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In this paper, we reflect on research in decision analysis that has appeared in Management Science and its impact on decision analysis practice and applications. We consider professional applications of decision analysis in business and government settings as well as everyday conversational applications. This paper was accepted by David Simchi-Levi, Special Section of Management Science: 65th Anniversary.

Clinicians regularly have to trade benefits and harms to choose between testing and treatment strategies. This process is often done by making global and implicit judgments. A decision analysis is an analytic method that makes this process more explicit, reproducible, and evidence-based. While clinicians are unlikely to conduct their own decision analysis, they will read publications of such analyses or use guidelines based on them. This review outlines the anatomy of a decision tree and provides clinicians with the tools to critically appraise a decision analysis and apply its results to medical decision making. Clinicians reading about a decision analysis can make two judgments. The first judgment is about the credibility of the methods, such as whether the decision analysis addressed a relevant clinical question, included all important outcomes, used the current best evidence to derive variables in the model, and adopted the appropriate time horizon. The second judgment is about rating confidence in the preferred course of action by determining the certainty in the model variables, whether the results are robust in sensitivity analyses and if the results are applicable to a specific patient. Results from a valid and robust decision analysis can inform both guideline panels and the patient-clinician dyad engaged in shared decision-making.

The ecosystem service (ES) concept has increasingly been applied in environmental planning, while there are several decades of experience in applying multi-criteria decision analysis (MCDA) in complex planning situations. The aim of this article is to assess how the ES concept has been used in water management projects together with MCDA and to examine the experiences gained and make recommendations to overcome any identified challenges. Our conclusions are based on a systematic analysis of 23 articles that were selected among 206 articles focused on water-related studies using, for example, the terms multi-criteria and ecosystem services in the title, abstract or keywords. Here, we explore (i) at what level of detail ESs are included in the decision hierarchy, (ii) the pros and cons of the complementary use of the two approaches, and (iii) how the potential challenges related to the use of MCDA, such as the large number of criteria, double-counting, or assigning criteria weights, are addressed in the selected cases. The results reveal large differences between the case studies. It is shown that only a few case studies used ES categories to classify criteria in the decision hierarchy, that these cases included different numbers of ES criteria and non-ES criteria, and that most case studies elicited stakeholder preferences in MCDA. Although the paper focuses on water management projects, the conclusions regarding the advantages and pitfalls of the complementary use of the methods, as well as our recommendations, are also applicable to other environmental management contexts.

Background Traditionally, the economic value of health technologies is assessed with cost-effectiveness (CE) and budget impact (BI) analyses. However, the evaluation of rare disease therapies often considers novel value criteria. Multi-criteria decision analysis (MCDA) is a promising tool in the assessment of value criteria that typically cannot be captured with traditional approaches. Objectives The objective of this research was to investigate the criteria and scoring functions applied in value frameworks and MCDA tools relevant to the evaluation of rare disease therapies. The aim was to gain a better understanding of the domains and measurement of commonly referenced novel value criteria. Methods A systematic literature review was performed covering the period from 2013 to 2019. MCDA or value framework articles and structured review papers on orphan-drug-specific MCDA articles were reviewed. Information sources included MEDLINE, Embase, Scopus, and 26 other gray literature sources. A descriptive review of identified criteria and scoring functions was performed, with special focus on “novel” value criteria that are traditionally not considered in CE or BI analyses. Results In total, 15 relevant value frameworks and MCDA tools were identified. These studies included a large number (n = 56) of individual value criteria. The most commonly included novel criteria were unmet medical need, severity of disease, and reduction in uncertainty. The identified scoring functions (measurement methods) for novel criteria were highly heterogeneous and tailored. Standardized scoring functions were not observed. Additionally, the studies did not provide their rationale for choosing a specific scoring function for a criterion. Conclusions MCDA is a promising tool to include novel value criteria into the health technology assessment of therapies for rare diseases. To support the development of a transparent and justified evaluation process, scoring functions should be further investigated. Supplementary Information The online version contains supplementary material available at 10.1007/s41669-021-00271-w.

Purpose This purpose of this study is to structure complex problems to be solved with greater efficiency, optimising the relationship between root causes (RC) relevance of the problem and utilisation of human resources to treat them, minimising the use of manpower in problem-solving activity and thus contributing to greater productivity within organisations. Design/methodology/approach The authors built an approach under the concepts of theory of constraints and multiattribute and multiobjective decision-making methods that were applied in a real complex problem of the low development of Brazilian space industry, by theoretical perspective. Also, the authors submitted it in a simulation environment to assess in which situations it is successful considering number of problem’s RC, system complexity and number of people in the system. Findings The approach was successful on the real case, finding the optimal relationship between the RC relevance and the number of people involved to treat them. For certain complex problem inputs configurations, simulation results reveal that the approach is reliable obtaining more than 95% chance of success in finding the optimal relationship, when comparing with traditional prioritising methods. Originality/value This approach introduces an unprecedented way to locate and evaluate non-physical constraints within a system, which is used to determine RC relevance, as well as an unprecedented way of defining a single optimal solution for structuring a problem, considering the relevance of RC and the use of human resources. The approach is useful for organisations in general which often need managing complex problems with few resources.

