Decision Procedure Research Articles

Community Engagement in Local Governance

Community engagement in local governance is crucial for effective and inclusive decision-making. Lack of Awareness and Information has been found that limited knowledge can have its community members may not fully understand the governance processes or their rights and responsibilities. Information gaps have been found that the insufficient information about local issues and decision-making processes can hinder meaningful participation. Low Participation Rates have been found that apathy and disinterest is that some community members may be disengaged due to a lack of trust in the system or a perception that their participation will not make a difference. Barriers to access has been found that the physical, economic, or social barriers may prevent certain groups from participating, such as the elderly, disabled, or economically disadvantaged. During the initial phase (2008-2013) of the Local Governance and Community Development Program (LGCDP) in Nepal, social mobilization emerged as one of its most effective components. Approximately 1 million citizens actively influenced local government decisions, leading many local bodies to develop annual plans that more accurately addressed the needs of citizens, particularly marginalized and disadvantaged groups. However, there remain several lessons to be learned in addressing the challenges of implementation. It is crucial to harmonize local social mobilization efforts and enhance the quality and support of social mobilization initiatives across Nepal. Addressing the challenges of community engagement in local governance involves a multifaceted approach that includes enhancing communication, building capacity, fostering inclusivity, and ensuring accountability. Public education campaigns have been found to Conduct outreach programs to educate community members about their rights, governance processes, and how they can participate. Workshops and training have been identified to offer workshops and training sessions to increase understanding of local governance and decision-making procedures. Diverse engagement channels have to utilize multiple platforms for engagement, including town hall meetings, online forums, surveys, and community events, to reach a broader audience. Inclusive scheduling has to schedule meetings and events at times that are convenient for different community groups, including evenings and weekends.

Innovative Marketing Strategies in the Competitive Economic Environment in Sustainable Development Goals (SDGs): A Case Study of Top Glove Manufacturers in Malaysia

Objective: The aim is to identify the key marketing strategies implemented by the manufacturers to achieve growth and sustainable development goals (SDGs) market leadership by glove-producing companies in Malaysia in the highly competitive global marketplace. Theoretical Framework: This paper contributes to the existing literature on the marketing strategies practiced in the manufacturing industries, mainly focusing on the glove manufacturing industry in Malaysia, which ranks as the global supplier of hand gloves in the industrial and medical categories. Taken in total, this work provides a refined appreciation of marketing and innovation’s relationship within a particular sector, and it does so by examining how those Malaysian glove makers who contest global markets leverage creativity in their marketing strategies in response to supply chain interruptions, approving bodies, and shifts in international buying trends. Methodology: Following a case study approach, this study focuses on large glove manufacturers in Malaysia; Top Glove is a crucial player. Hence, secondary sources will be adopted during the data collection stage. Perceptual data is also gathered from workers of key executives, marketing managers, and other industry professionals, which gives a picture of these business entities' marketing campaigns and strategic decision-making procedures. Industry journals, company reports, and market study papers validate secondary data. Results and Discussion: This study reveals that policymakers, manufacturing businesses, and marketing scholars can learn about the necessity of niche-based policies and friendly legal environments to boost innovation and competitiveness within manufacturing industries. It provides recommendations on creating a favorable climate for employing innovative marketing strategies and the possible enlargement of home-based businesses internationally. Originality/Value: Based on the findings and insights gathered from the research on innovative marketing strategies in the competitive global economic environment, particularly focusing on the case study of top glove manufacturers in Malaysia, several recommendations can be proposed to guide businesses in enhancing their marketing approaches and organizational performance

Anti-factor is FPT Parameterized by Treewidth and List Size (but Counting is Hard)

