Decision Analysis Research Articles

Development of a decision support tool to compare diagnostic strategies for establishing the herd status for infectious diseases: An example with Salmonella Dublin infection in dairies

The diagnosis of infectious diseases at herd level can be challenging as different stakeholders can have conflicting priorities. The current study proposes a “proof of concept” of an approach that considers a reasonable number of criteria to rank plausible diagnostic strategies using multi-criteria decision analysis (MCDA) methods. The example of Salmonella Dublin diagnostic in Québec dairy herds is presented according to two epidemiological contexts: (i) in herds with no history of S. Dublin infection and absence of clinical signs, (ii) in herds with a previous history of infection, but absence of clinical signs at the moment of testing. Multiple multiparty exchanges were conducted to determine: 1) stakeholders’ groups; 2) the decision problem; 3) solutions to the problem (options) or diagnostic strategies to be ordered; 4) criteria and indicators; 5) criteria weights; 6) the construction of a performance matrix for each option; 7) the multi-criteria analyses using the visual preference ranking organization method for enrichment of evaluations approach; 8) the sensitivity analyses, and 9) the final decision. A total of nine people from four Québec’s organizations (the dairy producers provincial association along with the DHI company, the ministry of agriculture, the association of veterinary practitioners, and experts in epidemiology) composed the MCDA team. The decision problem was “What is the optimal diagnostic strategy for establishing the status of a dairy herd for S. Dublin infection when there are no clinical signs of infection?”. Fourteen diagnostic strategies composed of the three following parameters were considered: 1) biological samples (bulk tank milk or blood from 10 heifers aged over three months); 2) sampling frequencies (one to three samples collection visits); 3) case definitions to conclude to a positive status using imperfect milk- or blood-ELISA tests. The top-ranking diagnostic strategy was the same in the two contexts: testing the bulk tank milk and the blood samples, all samples collected during one visit and the herd being assigned a S. Dublin positive status if one sample is ELISA-positive. The final decision favored the top-ranking option for both contexts. This MCDA approach and its application to S. Dublin infection in dairy herds allowed a consensual, rational, and transparent ranking of feasible diagnostic strategies while taking into account the diagnostic tests accuracy, socio-economic, logistic, and perception considerations of the key actors in the dairy industry. This promising tool can be applied to other infectious diseases that lack a well-established diagnostic procedure to define a herd status.

Long-Term Costs of Minimally Invasive Sacral Colpopexy Compared to Native Tissue Vaginal Repair With Concomitant Hysterectomy

Study ObjectiveTo determine the long-term costs of hysterectomy with minimally invasive sacrocolpopexy (MISCP) versus uterosacral ligament suspension (USLS) for primary uterovaginal prolapse repair. DesignA hospital-based decision analysis model was built using TreeAge Pro (TreeAge Software Inc, Williamstown, MA). Those with prolapse were modeled to undergo either vaginal hysterectomy with USLS or minimally invasive total hysterectomy with sacrocolpopexy (MISCP). We modeled the chance of complications of the index procedure, prolapse recurrence with the option for surgical retreatment, complications of the salvage procedure, and possible second prolapse recurrence. The primary outcome was cost of the surgical strategy. The proportion of patients living with prolapse after treatment was the secondary outcome. SettingTertiary center for urogynecology. PatientsFemale patients undergoing surgical repair by the same team for primary uterovaginal prolapse. InterventionsComparison analysis of estimated long-term costs was performed. Measurements and Main ResultsOur primary outcome showed that a strategy of undergoing MISCP as the primary index procedure cost $19 935 and that undergoing USLS as the primary index procedure cost $15 457, a difference of $4478. Furthermore, 21.1% of women in the USLS group will be living with recurrent prolapse compared to 6.2% of MISCP patients. Switching from USLS to MISCP to minimize recurrence risk would cost $30 054 per case of prolapse prevented. Additionally, a surgeon would have to perform 6.7 cases by MISCP instead of USLS in order to prevent 1 patient from having recurrent prolapse. ConclusionThe higher initial costs of MISCP compared to USLS persist in the long term after factoring in recurrence and complication rates, though more patients who undergo USLS live with prolapse recurrence.