We compare how several forms of multicriteria decision analysis (MCDA) can enhance the practice of alternatives assessment (AA). We report on a workshop in which 12 practitioners from US corporations, government agencies, NGOs, and consulting organizations applied different MCDA techniques to 3 AA case studies to understand how they improved the decision process. Participants were asked to select a preferred alternative in each case using a different decision analysis approach: their unaided decision-making method, individual or lightly facilitated group multiattribute value theory (MAVT), and more extensively facilitated group structured decision making (SDM). Surveys conducted after each exercise revealed that participants were positive toward the use of formal decision-making methods for AA, reporting meaningful increases in their understanding of the trade-offs involved and their own values. Participants also reported challenges with each approach. While the MCDA techniques were reported to enhance transparency and communication, they did not consistently lead to higher satisfaction with a decision and/or outcome, and they were not more likely to be adopted within their organizations than unaided approaches. More formal decision-making methods have promise in the context of AA, but practitioners will need more guidance to use such tools successfully. Practitioners will also need to define what "success" constitutes; different approaches may be called for depending on whether the objective is increased understanding, satisfaction with the outcome, satisfaction with the process, or something else. Integr Environ Assess Manag 2021;17:27-41. © 2020 SETAC.

Objective: To provide a resource to educate clinical decision makers about the analyses and models that can be employed to support data-driven choices. Data Sources: Published studies and literature regarding decision analysis, decision trees, and models used to support clinical decisions. Review Methods: Decision models provide insights into the evidence and its implications for those who make choices about clinical care and resource allocation. Decision models are designed to further our understanding and allow exploration of the common problems that we face, with parameters derived from the best available evidence. Analysis of these models demonstrates critical insights and uncertainties surrounding key problems via a readily interpretable yet quantitative format. This 11th installment of the Evidence-Based Medicine in Otolaryngology series thus provides a step-by-step introduction to decision models, their typical framework, and favored approaches to inform data-driven practice for patient-level decisions, as well as comparative assessments of proposed health interventions for larger populations. Conclusions: Information to support decisions may arise from tools such as decision trees, Markov models, microsimulation models, and dynamic transmission models. These data can help guide choices about competing or alternative approaches to health care. Implications for Practice: Methods have been developed to support decisions based on data. Understanding the related techniques may help promote an evidence-based approach to clinical management and policy.

When setting priorities for health, there is broad agreement that a range of social values and ethical principles beyond clinical and cost-effectiveness matter, but exactly how health technology assessment (HTA) should account for a broader set of criteria remains an area of ongoing debate. In light of this, we welcome a recent review paper by Baltussen et al. evaluating the potential of different multi-criteria decision analysis (MCDA) approaches to enable HTA agencies to incorporate a broader set of values in their appraisals. The authors describe three approaches to MCDA—qualitative MCDA, quantitative MCDA, and MCDA with decision rules—laying out their relative advantages and disadvantages and providing recommendations for how they can best be implemented. While we endorse many of the authors' assessments and conclusions, including the critical role of deliberation in any MCDA approach and the undertaking of qualitative MCDA at a minimum, we take a stronger position regarding the flaws of quantitative MCDA and strongly caution against it. We find quantitative MCDA antithetical to at least two of the ways MCDA is intended to improve HTA recommendations: (i) enhancing quality and (ii) promoting transparency. Quantitative MCDA may mask the complex tradeoffs that exist within and between decision criteria and remain generally inaccessible to those who are not well-versed in its technical methods of appraisal. We advocate for a predominantly qualitative approach to MCDA appraisal centered around deliberation and supplemented with decision aids to help account for health opportunity costs.

In this work, we show the usefulness of a new Multi-Criteria Decision Analysis (MCDA) method, called Unweighted TOPSIS (uwTOPSIS), in helping decision making, aiming at achieving the objectives of ...

Several practical problems incorporate uncertainty and erroneous information within its framework of definition. Many distinguished models have already been developed to manage such unreliability, like probabilistic models, and so forth. However, to revitalize the problems where uncertainty is not probabilistic, or the information is vaguely accessible, other tools like fuzzy logic and fuzzy linguistic-based approach have emerged. The efficacy of a linguistic-based approach pertains to a problem of decision making involving qualitative information where precise numerical modeling appears to be inappropriate. The 2-tuple linguistic model formalized by Herrera and Martinez in year 2000, has upgraded several linguistic processes for solving complex decision-making issues. Its successful application in different fields has impelled several researchers to work in its extension. This study aims to offer a systematic literature review on a 2-tuple linguistic model as well as discuss its extensions. The manuscript lays out a brief review of 188 articles published between 2000 and 2019. Based on the 188 selected articles, the following questions can be justified: (1) What is the fuzzy linguistic approach? (2) What is the linguistic model based on the 2-tuples and its foundation? (3) Why the 2-tuple model preferred over other existing classical linguistic models? (4) What are the extensions of the 2-tuple model? Moreover, (5) What are the major applications of a 2-tuple model witnessed in varied disciplines, specifically decision analysis? Therefore, seeking answers to these questions indicate current trends in research. Further, it provides the direction for scholars to work in this field and overcome the challenges associated with the prevailing 2-tuple model.