AbstractIn the general AntiFactor problem, a graph G and, for every vertex v of G, a set $$X_v\subseteq {\mathbb {N}}$$ X v ⊆ N of forbidden degrees is given. The task is to find a set S of edges such that the degree of v in S is not in the set $$X_v$$ X v . Standard techniques (dynamic programming plus fast convolution) can be used to show that if M is the largest forbidden degree, then the problem can be solved in time $$(M+2)^{{{\text {tw}}}}\cdot n^{{\mathcal {O}}(1)}$$ ( M + 2 ) tw · n O ( 1 ) if a tree decomposition of width $${{\text {tw}}}$$ tw is given. However, significantly faster algorithms are possible if the sets $$X_v$$ X v are sparse: our main algorithmic result shows that if every vertex has at most $$x$$ x forbidden degrees (we call this special case AntiFactorx), then the problem can be solved in time $$(x+1)^{{\mathcal {O}}({{\text {tw}}})}\cdot n^{{\mathcal {O}}(1)}$$ ( x + 1 ) O ( tw ) · n O ( 1 ) . That is, AntiFactorx is fixed-parameter tractable parameterized by treewidth $${{\text {tw}}}$$ tw and the maximum number $$x$$ x of excluded degrees. Our algorithm uses the technique of representative sets, which can be generalized to the optimization version, but (as expected) not to the counting version of the problem. In fact, we show that #AntiFactor1 is already #$$[1]$$ [ 1 ] -hard parameterized by the width of the given decomposition. Moreover, we show that, unlike for the decision version, the standard dynamic programming algorithm is essentially optimal for the counting version. Formally, for a fixed nonempty set $$X$$ X , we denote by $$X$$ X -AntiFactor the special case where every vertex v has the same set $$X_v=X$$ X v = X of forbidden degrees. We show the following lower bound for every fixed set $$X$$ X : if there is an $$\epsilon >0$$ ϵ > 0 such that #$$X$$ X -AntiFactor can be solved in time $$(\max X+2-\epsilon )^{{{\text {tw}}}}\cdot n^{{\mathcal {O}}(1)}$$ ( max X + 2 - ϵ ) tw · n O ( 1 ) given a tree decomposition of width $${{\text {tw}}}$$ tw , then the counting strong exponential-time hypothesis (#SETH) fails.

You Don’t Know Jack about Bandwidth

If you are an ISP and your customers hate you, take heart: This is now a solvable problem.

List coloring based algorithm for the Futoshiki puzzle

Given a graph G=(V, E) and a list of available colors L(v) for each vertex v\in V, where L(v) \subseteq {1, 2, ..., k}, List k-Coloring refers to the problem of assigning colors to the vertices of $G$ so that each vertex receives a color from its own list and no two neighboring vertices receive the same color. The decision version of the problem, List k-Coloring, is NP-complete even for bipartite graphs. As an application of list coloring problem we are interested in the Futoshiki Problem. Futoshiki is an NP-complete Latin Square Completion Type Puzzle. Considering Futoshiki puzzle as a constraint satisfaction problem, we first give a list coloring based algorithm for it which is efficient for small boards of fixed size. To thoroughly investigate the efficiency of our algorithm in comparison with a proposed backtracking-based algorithm, we conducted a substantial number of computational experiments at different difficulty levels, considering varying numbers of inequality constraints and given values. Our results from the extensive range of experiments indicate that the list coloring-based algorithm is much more efficient.

Decision trees for determining the fate of laboratory animals.

Facilities involved in laboratory animal research often face ethical challenges such as: what should I do with the animals that are no longer suitable for experimental purposes? One of the common answers to this question is to kill them. And while numerous scientifically justifiable reasons exist for killing laboratory animals, we must not overlook our ethical responsibility towards these sentient beings. Animal facility managers and scientists frequently find themselves in a moral dilemma, torn between furthering their research and addressing the well-being of experimental animals required for their studies. We elaborated a concept consisting of six decision trees and recommendations for making informed decisions about the need to kill laboratory animals in research facilities, considering legal and ethical considerations. The concept is based on the German regulatory perspective. However, the measures and decisions for animal welfare can be implemented in all laboratory animal facilities. These recommendations suggest several courses of action, including implementing consistent breeding plans, exploring alternative uses, reassigning surplus animals and their organs, and establishing appropriate housing capacity limits that ensure species-appropriate care. We encourage scientists and animal facility managers to develop and implement decision-making frameworks and procedures tailored to their specific facilities, in the hope that this work will promote a thoughtful and responsible approach to the complex challenges associated with the killing of laboratory animals, advancing scientific progress and the humane treatment of these animals.

