Single Decision Maker Research Articles

PROTECTION OF WOMEN'S AND CHILDREN'S RIGHTS BASED ON SYSTEM INTERCONNECTION: A New Paradigm of Execution of Women and Children's Rights after Divorce

The implementation of post-divorce decisions in the religious court demands great attention because the system for implementing decisions in divorce cases still needs to be stronger. The cost of executing the decision is not commensurate with the nominal (the ex-husband must pay “the obligation” to his ex-wife). As a result, court decisions become “useless”, which are only authoritative in writing but weak in implementation. Through this paper, the author wants to explain a new concept related to the fulfillment of the rights of women and children after divorce based on an interconnected system so that post-divorce women and children are guaranteed their fulfillment of rights without going through the process of execution in court. This research is normative research through a statutory approach and a conceptual approach. This research shows that ensuring the women's and children's rights fulfillment requires an interconnected system. Courts must involve non-judicial institutions in an integrated manner following their respective authorities. This new framework makes institutions outside the judiciary as external partners in implementing single identity-based court decisions.

The Emergency Performance of the Hungarian Ambulance Service during the COVID-19 Pandemic.

The COVID-19 pandemic had a considerable impact on the whole health sector, particularly on emergency services. Our aim was to examine the performance of the Hungarian National Ambulance Service during the first four waves of the pandemic. We defined the 2019 performance of the service as the baseline and compared it with the activity during the pandemic years of 2020 and 2021. The data contained deliveries related to acute myocardial infarction, hemorrhagic stroke, ischemic stroke, overall non-COVID-related ambulance deliveries, COVID screenings performed by the ambulance service, and COVID-related ambulance deliveries. The data were aggregated for each week of the investigated time period and stratified by gender and age. Compared with the pre-pandemic era, we found a significant increase in all three medical conditions and overall deliveries (p < 0.001 in all cases). As a result of the increased burden, it is important for emergency services to prepare for the next global epidemic and to improve organizational performance and rescue activities. The Hungarian example highlights that in a pandemic, it can be beneficial to organize the emergency care of a country or a larger region under a single provider with a single decision maker supported by business intelligence.

Optimal outpatient capacity allocation under the capacity-addition policy

Outpatient departments are under great pressure to meet increasing demand with limited medical resources. Facing a high-demand environment, the capacity-addition policy (CAP) has been widely applied, where outpatients allocate additional capacity in a demand-driven approach. Under the CAP, outpatient managers estimate an effective demand. Additional capacity is determined depends on the relationship between the estimate and regular capacity. Moreover, regular capacity is also a decision variable, which leads to great difficulties when conducting capacity allocation. The joint decisions on the optimal regular capacity, its allocation scheme of different phases and proper rule for estimating the effective demand become important concerns for outpatient managers, under uncertainties in demand and patient no-show. This paper addresses the problem towards maximizing the expected profit composed of expected revenue minus the costs of idleness and overwork. Several properties are proposed to address the difficulty in the infinite solution space. Properties help transform the joint decisions into orderly single decisions and prove the unimodality of the objective. Algorithms are designed accordingly. Experiments indicate that it is better to pre-offset the effect of no-show rather than consider its reduction in the determination of additional capacity. Outpatient managers need to enlarge the estimate of effective demand, and 1.1 to 1.2 times of the total expected demand is suggested, where outpatients can balance the expected overload with the expected profit. The optimal regular capacity is stable to the variety of no-show probability.

Dynamics and Processes in Operations Control Centers in Urban Public Transport: Potentials for Improvement

Disruptions in public transport operations occur every day. Thus, providing a reliable system is a challenge for operations and planning. This paper gives insights into the dynamics and processes of operations control centers in public transport to reveal potentials for further improvement in reliability. Therefore, directors were interviewed, dispatchers observed, and operations documentation was studied. It has become obvious that the process of dispatching has four different types of call signals (assault, accident, missing replacement, and wish-to-talk) corresponding to different kinds of incidents. The drivers use those call signals to contact the operations control center and initialize different procedures of communication between the dispatchers, drivers, and other involved parties. As the communication is mostly conducted via phone or radio, several improvements are possible, such as training in communications and increased use of information technology in operations. In planning tools, the handling of incidents is marginally supported. As all kinds of incidents can affect the service, they should be represented in planning tools to design more reliable public transport systems. However, they do not need to be represented in full detail. Verbal communication could mostly be reduced to single decisions. Accidents, for example, influence the operation by delayed vehicles and blocked ways. The findings of this work allow a better understanding of operations control centers and reveal their potentials for improvement.

