Set Of Decisions Research Articles

Combinatorial design of the MAUT and PAMSSEM II methods for multiple attributes group decision making with probabilistic linguistic information

This paper considers the application of Probabilistic Linguistic Term Sets (PLTS) in Multiple Attribute Group Decision Making (MAGDM) when the weights cannot be determined. Firstly, as an improvement of PROMETHEE, the PAMSSEM method has substantial advantages in handling mixed data and missing data. It can not only make use of the preference threshold as well as the irrelevant threshold to express the preference degree of the decision-maker (DM) but likewise express the compensation limit that DM is willing to accept through the rejection threshold. Simultaneously, the MAUT method cleverly employs the marginal utility function to redistribute the evaluation values under the same attribute within a fixed interval, making the decision matrix standard and reliable. To comprehensively utilize the advantages of these two methods, we integrated the PAMSSEM II and MAUT methods in the probabilistic linguistic environment to form the PL-MAUT-PAMSSEM II method followed by applying it to the problem of inbound tourism destination selection. Secondly, in the process of data processing, an equivalent conversion function will be used to convert the PLTS and the probabilistic hesitant fuzzy set (HPFS) to each other to make the calculation between the matrices feasible and use the entropy method to calculate the weights to ensure the decision-making process. Finally, taking the sample deviation value as the inspection standards, the big data analysis compared with other methods is carried out through a large number of sample examples to scientifically validate the superiority of the newly proposed method.

EQUALITY IN THE STREETS Using Proportionality Analysis to Regulate Street Policing

EQUALITY IN THE STREETS Using Proportionality Analysis to Regulate Street Policing

Anticollision Decision and Control of UAV Swarm Based on Intelligent Cognitive Game.

UAV swarm anticollision system is very important to improve the flight safety of the whole swarm formation, while the existing system design methods are still insufficient in realizing autonomous and cooperative anticollision. Based on the cognitive game theory, an intelligent decision-making and control method for UAV swarm anticollision is designed. Firstly, by using the idea of swarm intelligence, basic flight behaviors of UAV swarm are defined as five basic flight rules, such as cohesion, following, self-guidance, dispersion, and alliance. Further, the cognitive security domain of UAV swarm is constructed by setting the overall anticollision rules of the swarm and the anticollision rules of individual members. On this basis, the anticollision problem of UAV swarm is transformed into a game problem involving two parties, and the solution method of decision and control strategy set is proposed. Finally, the stability of anticollision decision and control method is proved through eigenvalue theory. The simulation results show that the method proposed in this paper can effectively realize the autonomous cooperative anticollision of UAV swarm and also has good algorithm real-time solution ability while ensuring flight safety.

A non-cooperative behavior management method for multi-attribute large group decision-making

In multi-attribute large group decision-making (MALGDM), the ideal state indicates a high degree of consensus among a set of decision-makers (DMs). It is complex to reach consensus because the number of decision attributes and DMs increases. Thus, we developed a novel consensus model to manage the decision-making in large group based on the non-cooperative behavior. The improved clustering method takes account of the similarities among different DMs. Similar DMs will be grouped into the same group. The consensus threshold is determined from an objective and subjective aspect to judge whether the consensus reaching process continues. With the introduction of three non-cooperative behaviors, we investigated a non-cooperative behavior detection method under the change of consensus level. Base on the number of DMs who are willing to change their preliminary views and the change value of consensus level, the non-cooperative degree of subgroup can be computed. According to the non-cooperative degree, the subgroups’ weight can be modified to raise the consensus level. Meanwhile, the subgroup is allowed to change. Based on the adjustment amount of DMs’ opinions, whether decision maker (DM) belongs to this subgroup is recalculated. Finally, an emergency decision-making problem in flood disaster is applied to manifest the feasibility and distinctive features of the proposed method.