Bargaining with Voluntary Disclosure and Endogenous Matching

We investigate a bargaining setting between an “informed” player, who has private information, and an “uninformed” player. The informed player has the option to truthfully disclose private information in two unique environments. In the first environment, the informed player is randomly matched with an uninformed player and, after matching, can voluntarily disclose private information prior to a negotiation taking place. In the second environment, the informed player can voluntarily disclose private information before any endogenous matching between players (and subsequent negotiation) takes place. We begin by examining these environments theoretically. When disclosure occurs after matching, we show that low informed types should disclose more often than high informed types to avoid disagreement. However, when disclosure occurs before matching, for a range of parameter values, we show that high informed types should disclose more than low informed types to secure a better match. We test these predictions in a controlled human-subjects experiment and verify many of the theoretical predictions. Among other results, we find that low informed types do indeed disclose their private information to avoid disagreement and that, when it is in their interest to do so, high informed types disclose their private information to secure a better match. Another key insight is that high uninformed types benefit from disclosure regardless of whether it occurs before or after matching. This paper was accepted by Axel Ockenfels, behavioral economics and decision analysis. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03566 .

Intentionality and Autonomy in Husserl’s Phenomenology: A Comprehensive Analysis of Conscious Decisions and the Transcendental Ego

This essay embarks on a thorough exploration of Edmund Husserl’s seminal contributions to the philosophical discourse on consciousness, with a particular focus on the dynamics of conscious decisions within the framework of phenomenology. By delving into Husserl’s nuanced examination of consciousness—its temporal structure, the nature of self-awareness, and the foundational concept of intentionality—the analysis reveals the intricate ways in which Husserl posits the transcendental ego as the nexus of meaning, judgment, and perception. The discussion illuminates how Husserl’s theory of intentionality and the intentional act’s matter and quality serve as the cornerstone for understanding the will’s engagement with the world. Through a detailed exposition of Husserl’s ideas on the phenomenological reduction, the essay articulates the active role of consciousness in constituting reality and the ethical dimensions underpinning the exercise of free will. The paper argues that Husserl’s insights into conscious decision-making challenge conventional views by framing free will within the contexts of knowledge, ethical deliberation, and the ego’s autonomy. This essay contributes to the ongoing dialogue between phenomenology and contemporary philosophy of mind by highlighting Husserl’s profound impact on our understanding of consciousness, agency, and the existential significance of human decisions. Keywords: Edmund Husserl, Phenomenology, Conscious Decisions, Transcendental Ego, Intentionality, Free Will, Consciousness, Epistemology, Temporality, Rationality, Autonomy, Phenomenological Reduction (Epoché), Intersubjectivity, Self-awareness

Sophia, A Female Robot With Artificial Intelligence In View of Sociology of Government

Sophia, a humanoid robot in the form of a woman who is equipped with artificial intelligence Sophia is good at numbers, performing repetitive tasks, interacting, communicating, and displaying attractive facial expressions. numbers, performing repetitive tasks, interacting and communicating in an interesting way as well as displaying facial expressions with various emotions. facial expressions with various emotions. AI in the context of sociology has unwittingly changed social life. social life. The existence of AI in the implementation of e-Government changes the pattern of communication in public services, affecting the socio-cultural ties that exist in the society. services, affecting previously strong socio-cultural ties and forming new communities. This phenomenon reflects the parameters of modernity, indicating changes in the order of the social system due to technological interference. This qualitative research aims to find out how the form of Social Interaction in Artificial Intelligence-Based Public Services (AI)-based Public Services is formed. Social Interaction of Artificial Intelligence-Based Public Services (AI) from the perspective of the sociology of government? The results The results concluded that AI is able to complete work without direct human interaction, or directly with humans. The use of AI technology in public services has potential benefits, such as increasing efficiency, service quality, and benefits for the community. Through the system Automated Customer service, Big Data Analysis for Decision Making, Security Detection Kiiminal Detection, Efficient Transportation Administration, Better Health Care and Diagnosis Society is increasingly responsive to the utilization of AI in public services, this is driven by increasing public awareness of the benefits of AI and the risks associated with AI utilization, such as loss of workers.

Analysis of the Relationships between Factors Contributing to Marine Casualties

This paper presents a model for assessing the impact of various factors on maritime accidents. This paper discusses the issue of maritime transport and its risks. The taxonomy of causes and consequences used in maritime transport is explained. Two mathematical principles were used, i.e., multi-criteria decision analysis (MCDA) and principal component analysis (PCA). The analyses were carried out using the Promethee/Gaia method. The connection between causes and accidents constituted the decision problem. The evaluation criteria, a set of actions and preferences, were identified based on the method principles. The results of this research include rankings of causal chains. They were analysed in multi-criteria and single-criteria dimensions. The outcomes are presented numerically and graphically. Such research contributes to improving safety at sea. It allows us to understand how a particular transport system works. Conclusions can be drawn and measures can be initiated to change the situation in the future.