Designing for Mutually Beneficial Decision Making in Human-Agent Teaming

This paper presents a joint decision-making framework between human and artificial intelligent agents in an effort to create a cohesive team uninhibited by each other’s actions. Based on the well-known Recognition Primed Decision-Making Model, our framework expands upon RPD’s single decision maker to be more Human-Agent Teaming (HAT) oriented. Specifically, our framework includes three layers of shared cognition to ensure both a consistent level of transparency between members and the efficient completion of the task. The first layer provides itself as a foundation of expectations that provides familiarity recognition in a situation. The second layer categorizes the environmental features into relevant decisions informing the symbiotic nature of who should and how to enact decisions collaboratively, which is the third layer. Altogether, this mutually beneficial decision-making model emphasizes transparency so that both humans and artificial agents are equal partners in completing tasks in unique situations.

HEART Hybrid Methods for Assessing Human Reliability in Coal-Fired Thermal Power Plant Process

The assessment of human reliability is crucial in serious processes and operations, such as planned maintenance, unplanned maintenance, and troubleshooting in a coal-fired thermal power plant, as the nature of these processes poses significant threats. When the literature is examined, the evaluation of human reliability in any type of power plant, especially coal-fired thermal power plants, is limited. In order to fill this gap, we systematically assessed human reliability in an accident that occurred during a repair of a tube failure in a boiler in a coal-fired thermal power plant. The HEART (human error assessment and reduction technique) method was used in a hybrid way alongside the fuzzy AHP and SWARA (step weight assessment ratio analysis) methods. Although the HEART method is a practical, understandable, and easy-to-implement human reliability assessment method, the APOE (assessment of the proportion effect) value depends on the decision of a single decision maker. This study aimed to eliminate this deficiency and compare human error possibilities using HEART–fuzzy AHP and HEART–SWARA methods. This hybrid method can be used in the operation of all coal-fired thermal power plants and provides practical contributions to minimize human error.

A modelling framework to support integrated design of production systems at early design stages

Multidisciplinary, large scale, and dynamic essence of production-logistic systems make their design knowledge complex. As a result, designers from different disciplines mostly design these systems with sequential approaches. This does not address the impact of single design decisions on overall system performance, which may lead to inconsistencies between different disciplines or failures. This paper aims to realise the integrated design of such systems by introducing a framework that incorporates Systems Engineering and Object-Oriented methods to develop a model that holistically embodies design knowledge of such systems. This model is constructed in Finite-State-Machine formalism to achieve an executable architecture and integrated with optimization models to allow simulation of alternatives and to observe the impact of design decisions on system behaviour. Supportive algorithms are introduced for refinements of design alternatives according to the simulation results. A fuzzy assessment approach is introduced to also assess the alternatives against qualitative criteria. The framework integrates simulation and fuzzy assessment results and performs a multi-criteria assessment to select an alternative for the detailed design. Therefore, the framework can stand as a decision support framework at early design stages, giving insights to designers about the impact of single design decisions on system overall performance and satisfaction of various objectives.

Examining the Role of Single versus Dual Decision-Making Approach for Patient Care

Decisions related to patient treatment during their hospital stay are done by two key decision-makers: The attending physician (AP) and the operating physician (OP). Under the single decision-making approach (S-DMA), the AP and OP are the same, whereas, under the dual decision-making approach (D-DMA), the AP and OP are different. In recent years, hospitals have seen an increasing trend towards D-DMA over S-DMA approach to accommodate physician schedules. While research outside healthcare operations management has argued for benefits from both S-DMA and D-DMA approaches, very little knowledge exists on what is effective during healthcare delivery. In this study, we address this gap by investigating the DMA within a hospital through physicians effectively improves patient care outcomes. Using detailed data from the state of Florida on cardiology patients over three years, we find that a S-DMA is associated with reduced patient length of stay, treatment cost, and mortality. This runs counter to some of the trends observed in practice. Further, we find that the effect is more significant for patients with greater disease complexity. A follow-up post-hoc analyses also reveal interesting insights: (1) S-DMA is more beneficial for community hospitals than teaching and academic hospitals (2) S-DMA is more critical than physician specialization. Our results are robust to alternate explanations and demonstrate the role that physicians can play in effective care treatment during a hospital stay.

