An integrated framework for environmental multi-impact spatial risk analysis

Quantitative risk analysis is being extensively employed to support policymakers and provides a strong conceptual framework for evaluating decision alternatives under uncertainty. Many problems involving environmental risks are, however, of a spatial nature, i.e., containing spatial impacts, spatial vulnerabilities, and spatial risk-mitigation alternatives. Recent developments in multicriteria spatial analysis have enabled the assessment and aggregation of multiple impacts, supporting policymakers in spatial evaluation problems. However, recent attempts to conduct spatial multicriteria risk analysis have generally been weakly conceptualized, without adequate roots in quantitative risk analysis. Moreover, assessments of spatial risk often neglect the multidimensional nature of spatial impacts (e.g., social, economic, human) that are typically occurring in such decision problems. The aim of this article is therefore to suggest a conceptual quantitative framework for environmental multicriteria spatial risk analysis based on expected multi-attribute utility theory. The framework proposes: (i) the formal assessment of multiple spatial impacts; (ii) the aggregation of these multiple spatial impacts; (iii) the assessment of spatial vulnerabilities and probabilities of occurrence of adverse events; (iv) the computation of spatial risks; (v) the assessment of spatial risk mitigation alternatives; and (vi) the design and comparison of spatial risk mitigation alternatives (e.g., reductions of vulnerabilities and/or impacts). We illustrate the use of the framework in practice with a case study based on a flood-prone area in northern Italy

In the opponent's shoes: increasing the behavioral validity of attackers' judgments in counterterrorism models

A key objective for policymakers and analysts dealing with terrorist threats is trying to predict the actions that malicious agents may take. A recent trend in counterterrorism risk analysis is to model the terrorists' judgments, as these will guide their choices of such actions. The standard assumptions in most of these models are that terrorists are fully rational, following all the normative desiderata required for rational choices, such as having a set of constant and ordered preferences, being able to perform a cost-benefit analysis of their alternatives, among many others. However, are such assumptions reasonable from a behavioral perspective? In this article, we analyze the types of assumptions made across various counterterrorism analytical models that represent malicious agents' judgments and discuss their suitability from a descriptive point of view. We then suggest how some of these assumptions could be modified to describe terrorists' preferences more accurately, by drawing knowledge from the fields of behavioral decision research, politics, philosophy of choice, public choice, and conflict management in terrorism. Such insight, we hope, might help make the assumptions of these models more behaviorally valid for counterterrorism risk analysis

Behavioural analytics: Exploring judgments and choices in large data sets

The ever-increasing availability of large data-sets that store users’ judgements (such as forecasts and preferences) and choices (such as acquisitions of goods and services) provides a fertile ground for Behavioural Operational Research (BOR). In this paper, we review the streams of Behavioural Decision Research that might be useful for BOR researchers and practitioners to analyse such behavioural data-sets. We then suggest ways that concepts from these streams can be employed in exploring behavioural data-sets for (i) detecting behavioural patterns, (ii) exploiting behavioural findings and (iii) improving judgements and decisions of consumers and citizens. We also illustrate how this taxonomy for behavioural analytics might be utilised in practice, in three real-world studies with behavioural data-sets generated by websites and online user activity

Modeling the values of private sector agents in multi-echelon humanitarian supply chains

© 2018 Elsevier B.V. Humanitarian organizations (HOs) increasingly look to engage private sector supply chains in achieving outcomes. The right engagement approach may require knowledge of agents' preferences across multi-echelon supply chains to align private sector value creation with humanitarian outcomes. We propose a multi-attribute value analysis (MAVA) framework to elucidate such preferences. We formalize this approach and apply it in collaboration with a HO pilot aiming to facilitate better private sector availability of malaria rapid diagnostic tests in Uganda. We demonstrate how HOs could use criteria weights and value functions from MAVA for project evaluation; in the process, we reveal business model insights for importers, distributors, and retailers in the pilot. We also show how MAVA facilitates the impact assessment of hypothetical options (i.e., combinations of products, services, and subsidies) to guide HO resource deployment. This paper offers the first attempt, to our knowledge, to develop quantitative measures for economic and non-economic objectives involving all agents in a multi-echelon supply chain, either humanitarian or commercial. We hope that this initial step stimulates further research to validate results and develop the framework proposed