Interval-valued picture fuzzy decision-making framework with partitioned maclaurin symmetric mean aggregation operators

Interval-valued picture fuzzy (IVPF) set is an extension of the picture fuzzy set theory used to represent uncertainty and vagueness in the processes of decision-making. This study focuses on exploring the interrelationships among multiple IVPFSs and criteria partitions. We investigate the IVPF partitioned Maclaurin symmetric mean operator and the weighted IVPF partitioned Maclaurin symmetric mean operator and discuss their respective properties. Subsequently, we identify certain special cases of these operators based on IVPF sets. Furthermore, we deploy a multi-criteria decision-making procedure utilizing the suggested IVPF partition operators. Through a numerical example, we demonstrate the practicality and validity of the presented approach. Finally, a thorough comparison with existing approaches is conducted to elucidate the superiority of the proposed method.

Creating airlines’ innovation: linking employees’ perspective of service quality, intellectual capital and innovation performance

ABSTRACT This study investigates various dimensions, including service quality, absorptive capacity, innovation performance, and intellectual capital (specifically organisational capital, social capital, and human capital), within the context of the airline industry. A survey questionnaire was administered to airline employees during the COVID-19 pandemic. The findings indicate that service quality has the potential to generate favourable innovation outcomes, such as increased absorptive capacity, thereby further enhancing employee innovation performance. Service quality is also found to have a simultaneous positive impact on human capital, leading to increased innovation performance and absorptive capacity. Therefore, organisations should prioritise the development of absorptive capacity to enhance both human capital and the accumulation of intellectual capital. Furthermore, both organisational capital and human capital play a vital role in these intricate strategy decision procedures. These factors play a crucial role in strengthening the connections between service quality and innovation performance.

AI-Based Learning Model for Sociocybernetic Systems in Web of Things: An Efficient and Accurate Decision-Making Procedure

AI-Based Learning Model for Sociocybernetic Systems in Web of Things: An Efficient and Accurate Decision-Making Procedure

Towards integrated multi-risk reduction strategies: A catalog of flood and earthquake risk mitigation measures at the building and neighborhood scales

The integration of risk reduction measures for earthquakes and floods is a promising concept in the field of multi-hazard disaster risk reduction. However, existing studies mostly discuss it conceptually, often missing a focus on decision-making. This paper introduces a novel approach by examining synergies and trade-offs between earthquake and flood risk reduction measures. The paper first conducts a detailed analysis of selected measures for these two hazards. It then explores how these individual risk mitigation measures can be strategically integrated at the building level to maximize synergies, potentially resulting in time and cost savings. Furthermore, it discusses the limitations of current decision-making procedures for single-hazard risk mitigation selection when applied to multi-hazard risk (MHR) scenarios. To address these challenges, the study proposes new decision variables to be incorporated into existing Multi-Criteria Decision-Making frameworks with the aim of facilitating strategic risk reduction prioritization for MHR scenarios, ensuring sustainability and community resilience. To aid implementation (i.e., the phase in which the measure would be selected and tested in a series of case study buildings), a catalog of analyzed risk reduction measures is developed and provided as Supplementary Material. In future developments, this catalog will facilitate the integration of these measures into decision-support tools for evidence-based multi-hazard disaster risk reduction.

Shrouded in secrecy – does the comitology procedure for GDPR adequacy decisions fit its purpose?

With the entry into force of Directive 95/46/EC, the EU based its approach toward data transfers on adequacy decisions, unilateral acts of the European Commission, issued as implementing acts. The EU co-legislators subsequently copied this model into the GDPR and the LED. Since the very beginning, the adequacy procedure involves a comitology phase in which a committee consisting of representatives of Member States expresses its opinion about the Commission's draft implementing act. I argue that adequacy, designed as a technical process, evolved into a tool in which politics, including economic relations and commercial interests, play an ever-greater role. This goes against the concept of comitology, the legitimacy of which is built on denying the political nature of what is delegated. Taking into account the above, as well as other shortcomings of the EU adequacy model, I argue that it is the right time to rethink it. There is also the need for a separate discussion regarding the role of the Article 93 Committee in the adequacy procedure, to be conducted together with the debate on the role and accountability of the European Commission.

On Cauchy Problem Solution for a Harmonic Function in a Simply Connected Domain with Multi-Component Boundary

Here, we present an investigation of the Cauchy problem solvability for the Laplace equation in a simply connected plane domain with a multi-component boundary. We reduce our investigation to solution of two singular integral equations. If the problem is resolvable, then we restore its solution via the integral Cauchy formula. We present the examples of the solvable problems. The construction involves an auxiliary approximate conformal mapping or the solution of certain singular integral equations.