Remarks on the inconsistency measure of the Analytic Hierarchy Process

The Analytic Hierarchy Process (AHP) is a popular Multi-Criteria Decision Making (MCDM) method. The workability of AHP made it suitable for solving complicated and elusive decision problems that subsequently led to its widespread applications in highly diverse fields. However, AHP has also received criticisms on various fronts, one of which is the rank reversal problem. When a replica of an existing alternative is introduced in the Multi-Criteria Decision (MCD) setting, it sometimes causes rank order reversal among alternatives. However, the addition of a replica of an alternative in the MCD setting is not limited to the rank reversal problem, but it also affects the inconsistency measure computed for the decision-maker (DM). An empirical study was conducted using AHP to measure the changes in the inconsistency of the DM on a well-defined and familiar MCD problem. The results indicate that when a replica is added to a pair-wise comparison matrix, the inconsistency of the DM reduces. It is found that there are two sources of inconstancy in a pair-wise preference matrix. One is intransitivity and another is the limitation of the 1–9 ratio scale. It is found that an inconsistency up to 50% is purely because of limitations of the ratio scale and higher inconsistencies are purely because of intransitivity in preferences defined by the DM. Therefore, the DMs should review and revise their preferences when their inconsistency exceeds 50%. This 50% threshold is also useful in deciding whether to apply a prediction algorithm to identify near consistent matrices. If the inconsistency of a matrix is above 50%, the prediction algorithms used to improve the consistency cannot be applied on the original inconsistent matrix because the source of inconsistency is intransitivity which means that the DM either does not have complete information about the problem or has not attended to the problem carefully.

Value Creation through Machine Learning Using Data Network Effects

The recently proposed theory of data network effects aims to account for how user value is created from the use of machine learning technology. The theory recognises the unique learning ability of machine learning, which uses large data sets for predictions and decision making. This paper offers a critical assessment of that theory, unearthing some of its strengths and limitations. The latter are transformed into a set of interrelated research questions that jointly constitute the proposed research programme into how to use machine learning technology in order to create and capture value. The paper contributes to the literature with an articulation of some novel knowledge gaps.

THE PRICE COMPONENT AS AN IMPORTANT VARIABLE OF THE NEGOTIATION PROCESS IN TOURISM

The negotiation process within the international tourism business involves a set of meetings, discussions and decisions, through which it is aimed at achieving the objectives and promoting the interests of the tourism market actors, by concluding advantageous contracts and ensuring a favourable prospect for business, if the negotiated price is a competitive one. In these terms, the price component becomes the most important variable of the negotiation process, as it can have both beneficial consequences on the act of negotiation and serious consequences on the outcome of this process. From this perspective, negotiators, at all times, resort to this variable to influence the results of the negotiations. This article addresses the topic of tourism products pricing and services within the international tourism business as a result of the negotiations between the actors of the tourism market, aiming to carry out a comprehensive analysis of this topic.

Approximation schemes for the joint inventory selection and online resource allocation problem

In this paper, we introduce and study the joint inventory selection and online resource allocation problem, which is characterized by two sequential sets of decisions that are irrevocably linked. First, a decision maker (DM) must select starting inventory levels for a set of available resources. Subsequently, the DM must match arriving customers to available resources in an online fashion so as to maximize expected reward. We first study the problem in its most general form, before focusing on a specific version that arises at Anheuser Busch InBev (ABI). This particular application of our general setting is referred to as the ABI Trailer Problem, and it considers how ABI ships its beer to vendors via third‐party delivery trucks. In this problem, ABI must select the weights of preloaded trailers of beer, which are then matched in an online fashion to the arriving third‐party delivery trucks. For the general setting, we develop simple and easy‐to‐implement approaches that come with robust worst‐case performance guarantees. For the ABI setting, we reveal a simplifying structural property related to the optimal matching policy, which gives rise to a natural adaptation of our original approach. We test the efficacy of these policies through extensive numerical experiments, where we find that our approaches are either near‐optimal or improve upon state‐of‐the‐art benchmarks. In particular, using a data set from ABI, we are able to generate instances of the ABI Trailer Problem, on which our algorithm has the potential to yield revenue improvements in the range of millions of dollars per year.

Distributed Nash Equilibrium Seeking in a Multi-Coalition Noncooperative Game Under Incomplete Decision Information

This brief studies a distributed Nash equilibrium (NE) seeking strategy for a multi-coalition noncooperative game with local decision sets. In the game model, each coalition consists of some agents and its cost funciton is determined by the sum of the local cost functions subject to the agents in the coalition, where each coalition serves as a virtual player and the actual decision-makers are the agents in the coalition. The cost of each coalition is to minimize its own cost function under the case that the agents in each coalition only know their local information and can not directly access the opponent’s decision information in other coalitions. To this end, a distributed seeking strategy that can be viewed a two-time scale system is developed to search the NE of the formulated multi-coalition game. The fast time-scale system is used to estimate each coalition’s pseudo-gradient by a connected interference graph that illustrates the interactions among the agents in each coalition. The slow time-scale system based on a projected psedudo-gradient dynamics is implemented to seek the NE, where the coalitions estimate other coalitions’ decisions by interacting only with their neighbors via a weight-balanced digraph. For this distributed NE seeking strategy, asymptotic convergence result is given by Lyapunov stability analysis. Finally, the theoretical results is demonstrated via a numerical example.

