renal Recipient Index (UKRRI: a composite score of age, dialysis requirement at listing, wait time on dialysis and diabetes status). Six blinded raters (two nephrologists, two transplant surgeons and two kidney transplant coordinators) scored clinical letters written at the time of transplant assessment across four domains (Clinician Belief of Appropriateness [CBA] for perceived suitability, perceived benefit, predicted short-term harm, predicted long-term harm: from 1 = worst, to 5 = best). The total CBA score was calculated as the mean score across the 4 domains. CBA scores were compared between raters using the intra-class correlation coefficient (ICC: for two-way agreement). Logistic regression models tested odds of poor outcome by CBA scores across 4 domains. Results Practicing clinicians were eager to participate, suggesting this type of research will be worthwhile and feasible. Among 30 KTR, the median total CBA transplant suitability score was 3 (IQR 3-4, range 1-5). Overall agreement was moderate (ICC for the total CBA score: 0.63, 95% CI 0.51-0.74; p < 0.001), but with substantial variation in CBA scores across the domains and between raters (Figure 1). In 3 of the 4 domains (perceived suitability: OR 0.91, 95% CI 0.48-1.73; predicted short-term: OR 0.70, 95% CI 0.33-1.51; and predicted long-term harms: OR 0.82, 95% CI 0.35-1.91), CBA scores adjusted for UKRRI were associated with poor outcome after transplantation in the expected direction. For predicted benefit, the point estimate was not in the expected direction; individuals with greater predicted benefits were more likely to have poor outcomes (OR 1.46, 95% CI 0.28-7.47). Unsurprisingly, given the small numbers in this pilot and the substantial influence of the donor organ on outcomes in kidney transplantation, all the confidence intervals were wide and crossed the null, indicating considerable uncertainty. Conclusion In patients undergoing assessment for kidney transplantation, measuring clinical judgement retrospectively is feasible, though subject to variability across domains and between raters. We did not identify any clear associations between retrospective assessment of CBA and outcome after kidney transplantation in this small pilot. In future work, we will develop and validate measures of clinical judgement, collect these measures prospectively, and examine the relationship of measured clinical judgement with health outcomes.

The lifetime health and economic burden of obesity in five European countries: what is the potential impact of prevention?

Estimating the burden of obesity in five European countries (Germany, Greece, the Netherlands, Spain and the UK) and the potential health benefits and changes in health care costs associated with a reduction in body mass index (BMI). A Markov model was used to estimate the long-term burden of obesity. Health states were based on the occurrence of diabetes, ischaemic heart disease and stroke. Multiple registries and literature sources were used to derive the demographic, epidemiological and cost input parameters. For the base-case analyses, the model was run for a starting cohort of healthy obese people with a BMI of 30 and 35 kg/m2 aged 40 years to estimate the lifetime impact of obesity and the impact of a one-unit decrease in BMI. Different scenario and sensitivity analyses were performed. The base-case analyses showed that total lifetime health care costs (for obese people aged 40 and BMI 35 kg/m2 ) ranged from €75 376 in Greece to €343 354 in the Netherlands, with life expectancies ranging from 37.9 years in Germany to 39.7 years in Spain. A one-unit decrease in BMI showed gains in life expectancy ranging from 0.65 to 0.68 year and changes in total health care costs varying from -€1563 to +€4832. The economic burden of obesity is substantial in the five countries. Decreasing BMI results in health gains, reductions in obesity-related health care costs, but an increase in non-obesity related health care costs, which emphasizes the relevance of including all costs in decision making on implementation of preventive interventions.

The gender wage gap and its effect on women’s entrepreneurship

ABSTRACT We demonstrate an inverse relationship between women’s financial equality status with men (as proxied by gender wage gap), and women’s entrepreneurship participation at the U.S. state level. Gender wage gap affects women’s opportunity cost of the entrepreneurship decision. In states where women’s status is lower, women’s opportunity cost of becoming an entrepreneur is lower because their wages are lower, increasing women’s entrepreneurship participation. We also demonstrate that states’ demographics are important factors, but only education is specific to women. Collectively, we find that women choose to be entrepreneurial when their status is lower, which reflects women’s economic resiliency.