Modelling multicriteria value interactions with Reasoning Maps

Idiographic causal maps are extensively employed in Operational Research to support problem structuring and complex decision making processes. They model means-end or causal discourses as a network of concepts connected by links denoting influence, thus enabling the representation of chains of arguments made by decision-makers. There have been proposals to employ such structures to support the structuring of multicriteria evaluation models, within an additive value measurement framework. However, a drawback of this multi-methodological modelling is the loss of richness of interactions along the means-end chains when evaluating options. This has led to the development of methods that make use of the structure of the map itself to evaluate options, such as the Reasoning Maps method, which employs ordinal scales and ordinal operators for such evaluation. However, despite their potential, Reasoning Maps cannot model explicitly value interactions nor perform a quantitative ranking of options, limiting their applicability and usefulness. In this article we propose extending the Reasoning Maps approach through a multilinear evaluation model structure, built with the MACBETH multicriteria method. The model explicitly captures the value interactions between concepts along the map and employs the MACBETH protocol of questioning to assess the strength of influence for each means-end link. The feasibility of the proposed approach to evaluate options and to deal with multicriteria interactions is tested in a real-world application to support the construction of a population health index

Structuring contrasting forest stakeholders' views with the Strategic Options Development and Analysis (SODA) approach

The study reported here aimed at presenting the structuring of a complex problem that emerges from contrasting perspectives of different stakeholders on the use and conservation of native forests in a context where regulations restrict their management, as occurs in Santa Catarina State, Brazil. The methodology adopted in this work consisted both in the construction of a causal map, based on interviews with stakeholders of Santa Catarina native forests, and in the analysis of the map using techniques of the Strategic Options Development and Analysis (SODA) approach. The analyses carried out indicated that the economic valuation of forest resources as well as the monitoring of forest cover are key issues for the management of Santa Catarina’s native forests. In addition, the information generated by the causal map analysis can assist not only the process of designing innovative and all-inclusive policies for the management of native forests, but also the modeling process based on Systems Dynamics in order to evaluate the impacts of policies on the dynamics that govern the conservation and use of the resources of native forests. The adopted SODA approach also proved to be effective in structuring the complex problem situation addressed in this study

On the learning patterns and adaptive behavior of terrorist organizations

© 2019 Elsevier B.V. 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

A framework for supporting health capability-based planning: Identifying and structuring health capabilities

The COVID-19 pandemic has highlighted that health security systems must be redesigned, in a way that they are better prepared and ready to cope with multiple and diverse health threats, from predictable and well-known epidemics to unexpected and challenging pandemics. A powerful way of accomplishing this goal is to focus the planning on health capabilities. This focus may enhance the ability to respond to and recover from health threats and emergencies, while helping to identify the level of resources required to maintain and build up those capabilities that are critical in ensuring the preparedness of health security systems. However, current attempts for defining and organizing health capabilities have some important limitations. First, such attempts were not designed to consider diverse scenarios and multiple health threats. Second, they provide a limited representation of capabilities and lack a systemic perspective. Third, they struggle to identify capability and resource gaps. In this article, we thus propose a new framework for identifying and structuring health capabilities and support health capability planning. The suggested framework has three main potential benefits. First, the framework may help policymakers in planning under high levels of uncertainty, by considering multiple realistic and stressful scenarios. Second, it can provide risk analysts with a more comprehensive representation of health capabilities and their complex relationships. Third, it can support planners in identifying resource and capability gaps. We illustrate the use of the framework in practice considering an outbreak scenario caused by three different health threats (COVID-19, Ebola, and Influenza viruses)

Multi-criteria decision analysis for strategic decision making

In this chapter we discuss the use of MCDA for supporting strategic decision making, particularly within strategy workshops. The chapter begins by exploring the nature of strategic decisions and the characteristics of the strategic decision making process. Specifically, we examine the technical issues associated with the content of strategic decisions, and the social aspects that characterise the processes within which they are created. These features lead us to propose a number of adaptations to the standard MCDA approach if it were to be used at a more strategic level. We make suggestions on how to implement these proposals, and illustrate them with examples drawn from real-world interventions in which we have participate das strategic decision support analysts

“On‐the‐spot” modeling and analysis: the facilitated modeling approach

Since the 1980s, an alternative to the traditional “expert” mode of model‐based consultancy has been suggested, where the management scientist acts both as an analyst and as a facilitator throughout the intervention. This alternative approach uses facilitated modeling as the main intervention tool, which requires the management scientist to carry out the whole intervention jointly with a team drawn from the client organization: from helping to structure and define the nature of the problem situation of interest, to supporting the evaluation of priorities and development of plans for subsequent implementation. This mode of engagement is particularly suitable for supporting the development of strategy, the analysis of policy issues, or the evaluation of high‐stake decisions. This article discusses the facilitative modeling paradigm in management science. Drawing on research scattered across a range of publications and domains, we provide a formal definition of facilitated modeling, examine its general characteristics, and briefly review a family of well‐established facilitated modeling approaches. We then discuss key issues to consider when designing facilitated modeling interventions, and identify some directions for future research