Appointed learning for the common good: Optimal committee size and monetary transfers

A population of identical individuals must choose one of two alternatives under uncertainty about the state of the world. Individuals can acquire different levels of costly information and complete contracts are not feasible. For such a setup, we investigate how vote delegation to a committee and suitable monetary transfers for its members can ensure that high or optimal levels of information are (jointly) acquired. We show that for a (stable) committee that uses the majority rule to maximize the probability of choosing the right alternative and then to minimize aggregate information acquisition costs, its size must be small in absolute terms (if full learning is possible) and small relative to population size (if only partial learning is possible). Yet committees must never be made up of one member, so the tyranny of a single decision-maker can be avoided. Our analysis identifies both the potential and some of the limitations of monetary transfers in committee design.

Collegiality and efficiency in bureaucracy

This article addresses the relation between the design of regulatory agencies and efficiency, arguing that authority concentrated to a single individual outperforms more collegial decision-making when the regulated firms’ interests are aligned. The tentative explanation is that concentrated leadership reduces the risk for capture. This argument is developed from an empirical case on the markets for mobile and fixed broadband. In the mobile market, the regulated firms are similarly positioned, whereas in the fixed broadband market, the firms typically have adversarial positions, with an incumbent being challenged by entrants. A statistical analysis of regulatory agencies in 33 European countries lends support to the argument that regulation of mobile broadband benefits from having a single decision-maker whereas a bureaucratic regulation with more collegiality functions as well for the fixed broadband.

Application for polymer industry to hold nearly 40% of the bio-based platform chemicals market; demand to grow by 8% CAGR through 2031

Today, more leaders, managers, and directors can decide, even though they cannot decide for every situation. When decisions are scattered and unstructured, decisions become complicated and so that a single decision-maker based on expertise alone cannot address them adequately. This research proposes the Electronic Decision System (EDS) for effective data visualization and analysis process. EDS helps to decide during a complicated situation using Big Data Analytics-based Artificial Neural Network Framework (BDA-ANNF). The BDA-ANNF collects and analyses the data provided, and Artificial Neural Network (ANN) is used to predict and take accurate decisions in every complicated situation. These systems allow decision-makers to understand the market, direct and indirect climate, analyze the numerous trade-offs in the sector, evaluate costs linked to the various choices for decision-making, and eventually help people improve their decision-making outcome. EDS's approaches and techniques share one key challenge that must successfully provide consumers and policymakers with an appropriate and understandable simulation solution. The finding indicates that the BDA-ANNF decides the complicated situation up to the accuracy rate of 95.23%, which is not attainable by existing methods, and the error rate is 4.77%.

From single decisions to sequential choice patterns: Extending the dynamics of value-based decision-making

Every day, we make many value-based decisions where we weigh the value of options with other properties, e.g. their time of delivery. In the laboratory, such value-based decision-making is usually studied on a trial by trial basis and each decision is assumed to represent an isolated choice process. Real-life decisions however are usually embedded in a rich context of previous choices at different time scales. A fundamental question is therefore how the dynamics of value-based decision processes unfold on a time scale across several decisions. Indeed, findings from perceptual decision making suggest that sequential decisions patterns might also be present for vale-based decision making. Here, we use a neural-inspired attractor model as an instance of dynamic models from perceptual decision making, as such models incorporate inherent activation dynamics across decisions. We use the model to predict sequential patterns, namely oscillatory switching, perseveration and dependence of perseveration on the delay between decisions. Furthermore, we predict RT effects for specific sequences of trials. We validate the predictions in two new studies and a reanalysis of existing data from a novel decision game in which participants have to perform delay discounting decisions. Applying the validated reasoning to a well-established choice questionnaire, we illustrate and discuss that taking sequential choice patterns into account may be necessary to accurately analyse and model value-based decision processes, especially when considering differences between individuals.

