Toward robust decision-making in safe and sustainable by design (SSbD): current state and recommendations for MCDA integration

Participatory management is a working method of paramount importance based on the principles of knowledge sharing and accountability for addressing the sustainable management of the fishery sector. To approach this multidimensional problem we applied two Multi Criteria Decision Analysis (MCDA) methods, the Analytic Hierarchy Process (AHP) and the Non-Structural Fuzzy Decision Support System (NSFDSS), which were applied incorporating uncertainty to generate probabilistic rankings. The NSFDSS technique was applied for the first time to address a fishery problem. Two surveys were carried out among Mediterranean Advisory Council (MEDAC) stakeholders with different backgrounds. By the two surveys we: i) made an AHP test for exploring stakeholders’ perception of the objectives and indicators used in the monitoring of the stocks, ecosystem, and fisheries, and ii) introduced the NSFDSS technique, gathering feedback on stakeholders’ preferences on management options for improving fishery sustainability (e.g., reducing discards, improving ecosystem state and economic yield in the long term). In the AHP the respondents were asked to evaluate the importance of one objective against another according to a scale of semantic scores from 1 to 5, whereas a simpler scoring scale, with only 3 possible options, was used in the NSFDSS. The two MCDA methods were proven to be useful to elicit stakeholders’ view on the potential effects of key issues on economic and environmental fishery sustainability. The results showed stakeholders’ awareness of the fact that the reproductive potential should be secured by checking mortality and/or fishing intensity. Consistently, among the ecological indicators that are tracking the fisheries policy objectives, a higher rank was attributed to “mean size of the spawners,” while cost efficiency was considered to be essential for improving profits. Regarding the economic indicators, stakeholders gave higher priority to “revenue” in comparison to “production (catches),” which is a sign of awareness that increasing fish production does not necessarily turn into increased revenue. Among the different management strategies, “fleet withdrawal” (scraping) was considered as the worst option, while the “combination of measures” was considered to be the best alternative for achieving a sustainable fishery in the long term.

Human decisions about the design, deployment, and governance of AI systems are increasingly complex, involving multiple desiderata such as accuracy, fairness, transparency, cost, and environmental impact. This paper proposes a novel and scalable framework for managing this complexity through application of resources from the field of Multi-Criteria Decision Analysis (MCDA). After an introduction to the “ n > 2 Criteria Problem” in decision-making about AI, an introduction to MCDA methods, and a review of prior applications of MCDA to decision support in decisions involving AI, we lay out our own novel and scalable framework for applying MCDA to such decisions. We illustrate the framework through application to four simulated scenarios involving a medical institution’s choice among AI-powered diagnostic tools with varying performance on accuracy, fairness, transparency, and cost. We apply five multi-criteria decision analysis (MCDA) methods — namely, the Weighted Sum Model (WSM), the Analytic Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the VIKOR method (VlseKriterijumska Optimizacija I Kompromisno Resenje), and the Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) — to rank ten AI models under four distinct weight configurations reflecting realistic deployment contexts. Sensitivity analyses are then conducted to examine how variations in criterion weight assignments affect model rankings across methods and scenarios. In concluding sections we discuss the scalability of these techniques for design, deployment, and policy decisions involving AI, as well as the likely limitations of MCDA methods for decision support involving AI.

The sustainability evaluation of engineering processes and structures is a multifaceted challenge requiring the integration of diverse and often conflicting criteria. To address this challenge, Multi-Criteria Decision-Making (MCDM) methods have emerged as effective tools. However, the selection of the most suitable MCDM approach for problems involving multiple criteria is critical to ensuring robust, reliable, and actionable outcomes. Equally significant is the choice of a proper normalization technique, which plays a pivotal role in determining the robustness and reliability of the results. This study investigates the impact of common MCDM tools on the decision-making process concerning diverse aspects of sustainability. It also examines how different normalization methods influence the final outcomes. Sustainability in this context is understood as a trade-off among five key dimensions: performance, environmental impact, economic impact, social impact, and circularity. The outcome of the MCDM process is represented by an aggregated metric, referred to as the Sustainability Index (SI). This index offers a comprehensive and robust framework for evaluating sustainability and facilitating decision-making when conflicting criteria are present. To assess the effects of implementing different MCDM and normalization choices on the sustainability assessment, a dataset from the aviation sector is employed. Specifically, a typical aircraft component is analyzed as a case study for holistic sustainability assessment, utilizing data that represent the various dimensions of sustainability mentioned above, for this component. Additionally, the study investigates the influence of initial data variations and weight variations within the MCDM process on the results. The results indicate that, overall, the different MCDM and normalization methods lead to similar outcomes when applied to the design alternatives. However, a deeper dive into the results reveals that the weighted sum method, when paired with min-max normalization, appears to be more appropriate, based on the use case involved for the present investigation, due to its robustness regarding small variations in the initial data and its sensitivity to large ones. This research underscores the critical importance of selecting appropriate MCDM tools and normalization methods to enhance transparency, robustness, reliability, and consistency of sustainability assessments within a holistic framework.