Study of the value component of the value-target management of the foreign economic activity of the enterprise

Introduction. Management of foreign economic activity is successful and effective when it is aimed at achieving corporate values and goals. Value-target management is an effective tool for increasing the efficiency of a company’s foreign economic activity and its competitiveness in foreign markets. Today, there is still a large percentage of domestic enterprises that are not guided by values in their activities or whose values are boring abstract phrases that even managers do not follow. Since domestic enterprises usually focus only on goals, they cannot use the potential of their employees as efficiently as possible. The purpose of the paper is to investigate and analyze the value component of the value-target management model of the enterprise’s foreign economic activity, as well as to determine how the declared values in the field of foreign economic activity should be supported and implemented at the enterprise. Results. The paper covers the general theoretical aspects related to the value-target management of the enterprise’s foreign economic activity, the value component of the value-target management model of the enterprise’s foreign economic activity is considered in detail: the essence of the concept of «corporate values» is defined, their role and importance in the process of forming a cohesive motivated FEA managers team are determined, the values by which Ukrainian companies are guided in their activities are studied, the key features of value-oriented management are analyzed, and it is also determined how the declared values in the field of foreign economic activity should be supported and implemented at the enterprise. Conclusion. The application of value-oriented management in the management of foreign economic activity of the enterprise allows to unite the team of FEA managers, increase their involvement and loyalty, reduce the costs of ineffective decisions, attract motivated specialists of a high level of professionalism, increase the satisfaction of customers, partners, and employees, ensure high quality of work and, as a result, increase the productivity and efficiency of the FEA managers team and achieve better financial results from foreign economic activity.

A resilient inventory management of pharmaceutical supply chains under demand disruption

Inspired by the global supply chain disruptions caused by the COVID-19 pandemic, we study optimal procurement and inventory decisions for a pharmaceutical supply chain over a finite planning horizon. To model disruption, we assume that the demand for medical drugs is uncertain and shows spatiotemporal variability. To address demand uncertainty, we propose a two-stage optimization framework, where in the first stage, the total cost of pre-positioning drugs at distribution centers and its associated risk is minimized, while the second stage minimizes the cost of recourse decisions (e.g., reallocation, inventory management). To allow for different risk preferences, we propose to capture the risk of demand uncertainty through the expectation and worst-case measures, leading to two different models, namely (risk-neutral) stochastic programming and (risk-averse) robust optimization. We consider a finite number of scenarios to represent the demand uncertainty, and to solve the resulting models efficiently, we propose L-shaped decomposition-based algorithms. Through extensive numerical experiments, we illustrate the impact of various parameters, such as travel time, product’s shelf life, and waste due to transportation and storage, on the supply chain resiliency and cost, under optimal risk-neutral and risk-averse policies. These insights can assist decision makers in making informed choices.

The Broader Opportunity Costs in the Broader Cost-Effectiveness Analysis Framework.

The traditional cost-effectiveness analysis framework usually takes a healthcare system perspective, where the aim is to maximise population health from a fixed budget allocated to healthcare. Extensions to this framework have been suggested, including: (i) incorporating impacts that fall outside the healthcare sector; (ii) accounting for outcomes beyond health; and (iii) assessing equity considerations. Several alternatives have been proposed that serve these purposes, for example, the extended "impact inventory", the "beyond-the-QALY" approach and the distributional cost-effectiveness analysis. This paper aims to develop a comprehensive framework that incorporates into the cost-effectiveness analysis framework some of its most advocated extensions and provides a means of arriving at a unidimensional cost-effectiveness analysis result measure. Building on previous work, I proposed a framework that explicitly incorporates the full extent of the opportunity costs that arise when new dimensions and distributional concerns are included in cost-effectiveness analyses. A hypothetical example is provided as a way of illustration. Operationalising the proposed framework requires system-wide representative values and/or robust estimates concerning: (i) selecting dimensions; (ii) measuring opportunity costs associated with each dimension; (iii) quantifying equity weights and percentages of beneficiaries and losers meeting equity considerations; and (iv) attaching monetary values to dimensions measured using a non-monetary metric. Extending the cost-effectiveness analysis framework entails extending the measurement of the opportunity costs of funding decisions. This implies populating an ambitious puzzle that in some cases poses fundamental conceptual and empirical questions. Potential routes of further research that might facilitate such undertaking are proposed.

Attention, institutional friction, and punctuated equilibrium in China's budget: Changes in social security and employment expenditure

AbstractBased on the literature on punctuated equilibrium theory (PET), we conducted an empirical analysis on the changes in China's social security and employment budget expenditure at the subnational level from 2009 to 2020. We calculated the kurtosis value for 31 provinces to check whether they conform to PET. We used the number of policy documents and resource congestion as indicators of attention allocation and decision cost, respectively, to determine the impacts they have on budget changes. We then performed regression with a fixed effect model. The results show that the budgetary changes of only 20 provinces obey a leptokurtic distribution. Moreover, the allocation of attention by decision makers and institutional friction can explain this pattern of change. This article enriches the empirical study and extends its applicability to PET at the subnational level, especially in authoritarian regimes such as China, and provides some evidence for subsequent research on selecting indicators.Related ArticlesKwon, Sung‐Wook, and Sylvia Gonzalez‐Gorman. 2019. “Influence of Local Political Institutions on Policy Punctuation in Three Policy Areas.” Politics & Policy 47(2): 300–25. https://doi.org/10.1111/polp.12295.Modlin, Steve. 2008. “Defining Involvement of County Commissioners in the Budget Formulation Process.” Politics & Policy 36(6): 1044–65. https://doi.org/10.1111/j.1747‐1346.2008.00149.x.Moreno‐Enguix, María Del Rocío, and Laura Vanesa Lorente Bayona. 2017. “Factors Affecting Public Expenditure Efficiency in Developed Countries.” Politics & Policy 45(1): 105–43. https://doi.org/10.1111/polp.12194.

