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Exploring the future: Runtime scenario selection for complex and time-bound decisions

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... The decision-making environment in the immediate response phase is particularly challenging. This is due to decision density (exceptional number of decisions to be made (Comes, 2016)), urgency (many decisions must be taken under time pressure ), uncertainty (about the current and the future situation (Van de Walle and Comes, 2015)), limited resource (monetary and non-monetary (Chakravarty, 2014)), and potential consequences (decisions taken early on can continue to impact the entire response (Kowalski-Trakofler et al., 2003)). While the complexity of the location decision (Loree and Aros-Vera, 2018) highlights the need for decision support systems (DSSs) to support field-based decision-makers (hereinafter DMs) in the immediate response https://doi.org/10.1016/j.tre.2019.05.002 (Thompson et al., 2006), the above challenges pose specific requirements on DSSs ( Van de Walle and Turoff, 2008). ...
... Scenario analysis largely targets long-term decision-making and strategic planning purposes (e.g., Charles et al. (2016) and Ransikarbum and Mason (2016)). Only a very few studies have considered rapid scenario updates for decision-making in crises (Comes et al., 2015(Comes et al., , 2012. ...
... Although the specific characteristics of disasters contexts may imply constraints regarding datasets, disasters are not any more totally data lacking contexts (Van de Walle and Comes, 2015). The data that we used in our paper to build and validate the model is often available for different type of disasters through information sharing platforms. ...
Article
Locating distribution centers is critical for humanitarians in the immediate aftermath of a sudden-onset disaster. A major challenge lies in balancing the complexity and uncertainty of the problem with time and resource constraints. To address this problem, we propose a location allocation model that divides the topography of affected areas into multiple layers; considers constrained number and capacity of facilities and fleets; and allows decision-makers to explore trade-offs between response time and logistics costs. To illustrate our theoretical work, we apply the model to a real dataset from the 2015 Nepal earthquake response. For this case, our method results in a considerable reduction of logistics costs.
... Still, key challenges, particularly in the response to a disaster, remain collecting, analysing and providing actionable information to the people who need it. Finding reliable and trusted data that is relevant to support efficient response and coordination in emerging networks remains a challenge, particularly in in rapidly evolving situations [3]. Often, the filtering selection of informative content is done manually [4]. ...
... One common resilience framework is focused on "4 Es": Engagement, Education, Empowerment and Encouragement [11]. A community's capacity to gain trusted information through networked connections (engagement), to process and critically reflect on that information (education and encouragement), and to rapidly respond to emerging problems (empowerment) is more important for resilience than setting up detailed plans, which rarely foresee all contingencies [3,27]. New techniques using crowdsourcing and citizen science have emerged that enable these processes among citizens. ...
... The wellknown platform Humanitarian Tracker, for instance, was able to verify only 5,000 out of 80,000 citizen reports about Syria [34]. As such, there is a clear need for real-time methods verifying and evaluating the quality and trustworthiness, relevance and informativeness of data [3,2]. Messages need to be filtered or ranked in terms of criticality and reliability. ...
Article
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Communities have been described to be at the heart of the preparedness for and the response to disasters. The increasing connectedness has made communities more vulnerable for their dependence on a complex network of critical infrastructures. At the same time, this very connectedness has the potential to enable communities to self-organise, engage, and connect with other communities to improve their resilience. While the pathway to more resilience is promising and has many advocates, the response to crises and disasters, time and again reveals the challenges related to (i) ad-hoc switching from preparedness to response; (ii) ad-hoc connecting professional responders, communities, volunteers, and local authorities; and (iii) designing systems and tools that are tailored such that the feedback from local communities can be taken into account for coordination and planning. Therefore, a paradigm shift is needed in designing crisis and disaster management information systems linking ad-hoc response to longer-term planning, in which networks of communities are at the core of the process. This paper sets out to provide a critical review on community-resilience literature. From there, it develops a research design principles for information systems to improve community resilience.
... Scenarios selection for exercises and simulations in the field of disaster response need to be purposeful and relevant in addressing specific learning aims (Comes, Wijngaards, Maule, Allen & Schultmann, 2012). Purposeful scenarios respect time and resource constraints, correspond to unfolding dynamics of events in real-time, and contain context and elements affecting the process of decision-making (Comes, Wijngaards & Van de Walle, 2015). ...
... Scenario-based training and simulation exercises are powerful tools that enable organizations to (1) train executing relevant procedures and processes; and (2) practice decision-making in complex and dynamic environments (Lateef, 2010;Comes, Wijngaards & Van de Walle, 2015). Exercises provide insights into the organization's, team and individual performance. ...
... However, the scenario-based training in the field of disaster management is still lagging because of the complex nature of conditions associated with a disaster event (Comes et al., 2011, Luo et al., 2014. The high levels of uncertainty and risk factors related to disaster events are not easy to simulate in real-time or to cover the limitless number of existing scenarios (Comes et al., 2015). The lag in creating scenarios satisfying requirements such as effectiveness, agility, comprehensiveness and adaptability leads to negative effects on the outcome of the exercise and the organization's' performance in the field (. ...
Conference Paper
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Simulation exercises as a training tool for enhancing preparedness for emergency response are widely adopted in disaster management. This paper addresses current scenario design processes, proposes an alternative approach for simulation exercises and introduces a conceptual design of an adaptive scenario generator. Our work is based on a systematic literature review and observations made during TRIPLEX-2016 exercise in Farsund, Norway. The planning process and scenario selection of simulation exercises impact directly the effectiveness of intra-and interorganizational cooperation. However, collective learning goals are rarely addressed and most simulations are focused on institution-specific learning goals. Current scenario design processes are often inflexible and begin from scratch for each exercise. In our approach, we address both individual and collective learning goals and the demand to develop scenarios on different layers of organizational learning. Further, we propose a scenario generator that partly automates the scenario selection and adaptively responds to the exercise evolvement.
... This is one of the major advantages of model-based scenario development over intuitive approaches based on qualitative knowledge and expert insights (Van Notten et al., 2003). However, it is often necessary to prioritize a small number of these scenarios because (i) users can pay close attention only to a few, (ii) policy evaluation poses computational constraints (Carlsen et al., 2016b), and (iii) limited resources can be allocated more effectively for further scenario development (Comes et al., 2015). ...
... Trutnevyte (2013) chooses a small set of energymix scenarios by maximizing a diversity metric defined for each scenario as the Euclidean distance to a reference scenario. Comes et al. (2015) also focus on diversity and construct scenario equivalence classes where two scenarios are considered similar if the weighted average of differences between all outcome indicators (i.e. weighted Manhattan distance) is smaller than a certain threshold. ...
Article
Many-Objective Robust Decision Making (MORDM) is a prominent model-based approach for dealing with deep uncertainty. MORDM has four phases: a systems analytical problem formulation, a search phase to generate candidate solutions, a trade-off analysis where different strategies are compared across many objectives, and a scenario discovery phase to identify the vulnerabilities. In its original inception, the search phase identifies optimal strategies for a single reference scenario for deep uncertainties, which may result in missing locally near-optimal, but globally more robust strategies. Recent work has addressed this issue by generating candidate strategies for multiple policy-relevant scenarios. In this paper, we incorporate a systematic scenario selection procedure in the search phase to consider both policy relevance and scenario diversity. The results demonstrate an increased tradeoff variety besides higher robustness, compared to the solutions found for a reference scenario. Future research can routinize multi-scenario search in MORDM with the aid of software packages.