Multi-criteria decision analysis to select metrics for design and monitoring of sustainable ecosystem restorations

Approximately 3000 papers concerning multi-criteria decision analysis (MCDA) in the environmental field were identified through a series of queries in the Web of Science database and classified by MCDA method and environmental application using text mining in R. Stemming and stop word removal techniques were used to remove irrelevant text from the literature. Trends in MCDA methods (AHP/ANP, TOPSIS, outranking, MAUT/MAVT) associated with specific environmental applications (water, air, energy, natural resources, and waste management) or interventions/tools applications (stakeholders, strategies, sustainability, and GIS) were identified. The results show a linear growth in the share of MCDA papers in environmental science across all application areas. Furthermore, the results show that AHP/ANP and MAUT/MAVT are the most frequently mentioned MCDA methods in the literature. For environmental applications, the results showed that natural resource and waste management keywords were, respectively, the most and least commonly discussed applications within the MCDA papers. For intervention/tool applications, we found that keywords associated with ‘strategy’ and ‘GIS’ applications are, respectively, the most and least commonly discussed keywords within the MCDA papers. The authors found that MCDA method keywords were evenly distributed across the environmental and intervention/tool applications, indicating a lack of preference in the environmental field for use of specific MCDA methods. This paper demonstrates that text mining is an applicable tool to assess specific textual trends and patterns when analyzing larger bodies of MCDA literature.

Maps are often used as decision support tools in both, desktop geographic information systems (GIS) and mobile GIS environments. The decision support capabilities of current location-based services (LBS) are limited to navigation support and database querying with no analytic evaluation of the attractiveness of alternative destinations being offered. This chapter demonstrates how LBS can be extended with specific decision support functionality, namely multi-criteria decision analysis (MCDA). MCDA was recently transferred to the mobile GIS platform illustrating how LBS user preferences can be represented by the parameters in a MCDA method and will lead to personalized decision outcomes. An extension to a collaborative crisis management scenario is proposed, in which mobile decision-makers have MCDA tools at hand to help them make more informed choices. This chapter describes the scenario and derives a client/server architecture as well as the user interface and map design for a mobile decision support system for emergency responseKeywordsGeographic Information SystemDecision Support ToolMulticriteria DecisionOrdered Weighted AverageSpatial Decision Support SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The main objectives of this article are 2-fold. First, we explore the application of multicriteria decision analysis (MCDA) methods in different areas of health care, particularly the adoption of various MCDA methods across health care decision making problems. Second, we report on the publication trends on the application of MCDA methods in health care. PubMed was searched for literature from 1960 to 2019 in the English language. A wide range of keywords was used to retrieve relevant studies. The literature search was performed in September 2019. Articles were included only if they have reported an MCDA case in health care. The search yielded 8,318 abstracts, of which 158 fulfilled the inclusion criteria and were considered for further analysis. Hybrid methods are the most widely used methods in health care decision making problems. When it comes to single methods, analytic hierarchy process (AHP) is the most widely used method followed by TOPSIS (technique for order preference by similarity to ideal solution), multiattribute utility theory, goal programming, EVIDEM (evidence and value: impact on decision making), evidential reasoning, discrete choice experiment, and so on. Interestingly, the usage of hybrid methods has been high in recent years. AHP is most widely applied in screening and diagnosing and followed by treatment, medical devices, resource allocation, and so on. Furthermore, treatment, screening and diagnosing, medical devices, and drug development and assessment got more attention in the MCDA context. It is indicated that the application of MCDA methods to health care decision making problem is determined by the nature and complexity of the health care problem. However, guidelines and tools exist that assist in the selection of an MCDA method.