Natural Language Inference with Transformer Ensembles and Explainability Techniques

Natural language inference (NLI) is a fundamental and quite challenging task in natural language processing, requiring efficient methods that are able to determine whether given hypotheses derive from given premises. In this paper, we apply explainability techniques to natural-language-inference methods as a means to illustrate the decision-making procedure of its methods. First, we investigate the performance and generalization capabilities of several transformer-based models, including BERT, ALBERT, RoBERTa, and DeBERTa, across widely used datasets like SNLI, GLUE Benchmark, and ANLI. Then, we employ stacking-ensemble techniques to leverage the strengths of multiple models and improve inference performance. Experimental results demonstrate significant improvements of the ensemble models in inference tasks, highlighting the effectiveness of stacking. Specifically, our best-performing ensemble models surpassed the best-performing individual transformer by 5.31% in accuracy on MNLI-m and MNLI-mm tasks. After that, we implement LIME and SHAP explainability techniques to shed light on the decision-making of the transformer models, indicating how specific words and contextual information are utilized in the transformer inferences procedures. The results indicate that the model properly leverages contextual information and individual words to make decisions but, in some cases, find difficulties in inference scenarios with metaphorical connections which require deeper inferential reasoning.

Unveiling political polarization on Twitter: Machine learning and sentiment analysis in presidential elections

The year 2024 stands out as a pivotal year marked by significant political transformations across the globe. Some countries, such as Mexico and the United States, could be deeply affected by political polarization and echo chambers. This study employed sentiment analysis and machine learning techniques to investigate political polarization on Twitter during the 2018 Mexican presidential election. The findings reveal that the winning candidate exhibited the highest level of polarization. This underscores the pivotal role of social media in elections. For some time now, social media platforms like Twitter have contributed to intensified political polarization and the creation of echo chambers. Further research is essential to understand the influence of polarization on voter decision-making and democratic procedures. Establishing ethical guidelines for using machine learning in policy analysis is critical to preserving the integrity of democratic processes while reaping the potential benefits of new technologies.

Hard Choices and Ultimate Ends

Abstract I propose a novel view on hard choices. It broadens the concept to include not only ‘classic’ hard choices but also transformative and aspirational choices. I argue that a choice is hard when an individual does not have an all-things-considered reason to choose one option over another and the objects of choice are ultimate ends. Construing hard choices in this way supports and explains the widely held assumption that, when faced with hard choices, it is impermissible to choose arbitrarily. More specifically, it shows that it is impermissible to choose arbitrarily by plumping, and yet legitimate to do so by picking. Understanding hard choices in the way I propose sheds light on the fact that there are moral and agential constraints that limit the space of permissible decision-making procedures when confronted with ‘classic’ hard, transformative, and aspirational choices. Delineating these constraints, in turn, can provide guidance to critically appraise current theories of practical rationality.

Edge open packing: Complexity, algorithmic aspects, and bounds

Given a graph G, two edges e1,e2∈E(G) are said to have a common edge e≠e1,e2 if e joins an endvertex of e1 to an endvertex of e2. A subset B⊆E(G) is an edge open packing set in G if no two edges of B have a common edge in G, and the maximum cardinality of such a set in G is called the edge open packing number, ρeo(G), of G. In this paper, we prove that the decision version of the edge open packing number is NP-complete even when restricted to graphs with universal vertices, Eulerian bipartite graphs, and planar graphs with maximum degree 4, respectively. In contrast, we present a linear-time algorithm that computes the edge open packing number of a tree. We also resolve two problems posed in the seminal paper (Chelladurai et al. (2022) [5]). Notably, we characterize the graphs G that attain the upper bound ρeo(G)≤|E(G)|/δ(G), and provide lower and upper bounds for the edge-deleted subgraph of a graph and establish the corresponding realization result.

Counter abstraction for regular open teams

We study open swarms and teams of multi-agent systems where agents may join or leave the system at runtime. Kouvaros et al. [1] defined the verification problem for such systems and showed it to be undecidable, in general. Also, they have found one decidable class of open multi-agent systems and provided a partial decision procedure for another. In the same vein we present a subclass of open teams called regular open teams for which reachability is decidable. This is shown by employing a counter abstraction technique wherein the regular open team is abstracted into a finite state multi-counter system.