... (Charles, 2010). L'enjeu de la prévision de demande dans un contexte de catastrophe humanitaire semble donc porter sur cet aspect des choses et sur la capacité à estimer a priori l'impact associé à la survenue de telle ou telle catastrophe (Comes et al. 2014). Plusieurs auteurs ont proposé des solutions permettant d'adresser le sujet de la prévision dans le monde humanitaire. ...
... Des méthodes formelles telles que La Prospective (Godet, 2000) ou une combinaison de la méthode Delphi et de la méthode d'analyse d'impact croisé (Bañuls et Turrof, 2011) sont des exemples de méthode formelle. Le risque inhérent à ce genre d'approche est sans nul doute la génération de scénarios inconsistants qui viendraient « noyer » l'utilisateur (Comes et al. 2014). Des traitements ex-post peuvent alors être mis en oeuvre pour limiter cet écueil. ...
Article
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Every year, more than 400 natural disasters hit the world. To assist those affected populations, humanitarian organizations store in advance emergency aid in warehouses. This PhD thesis provides tools for support decisions on localization and sizing of humanitarian warehouses. Our approach is based on the design of representative and realistic scenarios. A scenario expresses some disasters’ occurrences for which epicenters are known, as well as their gravity and frequency. This step is based on the exploitation and analysis of databases of past disasters. The second step tackles about possible disaster’s propagation. The objective consists in determining their impact on population on each affected area. This impact depends on vulnerability and resilience of the territory. Vulnerability measures expected damage values meanwhile resilience estimates the ability to withstand some shock and recover quickly. Both are largely determined by social and economic factors, being structural (geography, GDP, etc.) or political (establishment or not relief infrastructure, presence and strict enforcement of construction standards, etc.). We propose through Principal Component Analysis (PCA) to identify, for each territory, influential factors of resilience and vulnerability and then estimate the number of victims concerned using these factors. Often, infrastructure (water, telecommunications, electricity, communication channels) are destroyed or damaged by the disaster (e.g. Haiti in 2010). The last step aims to assess the disaster logistics impact, specifically those related to with: transportation flows capacity limitations and destruction of all or part of emergency relief inventories. The following of our study focuses on location and allocation of a warehouses’ network. The proposed models have the originality to consider potential resources and infrastructure degradation after a disaster (resilience dimension) and seek optimizing the equilibrium between costs and results (effectiveness dimension). Initially we consider a single scenario. The problem is an extension of classical location studies. Then we consider a set of probable scenarios. This approach is essential due to the highly uncertain character of humanitarian disasters. All of these contributions have been tested and validated through a real application case: Peruvian recurrent disasters. These crises, mainly due to earthquakes and floods (El Niño), require establishment of a first aid logistics network that should be resilient and efficient.
... These small-and-medium-scale crises constitute a very big percentage of emergency interventions by humanitarian organizations. For these particular crises, it is possible to build realistic scenarios based on past events as demonstrated by Charles (2010) or Comes et al. (2015). Other researchers such as Kovács and Spens (2007) and Peres et al. (2012) simply consider that for small-and medium-size disasters, future occurrences will generally be similar to those that had occurred in the past. ...
... We refer readers interested in this issue to Vargas Florez et al. (2014), where a concrete methodology is proposed. Based on the results of Kovács and Spens (2007), Balcik and Beamon (2008), Charles (2010), Peres et al. (2012) and Comes et al. (2015), we simply assume that realistic scenarios can be defined for recurrent disasters, including the following elements: ...
Article
Each year, more than 400 natural disasters hit the world. To be more responsive, humanitarians organize stocks of relief items. It is an issue to know the quantity of items to be stored and where they should be positioned. Many authors have tried to address this issue both in industrial and humanitarian environments. However, humanitarian supply chains today do not perform correctly, particularly as regards resilience and efficiency. This is mainly due to the fact that when a disaster occurs, some hazards can strongly impact the network by destroying some resources or collapsing infrastructure. The expected performance of the relief response is consequently strongly decreased. The problem statement of our research work consists in proposing a decision-making support model in artificial intelligence dedicated to the humanitarian world and capable of designing a coherent network that is still able to adequately manage the response to a disaster despite failures or inadequacies of infrastructure and potential resources. This contribution is defined through a Stochastic Multi-Scenarios Program as a core and a set of extensions. A real-life application case based on the design of a humanitarian supply chain in Peru is developed in order to highlight the benefits and limits of the proposition.
... The combination of scenario planning and decision analysis has been a frequent focus (Wright and Goodwin, 1999;French et al., 2010;Stewart et al., 2010;Williamson and Goldstein, 2012;French, 2015). Several authors have noted the potential of this approach to structure analyses for nuclear emergency management (Carter and French, 2003a;Haywood, 2010;Comes et al., 2013;Comes et al., 2015). However, these references have tended to use more quantitative and probabilistic methods than are not yet computationally feasible in the first few hours of an accident. ...
... Reflecting on our observations at all the workshops and the remarks made at the second one particularly, plots that contour emergency reference levels and thus are focused on potential actions and countermeasures may be more helpful than simple maps of dose or deposition. Several authors identified in our literature review also emphasised the need for actionoriented plots (Carter and French, 2003b;Haywood, 2010;Comes et al., 2013;Comes et al., 2015). ...
... The data is modelled by stochastic and robust approaches in %50 of the reviewed papers that address multiple objectives. Although stochastic and robust methods are strongly recommended for uncertain contexts, it has been proven that they have a limited applicability in sudden-onset disasters response even in the relief phase, owing to a lack of access to (a) probability distributions (for stochastic approaches) and (b) data/computational resources (for robust approaches) [73]. ...
... Group decision-making processes can be thought of as the series of activities or steps by which information is collected, a decision is made and action is taken on the basis of the decision [73]. Fig. 2 shows a typical group decision-making process in the humanitarian context [2,8]. ...
Article
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In the humanitarian response, multiple decision-makers (DMs) need to collaborate in various problems, such as locating temporary relief distribution centres (RDCs). Several studies have argued that maximising demand coverage, reducing logistics costs and minimising response time are among the critical objectives when locating RDCs after a sudden-onset disaster. However, these objectives are often conflicting and the trade-offs can considerably complicate the situation for finding a consensus. To address the challenge and support the DMs, we suggest investigating the stability of non-dominated alternatives derived from a multi-objective model based on Monte Carlo Simulations. Our approach supports determining what trade-offs actually matter to facilitate discussions in the presence of multiple stakeholders. To validate our proposal, we extend a location-allocation model and apply our approach to an actual data-set from the 2015 Nepal earthquake response. Our analyses show that with the relative importance of covering demands ≤0.4, the trade-offs between logistics costs and response time affects the numbers and locations of RDCs considerably. We show through a small experiment that the outputs of our approach can effectively support group decision-making to develop relief plans in disasters response.
... Credibility can be derived by verifiability and reliability of the source (Schoemaker, 1993). To balance timeliness and potential impact versus credibility, precision and granularity, information management should make trade-offs transparent at run-time by explicitly challenging the adequacy of the information and the constraints on the process (Comes, Wijngaards & Van de Walle, 2014). Disaster responders operate under pressure and strain, including risks for personal safety and well being of responders on-site. ...
... The form and type of information depends on the decision-makers' preferences, their access to technology (such as internet connection, bandwidth or printers) and the time available. Distributed techniques for scenario construction (Comes, Wijngaards & Van de Walle, 2014) enable the integration of experts from various agencies and authorities, bringing together local experts, professional responders, volunteers that work onsite or remotely (such as the Digital Humanitarian Network, a global network of volunteers ), and scientists with different backgrounds. ...