PurposeThe aim of this paper was to investigate the criteria and sub-criteria with the most impact on determining a suitable location for refugee camps. This paper also analysed the relationships between the main criteria used in the selection process.Design/methodology/approachThis study applied a combination of fuzzy methods and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Network Process (ANP) methods as tools for multiple-criteria decision analysis. A questionnaire was distributed to field workers in an international humanitarian organization team.FindingsFive main criteria and twenty sub-criteria were defined. Between them, the highest ranked sub-criteria were long-term planning, optimal distribution and opportunity for growth. These findings were specific to the interviewed respondents of presented research at the time the data were collected and offer a potential research design for future research examining different organizations and teams.Research limitations/implicationsThe methods and evaluation were based on human opinions that were potentially biased.Practical implicationsThe results of this study could be useful to government organizations, UN agencies, humanitarian organizations and other decision-making parties in selecting camp locations for refugees or internally displaced people according to how the importance of particular sub-criteria is understood.Originality/valueNew sub-criteria were included in this research. To date, the combination of fuzzy, DEMATEL and ANP methods has not been previously used in relation to these issues. Scientific knowledge concerning refugee camp siting problems is limited. This research extends this knowledge with the involvement of humanitarian workers as respondents. This paper also offers organizations a process for solving complex decision-making problems with long-term results or effect.

The threat to national security posed by terrorists makes the design of evidence-based counter-terrorism strategies paramount. As terrorist organizations are purposeful entities, it is crucial to understand their decision processes if we want to plan defenses and counter-measures. In particular, there is evidence that terrorist organizations are both adaptive in their behavior and driven by multiple objectives in their actions. In this paper, we use insights from learning theory and compare several different reinforcement learning models regarding their ability to predict terrorist organizations’ actions. Using data on target choices of terrorist attacks and two different objectives (renown and revenge), we show that a total reinforcement learning with power (Luce) choice probabilities and information discounting can be used to model the adaptive behavior of terrorist organizations. The model renders out-of-sample predictions which are comparable in their validity to those observed for learning in laboratory studies. We draw implications for counter-terrorism strategies by comparing the predictive validity of the different models and their calibrated parameters. Our results also offer a starting point for studying the convergence process in game theoretic analyses of conflicts involving terrorists.

The paper provides an overview of known heuristic techniques and different views on decision-making based on heuristics. We discuss heuristics of the “heuristics and biases” program introduced by Daniel Kahneman and Amos Tversky, and heuristics of the “fast and frugal heuristics” program developed by Gerd Gigerenzer and colleagues. We propose the idea that heuristics, being a natural instrument of human assessment, may be applied not only for the simplification of the decision process, but also in deliberate strategies without sufficiently reducing the complexity of a problem. In light of this, we give an overview of the Analytic Network Process methodology as a cognitive process. The Analytic Network Process is a descriptive psychophysical process developed by the mathematician, a distinguished University Professor at the University of Pittsburgh, Thomas L. Saaty in the 1980s, and it is used for large-scale, multi-criteria decision analysis. It represents the decision problem as a network structure with dependences and feedbacks between included criteria and alternatives. Whether applying heuristics may help to answer questions like “which of the two elements influence more”, pairwise comparisons of considered elements give more accurate answers on “how strong is this influence”. The aim of this article is to draw the attention of psychologists to the interdisciplinary research of the Analytic Network Process that has only been previously studied as a mathematical algorithm, and not as a cognitive process.

We explore some recent, and not so recent, developments concerning the use of probability forecasts and their combination in decision making. Despite these advances, challenges still exist. We expand on some important challenges influencing the “goodness” of combined probability forecasts such as miscalibration, dependence among forecasters, and selection of an appropriate evaluation measure while connecting the processes of aggregating and evaluating forecasts to decision making. Through three important applications from the domains of meteorology, economics, and political science, we illustrate state-of-the-art usage of probability forecasts: how they are combined, evaluated, and communicated to stakeholders. We expect to see greater use and aggregation of probability forecasts, especially given developments in statistical modeling, machine learning, and expert forecasting; the popularity of forecasting competitions; and the increased reporting of probabilities in the media. Our vision is that increased exposure to and improved visualizations of probability forecasts will enhance the public’s understanding of probabilities and how they can contribute to better decisions.

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.

Building redundant capacity into an organization’s information technology (IT) infrastructure is a standard part of business continuity planning (BCP). Traditionally, cost concerns have dominated the decision of where to locate the redundant facilities. However; recently managers are becoming more aware of the fact that the very issues that make the main IT facilities vulnerable to disruption (i.e. man-made or natural disasters) are likely to impact the redundant (back-up facilities as well. This complicates the process of selecting redundant facility location(s). The problem is essentially a multi-criteria decision problem, and can be addressed using the location analysis techniques that have been used in other domains in the past. Meanwhile, what make this context somewhat unique are the decision criteria and the rather subjective nature of the decision process. This paper provides a simple decision model for the problem, and illustrates the model with a case where relevant decision criteria are identified and the solution is obtained using a mix of objective and subjective decision techniques. We believe the paper is valuable because it presents an actionable methodology for practitioners involved in BCP.

Poliheuristic theory addresses the “why” and “how” of decision making. It focuses on how decision makers use heuristics en route to choice by addressing both the process and the choice related to the decision task. More specifically, decision makers use a two-stage process wherein a more complicated choice set is reduced to one that is more manageable through the use of these heuristics, or cognitive shortcuts. In the second stage, decision makers are more likely to employ maximizing and analytical strategies in making a choice. Poliheuristic theory also focuses on the political consequences of decision making, arguing that decision makers will refrain from making politically costly decisions. While poliheuristic theory helps us better understand how decision makers process information and make choices, it does not specifically address how choice sets and decision matrices were created in the first place. Applied decision analysis (ADA) rectifies this shortcoming by focusing on how leaders create particular choice sets and matrices and then how they arrive at a choice. It does so by first identifying the decision maker’s choice set or decision matrix; that is, the alternatives or options available to choose from as well as the criteria or dimensions upon which the options will be evaluated. ADA then focuses on uncovering the decision maker’s decision code through the use of multiple decision models. Combining poliheuristic theory with ADA allows researchers to more fully explain decision making in general and crisis decision making in particular. An application of poliheuristic theory and ADA to decision making pertaining to the Fukushima nuclear disaster reveals that even in this high-stress crisis environment decision makers followed the two-stage process as predicted by poliheuristic theory. More specifically, in the first stage, decision makers simplified the decision task by resorting to cognitive heuristics (i.e., decision making shortcuts) to eliminate politically damaging alternatives such as voluntary evacuation. In the second stage, decision makers conducted a more analytical evaluation of the compulsory evacuation options.