This research aims to leverage existing data sources to provide new insights into the usage of Multi-Criteria Decision Analysis (MCDA) in the built environment without creating a new data collection process. The study is focused upon built environment vocations, spanning the disciplines of architecture, construction management, urban planning, real estate, facilities management, and the numerous engineering domains that support capital projects. The method of the study utilized Google search engine data from Google Trends and academic literature metadata from Web of Science, which was analyzed using Bibliometrix via the Biblioshiny interface. These datasets were cross-referenced with original data collected from a Qualtrics delivered industry questionnaire to develop a more comprehensive understanding of MCDA adoption within the built environment. The research presents a ranked list of MCDA methods used within built environment vocations in order of level of adoption, with the top five being PROMETHEE, House of Quality, Monte Carlo Simulations, TOPSIS, and Utility Theory. The results include Biblioshiny visualizations for MCDA methods studied within built environment academic papers and findings related to tool associations via key clusters, such as GIS and drone based data collection tool usage, in concert with MCDA methods. The paper presents evidence that MCDAs are more frequently used in environmental science, geography, management, and sustainability studies within the built environment relative to other design or construction oriented use cases. By integrating diverse data sources—academic publications, search engine trends, and industry responses—the research explores a multi-faceted perspective on the current state of MCDA usage, highlighting the gaps between academic focus, industry practices, and public interest. The research notes that MCDAs are often considered on an individual basis instead of approached broadly as a set of mathematical tools, possibly serving as a barrier to a more expansive understanding of the means and methods practitioners might employ.

The transparent practice of involving stakeholders in the decision making process can largely aid in eliminating opposition against sensitive and highly-debated transportation projects. Multi-Criteria Decision Analysis (MCDA), in particular, is able to consider opposing points of view of multiple stakeholders. This paper presents the research done in the realm of MCDA with the objective of identifying the appropriate technique within the MCDA process that can best aid in building consensus among stakeholders. The work is done by examining a number of widespread techniques used at each MCDA step through its application to a real-case study. Assessing stakeholders' perceptions of each of these techniques is conducted throughout the analysis process. After presenting the case study decision context and the application of the MCDA techniques results in detail, the paper concludes with matrix recommendations for building consensus via MCDA. The research finds weight elicitation via pairwise comparison, scaling via modeling and utility function, and amalgamation via the probabilistic impact index method perform best with regards to providing confidence and building consensus among stakeholders.

Siting of a central healthcare waste incinerator using GIS-based Multi-Criteria Decision Analysis