Multi-Criteria Decision Analysis of an Innovative Additive Manufacturing Technique for Onboard Maintenance

Access to spare parts in the maritime industry is limited throughout most of a ship’s life cycle. The limitation is caused by both the geographical distance of vessels from suppliers and the often limited turnaround time during which parts can be delivered. Manufacturing some parts onboard is possible, but it is a time-consuming and labour-intensive process. Advanced manufacturing techniques could be used to improve access to spare parts at sea by combining the desirable materials properties and flexibility of Direct Energy Deposition (DED) and the higher dimensional tolerances of Computer Numerical Control (CNC) manufacturing. The present study assesses the comparative viability of onboard implementation of advanced manufacturing techniques for offshore assets as a capital investment in different modes against an option of no onboard advanced manufacturing using a multi-criteria decision analysis method. To this end, a Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) is employed considering the techno-economic and environmental aspects of the decision-making process as well as the inherent challenges that come with a new area of research. Finally, the challenges, opportunities, and pathways to onboard maintenance using additive manufacturing are discussed within the scope of the sustainable future for ship and offshore energy assets.

Cost-Utility Analysis of Early Detection with Ultrasonography of Differentiated Thyroid Cancer: A Retrospective Study on a Korean Population.

There is debate about ultrasonography screening for thyroid cancer and its cost-effectiveness. This study aimed to evaluate the cost-effectiveness of early screening (ES) versus symptomatic detection (SD) for differentiated thyroid cancer (DTC) in Korea. A Markov decision analysis model was constructed to compare the cost-effectiveness of ES and SD. The model considered direct medical costs, health outcomes, and different diagnostic and treatment pathways. Input data were derived from literature and Korean population studies. Incremental cost-effectiveness ratio (ICER) was calculated. Willingness-to-pay (WTP) threshold was set at USD 100,000 or 20,000 per quality-adjusted life year (QALY) gained. Sensitivity analyses were conducted to address uncertainties of the model's variables. In a base case scenario with 50 years of follow-up, ES was found to be cost-effective compared to SD, with an ICER of $2,852 per QALY. With WTP set at $100,000, in the case with follow-up less than 10 years, the SD was cost-effective. Sensitivity analysis showed that variables such as lobectomy probability, age, mortality, and utility scores significantly influenced the ICER. Despite variations in costs and other factors, all ICER values remained below the WTP threshold. Findings of this study indicate that ES is a cost-effective strategy for DTC screening in the Korean medical system. Early detection and subsequent lobectomy contribute to the cost-effectiveness of ES, while SD at an advanced stage makes ES more cost-effective. Expected follow-up duration should be considered to determine an optimal strategy for DTC screening.

Reinforcement Learning Algorithm to Optimize Players' Tactical Decisions and Round Planning in Tennis Matches

This paper explores the application of the Ranking Genetic Optimization Reinforcement Learning (RGORL) algorithm to optimize players' tactical decisions and round planning in tennis matches. Leveraging evolutionary principles and reinforcement learning techniques, RGORL offers a data-driven framework for enhancing on-court performance. Extensive simulations demonstrate the algorithm's effectiveness in improving match outcomes, points won percentages, and games won percentages. Results illustrate a steady improvement in fitness scores over successive generations, indicating RGORL's ability to evolve and refine strategies over time. Analysis of tactical decisions reveals the superiority of strategies such as the "Net Approach" in terms of win rates, points won percentages, and games won percentages. Through extensive simulations, RGORL demonstrates a notable improvement in match outcomes, with a maximum increase of 13% in win rates. Analysis of tactical decisions reveals significant enhancements in points won percentages, with improvements of up to 34% observed across various strategies, notably the "Net Approach." Furthermore, the algorithm achieves substantial gains in games won percentages, with increases of up to 25% recorded.

