Decision-making Model Research Articles

Corruption: A situational action view

Abstract This article presents an integrative, analytic, and interactive approach to explaining corruption. We find that extant research, while empirically rich, often lacks grounding on the micro level of analysis, operates on unspecified or implausible decision-making models, does not clearly distinguish between proximal and distal causes, and does not attempt to study the interactions between individual propensities and corruption-conducive settings (situational analysis). The theoretical traditions of rational choice and culture provide insight into some but not all corrupt acts, and are limited in the extent to which they explain why and how an individual chooses to act corruptly. Drawing on analytic criminology, we propose situational action theory (SAT) as an alternative explanation of corruption as moral action that forefronts rule guidance, conditional relevance of controls, and the interplay of people and settings. This conceptual framework will allow researchers to build on existing empirical work while distinguishing causes of corruption from its mere correlates. The article concludes with some reflections on bridging the gap between situational theory and empirical research into corruption.

Synergizing GIS and genetic algorithms to enhance road management and fund allocation with a comprehensive case study approach

This study identifies a critical knowledge gap, revealing how the deterioration of roads, compounded by extensive usage and additional factors, poses significant risks to the road networks’ functionality. Without a robust fund allocation and prioritization strategy, the extent of this risk may be overlooked, adversely affecting the performance of essential infrastructure elements. Our research introduces an integrated decision-making model for existing road infrastructures to address this gap. This innovative approach combines a Geographic Information System (GIS)-based road management model with a fund allocation prioritization strategy, enhanced by an optimization engine via a genetic algorithm. The primary aim is to precisely determine Maintenance and Repair (M&R) interventions tailored to the condition states, thereby improving the Pavement Condition Index (PCI) of the road segments. The research is structured around three key objectives: (1) develop a detailed GIS-based road management database incorporating inspection data and attributes of road infrastructure for proactive M&R decision-making; (2) efficiently allocate funds to maintain service delivery on deteriorated roads; and (3) pinpoint the optimal type and timing of M&R interventions to boost the condition and performance of the road segments. Anticipated results will provide asset managers with a comprehensive decision support system for executing effective M&R practices.

Integrating Public Health Data and Epidemiological Models in Business Decision-Making Post COVID-19

Objective: Propose the integration of public health data and epidemiological models into decision-making processes for health-related businesses in the Panamanian economy during future pandemics. Theoretical Framework: The analysis of the relationship between public health and the economy, through econometric models, basic reproduction number, effective reproduction number, and their interaction in health crisis contexts, provides a foundation for understanding the use of epidemiological data in strategic crisis management. Method: The methodology involves a quantitative approach using time series and predictive models applied to secondary data from the Ministry of Health of Panama on COVID-19 cases between march-april 2020. Results and Discussion: A strong relationship was found between hospitalized cases in regular wards and ICUs, consistent with findings from Kucharski et al. (2020) and Petrucci et al. (2020). This suggests that an increase in direct infections translates to higher demand for hospital services, enabling the anticipation of risks and strategic planning for staff and supplies. Research Implications: The implementation of models that account for trends, seasonality, cycles, and irregularities, complemented by the analysis of relationships between variables, facilitates the development of predictive models that incorporate epidemiological indicators and their effects on any economic sector, particularly health-related ones. Originality/Value: This study proposes a comprehensive framework that combines public health data with econometric models, offering an innovative tool for business decision-making during health crises.

Day-ahead statistical forecasting of algal bloom risk to support reservoir release decisions in a highly engineered watershed.