Conference Paper
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Modern societies are increasingly threatened by disasters that require rapid response through ad-hoc collaboration among a variety of actors and organizations. The complexity within and across today’s societal, economic and environmental systems defies accurate predictions and assessments of damages, humanitarian needs, and the impact of aid. Yet, decision-makers need to plan, manage and execute aid response under conditions of high uncertainty while being prepared for further disruptions and failures. This paper argues that these challenges require a paradigm shift: instead of seeking optimality and full efficiency of procedures and plans, strategies should be developed that enable an acceptable level of aid under all foreseeable eventualities. We propose a decision- and goal-oriented approach that uses scenarios to systematically explore future developments that may have a major impact on the outcome of a decision. We discuss to what extent this approach supports robust decision-making, particularly if time is short and the availability of experts is limited. We interlace our theoretical findings with insights from experienced humanitarian decision makers we interviewed during a field research trip to the Philippines in the aftermath of Typhoon Haiyan.
... Global networks of alliances and hostilities are becomingly increasingly blurred and deeply layered. In this multi-polar world with its 'multiple modernities' (Casanova, 2011), hard to predict discontinuities (van Notten et al., 2005), and collapsed decision-making timing (Comes et al., 2014), the famous and controversial thesis of Huntington (1996) that we are facing a 'clash of civilizations' is often read by non-Westerners as a conceptual shorthand, as a reflex of the West's hard-to-die attitude of thinking that any global narrative that challenges their own is, ipso facto, an oppositional one (Yije, 2010)and thus ultimately as an instrumental theoretical construct which has been shaped up to serve specific ideological purposes (Adib-Moghaddam, 2008), and which may be possibly supported only from a Western perspective serving Western interests (Fox, 2001). A common basis for a true dialogue in terms of cultural values is indispensable for future peaceful coexistence (Anthony, 2010), as the persistence of oppositional narratives on the Western side naturally paves the way to dialectic, and often armed counterparts (Aydin and Özen, 2010). ...
Article
In this paper, we present an innovative data processing architecture, the Activation & Competition System (ACS), and show how this methodology allows us to reconstruct in detail some aspects of the fine grained structure of global relationships in the world order perspective, on the basis of a minimal dataset only consisting of the values of five publicly available indicators for 2007 for the 118 countries for which they are jointly available. ACS seems in particular to qualify as a valuable tool for the analysis of inter-country patterns of conflict and alliances, which may prove of special interest in the current situation of global strategic uncertainty in international relations.
... However, these SAR guidelines do not characterize the information items needed by first responders to obtain the overview of the situation (Departmentet;FEMA, 2006). Many frameworks that capture information needs were developed for disaster management, especially for situational awareness during the response phase (Cinque, Esposito, Fiorentino et al., 2015;Comes, 2011;Comes, Wijngaards, & Van de Walle, 2015;George Stephen, 2010;Han Liangxiu, 2010;Lazreg, Radianti, & Granmo, 2015;Li, Becerik-Gerber, Krishnamachari, & Soibelman, 2014;Liang & Gao, 2010;Nunavath, Radianti, Comes et al., 2015;Upadhyay Rochan, 2008;. Some of these studies focused on improving information sharing and coordination among different first responders (Bharosa, Lee, & Janssen, 2010;Bharosa, Lee, Janssen et al., 2009;Chen, Sharman, Rao et al., 2007;Ley et al., 2014;Salmon, Stanton, Jenkins et al., 2011;Schryen, Rauchecker, & Comes, 2015;Van de Walle, Brugghemans, & Comes, 2016;Wex, Schryen, & Neumann, 2011;Zhang, 2011). ...
Article
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At the onset of an indoor fire emergency, the availability of the information becomes critical due to the chaotic situation at the emergency site. Moreover, if information is lacking, not shared, or responders are too overloaded to acknowledge it, lives can be lost and property can be harmed. Therefore, the goal of this paper is to identify information items that are needed for first responders during search and rescue operations. The authors use an educational building fire emergency as a case and show how first responders can be supported by getting access to information that are stored in different information systems. The research methodology used was a combination of literature review, fire drills participation, and semi-structured interviews with first responders from different emergency organizations. The results presented are identified information items and an information model.
... Similarly, Halim et al. (2015) put forward an optimization based worst-case scenario discovery approach. Comes et al. (2015) discuss the scenario selection task in the context of time sensitive decisions. Trutnevyte et al. (2016) highlight the importance of scenario selection in the context of scenario discovery and call for more research in this direction. ...
Article
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Scenario discovery is a model-based approach to scenario development under deep uncertainty. Scenario discovery relies on the use of statistical machine learning algorithms. The most frequently used algorithm is the Patient Rule Induction Method (PRIM). This algorithm identifies regions in an uncertain model input space that are highly predictive of model outcomes that are of interest. To identify these regions, PRIM uses a hill-climbing optimization procedure. This suggests that PRIM can suffer from the usual defects of hill climbing optimization algorithms, including local optima, plateaus, and ridges and valleys. In case of PRIM, these problems are even more pronounced when dealing with heterogeneously typed data. Drawing inspiration from machine learning research on random forests, we present an improved version of PRIM. This improved version is based on the idea of performing multiple PRIM analyses based on randomly selected features and combining these results using a bagging technique. The efficacy of the approach is demonstrated using three cases. Each of the cases has been published before and used PRIM. We compare the results found using PRIM with the results found using the improved version of PRIM. We find that the improved version is more robust to new data, can better cope with heterogeneously typed data, and is less prone to overfitting.
... Regarding the methodological dimension, we have two papers that cover trends in some of the most appropriated methodologies for foresight support systems support: Scenarios, Delphi Method and Prediction Markets. Comes et al. (2015-in this issue) address the complexity of strategic decisions and the resulting multitude of scenarios in foresight contexts. They take a decision-and action-oriented stance by proposing an approach to support making the tradeoffs between accuracy and resources spent by prioritizing scenarios based on their significance for a specific decision even on the basis of incomplete information. ...
... Since the seminal work of Kahneman and Tversky [3], researchers have identified a large number of biases that influence decision-making. Many have argued that computational support can help in such situations [4], [5]. However, the multitude of decision support tools and information products that is used to support responders has started to introduce biases of its own. ...
... What information is of concern and interest is changing rapidly and constantly. Simultaneously, the actual situation and the information about it evolve in a highly dynamic way, and the match between situation and information is far from perfect: information is typically lagging, uncertain, sometimes contradictory or missing, and in many cases it requires further interpretation (Comes et al., 2015). In other words, the dynamics of a crisis and the volatility of the situation, the information about it, and the aims organizations or individuals pursue impact the effectiveness of coordination (Comes et al., 2011; Van de Walle and Turoff, 2008). ...
Article
In responding to an emergency, the actions of emergency response teams critically depend upon the situation awareness the team members have acquired. Situation awareness, and the design of systems to support it, has been a focus in recent emergency management research. In this paper, we introduce two interventions to the core processes of information processing and information sharing in emergency response teams to analyze their effect on the teams’ situation awareness: (1) we enrich raw incoming information by adding a summary of the information received, and (2) we channel all incoming information to a central coordinator who then decides upon further distribution within the team. The effect of both interventions is investigated through a controlled experiment with experienced professional responders. Our results show distinctly different effects for information enrichment and centralization, both for the teams and for the coordinators within the team. While the interaction effects of both conditions cannot be discerned, it is apparent that processing non-enriched information and non-centralized information sharing leads to a worse overall team situation awareness. Our work suggests several implications for the design of emergency response management information systems.