Islamic Legal Traditionalism in the Consideration of Polygamous Validation (Analysis of Decisions on the Granting the Validation of Unregistered Marriage in Polygamy Cases After the Enactment of SEMA No. 3/2018)

Islamic Legal Traditionalism in the Consideration of Polygamous Validation (Analysis of Decisions on the Granting the Validation of Unregistered Marriage in Polygamy Cases After the Enactment of SEMA No. 3/2018)

Guidelines on the selection and inventory of social life cycle assessment indicators: a case study on flexible plastic packaging in the European circular economy

Abstract Purpose Emerging technologies are addressing current challenges to shift from a linear to a circular economy. However, the consideration of social aspects in this context is limited, and the prioritization of indicators appears arbitrary in the absence of clear and robust selection criteria. Following social life cycle assessment (S-LCA) principles, the aim of this paper is to guide the selection and inventory of social indicators based on a case study on evolving flexible packaging within the European circular economy. Methods To achieve the objective, the study involves several steps, including conducting a systematic literature review to identify key stakeholders, impact subcategories, indicators, and inventories for circular flexible plastic packaging. Multi-criteria decision analysis (MCDA) is applied to preselect a set of indicators, followed by stakeholder engagement in prioritizing impact indicators through participatory methods. Subsequently, a data collection procedure was established. Results and discussion This paper presents a materiality ranking for 19 social indicators tailored to the emerging circular flexible packaging. The result is a prioritization of nine of these indicators, including Existence of record of proof of age, Percentage of workers who are paid a living wage or above, and Existence of certified environmental management system. These nine indicators form the basis for the collection of respective inventory data for an entry-level assessment. Furthermore, guidelines on 11 procedural steps were formulated based on these findings. Moreover, challenges of synonymity and inconsistency in S-LCA terminology, besides inaccessible inventory data especially in the context of evolving recycling technologies, are identified as substantial barriers in the effort to streamline and perform S-LCA. Conclusions The paper concludes that prioritizing indicators is essential for entry-level and prospective assessments, especially when time and data are limited. Additionally, using primary inventory data to evaluate the foreground system and its direct impacts on stakeholders promotes accountability and corporate social responsibility. Furthermore, a set of procedural steps, from defining the system boundary to preparing the data questionnaire, serves as a valuable resource for practitioners across diverse industries. Recommendations In future research, these nine prioritized indicators can be applied to assess the social performance of various case studies along the value chain of circular flexible plastic packaging, covering food and non-food applications. In addition, the methodological guidelines for selecting and prioritizing indicators can be replicated across multiple industry sectors. Moreover, research effort should be directed towards establishing a methodological framework for prospective S-LCA in the context of emerging technologies.

The application of multi-criteria decision analysis in gaining a premier sort of stability in airplane safety

Improving the aeronautical safety has been of high importance for the experts. Different factors influence the static and dynamic stability of the plane. These factors play important roles in the plane safety. The static and dynamic safety include several factors. Taking all the technical criteria and elements into account, this article tries to find a suitable method for evaluating and eventually deciding on the best alternative. One of the methods of multi-criteria decision analysis is the concordance analysis (CA) which is an important way of evaluation in solving issues of different miscellaneous criteria that cannot be incorporated into one another. Also, Best-worst method (BWM), one of the most practical decision-making methods, is used in this article. Using the CA and BWMs and based on different criteria, this research prioritises the premier stability alternatives and uses them in the design and finally suggests a suitable alternative out of the influential linear and lateral ones. To do this, a combination of extant varieties including 15 criteria and three alternatives is used and the concordance and non-concordance collections and criteria are defined. The results showed that in addition to the fact that the use of both methods led to the same results; as a result, the linear, lateral and directional became the best options for aeroplane safety, respectively, for design purposes.

Supporting decision-making of collaborative robot (cobot) adoption: The development of a framework

Collaborative robots (cobots) are emerging in manufacturing as a response to the current mass customization production paradigm and the fifth industrial revolution. Before adopting this technology in production processes and benefiting from its advantages, manufacturers need to analyze the investment. Therefore, this study aims to develop a decision-making framework for cobot adoption, incorporating a comprehensive set of quantitative and qualitative criteria, to be used by decision-makers in manufacturing companies. To achieve that objective, a qualitative study was conducted by collecting data through interviews with key actors in the cobot (or advanced manufacturing technologies) adoption decision process in manufacturing companies. The main findings of this study include, firstly, an extensive list of decision criteria, as well as some indicators to be used by decision-makers, some of which are new to the literature. Secondly, a decision-making framework for cobot adoption is proposed, as well as a set of guidelines to use it. The framework is based on a weighted scoring method and can be customizable by the manufacturing company depending on its specific context, needs, and resources. The main contribution of this study consists in assisting decision-makers of manufacturing companies in performing more complete and sustained decision analyses regarding cobots adoption.