Predicting algal blooms in waterways receiving inflows from multiple sources is challenging since blooms and their drivers can originate from diverse sources. Models that mechanistically simulate the formation and transport of algal blooms are often computationally intensive, creating barriers to using them for daily decision-making. Given this challenge, we developed a statistical risk forecasting framework for the Caloosahatchee River and Estuary in southwest Florida, United States of America, which receives engineered water releases from the eutrophic Lake Okeechobee, as well as hydrologic inputs from the surrounding watershed. The forecasting approach considers two different hydrologic regimes (i.e., lake- versus runoff-dominated conditions) while maintaining structural simplicity such that water managers could readily apply the model for short-term decision-making. Using daily mean discharge at two United States Geological Survey stations and more than 14 years of discrete water quality sampling data with diverse temporal resolution from the South Florida Water Management District, two regression tree models were trained and tested for day-ahead bloom risk forecasting: one for conditions in which lake releases dominated, and one when watershed inputs dominated. For the model capturing lake-dominated conditions, the main bloom predictors were 30-day lagged total suspended solids at the lake and canal outlets averaged over transect residence time (R2=0.78 and RMSE=6.10μg/L). For the model predicting watershed-dominated conditions, 30-day lagged dissolved phosphorus load, and chlorophyll-a averaged over transect residence time at the canal outlet were the most important predictors of next-day chlorophyll-a (R2=0.49 and RMSE=14.50μg/L). Critically, algal blooms driven by Lake Okeechobee releases are potentially controllable, as they are related, at least in part, to the operation of water control structures. Thus, the strong performance of the model for lake-dominated conditions demonstrates its utility for informing release scheduling to mitigate downstream blooms.

Optimising design factors for mobile learning: evaluating approaches and criteria

This study presents a multicriteria decision-making model that integrates the decision-making trial and evaluation laboratory (DEMATEL) with the VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methodologies. The model aims to develop an influence framework for user interface design (UID) and validate the relationships among its components. The focus is on identifying key dimensions and criteria of UID in mobile learning and assessing their relative importance. The study evaluates 17 subcriteria after applying DEMATEL and VIKOR, as rated by 24 experts. A key finding is the visualisation of causal relationships, revealing that the colour scheme is notably influenced by font type, underscoring the colour scheme as the most critical factor in mobile learning interface design. Additionally, the study emphasises the importance of adopting continuous design approaches to improve interactive experiences and streamline mobile processes.

Epistemic reduction of the concept of ‘decision’

“Reduction” is a widely rejected view of how commonsense psychological notions relate to neuroscience. I argue that there is a particular view of reduction on which reduction of the key commonsense concept of ‘decision’ is a live option. In particular, I advance a version of epistemic reduction based on “connectability.” On this view, reduction relations are not logical relationships, but instead posited identifications that guide subsequent modeling efforts, and are substantiated if one can explain higher-level generalizations with lower-level models. I apply this view to the field of decision neuroscience, and in particular accumulation to bound models of decision-making. I show how this field is achieving the advantages of reductive explanation, including providing increasingly detailed and novel predictions, unifying distinct types of decisions under a single account, and explaining anomalies of rational decision-making. If I am right, the widespread anti-reductive attitude in philosophy of mind needs to be reconsidered.

Sustainable supplier selection and order allocation problem considering the agility and resilience dimensions: a novel multi-stage data-driven decision-making approach

This study focuses on the Supplier Selection and Order Allocation Problem (SSOAP) by considering important dimensions namely agility, resilience, and sustainability. The current article proposes a novel decision-making model to evaluate the performance of the raw material providers (RMPs) and specify the amount of orders. In the first stage, based on the literature and experts, the main aspects and criteria of the research problem are specified. Then, their weights are calculated using the fuzzy Best-Worst Method (BWM). In the next step, the fuzzy Normalized Weighted Sum (NWS) method is employed to compute the score of RMPs. Afterward, a mathematical model is proposed to select the most appropriate RMPs and specify the number of allocated orders. Then, to cope with the uncertainty, a data-driven robust fuzzy programming method is applied. Eventually, a new solution procedure named the Lexicographic Chebyshev Chance Constrained Fuzzy Goal Programming (CCFGP) is proposed to solve the suggested MOM. Results show that ‘Responsiveness’, ‘Cost’, ‘Quality’, ‘Robustness’, and ‘Reliability’ are selected as the most desirable criteria. Moreover, the results confirmed the efficiency and effectiveness of the developed approach. The results can present a perspective to managers on the role of agility, resilience, and sustainability in the SSOAP.