In mine design and planning, the identification of an appropriate Post-Mining Land Use (PMLU) is necessary and crucial to achieve environmental quality, socio-economic renewal, and social acceptance of mining projects. In this context, Multi-Criteria Decision Making (MCDM) methods support decision-makers and stakeholders, identifying the relevant factors and criteria, so that, different available alternatives can be evaluated, compared, and contrasted with each other. With the vision to enable its wide application, 15 mine profiles are identified which, combined with selected MCDM methods and relevant factors, results in a Multi-Criteria Decision Analysis (MCDA) framework for PMLU. In this preliminary framework, the MCDM methods selected are SIMUS, TOPSIS, and SMARTER. They serve different problems and, therefore, are used in different profiles: SIMUS is applied to complex profiles, TOPSIS to the lesser ones, and SMARTER is used due to its capacity of assigning weights to criteria based on Ranking Order Centroid calculations. This preliminary MCDA structure gives the possibility to include the complexity (technical and decisional) and a participatory process, for all stakeholders involved concerning PMLU.

Computational models and simulations often involve representations of decision-making processes. Numerous methods exist for representing decision-making at varied resolution levels based on the objectives of the simulation and the desired level of fidelity for validation. Decision making relies on the type of decision and the criteria that is appropriate for making the decision; therefore, decision makers can reach unique decisions that meet their own needs given the same information. Accounting for personalized weighting scales can help to reflect a more realistic state for a modeled system. To this end, this article reviews and summarizes eight multi-criteria decision analysis (MCDA) techniques that serve as options for reaching unique decisions based on personally and individually ranked criteria. These techniques are organized into a taxonomy of ratio assignment and approximate techniques, and the strengths and limitations of each are explored. We compare these techniques potential uses across the Agent-Based Modeling (ABM), System Dynamics (SD), and Discrete Event Simulation (DES) modeling paradigms to inform current researchers, students, and practitioners on the state-of-the-art and to enable new researchers to utilize methods for modeling multi-criteria decisions.

Biodiversity conservation decisions are difficult, especially when they involve differing values, complex multidimensional objectives, scarce resources, urgency, and considerable uncertainty. Decision science embodies a theory about how to make difficult decisions and an extensive array of frameworks and tools that make that theory practical. We sought to improve conceptual clarity and practical application of decision science to help decision makers apply decision science to conservation problems. We addressed barriers to the uptake of decision science, including a lack of training and awareness of decision science; confusion over common terminology and which tools and frameworks to apply; and the mistaken impression that applying decision science must be time consuming, expensive, and complex. To aid in navigating the extensive and disparate decision science literature, we clarify meaning of common terms: decision science, decision theory, decision analysis, structured decision-making, and decision-support tools. Applying decision science does not have to be complex or time consuming; rather, it begins with knowing how to think through the components of a decision utilizing decision analysis (i.e., define the problem, elicit objectives, develop alternatives, estimate consequences, and perform trade-offs). This is best achieved by applying a rapid-prototyping approach. At each step, decision-support tools can provide additional insight and clarity, whereas decision-support frameworks (e.g., priority threat management and systematic conservation planning) can aid navigation of multiple steps of a decision analysis for particular contexts. We summarize key decision-support frameworks and tools and describe to which step of a decision analysis, and to which contexts, each is most useful to apply. Our introduction to decision science will aid in contextualizing current approaches and new developments, and help decision makers begin to apply decision science to conservation problems.Las decisiones sobre la conservación de la biodiversidad son difíciles de tomar, especialmente cuando involucran diferentes valores, objetivos multidimensionales complejos, recursos limitados, urgencia y una incertidumbre considerable. Las ciencias de la decisión incorporan una teoría sobre cómo tomar decisiones difíciles y una variedad extensa de marcos de trabajo y herramientas que transforman esa teoría en práctica. Buscamos mejorar la claridad conceptual y la aplicación práctica de las ciencias de la decisión para ayudar al órgano decisorio a aplicar estas ciencias a los problemas de conservación. Nos enfocamos en las barreras para la aceptación de las ciencias de la decisión, incluyendo la falta de capacitación y de conciencia por estas ciencias; la confusión por la terminología común y cuáles herramientas y marcos de trabajo aplicar; y la impresión errónea de que la aplicación de estas ciencias consume tiempo y debe ser costosa y compleja. Para asistir en la navegación de la literatura extensa y dispar de las ciencias de la decisión, aclaramos el significado de varios términos comunes: ciencias de la decisión, teoría de la decisión, análisis de decisiones, toma estructurada de decisiones y herramientas de apoyo para las decisiones. La aplicación de las ciencias de la decisión no tiene que ser compleja ni debe llevar mucho tiempo; de hecho, todo comienza con saber cómo pensar detenidamente en los componentes de una decisión mediante el análisis de decisiones (es decir, definir el problema, producir objetivos, desarrollar alternativas, estimar consecuencias y realizar compensaciones). Lo anterior se logra de mejor manera mediante la aplicación de una estrategia prototipos rápidos. En cada paso, las herramientas de apoyo para las decisiones pueden proporcionar visión y claridad adicionales, mientras que los marcos de apoyo para las decisiones (p.ej.: gestión de amenazas prioritarias y planeación sistemática de la conservación) pueden asistir en la navegación de los diferentes pasos de un análisis de decisiones para contextos particulares. Resumimos los marcos de trabajo y las herramientas más importantes de apoyo para las decisiones y describimos el paso, y el contexto, del análisis de decisiones para el que es más útil aplicarlos. Nuestra introducción a las ciencias de la decisión apoyará en la contextualización de las estrategias actuales y los nuevos desarrollos, y ayudarán al órgano decisorio a comenzar a aplicar estas ciencias en los problemas de conservación.