Systems Reliability and Data Driven Analysis for Marine Machinery Maintenance Planning and Decision Making

Understanding component criticality in machinery performance degradation is important in ensuring the reliability and availability of ship systems, particularly considering the nature of ship operations requiring extended voyage periods, usually traversing regions with multiple climate and environmental conditions. Exposing the machinery system to varying degrees of load and operational conditions could lead to rapid degradation and reduced reliability. This research proposes a tailored solution by identifying critical components, the root causes of maintenance delays, understanding the factors influencing system reliability, and recognising failure-prone components. This paper proposes a hybrid approach using reliability analysis tools and machine learning. It uses dynamic fault tree analysis (DFTA) to determine how reliable and important a system is, as well as Bayesian belief network (BBN) availability analysis to assist with maintenance decisions. Furthermore, we developed an artificial neural network (ANN) fault detection model to identify the faults responsible for system unreliability. We conducted a case study on a ship power generation system, identifying the components critical to maintenance and defects contributing to such failures. Using reliability importance measures and minimal cut sets, we isolated all faults contributing over 40% of subsystem failures and related events. Among the 4 MDGs, the lubricating system had the highest average availability of 67%, while the cooling system had the lowest at 38% using the BBN availability outcome . Therefore, the BBN DSS recommended corrective action and ConMon as maintenance strategies due to the frequent failures of certain critical parts. ANN found overheating when MDG output was above 180 kVA, linking component failure to generator performance. The findings improve ship system reliability and availability by reducing failures and improving maintenance strategies.

Dual-Criteria Decision Analysis by Multiphotonic Effects in Nanostructured ZnO.

Simultaneous interrogation of pump and probe beams interacting in ZnO nanostructures of a two-wave mixing is proposed for dual-path data processing of optical signals by nonlinear optical effects. An enhancement in third-order nonlinear optical properties was exhibited by Al-doped ZnO thin films. Multiphoton absorption and nonlinear refraction were explored by the z-scan technique at 532 nm with nanosecond pulses. The evolution of the optical Kerr effect in the ZnO thin films was analyzed as a function of the incorporation of Al in the sample by a vectorial two-wave mixing method. Electrical and photoconductive effects were evaluated to further characterize the influence of Al in the ZnO solid samples. Potential applications of nonlinear optical parameters for encoding and encrypting information in light can be envisioned.

Assessing climate change vulnerability of coastal roads

Climate change is a global phenomenon, which affects in several ways different regions all around the world, beyond the rise in global temperature. Among the different climate change issues, the management of transport infrastructures is crucial. Particularly, their vulnerability against changes in climatic conditions should be assessed. Vulnerability indicators are based on the IPCC concept of vulnerability and can be defined as a function of Climate Exposure, Sensitivity, and Adaptive Capacity. These dimensions need to be addressed during the assessment making and can be modelled as a Multiple Criteria Decision Analysis (MCDA) problem. This study proposes an integrated approach of several MCDA methods as a possible tool for ranking the climate change vulnerability of coastal roads in Malta. The application covers six coastal roads in the islands of Malta, classified by three different MCDA methods. The results indicate that the proposed approach can produce a consistent ranking of the climate change vulnerability of coastal roads. The study provides policy and decision-makers with a definition of a coastal road, an inventory of such roads, a list of climate change impacts, and a mathematical model incorporating climate change vulnerability indicators. The model can be used to prioritize investment and plan climate change adaptation strategies for infrastructural works on coastal roads.

Impact of Normalization on Entropy-Based Weights in Hellwig's Method: A Case Study on Evaluating Sustainable Development in the Education Area.

Determining criteria weights plays a crucial role in multi-criteria decision analyses. Entropy is a significant measure in information science, and several multi-criteria decision-making methods utilize the entropy weight method (EWM). In the literature, two approaches for determining the entropy weight method can be found. One involves normalization before calculating the entropy values, while the second does not. This paper investigates the normalization effect for entropy-based weights and Hellwig's method. To compare the influence of various normalization methods in both the EWM and Hellwig's method, a study evaluating the sustainable development of EU countries in the education area in the year 2021 was analyzed. The study used data from Eurostat related to European countries' realization of the SDG 4 goal. It is observed that vector normalization and sum normalization did not change the entropy-based weights. In the case study, the max-min normalization influenced EWM weights. At the same time, these weights had only a very weak impact on the final rankings of countries with respect to achieving the SDG 4 goal, as determined by Hellwig's method. The results are compared with the outcome obtained by Hellwig's method with equal weights. The simulation study was conducted by modifying Eurostat data to investigate how the different normalization relationships discovered among the criteria affect entropy-based weights and Hellwig's method results.