A new Bayesian method for the estimation of emergency nurses’ thresholds and agreement in the context of telephone triage

IntroductionTriage is the process aimed at ensuring that patients receive a level and quality of care matching the urgency of their conditions. The present study focuses on telephone triage. We discuss the application of a new decision-making model to the task of telephone triage.MethodsThe model allows to estimate the nurse’s Belonging Threshold (BT), which quantifies the minimum level of severity of an emergency scenario that leads the nurse to activate a rescue vehicle with emergency devices. The BT can be used as an index of the possible tendency of the nurse to systematically over-or under-triage. The model also provides accurate estimations of the level of agreement between different nurses, and between the nurses and reference experts, net of the noise due to the possible differences between the nurses’ BTs.Results and discussionThe model and the related experimental procedure were applied to a sample of 21 emergency nurses at the SUEM 118 Operations Center in Venice. We discuss how the model can be useful to identify nurses who would benefit from a training to improve the consistency of their application of the protocol, as well as to identify specific emergency scenarios for which the assignment of priority codes was most problematic.

Decision-making model for production and operation of underground gold mines considering low-carbon condition.

Within the framework of a low-carbon transition and integrated mineral resource exploitation, this study presents an innovative system dynamics (SD) model designed to optimize decision-making and enhance profitability in underground gold mining operations. The novel approach seamlessly integrates critical subsystems, including reserves, mining, ore dressing, smelting, financial, and carbon reduction, offering a comprehensive framework for the analysis of efficiency and sustainability. Utilizing causal loop and system flow diagrams, the model elucidates the synergistic impacts of index variations on mine operational efficiency. The model is applied to a case study involving three mining areas within a specific gold mine in China, where sensitivity analysis identifies key indicators affecting profitability. Furthermore, it examines dynamic trends under varying carbon tax scenarios. The findings reveal that mining strategic adjustments can significantly enhance profitability, extend the operational lifespan of mines, and reduce emissions.

Ranking of Autonomous Technologies for Sustainable Logistics Activities in the Confectionery Industry

The food supply chain (FSC) faces significant challenges, including the short shelf life of products, stringent food safety standards, and the growing demand for online ordering. These challenges underscore the need for a resilient and sustainable FSC, particularly in the confectionery industry, which is further burdened by the demand for innovative and healthier products. The aim of this paper is to optimize material handling activities in warehouse operations within the confectionery industry by ranking and selecting adequate material handling equipment (MHE). This paper proposes a novel hybrid multi-criteria decision-making model that integrates the Simple Aggregation of Preferences Expressed by Ordinal Vectors Group Decision Making (SAPEVO-M), Fuzzy Analytic Hierarchy Process (FAHP), and Fuzzy COmprehensive distance-Based Ranking (FCOBRA) methods. The model was applied to a real-world case study involving four alternative solutions and twelve defined evaluation criteria. The application of the model identified the implementation of an Automated Guided Vehicle system (AGVs) as the optimal alternative, offering substantial automation of logistics activities and addressing identified company challenges. The engagement of AGVs is estimated to reduce operational costs by 20%, improve warehouse operation efficiency by 30%, and decrease CO2 emissions by 25%. The contribution of this paper lies in the development of a methodological framework for evaluating and selecting MHE, as well as in highlighting the importance of optimizing material handling processes in the confectionery industry.

Large language models: Tools for new environmental decision-making.