Recent studies on decision analytics frequently refer to the topic of behavioral decision making (BDM), which focuses on behavioral components of decision analytics. This paper provides a critical review of literature for re-examining the relations between BDM and classical decision theories in both normative and descriptive reviews. We attempt to capture several milestones in theoretical models, elaborate on how the normative and descriptive theories blend into each other, thus motivating the mostly prescriptive models in decision analytics and eventually promoting the theoretical progress of BDM—an emerging and interdisciplinary field. We pay particular attention to the decision under uncertainty, including ambiguity aversion and models. Finally, we discuss the research directions for future studies by underpinning the theoretical linkages of BDM with fast-evolving research areas, including loss aversion, reference dependence, inequality aversion, and models of quasi-maximization mistakes. This paper helps to understand various behavioral biases and psychological factors when making decisions, for example, investment decisions. We expect that the results of this research can inspire studies on BDM and provide proposals for mechanisms for the development of D-TEA (decision—theory, experiments, and applications).

Climate change is projected to increase flood risks in West Africa. The EU Horizon 2020 project FANFAR co-designed a pre-operational flood forecasting and alert system for West Africa in four workshops with 50–60 stakeholders from 17 countries, adopting a Multi-Criteria Decision Analysis (MCDA) process. Firstly, we aimed to find a robust configuration of the FANFAR system. We document empirical evidence of MCDA, including stakeholder analysis, jointly creating 10 objectives, and 11 FANFAR system configurations. Stakeholders found it most important that the system produces accurate, clear, and accessible flood risk information, well before floods. Monte Carlo simulation and sensitivity analyses helped identifying three configurations that were robust despite uncertainty of expert predictions and different stakeholder preferences, elicited in group sessions. Secondly, we investigated if problem structuring helps focus early technical system development. Although partly achieved, full MCDA was necessary to provide convincingly robust configurations. Thirdly, we critically analyzed MCDA based on literature from sustainability science and transdisciplinary research. Our proposed framework consists of three steps: co-design (joint problem framing), co-production (doing research), and co-dissemination and evaluation of integrated knowledge. MCDA met many requirements, but not all. In step 1, participatory MCDA with problem structuring provides a consistent methodology, and can identify stakeholders and shared objectives to foster joint understanding. MCDA successfully contributes to step 2 by combining interdisciplinary expert knowledge, integrating conflicting stakeholder preferences, handling uncertainty, and providing unambiguous, shared results. Many elements of step 3 are not met by MCDA. We discuss this framework and using MCDA for transdisciplinary hydrology research that engages with stakeholders and society.

This study presents a decision‐analytic framework for prioritizing conservation strategies. The framework is based on combining direct and indirect preference‐elicitation methods and analyzing inconsistencies between the methods. A case study with The Nature Conservancy's Chesapeake Bay (The United States) agriculture team is presented. Participants evaluated six strategies to engage with agribusinesses, farmers, and farm landowners and increase adoption of nutrient and soil conservation and stream and wetland restoration activities. Impact, feasibility, and risk criteria and performance measures were developed to compare the strategies. Participants individually evaluated the strategies using a multimethod approach. One method included direct ranking based on an intuitive assessment of the strategies. The second method included indirect ranking based on swing weighting and multicriteria analysis. Some participants made adjustments to reduce inconsistencies between the methods. Results show that final rankings were more consistent than initial rankings. Inconsistencies can be reduced by understanding potential bias in the direct method and clarifying assumptions in the indirect method. This study provides evidence that a multimethod approach can deliver useful insights to inform decisions.

Building a well-structured objectives hierarchy is central to multi-criteria decision analysis (MCDA). However, in the absence of a systematic methodology to support the process, this task has been described as “more art than science”. Objectives hierarchies often tend to become large and constraining the size of a hierarchy can be challenging. This paper proposes and illustrates the use of a set of methods to support the simplification of the hierarchies in contexts that are “data rich” and characterised by many objectives. The aim of using the proposed approach is to support decision analysts in developing an appropriately concise decision model for the further interactions with the stakeholders. Using data from two completed environmental cases we show retrospectively how qualitative (means-ends networks), semi-quantitative (relevancy analysis) and quantitative (correlation analysis, principal component analysis, local sensitivity analysis of weights) methods, used alone or in combination, can inform hierarchy development. We evaluate the potential benefits and challenges of each method and discuss the advantages and disadvantages of the simplification of an objectives hierarchy. Questionnaire-based relevancy analysis can be a useful method to identify and communicate important objectives in the early phases of an MCDA process with stakeholders, while correlation analysis can help to identify overlapping objectives, particularly in cases having many objectives and alternatives. It is intended that the methods support a facilitator in developing a clear understanding of the problem and also prompt deeper thinking about and discussion of the appropriate structure and content of an objectives hierarchy with the stakeholders involved.