Structuring the Decision-Making Process Using Quantitative Options Valuation

This study focuses on enhancing decision-making processes in the construction industry by investigating quantitative decision-making models. The construction industry is known for its diverse projects and inherent risks. Effective decision-making is crucial for project success, but it faces challenges due to various factors. The research explores biases and heuristics in decision-making, specifically in entrepreneurial and managerial contexts, with a focus on two biases: overconfidence and representativeness. Data collection involved surveys administered to entrepreneurs and managers in prominent industrial sectors. The surveys measured the levels of overconfidence and representativeness in decision-making. Additionally, the study examined commonly used decision-making models in construction, including multi-criteria decision analysis, decision support systems, decision trees, and mathematical optimization techniques. The objective was to gain insights into applying quantitative models and improve the understanding of decision-making processes in construction projects. The survey achieved a response rate of 54%, and participating managers were categorized based on their two-digit Standard Industrial Classification (SIC) codes, specifically in the 1300, 3400, 3500, 3600, and 3800 categories. Rigorous statistical analyses were conducted to evaluate potential response bias. Comparing usable responses to non-respondents using chi-square tests, no significant evidence of bias was found (χ^2 (4) = 3.973, p = .59). Moreover, a further analysis explored potential response bias across the broader set of five two-digit SIC categories, and again, no significant evidence of bias was observed (χ^2 (5) = 1.782, p = .878). The findings of this study contribute to the improvement of decision-making in construction projects and provide valuable insights into the practical application of quantitative models. By addressing biases and exploring effective decision-making approaches, this research aims to enhance project success within the complex construction industry.

A geospatial analysis of flood risk zones in Cyprus: insights from statistical and multi-criteria decision analysis methods

Over the past few decades, flood disasters have emerged as the predominant natural hazard in Cyprus, primarily driven by the escalating influence of climate change in the Mediterranean region. In view of this, the objective of this study is to develop a geospatial flood risk map for the island of Cyprus by considering 14 flood hazard factors and five flood vulnerability factors, utilizing geographic information systems (GIS) and remotely sensed datasets. A comparative assessment was conducted for hazard mapping, employing statistical methods of frequency ratio (FR) and FR Shannon’s entropy (FR-SE), and multi-criteria decision analysis method of fuzzy analytic hierarchy process (F-AHP). The main findings indicated that the FR method exhibited the highest predictive capability, establishing it as the most suitable approach for flood hazard mapping. Additionally, vulnerability factors were aggregated using F-AHP to generate the vulnerability map. The resulting flood risk map, which is the product of flood hazard and flood vulnerability, revealed that 9% of the island was located within highly risky regions, while 13.2% was classified as moderate risk zones. Spatial analysis of these high-risk areas indicated their concentration in the primary city districts of the island. Therefore, to mitigate future risks within these cities, an analysis of potential expansion zones was conducted, identifying the best-suited zone exhibiting the lowest risk. The generated flood risk map can serve as a valuable resource for decision-makers on the island, facilitating the integration of flood risk analysis into urban management plans.

Ten Years of Mediterranean Monk Seal Stranding Records in Greece under the Microscope: What Do the Data Suggest?

This paper presents the results of an analysis of stranding events of the Mediterranean monk seal Monachus monachus over a decade. The analysis involved categorization according to the cause of stranding and seasonality, the identification of hotspot stranding areas and an assessment of possible correlations between stranding events and environmental/climatic patterns using time series analysis. Moreover, Generalized Additive Models (GAMs) were applied to explore the effects of the size of small-scale fishing grounds, the number of species sightings, and the occurrence of reproduction sites on "human-related" strandings. Finally, special focus was put on the central part of the eastern Ionian Sea for the assessment of stranding hotspot areas by means of the Multi-Criteria Decision Analysis (MCDA) approach, based on different kinds of spatial information such as anthropogenic pressures and the location of breeding sites and feeding grounds. Time series analysis results revealed that oscillation indices, during the first half of the year, and sea surface temperature (SST) in the Mediterranean from October to December were positively correlated with monk seal stranding events. GAMs underlined that areas combining extended small-scale fishery grounds and a higher number of sightings were more likely to cause more strandings. Regarding spatial analyses, the central Aegean Sea was highlighted as a hotspot for "human-related strandings", while the MCDA approach emphasized that the southern coasts of Cephalonia and the gulf between Lefkada and mainland Greece were susceptible to subadult strandings.