This study represents the first exploration of Large Language Models (LLMs) in environmental decision-making, examining their potential benefits and limitations. To address environmental issues, we propose and compare two generalizable frameworks: an LLMs-assisted framework that leverages LLMs to augment human expertise and coding in traditional decision workflows, and an LLMs-driven framework that aims to automate optimization. Through a water engineering case study focusing on PFAS control, where Environmental Fluid Dynamics Code (EFDC) was used for water environment simulation, we illustrate how to instantiate these frameworks and assess their performance. The case study reveals generalizable insights about these frameworks. Results indicate that both frameworks can contribute to environmental decision-making optimization to varying degrees, though their applicability differs significantly when facing complex decision scenarios. The LLMs-assisted framework, which effectively regulates flow rates and achieves higher PFAS interception, demonstrates how AI can enhance human decision-making while preserving the essential role of domain expertise and professional judgment. In contrast, the LLMs-driven framework faces challenges in handling complex parameter optimization tasks due to constraints such as context window and maximum output length. The findings emphasize the advantages of integrating Artificial Intelligence (AI) with conventional environmental modeling and management practices. This work confirms a crucial principle: LLMs should enhance rather than replace human expertise, with the ultimate responsibility for environmental decisions remaining with humans. The originality of this research lies in its innovative methodological approach, which leverages process design and prompt engineering to integrate cutting-edge AI with conventional environmental models, establishing a foundation for responsible human-AI collaboration in environmental decision-making. While examining current strengths and limitations, this framework robustly generates optimized environmental decision strategies, marking a new exploration in the field.

A molecular fuzzy decision-making model for optimizing renewable energy investments towards carbon neutrality

A molecular fuzzy decision-making model for optimizing renewable energy investments towards carbon neutrality

Reinforcement learning applied to a situation awareness decision-making model

Reinforcement learning applied to a situation awareness decision-making model

Hierarchical Online Air Combat Maneuver Decision Making and Control Based on Surrogate-Assisted Differential Evolution Algorithm

One-to-one within-visual-range air combat of unmanned combat aerial vehicles (UCAVs) requires fast, continuous, and accurate decision-making to achieve air combat victory. In order to solve the current problems of insufficient real-time performance of traditional intelligent optimization algorithms for solving decision-making problems and the mismatch between the planning trajectory and the actual flight trajectory caused by the difference between the decision-making model and the actual aircraft model, this paper proposes a hierarchical on-line air combat maneuvering decision-making and control framework. Considering the real-time constraints, the maneuver decision problem is transformed into an expensive optimization problem at the decision planning layer. The surrogate-assisted differential evolution algorithm is proposed on the basis of the original differential evolution algorithm, and the planning trajectory is obtained through the 5 degrees of freedom (DOF) model. In the control execution layer, the planning trajectory is tracked through the nonlinear dynamic inverse tracking control method to realize the high-precision control of the 6DOF model. The simulation is carried out under four different initial situation scenarios, including head-on neutral, dominant, parallel neutral, and disadvantaged situations. The Monte Carlo simulation results show that the Surrogate-assisted differential evolution algorithm (SADE) can achieve a win rate of over 53% in all four initial scenarios. The proposed maneuver decision and control framework in this article achieves smooth flight trajectories and stable aircraft control, with each decision average taking 0.08 s, effectively solving the real-time problem of intelligent optimization algorithms in maneuver decision problems.

A deep learning model for assistive decision-making during robot-aided rehabilitation therapies based on therapists’ demonstrations