Counting vanishing matrix-vector products

Consider the following parameterized counting variation of the classic subset sum problem, which arises notably in the context of higher homotopy groups of topological spaces. Let v∈Qd be a rational vector, (T1,T2…,Tm) a list of d×d rational matrices, S∈Qh×d a rational matrix not necessarily square and k a parameter. The goal is to compute the number of ways one can choose k matrices Ti1,Ti2,…,Tik from the list such that STik⋯Ti1v=0∈Qh.In this paper, we show that this problem is #W[2]-hard for parameter k. As a consequence, computing the k-th homotopy group of a d-dimensional 1-connected topological space for d>3 is #W[2]-hard for parameter k. We also discuss a decision version of the problem and its several modifications for which we show W[1]/W[2]-hardness. This is in contrast to the parameterized k-sum problem, which is only W[1]-hard (Abboud-Lewi-Williams, ESA'14). In addition, we show that the decision version of the problem without parameter is an undecidable problem, and we give a fixed-parameter tractable algorithm for matrices of bounded size over finite fields, parameterized by the matrix dimensions and the order of the field.

Relating behaviour of data-aware process models

Data Petri nets (DPNs) have gained traction as a model for data-aware processes, thanks to their ability to balance simplicity with expressiveness, and because they can be automatically discovered from event logs. While model checking techniques for DPNs have been studied, more complex analysis tasks that are highly relevant for BPM are beyond methods known in the literature. We focus here on equivalence and inclusion of process behaviour with respect to language and configuration spaces, optionally taking data into account. Such comparisons are important in the context of key process mining tasks, namely process repair and discovery, and related to conformance checking. To solve these tasks, we propose approaches for bounded DPNs based on constraint graphs, which are faithful abstractions of the reachable state space. Though the considered verification tasks are undecidable in general, we show that our method is a decision procedure DPNs that admit a finite history set. This property guarantees that constraint graphs are finite and computable, and was shown to hold for large classes of DPNs that are mined automatically, and DPNs presented in the literature. The new techniques are implemented in the tool ada, and an evaluation proving feasibility is provided.

Terminological features of the concept of signal-to-noise ratio when processing ultra-wideband signals

Currently, when processing various signals and studying various types of receivers, there are many definitions for one of the basic characteristics of radio systems – signal-to-noise ratio (SNR). At the same time, the terminological feature of the SNR concept is that any definition within the framework of a locally solvable signal processing problem is correct. However, difficulties arise when comparing the effectiveness of one algorithm with another, when studying different signal models, or when measuring the SNR in different parts of the receiving system. Some researchers ignore this fact due to the apparent absolute clarity of this issue, which leads to incorrect comparison of various processing algorithms or the formation of obviously unrealizable requirements for the designed equipment. In each locally solved problem, the determination of SNR will be individual, and comparison of efficiency at a fixed SNR must be carried out with mandatory scaling of signal energies depending on the formulation of the problem. This paper systematizes the terminological features of the SNR concept in order to ensure uniformity of its application by researchers and engineers. The purpose of the work is to systematize the definition of SNR for various signal models and conditions for measuring this characteristic, to specify the results in terms of terminological features of UWB signal processing and to propose a model for recalculating the SNR depending on the stage of the signal transmission and reception process. The paper examines the determination of SNR at the input and output of optimal and matched filters, at the output of optimal receivers operating under conditions of various a priori uncertainties, as well as when processing various classes of signals. By determining the SNR at the output of the matched filter and the SNR at the output of the maximum likelihood receiver for a rectangular video pulse, the discrepancy between the signal energies, all other things being equal, is clearly demonstrated. A connection has been established between the SNR when processing ultra-wideband signals at the output of the optimal receiver with similar devices for processing other types of signals, as well as with the SNR at the input of the receiving system. Systematization of the terminological features of the SNR concept will allow theoretical researchers to choose SNR definitions that are as close as possible to practical applications when analyzing the effectiveness of synthesized algorithms, and will allow practicing engineers to correctly interpret the results of theoretical calculations when developing or testing radio engineering systems.