... Since the seminal work of Kahneman and Tversky [3], researchers have identified a large number of biases that influence decision-making. Many have argued that computational support can help in such situations [4], [5]. However, the multitude of decision support tools and information products that is used to support responders has started to introduce biases of its own. ...
Article
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Time and again, humanitarian decision-makers are confronted with stress and pressure, distorted, lacking and uncertain information, and thus they are working in conditions that are known to introduce or enforce biases. Decision analysis has been designed to overcome such biases, and a network of " digital responders " organized over the Internet has set out to improve judgments by providing better information. However, without any structured support to determine objectives, goals and preferences and detached from the context of operational decision makers , remote analysts may face the very biases they are trying to help overcome. This article sets out to identify biases that matter for humanitarian decision support, reflecting on the role of field-based decision-makers and digital responders. The most important biases are reviewed to provide an assessment on their role in the course of a disaster response. To this end, a literature review is combined with results from fieldwork in three humanitarian disasters. I identify areas that are particularly sensitive to reinforced biases, and others , where digital volunteers can likely help, and conclude the paper with an agenda for future research.
... For the specific † WHO Ebola Situation Reports available on http://www.who.int/csr/disease/ebola/situation-reports/en/ ‡ For case numbers in early April reported by WHO, see instance http://www.afro.who.int/en/clusters-a-programmes/dpc/epidemic-a-pandemic-purpose of the Ebola response, we have brought together a team with in humanitarian logistics [4], [5], collaborative decision support [6] and humanitarian information management [7], [8]. In the response to Ebola, we aim to conduct field research to better understand: ...
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The 2014 Ebola outbreak in West Africa is the largest ever in history, affecting multiple countries and to this date, the World Health Organization has registered more than 6,500 deaths attributed to Ebola. The challenges arising from this outbreak to responders worldwide do not follow the standard characterisation or response patterns of natural sudden onset vs. conflict disasters. Rather, it is a medical emergency, which is intertwined with multiple challenges in the sectors decision-making, coordination, logistics and information management. In this paper, we present our research framework, which is based on desk research and initial interviews with responders. This framework guides ongoing field research in Ghana (December2014), and Liberia (Spring 2015).
... Already in the 1950s, Lindblom (1959) had described that decision-makers confronted with such uncertainty are 'muddling through'. Participatory approaches to model design and scenario analysis have been advocated as a way ahead when the communities affected are clearly known (Comes et al., 2015b;Wright and Goodwin, 2009). Examples range from scenarios for water and Áood management (Haasnoot et al. 2011) to urban planning and resource management (Vervoort et al., 2010), approaches that rely on connecting communities and policymakers in the preparedness phase. ...
... (32)(33)(34)(35) Some have noted the potential of this approach to structure analyses for nuclear crisis management. (36)(37)(38)(39) However, these references have tended to use more quantitative and probabilistic methods that are not yet computationally feasible in the first few hours of a crisis. Also, it may be difficult to muster the expert judgments needed to initialize them quickly. ...
Article
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In any crisis, there is a great deal of uncertainty, often geographical uncertainty or, more precisely, spatiotemporal uncertainty. Examples include the spread of contamination from an industrial accident, drifting volcanic ash, and the path of a hurricane. Estimating spatiotemporal probabilities is usually a difficult task, but that is not our primary concern. Rather, we ask how analysts can communicate spatiotemporal uncertainty to those handling the crisis. We comment on the somewhat limited literature on the representation of spatial uncertainty on maps. We note that many cognitive issues arise and that the potential for confusion is high. We note that in the early stages of handling a crisis, the uncertainties involved may be deep, i.e., difficult or impossible to quantify in the time available. In such circumstance, we suggest the idea of presenting multiple scenarios.
... However, such datasets are often incomplete because of infrastructure disruptions and access difficulties [14]. Even if data is available, it needs to be formatted and shared to support coordination [16] so that response can be organized in an efficient and effective way [49]. ...
Conference Paper
Disasters are characterized by conflicting, uncertain, or lacking data. Nevertheless, humanitarian responders need to make rapid decisions. This is particularly true for the immediate response to a sudden onset disaster. Since most humanitarian decision support systems (DSS) make important assumptions on data availability and quality that are often not fulfilled in practice, decision-makers are largely left to their experience. In this paper, we identify three major challenges for an operational DSS to support distribution planning: (i) deep uncertainty; (ii) reflecting field conditions and constraints; and (iii) rapid humanitarian logistics modeling. We review the relevant theories and provide an outline of the system requirements to develop a system for operational responders to achieve targeted service level on distribution of disaster relief through proper utilization of resources, time and scheduling.
... Within disaster management one central resilience framework resonates well with the insights stemming from psychology. Disasters present shocks to communities that can hardly be predicted and planned for (Comes, Wijngaards, & Van de Walle, 2015;Hardy & Comfort, 2014). To respond to such stresses, Edwards developed '4 Es' and suggests that resilient communities focus on engagement, education, empowerment and encouragement (Edwards, 2009). ...
Article
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New information and communication technologies (ICT) have enabled communities to collect and share information and tap into a network of peers in unprecedented ways. For more than a decade, information has been recognized as a vital part of disaster relief, and recently ICTs have been described to improve the resilience of disaster-ridden societies. At the same time, the humanitarian turn towards technology also entails increasing remote management and centralization. This paper highlights social justice concerns and critically reviews the role and potential of technology as an enabler of community resilience. We start from a discussion on essential concepts around information technologies for resilience and social justice. Having established the core concepts, we trace the development of ICT for resilience across three time periods. We discuss how technology development and disaster management practices co-evolved and highlight implications for resilience and social justice.
... The method is based on two complementary approaches for designing adaptive plans: "Adaptive Policymaking" (planning and identification of early warning signals) and "Adaptation Pathways" (exploring and sequencing a set of alternatives). To fit to the setting of the implementation phase of a vaccine campaign, such methods need to be adapted to take into account real-time scenario construction (as, for instance, proposed by Comes,Wijngaards and Van de Walle, 2015) as well as the impact of shocks on forecasting and decision making (Durbach and Montibeller, 2017). ...
Article
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The purpose of this paper is to analyze how far technology and information enable, facilitate or support the planning and implementation decisions in humanitarian vaccine cold chains for vaccination campaigns. The authors specifically focus on three emerging technologies that have the potential to create more flexible conditions in the field, and identify the need to further explore the link between uncertainty, information and irreversibility. Design/methodology/approach: The authors present a basic structure for the analysis of cold chain disruptions in terms of three distinct yet connected layers of deficient infrastructure and capacity, information gaps and failures in decision making. The authors then review three humanitarian technologies and their impact on vaccine campaigns along these layers. From there, a research agenda is developed to address research gaps this review brought forward. Findings: Three critical research gaps in the areas of technology innovation for humanitarian vaccine cold chain management are presented. The authors argue that technology to improve capacity, information and decisions need to be aligned, and that the areas of uncertainty, information and irreversibility require further investigation to achieve this alignment. In this way, the paper contributes to setting the research agenda on vaccine cold chains and connects humanitarian logistics to technology, information management and decision making. Originality/value: This paper presents the humanitarian vaccine cold chain problem from an original angle by illuminating the implications of technology and information on the decisions made during the planning and implementation phases of a vaccine campaign. The authors develop an agenda to provide researchers and humanitarians with a perspective to improve cold chain planning and implementation at the intersection of technology, information and decisions.