BackgroundA promising approach to improving motor recovery during rehabilitation is the use of robotic rehabilitation devices. These robotic devices provide tools to monitor the patient’s recovery progress while providing highly standardized and intensive therapy. A major challenge in using these robotic devices is the ability to decide when to assist the user. In this context, we propose a Deep Learning-based solution that can learn from a therapist’s criteria when a patient needs assistance during robot-aided rehabilitation therapy.MethodsAn experimental session was conducted with diverse people who suffered from neurological conditions. The participants used an upper limb rehabilitation robot to play a point-to-point game. A therapist supervised the robot-aided rehabilitation exercises and assisted the participants when considered necessary. This assistance provided by the therapist was detected to label those trajectories that were assisted to train a Deep Learning model that learns from the therapist when to assist. A series of transformations have been applied to the trajectories performed by the participants to generalize the method. Furthermore, the trajectory data was divided into sequences to be introduced to the model and continuously infer whether the user needs assistance. The data acquired during the experimental sessions have been divided into two datasets to train and evaluate the model: intra-participants (80% training, 20% validation) and test participants. The architecture of the Deep Learning model is conceived to perform time-series classification. It consists of diverse one-dimensional convolutional layers, a convolutional attention mechanism, and a Global Average Pooling layer. In addition, the output layer has one neuron with the sigmoid activation function, whose output can be interpreted as a probability of assistance. The model proposed in this study has been evaluated according to different metrics. In addition, the impact of applying fine-tuning to adapt the assistance to each user has been evaluated with the test participants.ResultsThe proposed model achieved an accuracy of 91.39% and an F1-Score of 75.15% with the validation dataset during a sequence-to-sequence evaluation, surpassing other state-of-the-art architectures. When evaluating the trajectories collected in the test dataset, the method proposed achieved an accuracy of 76.09% and an F1-Score of 74.42% after applying fine-tuning to each participant.ConclusionsThe results achieved by our Deep Learning-based method show the feasibility of learning assistance decision-making from experimented therapists. Furthermore, fine-tuning can be applied to personalize the assistance to each user and improve the accuracy of the method presented when deciding whether to assist with the rehabilitation robot.

XplainLungSHAP: Enhancing Lung Cancer Surgery Decision Making with Feature Selection and Explainable AI Insights

Background: Lung cancer surgery often involves complex decision-making, where accurate and interpretable predictive models are crucial for assessing postoperative risks and optimizing outcomes. This study presents XplainLungSHAP, a novel framework combining SHAP (SHapley Additive exPlanations) and attention mechanisms to enhance both predictive accuracy and transparency. The aim is to support clinicians in preoperative evaluations by identifying and prioritizing key clinical features. Methods: The framework was developed using data from 470 patients undergoing lung cancer surgery. Key clinical features were identified through SHAP, ensuring alignment with medical expertise. These features were dynamically weighted using an attention mechanism in a neural network, enhancing their impact on survival predictions. The model’s performance was evaluated through accuracy, confusion matrices, and ROC analysis, demonstrating its reliability and interpretability. Results: The XplainLungSHAP model achieved an accuracy of 91.49%, outperforming traditional machine learning models. SHAP analysis identified critical predictors, including pulmonary function, comorbidities, and age, while the attention mechanism prioritized these features dynamically. The combined approach ensured high accuracy and offered actionable insights into survival predictions. Conclusions: XplainLungSHAP addresses the limitations of black-box models by integrating explainability with state-of-the-art predictive techniques. This framework provides a transparent and clinically relevant tool for guiding surgical decisions, supporting personalized care, and advancing AI applications in thoracic oncology.

생성형 AI를 활용한 초등학교 사회과의 교수학습 프레임워크 개발

The GLAD model developed in this study is based on the decision-making learning model of social studies and is divided into six stages as follows. The lesson planning stage includes verifying AI requirements, curriculum analysis, learner analysis, and learning environment analysis. The lesson design stage comprises setting learning objectives, presenting situations and recognizing problems, understanding facts and exploring knowledge, understanding and exploring values, developing alternatives and making decisions, social participation and outcome evaluation, as well as meta-reading and emotional experiences. In the lesson implementation stage, teacher activities involve guiding learning objectives, presenting decision-making scenarios, supporting knowledge exploration and derivation, facilitating value exploration and decision-making, assisting in the decision-making process, and confirming and evaluating activity records. Student activities include confirming learning objectives, identifying and defining problems, verifying hypotheses and deriving knowledge, recognizing and clarifying values, reviewing alternatives and predicting outcomes, and acting and observing based on their decisions. AI activities in this stage consist of guiding AI usage, providing examples and supporting materials, predicting outcomes based on hypotheses, anticipating results aligned with values, supporting the comparison and prediction of alternatives, and assisting in activity record analysis and evaluation. Finally, the lesson feedback stage includes lesson analysis, evaluation, and improvement.