Distributed Generalized Nash Equilibrium Seeking: An Operator-Theoretic Perspective

Generalized games model interactions between a set of selfish decision makers, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">players</i> or <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">agents</i> , where both the objective function <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">and</i> the feasible decision set of each player may depend on strategies of the competitors. Such games arise, for example, when agents compete for a share of some common but limited resources. For instance, consider a set of vehicles sharing the road, set of radio channels competing for bandwidth, or set of companies servicing an economic market. They can each be modeled as players/agents in a game, competing for a portion of available resources, that is, road capacity, total bandwidth, or market share. As resources are limited, player choices are bound together by a coupling-capacity constraint. Essentially, in a generalized game, or one with coupling constraints, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">set of available</i> choices an agent has depends on the choices of other agents. Thus, a player cannot simply optimize their own objective function without considering the decisions of the others, even though this objective might not depend on him or her. The relevant equilibrium (namely, the solution concept) for noncooperative decision making with coupling constraints is called a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">generalized</i> Nash equilibrium (GNE).

Good Practices to Counteract Epidemic Emergency in Mining Companies in Poland

The date assumed as the beginning of the pandemic in Poland is 4 March 2020, the date of the first confirmed case of the virus. This article presents the actions undertaken by the management of underground hard coal mining plants concerning the risk of epidemic related to SARS-CoV-2. This work shows a set of implemented recommendations, guidelines and decisions, which were established after the appearance of the first wave of cases in Poland. What is more, it discusses measures aiming at reducing the risk of spreading the coronavirus among the mineworkers. The suggestions for different variants of the decision-making process concerning the pandemic and which have an enormous impact on the operating expenses of the company are also made. The paper presents the results of the study on actions taken in individual mining companies and good work practices recommended to be applied.

STRATEGIC APPROACH TO CLIMATE RISKS IN TRANSPORT

Purpose. The aim of the work is to study the climate risks problem in transport systems and to form recommendations for strategic decisions on the adaptation of transport in Ukraine to the occurrence of adverse effects as a result of climate hazard. Research methods. The research has been carried out with the statistical analysis and synthesis methods. Results. The study of climate change in the world and Ukraine, which indicates the need for new effective methods and proposals that would take into account the peculiarities of the national transport system and ensure the implementation of the strategic approach concept to transport management in climate risks, has been carried out. A precautionary attitude to extreme weather is the basis for the strategic decisions formation at the national level. The main stages of the strategic approach to climate risks in transport have been formulated. Scientific novelty. The results of the study allow to form the scientific basis of the development strategy of Ukrainian transport industry in the conditions of natural dangers and risks growth. Practical significance. This paper determines the strategic measures of adaptation of the transport system to climatic risks, which are formed due to the implementation of a set of organizational, technical, construction and economic decisions. The results can be used in the development of management plans for transport and logistics companies in climate hazard.

Open Innovation moves in SMEs: How European SMEs place their bets?

Although Open Innovation (OI) may be a source of opportunities for Small and Medium Enterprises (SMEs), it also represents risks. How SMEs manage the balance between potential benefits and associated risks remains an overlooked topic in the literature. In parallel, recognizing the need for a more fine-grained level of analysis, we introduce the concept of OI moves to disentangle the OI projects of SMEs. An ‘OI move’ is defined as the set of managerial decisions and actions that are executed to reach specific innovation outputs in the framework of a collaboration with a single partner that is conducted as a part of an OI project. An OI move consist of the combination of two building blocks: a) the type of partner chosen to innovate with - ‘who is the SME innovating with’ and b) the exploitation mode - ‘who is exploiting the result’. Using the OI move as the level of the present analysis, we argue that different OI moves have the potential of delivering different benefits, but they also represent different levels of risk. To that end, the aim of our research is to explore how SMEs weigh benefits and risks at the level of OI move.On the basis of a unique data set of 106 in-depth case studies of innovation projects carried out by innovative SMEs located in Europe, 500 OI moves were identified. Clustering the extracted OI moves based on a typology formed by the combination of the type of partners and the exploitation mode revealed: a) benefits sought, b) risks encountered c) the type of partnership chosen.Findings of the study rely on the attention-based view of the firm as it provides an interesting lens through which OI is executed. Three main findings derive from the study. First, an SME initiates an OI move not only on the basis of the potential economic return. Albeit, it is the ‘attention capital’ required to create and capture value that is the SMEs' starting point in considering an OI partnership. Second, we find a ‘paradox’: SMEs tend to practice OI because they lack resources, but it is precisely the lack of resources that makes them reluctant to enter into attention-intensive OI partnerships even if return can be substantial. Third, OI moves involving communities and crowds are a ‘new playing field’ for SMEs. Because of their novelty, SMEs are reluctant to initiate such an OI move even though case-based evidence in our research demonstrates that SMEs using such collaborative forms can be quite successful.