Two-stage three-way enhanced multi-criteria classification optimization for risk-averse product design programming

Product design is essential in the product lifecycle. In product design, decision errors from designers are ineluctable due to the lack of sufficient knowledge and experience on design parameters. However, the penalties (e.g., expected risk loss costs) of design decisions have been overlooked as always. The result is that being risk-averse naturally becomes a major issue to be advocated. Meanwhile, the preferences of designers, representing rational cognition of product design, are crucial in product design. In response to the above two critical issues, a two-stage three-way enhanced multi-criteria classification optimization for risk-averse product design programming is proposed. In the first stage, the three-way decisions (3WD) theory is introduced to handle the relation between the penalties and design decisions, and provide an uncertain decision representing an intermediate attitude of designers on design parameters. Additionally, designers are endowed to preferentially consider design parameters that are profitable for the success of product design. The purpose of the first stage is to formulate multiple alternative design schemes. In the second stage, under the framework of the ELimination Et Choix Traduisant la REalité (ELECTRE) TRI, a multi-criteria classification optimization is constructed to cater to designers’ preferences. Subsequently, we can identify the optimal design scheme from these multiple alternative design schemes. The effectiveness and merits of the proposed approach are demonstrated with two illustrative examples and a substrate design case. As shown from the case studies, the proposed approach has a stable performance in terms of identifying the optimal design scheme in a stochastic environment.

Smoothed Online Optimization with Unreliable Predictions

We examine the problem of smoothed online optimization, where a decision maker must sequentially choose points in a normed vector space to minimize the sum of per-round, non-convex hitting costs and the costs of switching decisions between rounds. The decision maker has access to a black-box oracle, such as a machine learning model, that provides untrusted and potentially inaccurate predictions of the optimal decision in each round. The goal of the decision maker is to exploit the predictions if they are accurate, while guaranteeing performance that is not much worse than the hindsight optimal sequence of decisions, even when predictions are inaccurate. We impose the standard assumption that hitting costs are globally α-polyhedral. We propose a novel algorithm, Adaptive Online Switching (AOS), and prove that, for a large set of feasible δ > 0, it is (1+δ)-competitive if predictions are perfect, while also maintaining a uniformly bounded competitive ratio of 2~O (1/(α δ)) even when predictions are adversarial. Further, we prove that this trade-off is necessary and nearly optimal in the sense that any deterministic algorithm which is (1+δ)-competitive if predictions are perfect must be at least 2~Ω (1/(α δ)) -competitive when predictions are inaccurate. In fact, we observe a unique threshold-type behavior in this trade-off: if δ is not in the set of feasible options, then no algorithm is simultaneously (1 + δ)-competitive if predictions are perfect and ζ-competitive when predictions are inaccurate for any ζ < ∞. Furthermore, we discuss that memory is crucial in AOS by proving that any algorithm that does not use memory cannot benefit from predictions. We complement our theoretical results by a numerical study on a microgrid application.

Optimal scale selection based on multi-scale single-valued neutrosophic decision-theoretic rough set with cost-sensitivity

The selection of optimal scale has always been the essential problem of multi-scale system. However, most of the current studies only consider the consistency of the system, and ignore the cost information. Therefore, based on the ranking methods of single-valued neutrosophic number, this paper constructs a multi-scale dominant single-valued neutrosophic system. Furthermore, the test cost is defined according to the relative distance preference degree, and the risk cost of Bayesian theory introduced by decision-theoretic rough set is used as the decision cost of accepting, delaying and rejecting decisions. Therefore, we establish a multi-scale dominant single-valued neutrosophic decision-theoretic rough set based on test and decision cost. In addition, we also propose a scale updating algorithm to find out all consistent scales. Afterwards, we further raise an optimal scale selection algorithm based on the minimum total cost criterion. Finally, the algorithm is verified and analyzed by UCI data sets. The algorithm and model proposed in this paper further expand the application of single-valued neutrosophic rough set in multi-scale system, and provide a reference for subsequent research in this field.