Modeling multi-criteria decision analysis in residential PV adoption

BACKGROUND: In the context of the progressive movement towards patientcentred care, patient-specific decision support is an important focus of interest. Many diagnostic and treatment patient decision aids (PDAs) are now available to help patients make informed choice decisions. An increasing number of these are software-based, with some available online. Multi-Criteria Decision Analysis (MCDA) is a potentially useful technique on which to base a software-assisted PDA, especially when the decision is complex - as is the case in choosing the best treatment for non-small cell lung cancer – but it has so far been relatively little exploited in this area. The use of any from a number of existing MCDA-based software applications in the development and delivery of a MCDA-based interactive PDA can be an effective way of achieving “best-practice” or normative standards of decision making, such as 1) a well-constructed set of decision criteria or 2) logically consistent patient preferences. However, it also involves the use of resources such as the time and cognitive effort involved in decision-making. The comparative evaluation of alternative MCDA-based software applications in developing and delivering a PDA therefore involves trade-offs between decision effectiveness and decision resource criteria moving from the normative to the prescriptive. MCDA is an ideal tool for this meta-evaluation task as well as for the adoption decision itself. AIM: To analyse, as proof of concept, the use of MCDA for the development, implementation and evaluation of interactive PDAs in routine clinical practice. OBJECTIVES: 1. To assess the use with clinicians in the Spanish NHS of two alternative MCDA software applications which implement dissimilar MCDA techniques in the development of a PDA in routine clinical practice; 2. To assess the use with clinicians in the Spanish NHS of the same two alternative MCDA software applications in the implementation of a PDA in an environment replicating actual clinical consultations; 3. To build a meta-multi-criteria decision model based on the Decision Resources Decision Effectiveness Analysis (DRDEA) framework and assess the use of this model by clinicians in the Spanish NHS to make the choice between the two MCDA applications as the basis for a PDA. METHODS: 1) Two dissimilar MCDA software applications served as a basis for the development of a lung cancer clinical management PDA in close collaboration with two different groups of three clinicians from two different Spanish NHS hospitals (H1 and H2): 1) Expert Choice, which implements the Analytic Hierarchy Process (AHP) MCDA approach; 2) Annalisa in Elicia (ALEL), which implements the Simple Attribute Weighting (SAW) MCDA approach. The process of codevelopment of the PDA in hospitals H1 and H2 was documented; 2) Expert Choice was used to implement (i.e. deliver) the lung cancer clinical management PDA in three hypothetical consultations in hospital H1. In each consultation, one of the three clinicians involved in the development of the tool, with support by this researcher, guided a proxy patient (a non-clinical member of hospital staff) through the PDA. The same process was repeated with the MCDA software ALEL in hospital H2. The process of delivery of the PDA in hospitals H1 and H2 was documented; 3) This researcher built a meta-multi-criteria decision model based on the DRDEA framework to help clinicians choose between different MCDA software applications as the basis of a PDA. The MCDA approach used for this meta-model was Multi- Attribute Value Theory (MAVT). The model was implemented, using the software HiView 3, with three clinicians from hospital H3 for the choice between Expert Choice and ALEL as the basis of a lung cancer clinical management PDA. RESULTS: The thesis makes a three-fold contribution to research in patient-centred decision support. First, it presents two new MCDA software-based approaches to clinical decision support, based on joint work with clinicians in the Spanish NHS, for developing an interactive PDA for the clinical management of non-small cell lung cancer. Second, it describes the use of these decision support tools in the delivery of 5 an interactive PDA for the clinical management of non-small cell lung cancer in a hospital environment via simulated consultations between actual clinicians, with support from this researcher, and proxy lung cancer patients. Third, it presents and applies a new MCDA-based methodology for evaluating the use of alternative MCDA software applications in the development and delivery of interactive PDAs.

Among transportation researchers, pedestrian issues are highly significant, and various solutions have been proposed to address these challenges. These approaches include Multi-Criteria Decision Analysis (MCDA) and machine learning (ML) techniques, often categorized into two primary types. While previous studies have addressed diverse methods and transportation issues, this research integrates pedestrian modeling with MCDA and ML approaches. This paper examines how MCDA and ML can be combined to enhance decision-making in pedestrian dynamics. Drawing on a review of 1574 papers published from 1999 to 2023, this study identifies prevalent themes and methodologies in MCDA, ML, and pedestrian modeling. The MCDA methods are categorized into weighting and ranking techniques, with an emphasis on their application to complex transportation challenges involving both qualitative and quantitative criteria. The findings suggest that hybrid MCDA algorithms can effectively evaluate ML performance, addressing the limitations of traditional methods. By synthesizing the insights from the existing literature, this review outlines key methodologies and provides a roadmap for future research in integrating MCDA and ML in pedestrian dynamics. This research aims to deepen the understanding of how informed decision-making can enhance urban environments and improve pedestrian safety.

In recent years, decision makers, policy analysts, and other actors, have become increasingly aware of sustainability, and begun to combine economic, social and environmental criteria in their efforts to maintain competitiveness, long-term growth and development. In multiple stakeholder settings, the presence of diverse objectives and conflicting criteria often leads to a complex multi-criteria decision problem. The multi-criteria decision analysis (MCDA) offers an integrated framework to model and study sustainability criteria and related inter-criteria relationships. In this paper, we review some of the most significant literature on environmental sustainability, and categorise it to show how and why MCDA models are widely used and becoming increasingly popular. Our systematic analysis suggests that, there has been significant growth in environmental applications of MCDA in diverse areas, ranging from energy management and policy to land use, recycling management and sustainable tourism. Among the various MCDA methods and techniques, analytical hierarchy process, TOPSIS, and goal programming are the most frequently used approaches. Many authors use a combination of different MCDA techniques to balance various factors important to achieve sustainability related goals. We expect sustainability related criteria to be an essential consideration in most future multi-criteria models.

Bibliometric Study on Multicriteria Decision Analysis (MCDA) Applied to the Selection of Operational Means in a Military Environment