... At the same time, particularly the early chaotic phase of disasters is subject to 'severe' or 'deep' uncertainty ( Comes et al. 2015). The combination of uncertainty, time pressure, and proliferation of technology has wide implications for coordination. ...
Conference Paper
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The profusion of information technology has created new possibilities for local communities to self-organize and respond to disruptive events. Along with the opportunities, there is also a series of challenges that need to be addressed in order to improve societal resilience. One of these challenges is to make sense of the continuous stream of information to create a coherent understanding and improve coordination. The research presented in this paper focuses on the socio-technical requirements of IT platforms that support sensemaking and coordination. Using a comprehensive evaluation exercise based on real data from the 2017 Kenyan elections, we examine the development, workflows and use of this shared situational awareness in a group decision making process. In this manner, we identify requirements for resilience platforms and identify further research directions.
... Already in the 1950s, Lindblom (1959) had described that decision-makers confronted with such uncertainty are 'muddling through'. Participatory approaches to model design and scenario analysis have been advocated as a way ahead when the communities affected are clearly known (Comes et al., 2015b;Wright and Goodwin, 2009). Examples range from scenarios for water and flood management (Haasnoot et al. 2011) to urban planning and resource management (Vervoort et al., 2010), approaches that rely on connecting communities and policymakers in the preparedness phase. ...
... According to [25], a scenario consists of information about a situation (variables), their relations, and their development over time. Thus, an EM scenario is a description of an emergency situation and the context in which it is situated, of the following course of events and, possibly, of the actions to solve it. ...
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We present a framework to support creative design of emergency management scenarios. By creative design of scenarios we mean the process of imagining situations and describing them through models and stories. The framework supports the tasks of gathering and organizing knowledge about emergency management situations by automatically generating conceptual models, related to fragments of emergency scenarios. It leverages semantics-based techniques to enable a computational creativity approach. A software application was defined to support the activities of modeling scenarios by permitting to generate, organize, and query sets of these conceptual models, which we name mini-stories, and that can be adopted to inspire the activity of creative design. Selected mini-stories are blueprints for more detailed user scenario descriptions and models that can be used, for instance, for analysis or simulation. As a case study, we consider emergency management in smart cities. This is a challenging domain because smart cities are characterized by interconnected physical and virtual services forming complex ecosystems, which provide sophisticated services to the population and to institutions, manage public resources in a optimal way, and involve citizens in decisional processes. As a consequence, smart city ecosystems can be threatened by several hazards spanning from natural disasters, as tsunami and earthquakes, to anthropic events, as terrorist attacks. Ability of service providers and institutional operators to face and manage emergency situations is therefore a relevant issue. Simulation and analysis of both crisis events and executions of management plans are a promising approach to deal with these articulated problems. However, manual definition of models to base the analysis is a demanding activity due to the huge number of different situations to consider. It requires knowledge related to the crisis and emergency domains, to the context (e.g., a specific city and its current regulations) and ability in modeling tasks. All these aspects demand for tools to support modeling activities, and our proposal aims at fulfilling this need. In particular, the discussed framework uses in a integrated way three types of knowledge: structural knowledge, to support the construction of models based on design patterns; domain knowledge, here related to smart cities and emergency management and represented by means of ontologies; and contextual knowledge, related to specific aspects (e.g., localization) of the considered scenario and represented as rules. We validated the presented approach by means of experiments performed by real city planners.
... Scenario-based simulations hence present an ideal way to prepare decision-makers for complex and high-risk situations [1], [2]. At the same time, due to the complexity of strategic decisions and the resulting multitude of scenarios, there is always a need to select scenarios to fully work out in and choose the appropriate level of detail and granularity [3]. One of the challenges in the design of scenario-based training is thus to find the scenarios that maximise learning while adhering to given levels of complexity [4], reliability and fidelity [5] time or resources for the training [6]. ...
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Scenarios are designed to support decision-makers in gaining a better understanding of the consequences of their decisions. Scenario-based simulations, hence, present an ideal way to train decision-makers for complex and high-risk situations. Usually, training scenarios are human-authored which is limiting in terms of the number and diversity of the generated scenarios, in addition to being a time-and resource-consuming process. In this paper, we are introducing a first-order logic rule-based design and implementation of an automated scenario generator system to support designers of virtual, augmented and simulated reality humanitarian training setups. The system is based on knowledge extracted from publicly available humanitarian related databases. Moreover, the system can produce scenarios that adapt to user performance in addition to meeting certain training requirements.
... However, this means that information sharing and analysis is directed "upwards" to enable planning and coordination at a regional level. In highly volatile and risky environments, such as those driven by warfare and continuously shifting lines with limited access and resources, feedback to operational responders is provided too late, and too little [7][1] [6]. In response to delays within official reporting channels, alternative and social networks have been established to share important information via tools that are known and easily usable, such as WhatsApp. ...
Conference Paper
Humanitarian aid workers who try to provide aid to the most vulnerable populations in the Middle East or Africa are risking their own lives and safety to help others. The current lack of a collaborative real-time information system to predict threats prevents responders and local partners from developing a shared understanding of potentially threatening situations, causing increased response times and leading to inadequate protection. To solve this problem, this paper presents a threat detection and decision support system that combines knowledge and information from a network of responders with automated and modular threat detection. The system consists of three parts. It first collects textual information, ranging from social media, and online news reports to reports and text messages from a decentralized network of humanitarian staff. Second, the system uses deep neural network techniques to automatically detects a threat or incident and provide information including location, threat category, and casualties. Third, given the type of threat and the information extracted by the NER, a feedforward network proposes a mitigation plan based on humanitarian standard operating procedures. The classified information is rapidly redistributed to potentially affected humanitarian workers at any level. The system testing results show a high precision of 0.91 and 0.98 as well as an F-measure of 0.87 and 0.88 in detecting the threats and decision support respectively. We thus combine the collaborative intelligence of a decentralized network of aid workers with the power of deep neural networks.
... As resources are typically short in the heat of a disaster requirement, the run-time needs to be minimized of both the scenario creation and the mathematical model. Based on (Tietje, 2005;Comes et al., 2015), we conclude that the creation of valid scenario sets should include two complementary steps: ...
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Literature about humanitarian logistics (HL) has developed a lot of innovative decision support systems during the last decades to support decisions such as location, routing, supply, or inventory management. Most of those contributions are based on quantitative models but, generally, are not used by practitioners who are not confident with. This can be explained by the fact that scenarios and datasets used to design and validate those HL models are often too simple compared to the real situations. In this chapter, a scenario-based approach based on a five-step methodology has been developed to bridge this gap by designing a set of valid scenarios able to assess disaster needs in regions subject to recurrent disasters. The contribution, usable by both scholars and practitioners, demonstrates that defining such valid scenario sets is possible for recurrent disasters. Finally, the proposal is validated on a concrete application case based on Peruvian recurrent flood and earthquake disasters.
... Then, best acceptable decision sequences in terms of a proximity measure can be specified. Such sequences correspond to the elementary exploratory scenarios [9,12] of the overall network evolution. In this chapter, the latter approach, based on exploratory scenarios rather than normative, has been admitted, which corresponds to the situation where no strict planning horizon can be specified. ...