Quantum computing-Enhanced AI systems for advanced business intelligence applications

The convergence of quantum computing and artificial intelligence represents a transformative technological paradigm with unprecedented potential for business intelligence applications. This comprehensive review critically examines the revolutionary capabilities of quantum computing-enhanced AI systems in addressing complex computational challenges across multiple business domains. Through systematic analysis of emerging research, implementation frameworks, and interdisciplinary case studies, we investigate how quantum computing's unique computational mechanisms can fundamentally reshape data analysis, strategic decision-making, and predictive modeling. Our comprehensive examination reveals that quantum AI systems demonstrate remarkable potential to reduce computational complexity by up to 90%, enhance predictive accuracy by 60-75%, and provide unprecedented insights across financial, logistical, and strategic business intelligence domains. The research synthesizes evidence from multiple technological domains, highlighting the transformative potential of quantum-enhanced AI in solving previously intractable computational problems. By exploring technological capabilities, implementation challenges, and future research directions, this review provides a critical framework for understanding the emerging intersection of quantum computing and artificial intelligence in advanced business intelligence applications.

Optimising resource recovery from construction waste material reverse supply chain coordination games

The inefficiency of government, recycling, and recovery companies in managing construction waste, especially after an earthquake, highlights the operational challenges of reverse supply chain (RSC) in effectively recycling materials. This study aims to address these inefficiencies by proposing a decision-making supply chain model that enhances coordination within the RSC, and focusing on optimising the management of post-disaster construction waste. Grounded in market demand functions, the model explores three subsidy models to examine their impact on recycling efficiency, the recycling ratio, retail pricing of materials from recycled waste, and overall profit. The study uses manufacturing enterprises-leadership and subsidy-specific Stackelberg game models to find optimal strategies for minimising waste and maximising supply chain profit. The results show that government subsidies significantly influence recycling and pricing strategies within RSC operations, and governments should offer subsidies according to waste dispersion prioritisation and quantity of recoverable construction debris. Based on the study findings, the proposed RSC model substantially enhances recycling efficiency and profitability for both recycling and manufacturing firms. These findings validate the effectiveness of the model and emphasise the prospects for future research on an RSC framework for construction waste material.

Biodiversity Protection Practices in Supply Chain Management: A Novel Hybrid Grey Best–Worst Method/Axial Distance-Based Aggregated Measurement Multi-Criteria Decision-Making Model

Biodiversity, from genes to entire ecosystems, is crucial for a healthy planet. However, human activities, including business practices, are causing rapid biodiversity loss. This study focuses on selecting and integrating biodiversity protection practices into the supply chain, offering a chance to make positive changes for the environment and future generations. A new hybrid grey multi-criteria decision-making (MCDM) model is proposed in this paper, which combines the grey Best–Worst Method (BWM) for obtaining criteria weights and the grey Axial Distance-based Aggregated Measurement (ADAM) method for ranking alternatives (practices). The applicability of the proposed model for solving the defined problem was demonstrated by ranking nine practices according to seven criteria. The most effective supply chain management practices in the context of biodiversity conservation were supply chain policies (with a score of 0.044), biodiversity goal setting, monitoring, reporting, and transparency (0.039), and education and awareness raising (0.037). These practices are the best because they combine clear frameworks, measurable goals, and long-term cultural change for effective biodiversity conservation. The lowest ranked practice is compliance with legislation (0.006) since it represents a baseline, reactive approach rather than a proactive or innovative strategy for biodiversity conservation. This study provides a comprehensive framework and hybrid MCDM model that enhances theoretical knowledge and can serve as a basis for developing a practical tool for integrating, assessing, and prioritizing biodiversity-focused practices in supply chains. The main novelties of this paper are the extension of the ADAM method in the grey environment, the development of a new hybrid MCDM model that combines the grey BWM and grey ADAM method, the identification of biodiversity-oriented business strategies in supply chains and the criteria for their evaluation, and a framework for practice evaluation and selection.