The Executive Branch decisions in Brazil: A study of administrative decrees through machine learning and network analysis.

This paper dissects the potential of state-of-the-art computational analysis to promote the investigation of government’s administrative decisions and politics. The Executive Branch generates massive amounts of textual data comprising daily decisions in several levels and stages of the law and decree-making processes. The use of automated text analysis to explore this data based on the substantive interests of scholars runs into computational challenges. Computational methods have been applied to texts from the Legislative and Judicial Branches; however, there barely are suitable taxonomies to automate the classification and analysis of the Executive’s administrative decrees. To solve this problem, we put forward a computational framework to analyze the Brazilian administrative decrees from 2000 to 2019. Our strategy to uncover the contents and patterns of the presidential decree-making is developed in three main steps. First, we conduct an unsupervised text analysis through the LDA algorithm for topic modeling. Second, building upon the LDA results, we propose two taxonomies for the classification of decrees: (a) the ministerial coauthorship of the decrees to map policy areas and (b) the decrees’ fields of law based on a tagging system provided by the Brazilian Senate. Using these taxonomies, we compare the performance of three supervised text classification algorithms: SVM, Convolutional Neural Network, and Hierarchical Attention Network, achieving F1-scores of up to 80% when automatically classifying decrees. Third, we analyze the network generated by links between decrees through centrality and clustering approaches, distinguishing a set of administrative decisions related to the president’s priorities in the economic policy area. Our findings confirm the potential of our computational framework to explore N-large datasets, advance exploratory studies, and generate testable propositions in different research areas. They advance the monitoring of Brazil’s administrative decree-making process that is shaped by the president’s priorities and by the interplay among cabinet members.

On the use of Wasserstein distance in the distributional analysis of human decision making under uncertainty

The key contribution of this paper is a theoretical framework to analyse humans’ decision-making strategies under uncertainty, and more specifically how human subjects manage the trade-off between information gathering (exploration) and reward seeking (exploitation) in particular active learning in a black-box optimization task. Humans’ decisions making according to these two objectives can be modelled in terms of Pareto rationality. If a decision set contains a Pareto efficient (dominant) strategy, a rational decision maker should always select the dominant strategy over its dominated alternatives. A distance from the Pareto frontier determines whether a choice is (Pareto) rational. The key element in the proposed analytical framework is the representation of behavioural patterns of human learners as a discrete probability distribution, specifically a histogram considered as a non-parametric estimate of discrete probability density function on the real line. Thus, the similarity between users can be captured by a distance between their associated histograms. This maps the problem of the characterization of humans’ behaviour into a space, whose elements are probability distributions, structured by a distance between histograms, namely the optimal transport-based Wasserstein distance. The distributional analysis gives new insights into human behaviour in search tasks and their deviations from Pareto rationality. Since the uncertainty is one of the two objectives defining the Pareto frontier, the analysis has been performed for three different uncertainty quantification measures to identify which better explains the Pareto compliant behavioural patterns. Beside the analysis of individual patterns Wasserstein has also enabled a global analysis computing the WST barycenters and performing k-means Wasserstein clustering.

The Attribution Matrix and the joint use of Finite Change Sensitivity Index and Residual Income for value-based performance measurement

We present a model of performance measurement and attribution for delegated investments. We introduce an innovative two-dimensional approach that, on one hand, detects the (manager and client) decision effects, measuring the impact on the overall investment performance of the choices made by manager and client in a given period and, on the other hand, detects the (manager and client) period effects, measuring the impact on the period investment performance generated by the entire set of decisions made by manager and client. To accomplish the task, we employ two techniques of analysis and a three-step procedure: we first use the Finite Change Sensitivity Index (FCSI) for measuring the decision effects (step 1); then, we use the Residual Income (RI) approach for measuring the period effects (step 2); finally, we combine the two attribution dimensions into an Attribution Matrix (AM) (step 3) which contains the attribution values. An attribution value provides the amount of value added generated in a given period by the decisions made by the manager or by the investor in (the same or) another period. We apply the AM to an Italian fund, “Anima Italia A”, between 2013 and 2020.