Chapter
This paper presents an application of anticipatory networks to construct scenarios and select a best-compromise development strategy of complex information systems. We will provide a constructive approach to computing nondominated strategies that comply with the anticipatory preference structure. Scenario building and filtering process merges the statistical and judgmental forecasting of future decision problem parameters with the simulation of multicriteria decision making procedures. Anticipatory decision-making principles applied in multicriteria sustainable planning yield future visions that correspond to best-compromise management strategies. As a real-life case study we will present a detailed analysis of planning the future operation of an innovative digital knowledge platform. Platform’s performance is optimized with respect to multiple criteria related to financial sustainability, technological excellence, and social benefits. This platform has been developed within a recent EU Horizon-2020 research project.
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During an emergency response to a major oil spill accident, the features of the motion of the oil films affect the response decisions. A highly dynamic optimal solution is needed to tackle the continuous changes in the demand for emergency resources and transportation networks for logistics deliveries that must occur. To effectively balance the responsiveness and the total response cost in emergency operations, this paper proposes a dynamic multi-objective location-routing model to address new challenges, such as the time-varying conditions in the response to oil spills and the interrelationship between the decision-making environment and emergency operations. Since the problem is NP-hard, to efficiently obtain Pareto solutions, a novel implementation of a heuristic framework based on particle swarm optimization is developed to conduct numerical experiments. Additionally, to handle the multi-objective model, an alternative solution based on the cost performance method is adopted to help decision makers select the ideal options for Pareto solutions. A case study of a major oil spill accident that occurred in the Bohai Bay is conducted to demonstrate the application of the proposed model and approaches and the real-world implications.
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Policy makers often deal with a wide range of alternative probable future states for the entity they work for - a country, for example. To strive for the most desirable state, the policy makers must evaluate and rank all probable future scenarios. To this end, 'scenario methods' gained recent popularity are increasingly being employed. Nevertheless, currently, decisions made based on insight gained from scenarios are not made in an integrated systematic process. Current variants of the method help study the role of any research concept individually; thus, they do not provide a complete picture of the research situation. A more suitable variant of the method for today's world should provide a holistic view of the research situation by modelling possible links among research concepts. This paper, introduces a stepwise methodology that can guide the building, developing, and ranking of possible future scenarios, having factored in the possible causal interrelations among research concepts. The method is enriched with a combination of Fuzzy Cognitive Maps, a widely used soft computing method, and ELECTRE III, a popular method of Multi Attribute Decision Making. This paper also presents the results of the application of the proposed methodology for Iran's housing market, highlighting the advantages of the proposed methodology.
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Citizens' engagement in their neighbourhood community is pivotal for cities to effectively deal with future transitions. Knowing what is going on and having access to the neighbourhood network are important conditions for this. Although prior research has studied ways to foster information sharing between citizens, the underlying assumptions and design choices are often not made explicit. This research identifies design guidelines for playgrounds: physical and virtual spaces where citizens can exchange information about their neighbourhood. A focus group, a workshop and a case study of an existing playground design were performed in The Hague, NL, the context of this research. A set of eight guidelines was identified, covering how to select playground locations, which information to include, and how to design the interaction between citizens. These guidelines inform designers how to create urban playgrounds for citizens to meet, interact, and collaborate to create engaged communities.
Chapter
At the onset of an indoor fire emergency, the availability of the information becomes critical due to the chaotic situation at the emergency site. Moreover, if information is lacking, not shared, or responders are too overloaded to acknowledge it, lives can be lost and property can be harmed. Therefore, the goal of this paper is to identify information items that are needed for first responders during search and rescue operations. The authors use an educational building fire emergency as a case and show how first responders can be supported by getting access to information that are stored in different information systems. The research methodology used was a combination of literature review, fire drills participation, and semi-structured interviews with first responders from different emergency organizations. The results presented are identified information items and an information model.
Chapter
Literature about humanitarian logistics (HL) has developed a lot of innovative decision support systems during the last decades to support decisions such as location, routing, supply, or inventory management. Most of those contributions are based on quantitative models but, generally, are not used by practitioners who are not confident with. This can be explained by the fact that scenarios and datasets used to design and validate those HL models are often too simple compared to the real situations. In this chapter, a scenario-based approach based on a five-step methodology has been developed to bridge this gap by designing a set of valid scenarios able to assess disaster needs in regions subject to recurrent disasters. The contribution, usable by both scholars and practitioners, demonstrates that defining such valid scenario sets is possible for recurrent disasters. Finally, the proposal is validated on a concrete application case based on Peruvian recurrent flood and earthquake disasters.
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Objective: This article describes the origins and contributions of the naturalistic decision making (NDM) research approach. Background: NDM research emerged in the 1980s to study how people make decisions in real-world settings. Method: The findings and methods used by NDM researchers are presented along with their implications. Results: The NDM framework emphasizes the role of experience in enabling people to rapidly categorize situations to make effective decisions. Conclusion: The NDM focus on field settings and its interest in complex conditions provide insights for human factors practitioners about ways to improve performance. Application: The NDM approach has been used to improve performance through revisions of military doctrine, training that is focused on decision requirements, and the development of information technologies to support decision making and related cognitive functions.
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This article describes the origins and contributions of the naturalistic decision making (NDM) research approach. NDM research emerged in the 1980s to study how people make decisions in real-world settings. Method: The findings and methods used by NDM researchers are presented along with their implications. The NDM framework emphasizes the role of experience in enabling people to rapidly categorize situations to make effective decisions. The NDM focus on field settings and its interest in complex conditions provide insights for human factors practitioners about ways to improve performance. The NDM approach has been used to improve performance through revisions of military doctrine, training that is focused on decision requirements, and the development of information technologies to support decision making and related cognitive functions.
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Surprise takes many forms, all tending to disrupt plans and planning systems. Reliance by decision makers on formal analytic methodologies can increase susceptibility to surprise as such methods commonly use available information to develop single-point forecasts or probability distributions of future events. In doing so, traditional analyses divert attention from information potentially important to understanding and planning for effects of surprise. The authors propose employing computer-assisted reasoning methods in conjunction with simulation models to create large ensembles of plausible future scenarios. This framework supports a robust adaptive planning (RAP) approach to reasoning under the conditions of complexity and deep uncertainty that normally defeat analytic approaches. The authors demonstrate, using the example of planning for long-term global sustainability, how RAP methods may offer greater insight into the vulnerabilities inherent in several types of surprises and enhance decision makers’ ability to construct strategies that will mitigate or minimize the effects of surprise.
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The recently developed concepts of aggregate risk and cumulative risk rectify two limitations associated with the classical risk assessment paradigm established in the early 1980s. Aggregate exposure denotes the amount of one pollutant available at the biological exchange boundaries from multiple routes of exposure. Cumulative risk assessment is defined as an assessment of risk from the accumulation of a common toxic effect from all routes of exposure to multiple chemicals sharing a common mechanism of toxicity. Thus, cumulative risk constitutes an improvement over the classical risk paradigm, which treats exposures from multiple routes as independent events associated with each specific route. Risk assessors formulate complex models and identify many realistic scenarios of exposure that enable them to estimate risks from exposures to multiple pollutants and multiple routes. The increase in complexity of the risk assessment process is likely to increase risk uncertainty. Despite evidence that scenario and model uncertainty contribute to the overall uncertainty of cumulative risk estimates, present uncertainty analysis of risk estimates accounts only for parameter uncertainty and excludes model and scenario uncertainties. This paper provides a synopsis of the risk assessment evolution and associated uncertainty analysis methods. This evolution leads to the concept of the scenario–model–parameter (SMP) cumulative risk uncertainty analysis method. The SMP uncertainty analysis is a multiple step procedure that assesses uncertainty associated with the use of judiciously selected scenarios and models of exposure and risk. Ultimately, the SMP uncertainty analysis method compares risk uncertainty estimates determined using all three sources of uncertainty with conventional risk uncertainty estimates obtained using only the parameter source. An example of applying the SMP uncertainty analysis to cumulative risk estimates from exposures to two pesticides indicates that inclusion of scenario and model sources increases uncertainty of risk estimates relative to those estimated using only the parameter source. Changes in uncertainty magnitude may affect decisions made by risk managers.