The perfect match: interpersonal relationships and their impact on chain management

Within the German wine industry, more and more wine estates engage in collaborations to secure grape and wine qualities and quantities to meet the market demands, creating vertically coordinated supply chain networks. The focus of many scholars in this field is mainly set on structural arrangements and less on relational management mechanisms, as studies of structures and their implementation are more readily undertaken and quantified. Both structural and relational mechanisms and their influence on the different levels such as network-, dyad- and firm-level are researched. In the agri-food industry, small- and medium-sized businesses, often family-led, prevail, amplifying the influence of single decision-makers on cooperative decisions. In such a setting, personal relationships and social bonds are stronger than in classical B2B relationships in other industries. Research on chain management does not, generally, take the interpersonal or individual influence of decisions-makers into account. However, recent and emerging literature indicates an influence of managers on chain management. Hence, the questions arise how do managers affect decisions and how can the interpersonal influence be managed? This paper aims to expand the theoretical framework of chain management including the impacts of behavioural and interpersonal aspects in decision-making regarding relationship management.

Towards sustainable supply strategy in the food industry: the case of Finland

PurposeThis paper investigates how sustainability requirements modify supply strategies at different levels in the food industry companies. The paper integrates single supply decisions into firm-level supply strategy and views supply strategy as one coherent strategy in which the decision-making is based on the values and principles of a firm.Design/methodology/approachThe paper captures the decade of change and studies the renewal of supply strategy by utilising two qualitative data sets with a ten years of difference. The data contain 41 interviews from a retailer and 5 of its food supplier companies.FindingsResults confirm that a decade ago only minor attention was channelled towards sustainability whereas today sustainability is a key driver behind supply decisions and supply strategies because firms need to respond to sustainability requirements and challenges. The study presents a framework for analysing different levels (firm, category and supply market levels) of changes and sustainability requirements faced by food companies and their supply strategies.Originality/valuePreviously, sustainability has rarely been connected to firm-level supply strategy. This study presents the supply strategy elements that were important for Finnish food industry companies over a decade ago and matches these elements to the needs of today’s firms. As a result, the study incorporates the concept of sustainability with the supply strategy frameworks.

A review of partial information in additive multicriteria methods

Abstract The relevance of multiple criteria decision-making/aiding is reinforced by the prominence of these methods in a wide range of applications. Whether by solving problems with a single decision-maker (DM) or a group of DMs, additive modelling, based on value or utility functions, is the most traditional. However, applying this kind of method raises a critical issue: the difficulty in eliciting DM’s preferences and recommending a decision. Actually, it is a hard task for the DM to provide complete information regarding their preferences, because the DM may not be able to provide such information in the detailed way required, or even they may not be willing to do so. From this perspective, the emergence and growth of partial (incomplete or imprecise) information-based methods is indicative that these are a useful way of guiding decision-making as they require less cognitive input from a DM. Thus, this paper systematically reviews the literature on multicriteria decision methods that deal with partial information, focusing on the Multi-Attribute Value/Utility Theory context. Strategic research questions guide a bibliometric and content analysis of 105 peer-reviewed papers selected from the Web of Science (Main Collection). An integrated analysis of the results provides scholars, researchers and other professionals with a deeper comprehension of methodological advances and respective contributions, and of the main challenges and trends in this field of knowledge. Our analysis aims to show that when these methods are applied more reliable decision-making can be achieved.

Research on electronic decision system for effective data visualization and analysis process

Today, more leaders, managers, and directors can decide, even though they cannot decide for every situation. When decisions are scattered and unstructured, decisions become complicated and so that a single decision-maker based on expertise alone cannot address them adequately. This research proposes the Electronic Decision System (EDS) for effective data visualization and analysis process. EDS helps to decide during a complicated situation using Big Data Analytics-based Artificial Neural Network Framework (BDA-ANNF). The BDA-ANNF collects and analyses the data provided, and Artificial Neural Network (ANN) is used to predict and take accurate decisions in every complicated situation. These systems allow decision-makers to understand the market, direct and indirect climate, analyze the numerous trade-offs in the sector, evaluate costs linked to the various choices for decision-making, and eventually help people improve their decision-making outcome. EDS's approaches and techniques share one key challenge that must successfully provide consumers and policymakers with an appropriate and understandable simulation solution. The finding indicates that the BDA-ANNF decides the complicated situation up to the accuracy rate of 95.23%, which is not attainable by existing methods, and the error rate is 4.77%.