Purpose In this paper, the authors focus on training as a frequently used knowledge management tool. This paper aims to help training function to achieve excellence at the first attempt by evaluating and deciding on the most interesting method for each training action before engaging the investment. Design/methodology/approach The authors apply instructional theories to evaluate the relevance of training methods and explored the multiple criteria decision analysis methods, which is a mathematical approach, for the evaluation to be rational. An experimental research based on study cases is also presented to test the applicability and effectiveness of the model proposed. Findings A decisional model that allows to choose rationally the most appropriate training method for each case. It is based on Elicitation and Choice Translating Reality (ELECTRE I) method, which is a multi-criteria decision analysis method and uses criteria from First Principles developed by Merrill in 2002. Practical implications The proposed model may have several implications for the improvement of training performance, particularly in the context of quality management systems that require product compliance based on continuous improvement and risk-based approaches. It can, therefore, be used as a tool to control the quality of training process or control the risk relative to the execution of a training action or more generally as a tool to “check” that training methods chosen are the most appropriate to attempt the training objectives before “act” the training action. Originality/value The combination of a decision analysis system with the theory of instruction and the applicability to training process management.

Introduction: Orphan diseases are low-prevalence conditions with chronically debilitating or life-threatening consequences. Their treatments are generally called orphan drugs (OD). Health–technology assessment processes have traditionally considered cost-effectiveness analysis (CEA), when making reimbursement and pricing decisions for health-care plans. Valuing OD with standard CEA raises important issues due to uncertain evidence, inability to meet cost-effectiveness thresholds for reimbursement and high budget impact, among others. Multi-criteria decision analysis (MCDA) allows to overcome these issues and improve the technical and ethical quality of decisions regarding prioritization, coverage, and reimbursement of OD. Areas covered: A scoping review was conducted in order to characterize MCDA frameworks for assessing OD and implementation experiences. We reviewed electronic databases (Medline, Embase, Cochrane Library, EBSCO, CINAHL, EconLit, Web of Science, LILACS, Google Scholar) key journals (Orphanet Journal of Rare Diseases and Value in Health) and organization repositories. Expert opinion: The theoretical framework for MCDA considers areas related to characteristics of orphan diseases and their technologies’ clinical and economic impact. Participation processes are critical in incorporating societal values in weighting different dimensions and constructing decision rules. Local implementation pilots considering different stakeholders are necessary in order to pinpoint specific barriers and opportunities.

Background Multi-criteria decision analysis (MCDA) concepts, models and tools have been used increasingly in health technology assessment (HTA), with several studies pointing out practical and theoretical issues related to its use. This study provides a critical review of published studies on MCDA in the context of HTA by assessing their methodological quality and summarising methodological challenges. Methods A systematic review was conducted to identify studies discussing, developing or reviewing the use of MCDA in HTA using aggregation approaches. Studies were classified according to publication time and type, country of study, technology type and study type. The PROACTIVE-S approach was constructed and used to analyse methodological quality. Challenges and limitations reported in eligible studies were collected and summarised; this was followed by a critical discussion on research requirements to address the identified challenges. Results 129 journal articles were eligible for review, 56% of which were published in 2015–2017; 42% focused on pharmaceuticals; 36, 26 and 18% reported model applications, issues regarding MCDA implementation analyses, and proposing frameworks, respectively. Poor compliance with good methodological practice ( Discussion Results highlight the need for advancement in robust methodologies, procedures and tools to improve methodological quality of MCDA in HTA studies. Research pathways include developing new model features, good practice guidelines, technologies to enable participation and behavioural research.

The recent benefit-risk framework (BRF) developed by the Food and Drug Administration (FDA) is intended to improve the clarity and consistency in communicating the reasoning behind the FDA's decisions, acting as an important advancement in US drug regulation. In the PDUFA VI implementation plan, the FDA states that it will continue to explore more structured or quantitative decision analysis approaches; however, it restricts their use within the current BRF that is purely qualitative. By contrast, European regulators and researchers have been long exploring the use of quantitative decision analysis approaches for evaluating drug benefit-risk balance. In this paper, we show how quantitative modelling, backed by decision theory, could complement and extend the FDA's BRF to better support the appraisal of evidence and improve decision outcomes. After providing relevant scientific definitions for benefit-risk assessment and describing the FDA and European Medicines Agency (EMA) frameworks, we explain the components of and differences between qualitative and quantitative approaches. We present lessons learned from the EMA experience with the use of quantitative modelling and we provide evidence of its benefits, illustrated by a real case study that helped to resolve differences of judgements among EMA regulators.

In the trend of digital marketing, the back and front office activities are automized. For any business organization of various departmental activities, finance and marketing play a vital role. Finance and marketing are the most important functional areas of operations in any business organization, as they directly impact the financial growth as well as market steadiness of the business. As very crucial decisions taken in these functional areas of operations affect the other departments, the decisions in these financial marketing should be taken with utmost care. Henceforth, this results in greater impact on economic growth of the organization. Therefore, the decisions in finance and marketing activities should consider various factors (critical to quality) before arriving at a conclusion. In order to attain the final decision, multi-criteria decision-making (MCDM) methods can be applied. These MCDM methods consider the conflicting factors to evaluate the finance and marketing activities.