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Since its origins, decision makers have broadly used the Delphi method as a collaborative technique for generating important events and scenarios about what may happen in the future. This is a complex process because of the different interrelations and the potential synergetic effects among the relevant events related to a decision. This fact, along with the uncertainty about the occurrence or non-occurrence of the events, makes the scenario generation task a challenging issue in Delphi processes. In the 1960's, Cross-Impact Analysis (CIA) appeared as a methodological tool for dealing with this complexity. CIA can be used for creating a working model out from a set of significant events. CIA has been combined with other methodological approaches in order to increase its functionality and improve its final outcome. In this paper, the authors propose a new step-by-step model for scenario-analysis based on a merger of Turoffs alternative approach to CIA and the technique called interpretive Structural Modeling (ISM). The authors' proposal adds tools for detecting critical events and for producing a graphical representation to the previous scenario-generation methods based on CIA. Moreover, it allows working with large sets of events without using large computational infrastructures. The authors present sufficient information and data so that anyone who wishes to may duplicate the implementation of the process. Additionally they make explicit a set of requirements for carrying out a Delphi process for a group to develop a set of significant events, collectively make the estimations of cross impacts, and to support a continuous planning process within an organization. They use two examples to discuss operational issues and practical implications of the model.
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Behavioural studies have shown that while humans may be the best decision makers on the planet, we are not quite as good as we think we are. We are regularly subject to biases, inconsistencies and irrationalities in our decision making. Decision Behaviour, Analysis and Support explores perspectives from many different disciplines to show how we can help decision makers to deliberate and make better decisions. It considers both the use of computers and databases to support decisions as well as human aids to building analyses and some fast and frugal tricks to aid more consistent decision making. In its exploration of decision support it draws together results and observations from decision theory, behavioural and psychological studies, artificial intelligence and information systems, philosophy, operational research and organisational studies. This provides a valuable resource for managers with decision-making responsibilities and students from a range of disciplines, including management, engineering and information systems.
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In scenario planning, causal mapping has long been used as a means to elicit the worldviews of multiple experts, facilitate discussion, and challenge and improve mental models. Large and complex causal maps, however, are difficult to analyze. This paper proposes a novel method for scenario building, based on Fuzzy Cognitive Maps, that combines intuitive, cognitive mapping techniques with formal, quantitative analysis. The proposed method helps scenario planners to integrate the qualitative and partial knowledge of multiple individuals and overcome information processing limitations. The feasibility of the proposed approach is investigated with two scenario studies on solar photovoltaic panels.
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We consider the problem of determining the structure of clustered data, without prior knowledge of the number of clusters or any other information about their composition. Data are represented by a mixture model in which each component corresponds to a different cluster. Models with varying geometric properties are obtained through Gaussian components with different parameterizations and cross-cluster constraints. Noise and outliers can be modeled by adding a Poisson process component. Partitions are determined by the EM (expectation-maximization) algorithm for maximum likelihood, with initial values from agglomerative hierarchical clustering. Models are compared using an approximation to the Bayes factor based on the Bayesian Information Criterion (BIC); unlike significance tests, this allows comparison of more than two models at the same time, and removes the restriction that the models compared be nested. The problems of determining the number of clusters and the clustering method are solved simultaneously by choosing the best model. Moreover, the EM result provides a measure of uncertainty about the associated classification of each data point.
Article
Scenarios provide a commonly used and intuitively appealing means to communicate and characterize uncertainty in many decision support applications, but can fall short of their potential especially when used in broad public debates among participants with diverse interests and values. This paper describes a new approach to participatory, computer-assisted scenario development that we call scenario discovery, which aims to address these challenges. The approach defines scenarios as a set of plausible future states of the world that represent vulnerabilities of proposed policies, that is, cases where a policy fails to meet its performance goals. Scenario discovery characterizes such sets by helping users to apply statistical or data-mining algorithms to databases of simulation-model-generated results in order to identify easy-to-interpret combinations of uncertain model input parameters that are highly predictive of these policy-relevant cases. The approach has already proved successful in several high impact policy studies. This paper systematically describes the scenario discovery concept and its implementation, presents statistical tests to evaluate the resulting scenarios, and demonstrates the approach on an example policy problem involving the efficacy of a proposed U.S. renewable energy standard. The paper also describes how scenario discovery appears to address several outstanding challenges faced when applying traditional scenario approaches in contentious public debates.
Article
Solving complex decision problems is a demanding task; it requires determining and evaluating the consequences of decision alternatives. To this end, uncertain factors that can only partly be influenced by the decision makers, and their interdependencies need to be considered. Scenarios focus on this part of the decision problem; they enable a systematic exploration of a multitude of possible future developments that are relevant for the decision including external events and decisions made. Scenarios are particularly useful when the problem is pervaded by severe uncertainties that cannot be quantified. For the evaluation of alternatives, multiple objectives and the potentially diverging preferences of the involved actors need to be respected. Multi-criteria decision analysis aims at structuring the problem, evaluating the alternatives and supporting decision makers pursuing multiple goals. We propose an approach integrating scenarios and multi-criteria decision analysis that focuses on the robustness of alternatives in complex, dynamic, uncertain and time-bound situations. In this integrated framework, the scenarios provide the basis for evaluating a set of alternatives. Ideally, the set of scenarios considered captures all possible future developments. To appropriately explore this set, formal or analytical approaches to scenario construction generate a large number of scenarios. This challenges the decision makers' information-processing capacity. To support them in managing the richness of information, a two-fold approach that uses selection and aggregation is presented. By using a selection method, the scenarios that are deemed most relevant are identified, and their evaluations are presented in detail to decision makers. This approach is complemented by an aggregation of scenario evaluations on the basis of the decision makers' preferences. We present two approaches to facilitate the preference elicitation process. Copyright
Conference Paper
Decision making under uncertainty is fraught with pitfalls for human thinking: biases prevail. The combination of a scenario-based approach with multi-criteria decision analysis assists in making value judgements, trade-offs and uncertainties explicit. Scenarios, which are constructed in a distributed manner involving multiple experts from different domains, assist in overcoming e.g. the prominence effect and confirmation bias. Furthermore, support is provided to handle the uncertainty associated with each scenario without imposing unjustified assumptions on each piece of information. We develop a relative reliability concept, which differs from standard probability assessments as it is sensitive to the context, such as the decision problem at hand, the decision makers' requirements and the available information. This approach maintains the flexibility of the distributed system by allowing the experts to adapt the information they provide and the likelihood assessments thereof to the situation. Our approach is illustrated by an emergency management example.