Extension of the TOPSIS method to group decision-making

TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) is a very practical decision support method used in several areas of life. This method already exists in the literature in the context of a single decision maker. In order to adapt this method to group decision making, which can be easily applied in various situations, this work extended the TOPSIS method to group decision making using the quadratic mean and the geometric mean. In this work, numerical applications have been made and interesting results have been obtained.

Utility and mechanism design in multi-agent systems: An overview

Future cities promise to be more autonomous than ever, largely owing to our ability of coordinating complex systems in real time: fleets of self-driving cars will offer on-demand transportation services, delivery drones will fly parcels in our skies, power plants will provide renewable energy reliably. In many of these systems, there is no single decision-maker with full information and authority. Instead, the system performance greatly depends on the decisions made by interacting entities with local information and limited communication capabilities. Game theory, intended as the study of multi-agent decision-making, is a fitting paradigm to tackle many of the associated challenges. Moving from this observation, in this paper we review how tools and ideas from game theory can be brought to bear on the coordination of multi-agent systems. At the heart of the proposed approach is the design and influence of agents’ preferences so that their local optimization induces a desirable system behavior. Its applicability spans a variety of settings irrespective of whether the decision makers are strategic (e.g., drivers in a road network), or not (e.g., delivery drones). Along the way, we also discuss future research directions and connections with related research areas including algorithmic game theory, incentive and mechanism design, economics, computational complexity, and approximation algorithms.

Explainable Artificial Intelligence–A New Step towards the Trust in Medical Diagnosis with AI Frameworks: A Review

Machine learning (ML) has emerged as a critical enabling tool in the sciences and industry in recent years. Today’s machine learning algorithms can achieve outstanding performance on an expanding variety of complex tasks–thanks to advancements in technique, the availability of enormous databases, and improved computing power. Deep learning models are at the forefront of this advancement. However, because of their nested nonlinear structure, these strong models are termed as “black boxes,” as they provide no information about how they arrive at their conclusions. Such a lack of transparencies may be unacceptable in many applications, such as the medical domain. A lot of emphasis has recently been paid to the development of methods for visualizing, explaining, and interpreting deep learning models. The situation is substantially different in safety-critical applications. The lack of transparency of machine learning techniques may be limiting or even disqualifying issue in this case. Significantly, when single bad decisions can endanger human life and health (e.g., autonomous driving, medical domain) or result in significant monetary losses (e.g., algorithmic trading), depending on an unintelligible data-driven system may not be an option. This lack of transparency is one reason why machine learning in sectors like health is more cautious than in the consumer, e-commerce, or entertainment industries. Explainability is the term introduced in the preceding years. The AI model’s black box nature will become explainable with these frameworks. Especially in the medical domain, diagnosing a particular disease through AI techniques would be less adapted for commercial use. These models’ explainable natures will help them commercially in diagnosis decisions in the medical field. This paper explores the different frameworks for the explainability of AI models in the medical field. The available frameworks are compared with other parameters, and their suitability for medical fields is also discussed.

Social impacts of algorithmic decision-making: A research agenda for the social sciences

Academic and public debates are increasingly concerned with the question whether and how algorithmic decision-making (ADM) may reinforce social inequality. Most previous research on this topic originates from computer science. The social sciences, however, have huge potentials to contribute to research on social consequences of ADM. Based on a process model of ADM systems, we demonstrate how social sciences may advance the literature on the impacts of ADM on social inequality by uncovering and mitigating biases in training data, by understanding data processing and analysis, as well as by studying social contexts of algorithms in practice. Furthermore, we show that fairness notions need to be evaluated with respect to specific outcomes of ADM systems and with respect to concrete social contexts. Social sciences may evaluate how individuals handle algorithmic decisions in practice and how single decisions aggregate to macro social outcomes. In this overview, we highlight how social sciences can apply their knowledge on social stratification and on substantive domains of ADM applications to advance the understanding of social impacts of ADM.