Based on the principles of three-way decision as thinking in threes, this paper proposes a framework of trilevel multi-criteria decision analysis (3LMCDA). The main motivations and contributions are a new understanding of multi-criteria decision analysis at three levels: 1) single-criterion-level analysis with respect to individual criteria, 2) multi-criteria-level aggregation by pooling results from many criteria, and 3) multi-methods-level ensemble of different multi-criteria decision analysis methods. A full understanding of multi-criteria decision analysis requires understandings at the three levels. While the existing studies have been focused on the first two levels, new efforts at the third level may enable us to take advantages of many different multi-criteria decision analysis methods.

In the context of sustainability and long term survival, knowledge based decision making has been recognized as a significant factor. Its application in organizational settings specifically through knowledge management, process management, and decision making presents one of the core organizational capabilities. This chapter explores more deeply the synergy that occurs between the knowledge based decision making and its contribution to sustainability. The extent of knowledge based decision making application is evaluated, and its relation to the economic, social and environmental sustainability, in the multi case analysis of four Croatian firms from two service industries: telecommunications and insurance.

A comprehensive simulation meta model for transition planning and decision analysis is proposed and outlined. This model is designed to provide insight on the effects of a proposed agenda ahead of time, and to support an optimization of means and resources available to decision makers in governments, organizations, businesses, or to private persons to reach their goals across different time horizons and considering different quantities of relevance.

Energy systems are popularly studied to maintain and improve the energy supply, which is an essential and irreplaceable element for modern society. With the development of technology, stakeholders need to face more prioritization and optimization problems in the establishment, operation, and maintenance process of the energy system. To make various and complicated decisions with multiple criteria to consider in the life cycle of an energy system, multi-criteria decision-making (MCDM) methods have been widely applied. Since the MCDM method is used in the research and development process for an electricity utility company in 1991, MCDM methods have been extended, improved, and adopted in an increasing number of studies regarding the energy system. In order to provide a clear reference for researchers in the related field, this study overviewed 1995 literature solving problems related to the energy system by applying MCDM methods. The definition and classification of energy systems have been clearly explained with illustrations and examples. The general framework of MCDM is introduced, and the MCDM methods used in the studies concerning energy systems are summarized and explained. To comprehensively analyze the relative works of literature, all articles using MCDM methods to solve energy system-related problems are collected and analyzed by conducting a bibliometric analysis. In the bibliometric analysis, the co-citation networks of journals, publications, and authors and the co-occurrence network of keywords are examined and evaluated. As the result, the MCDM has been proven a feasible method for energy system; multi-criteria decision analysis for energy system has been shown as a heated topic, and the most highly cited papers, authors, research countries/regions, and sources are identified.

Multi-Criteria Decision Analysis is a decision-making technique that is used by practitioners and frequently discussed and applied by researchers. This present study attempts to review the literature of applications of MCDA techniques in the area of sustainability using citation analysis. For the data collection, the Scopus database was used. With the help of related keywords, 701 research papers were identified and found suitable for analysis. These papers were then analyzed based on country, author, citation, year, and other criteria to demonstrate the pattern of research applying MCDA in sustainability. The CiteSpace tool was also used to present the clusters, citation analysis, and keywords as bursts. The findings of the present study will help future researchers better explore the field.

International audience; AbstractThere is a growing demand for moving towards sustainable agri-food systems which per nature covers a complex network of activities and domains; such systems will benefit from multi-criteria decision analysis (MCDA) methods. Although some reviews on MCDA in agri-food research have been published, none of them covered the whole value chain. In this article, a corpus of 954 articles published by INRA scientists from 2007 to 2017 was used to study the diversity and potentiality of MCDA techniques. For the first time, experts from more than 10 agri-food domains worked altogether to annotate the articles, carry out a multivariate analysis, and finally interpret the statistical results to identify the specificities of certain domains and the complementarities between domains and to suggest avenues for future agri-food research. One-third of the studies were based only on a list of indicators, even when their purpose was to choose, sort, or rank options. Regardless of the scientific discipline in the agri-food sector, MCDA studies rarely considered temporal dynamics, spatial scale changes, or stakeholder contributions. As the agri-food system becomes increasingly sustainable in the near future, the use of MCDA methods will accelerate. To become more effective, they will have to include ecosystem services, even outside the scope of ecological studies. Similarly, MCDA studies will need to include participatory science to involve stakeholders (i.e., public authorities, governmental agencies) and end-users (i.e., farmers, producers, industrials, consumers) in the construction of the multi-criteria evaluation but also in the resulting decisions.

Decision making in health care is complex, and substantial uncertainty can be involved. Structured, systematic approaches to the integration of available evidence, assessment of uncertainty, and determination of choice are of significant benefit in an era of "value-based care." This is especially true for otolaryngology-head and neck surgery, where technological advancements are frequent and applicable to an array of subspecialties. Decision analysis aims to achieve these goals through various modeling techniques, including (1) decision trees, (2) Markov process, (3) microsimulation, and (4) discrete event simulation. While decision models have been used for decades, many clinicians and researchers continue to have difficulty deciphering them. In this review, we present an overview of various decision analysis modeling techniques, their purposes, how they can be interpreted, and commonly used syntax to promote understanding and use of these approaches. Throughout, we provide a sample research question to facilitate discussion of the advantages and disadvantages of each technique.