Article
This paper first describes current practice in decision analysis and argues that nothing in the technique's application is likely to challenge the strategic decision maker's current worldview of the course of future events that are modelled in the decision tree. By contrast, a scenario planning intervention in an organization has the potential to increase perceived threat and thus lead to a step change in strategic decision making. Strategic decisions are made against a backcloth of the operation of psychological processes that act, it is argued, to reduce the perceived level of environmental threat and result in strategic inertia. For this reason, it is recommended that scenario planning should be adopted as a standard procedure because of its ability to challenge individual and organizational worldviews. The use of scenario planning prior to conventional decision analysis is termed as ‘future-focussed thinking’, and parallels are drawn between the current advocated approach and that of Keeney's value-focussed thinking. Both serve to prompt the creation of enhanced options for subsequent evaluation by conventional decision analytic techniques. Copyright © 1999 John Wiley & Sons, Ltd.
Article
Sometimes experts, decisionmakers, and analysts are confronted with policy problems that involve deep uncertainty. Such policy problems occur when (1) the future is not known well enough to predict future changes to the system, (2) there is not enough knowledge regarding the appropriate model to use to estimate the outcomes, and/or (3) there is not enough knowledge regarding the weights stakeholders currently assign to the various criteria or will assign in the future. This paper presents an MCDA approach developed to deal with conditions of deep uncertainty, which is called Exploratory Multi-Criteria Decision Analysis (EMCDA). EMCDA is based on exploratory modelling, which is a modelling approach that allows policy analysts to explore multiple hypotheses about the future world (using different consequence models, different scenarios, and different weights). An example of a policy problem that can benefit from this methodology is decision making on innovations for improving traffic safety. In order to improve traffic safety, much is expected from Intelligent Speed Adaptation (ISA), an in-vehicle system that supports the driver in keeping an appropriate speed. However, different MCDA studies on ISA give different results in terms of the estimates of real-world safety benefits of ISA and the willingness of stakeholders (e.g. the automotive industry) to supply ISA. The application of EMCDA to the implementation of ISA shows that it is possible to perform an MCDA in situations of deep uncertainty. A full analysis taking into account the complete uncertainty space shows that the best policy is to make mandatory an ISA system for young drivers (less than 24 years of age) that restricts them from driving faster than the speed limit. Based on different assumptions, the analysis also shows that ISA policies should not target older drivers. Copyright © 2010 John Wiley & Sons, Ltd.
Article
The purpose of this paper is to provide the main results of a study concerning the risk of chlorine transport by train in France. The specific problem of chlorine transport is presented in the framework of a general model for assessing the risk in the transport of dangerous materials. The probability of accidents followed with a chlorine release involving fatalities are put in perspective with other risks having potential health effects on the public. Two types of application of the model are envisaged in relation to the management of risk: the selection of protective measures through a cost-effectiveness approach and the use of the model for a better planning of decisions in an accident situation.
Article
Scenario planning has formed a growing area of interest on the interface of academia and public and private sector policy-making. While methodological approaches are well covered in the academic literature, less attention has been paid to studying the use, impacts and effectiveness of scenario planning in public policy-making. This article combines preliminary findings from a review of evaluative scenario literature with workshop discussions among scenario practitioners, using environmental relevant policies as a case study. Subject to the nascent evaluative scenario literature, our preliminary findings highlight that scenario planning still is often executed in a rather ad-hoc and isolated manner and is mostly geared towards indirect decision support such as agenda-setting and issue-framing. The slim evidence base aggravates the assessment, but the potential of scenario planning to prepare public policy-making for the uncertainties and surprises of future developments and better manage complex decisions involving conflicting societal interests is clearly not fully utilized. Political and institutional context factors need to be treated with greater care in the future. Making better decisions under conditions of deep uncertainty does not only require rigorous analysis, but also political will and more stable institutional settings and organisational capacities to build up trust and experience with adaptive, flexible process formats. We synthesize our analysis with a discussion of further research needs.
Article
—We are primarily concerned with the problem of aggregating multicriteria to form an overall decision function. We introduce a new type of operator for aggregation called an ordered weighted aggregation (OWA) operator. We investigate the properties of this operator. We particularly see that it has the property of lying between the “and,” requiring all the criteria to be satisfied, and the “or,” requiring at least one of the criteria to be satisfied. We see these new OWA operators as some new family of mean operators.
Article
Informal/Interdependent: Learning, High Abstraction. In this domain we have the abstraction of shared experiences, values and beliefs. This is the domain of the shadow or informal organization, that complex network of obligations, experiences and mutual commitments without which an organization could not survive. Trust in this domain is a naturally occurring phenomenon as all collaboration is voluntary in nature. In some primitive societies the symbols are stories, often unique to a particular family who train their children to act as human repositories of complex stories that contain the wisdom of the tribe. The ability to convey high levels of complexity through story lies in the highly abstract nature of the symbol associations in the observer's mind when s/he hears the story. It triggers ideas, concepts, values and beliefs at an emotional and intellectual level simultaneously. A critical mass of such anecdotal material from a cohesive community can be used to identify and codify simple rules and values that underlie the reality of that organization's culture (Snowden, 1999). At its simplest manifestation this can be a coded reference to past experience. "You're doing a Margi" may be praise or blame – without context the phrase is meaningless, with context a dense set of experiences is communicated in a simple form.
Article
Scenario planning can be a useful and attractive tool in strategic management. In a rapidly changing environment it can avoid the pitfalls of more traditional methods. Moreover, it provides a means of addressing uncertainty without recourse to the use of subjective probabilities, which can suffer from serious cognitive biases. However, one underdeveloped element of scenario planning is the evaluation of alternative strategies across the range of scenarios. If this is carried out informally then inferior strategies may be selected, while those formal evaluation procedures that have been suggested in relation to scenario planning are unlikely to be practical in most contexts. This paper demonstrates how decision analysis can be used to structure the strategy evaluation process in a way which avoids the problems associated with earlier proposals. The method is flexible, versatile and transparent and leads to a clear and documented rationale for the selection of a particular strategy.
Article
This paper reports the findings of two experimental investigations into the efficacy of a causal cognitive mapping procedure as a means for overcoming cognitive biases arising from the framing of strategic decision problems. In Study 1, final year management studies undergraduate students were presented with an elaborated strategic decision scenario, under one of four experimental conditions: positively vs. negatively framed decision scenarios, with prechoice vs. postchoice mapping task orders (i.e., participants were required to engage in cognitive mapping before or after making a decision). As predicted, participants in the postchoice mapping conditions succumbed to the framing bias whereas those in the prechoice mapping conditions did not. Study 2 replicated and extended these findings in a field setting, on a sample of senior managers, using a decision scenario that closely mirrored a strategic dilemma currently facing their organization. Taken together, the findings of these studies indicate that the framing bias is likely to be an important factor in strategic decision making, and suggest that cognitive mapping provides an effective means of limiting the damage accruing from this bias. Copyright © 1999 John Wiley & Sons, Ltd.
Article
There are two broad categories of risk affecting supply chain design and management: (1) risks arising from the problems of coordinating supply and demand, and (2) risks arising from disruptions to normal activities. This paper is concerned with the second category of risks, which may arise from natural disasters, from strikes and economic disruptions, and from acts of purposeful agents, including terrorists. The paper provides a conceptual framework that reflects the joint activities of risk assessment and risk mitigation that are fundamental to disruption risk management in supply chains. We then consider empirical results from a rich data set covering the period 1995–2000 on accidents in the U. S. Chemical Industry. Based on these results and other literature, we discuss the implications for the design of management systems intended to cope with supply chain disruption risks.