Fine-grained modeling and optimization for intelligent resource management in big data processing

Big data processing at the production scale presents a highly complex environment for resource optimization (RO), a problem crucial for meeting performance goals and budgetary constraints of analytical users. The RO problem is challenging because it involves a set of decisions (the partition count, placement of parallel instances on machines, and resource allocation to each instance), requires multi-objective optimization (MOO), and is compounded by the scale and complexity of big data systems while having to meet stringent time constraints for scheduling. This paper presents a MaxCompute based integrated system to support multi-objective resource optimization via fine-grained instance-level modeling and optimization. We propose a new architecture that breaks RO into a series of simpler problems, new fine-grained predictive models, and novel optimization methods that exploit these models to make effective instance-level RO decisions well under a second. Evaluation using production workloads shows that our new RO system could reduce 37--72% latency and 43--78% cost at the same time, compared to the current optimizer and scheduler, while running in 0.02-0.23s.

Inductive risk: does it really refute value-freedom?

The argument from inductive risk is considered to be one of the strongest challenges for value-free science. A great part of its appeal lies in the idea that even an ideal epistemic agent—the “perfect scientist” or “scientist qua scientist”—cannot escape inductive risk. In this paper, I scrutinize this ambition by stipulating an idealized Bayesian decision setting. I argue that inductive risk does not show that the “perfect scientist” must, descriptively speaking, make non-epistemic value-judgements, at least not in a way that undermines the value-free ideal. However, the argument is more successful in showing that there are cases where the “perfect scientist” should, normatively speaking, use non-epistemic values. I also show that this is possible without creating problems of illegitimate prescription and wishful thinking. Thus, while inductive risk does not refute value-freedom completely, it still represents a powerful critique of value-free science.

Decision Support System Based on Spherical 2-tuple Linguistic Fuzzy Aggregation Operators and their Application in Green Supplier Selection

In this manuscript, we give the idea of Spherical 2-tuple linguistic fuzzy set (S2TLFS) for the multi criteria decision making (MCDM) problem with the information. We utilized some operation to define some Spherical 2-tuple linguistic fuzzy (S2TLF) aggregation operators (AOs). We discussed some properties of the developed operators. Then, to solve an MCDM problem using the Spherical 2-tuple linguistic information, we proposed an approach, and utilized these operators. Lastly, a numerical example of the green supplier selection for chemical processing industry is given to show the advantage of the defined approach and to show its practicability and performance

Health Inequalities: To What Extent are Decision-Makers and Economic Evaluations on the Same Page? An English Case Study.

Economic evaluations have increasingly sought to understand how funding decisions within care sectors impact health inequalities. However, there is a disconnect between the methods used by researchers (e.g., within universities) and analysts (e.g., within publicly funded commissioning agencies), compared to evidence needs of decision makers in regard to how health inequalities are accounted for and presented. Our objective is to explore how health inequality is defined and quantified in different contexts. We focus on how specific approaches have developed, what similarities and differences have emerged, and consider how disconnects can be bridged. We explore existing methodological research regarding the incorporation of inequality considerations into economic evaluation in order to understand current best practice. In parallel, we explore how localised decision makers incorporate inequality considerations into their commissioning processes. We use the English care setting as a case study, from which we make inference as how local commissioning has evolved internationally. We summarise the recent development of distributional cost-effectiveness analysis in the economic evaluation literature: a method that makes explicit the trade-off between efficiency and equity. In the parallel decision-making setting, while the alleviation of health inequality is regularly the focus of remits, few details have been formalised regarding its definition or quantification. While data development has facilitated the reporting and comparison of metrics of inequality to inform commissioning decisions, these tend to focus on measures of care utilisation and behaviour rather than measures of health. While both researchers and publicly funded commissioning agencies are increasingly putting the identification of health inequalities at the core of their actions, little consideration has been given to ensuring that they are approaching the problem in a consistent way. The extent to which researchers and commissioning agencies can collaborate on best practice has important implications for how successful policy is in addressing health inequalities.Supplementary InformationThe online version contains supplementary material available at 10.1007/s40258-022-00739-8.