The purpose of this study is to analyse the factors that have an impact on technology adoption for e-books utilizing the Analytic Hierarchy Process and Multiple Regression Analysis methods. Findings indicate that perceived usefulness and ease of use are the most significant determinants in using e-books. Of key significance is that Analytic Hierarchy Process results show that consumers make pairwise comparisons, adding environmental concerns to the selection process. Recognizing the importance of all these factors is valuable to e-book developers and marketers in presenting products that meet all consumer choice criteria. The Analytic Hierarchy Process provides researchers with a more thorough decision-making analysis.

When decision sciences are applied to concrete problems, Decision Makers (DM), concerned stakeholders, and the general public typically expect clear recommendations. As emphasized in particular in the literature on ethical dimensions of Operational Research (OR) practice, such recommendations are unavoidably conditioned by norms or normative conceptions. Although an extensive literature is devoted to promoting certain norms designed to be largely accepted by decision analysts, studies specifically devoted to determine, at a general level, how decision analysts can decide which norms should underlie their work, are found lacking. To make up for this lacuna, we flesh out the concept of justification. We develop requirements that any justification should satisfy to qualify for being able to justify norms on which recommendations can rest. We then introduce and recommend a series of practical rules that decision analysts should abide by, on the basis of which, in a given decision situation, a decision analyst can decide, together with the DM, whether a given norm underlying a given recommendation can be adopted.

A leader’s approach to foreign policy decision-making is a critical explanatory factor in understanding why certain decisions are made. While several tools are available to analysts who wish to examine the process by which decision makers settle upon their chosen alternative, one of the most compelling is applied decision analysis (ADA), which allows scholars to uncover the unique “decision DNA” associated with a given leader. This chapter surveys the literature that has used the ADA methodology to examine questions of foreign policy decision-making. It pulls from twenty studies of leaders’ decisions—with more than twenty different leaders, ranging from Winston Churchill to Mao Zedong to Osama bin Laden—which comprise more than one hundred total unique decisions, examining and discussing the findings of each. It draws inferences about which decision rules best explain leaders’ policy choices, concluding that the works in question show overwhelming support for the poliheuristic theory of decision; and it discusses how future scholars can build on the ADA research program and how this information can best be used by policymakers.

On behalf of the Editorial Board, we welcome readers to the inaugural issue of the Springer journal Environment Systems and Decisions. Updating the tradition of The Environmentalist (1981–2012), Environment Systems and Decisions will feature a revised scope focused on integrative decision making for environmental, social, technological, and economic systems. While many decision-oriented journals continue to focus on particular theories or methods, Environment Systems and Decisions will have an applied, systems-wide orientation. The topics will span a variety of industry, government, and military application domains with the utilization of decision analysis, systems analysis, risk assessment, risk management, risk communication, resilience analysis, policy analysis, environmental analysis, economic analysis, engineering, and the social sciences. The audience of Environment Systems and Decisions represent many application domains, as represented by the new name. For instance, Environment is intended to imply not only the natural environment, but also the environment in which a problem, decision, or innovation exists. By Systems we mean any type of systems, whether natural or manmade, technical or social, or concrete or abstract, or combinations thereof. Finally, Decisions emphasizes the behavioral and human elements of these subjects, and how the efforts featured in this journal can be leveraged to bring about significant positive changes to human welfare and the environment. Several upcoming special issues will explore themes relevant to our new scope. For instance, one upcoming issue will seek to capture the topic of sustainability from a quantitative, systems-based perspective. Another issue will investigate the topic of cyber security, which has traditionally focused on the development of technologies while largely ignoring the human aspects of the problem. Other topics to be explored in the future include multi-scale decision making and the value of information. This inaugural issue focuses on scenario analysis. Uncertainty is a universal theme or concept, cutting across disciplines and playing out at different scales of time and location. Questions regarding what the future might hold have fascinated scholars for centuries, yet scenario analysis as a formal discipline of investigating and addressing future scenarios is relatively new. Differing schools of thought exist in the field of scenario analysis, and there is no consensus on how and when to conduct a scenario analysis. How do we elicit, formulate, and identify scenarios? How do we account for stakeholder biases? How can we manage the uncertainty? What are the best ways to mitigate future risks and maximize future opportunities? We seek to address these questions through this special issue and provide insights on how to effectively utilize theories, methods, and applications of scenario analysis to better negotiate the risks and benefits associated with emergent and future conditions. The lineup of articles of this special issue is intentionally varied and crosscutting. We start with a comprehensive review of the scenario analysis literature from Tourki et al., who examine trends overtime in the nature of published articles. This serves as a jumping off point to explore the different methods and applications of the field. In an article from McCreight, practical methods and best practices of scenario development are outlined and applied I. Linkov (&) Z. A. Collier US Army Engineer Research and Development Center, Concord, MA, USA e-mail: Igor.Linkov@usace.army.mil

Health economics is a discipline of economics applied to health care. One method used in health economics is decision tree modelling, which extrapolates the cost and effectiveness of competing interventions over time. Such decision tree models are the basis of reimbursement decisions in countries using health technology assessment for decision making. In many instances, these competing interventions are diagnostic technologies. Despite a wealth of excellent resources describing the decision analysis of diagnostics, two critical errors persist: not including diagnostic test accuracy in the structure of decision trees and treating sequential diagnostics as independent. These errors have consequences for the accuracy of model results, and thereby impact on decision making. This paper sets out to overcome these errors using color to link fundamental epidemiological calculations to decision tree models in a visually and intuitively appealing pictorial format. The paper is a must-read for modelers developing decision trees in the area of diagnostics for the first time and decision makers reviewing diagnostic reimbursement models.