Tina Comes

Decision maps: A framework for multi-criteria decision support under severe uncertainty

In complex strategic decision-making situations the need for well-structured support arises. To evaluate decision alternatives, information about the situation and its development must be determined, managed and processed by the best available experts. For various types of information different reasoning principles have been developed: deterministic, probabilistic, fuzzy and techniques for reasoning under ignorance (i.e., the likelihood of an event cannot be quantified). We propose a new approach based on Decision Maps supporting decision makers under fundamental uncertainty by generating descriptions of different possible situation developments (scenarios) in a distributed manner. The scenarios are evaluated using Multi-Criteria Decision Analysis techniques.

A composite indicator model to assess natural disaster risks in industry on a spatial level

In the event of natural disasters, industrial production sites can be affected by both direct physical damage and indirect damage. The indirect damage, which often exceeds the direct ones in value, mainly arises from business interruptions resulting from the impairment of information and material flows as well as from domino effects in interlaced supply chains. The importance of industry for society and the domino effects often result in severe economic, social, and environmental consequences of industrial disasters making industrial risk management an important task for risk managers at the administrative level (e.g. civil protection authorities). Since the possible industrial disaster damage depends not only on hazard and exposure but also on the vulnerability of a system, an effective and efficient industrial risk management requires information about the system’s regionalized vulnerability. This paper presents a new methodology for structural industrial vulnerability assessment based on production factors that enables to assess the regional industrial disaster vulnerability. In order to capture industry-specific vulnerability factors and to account for the processes underlying regional industrial vulnerability, a two-stage approach is developed. This approach combines a composite indicator model to assess sector-specific vulnerability indices (Vs) with a new regionalization method. The composite indicator model is based on methodologies from the field of multicriteria decision analysis (MultiAttribute Value Theory) and the Decision-Making Trial and Evaluation Laboratory Method is applied to correct the (Vs) for interdependencies among the indicators. Finally, the developed approach is applied to an exemplar case study and the industrial vulnerability of 44 administrative districts in the German federal state of Baden-Wuerttemberg is assessed.

Trapezoidal fuzzy DEMATEL method to analyze and correct for relations between variables in a composite indicator for disaster resilience

Indicator systems of disaster vulnerability are important for monitoring and increasing the capacity in risk management. Various composite indicators have been developed to operationalize social vulnerability at national and sub-national level. Problems with relations between the sub-indicators of the composite indicator are a common phenomenon. The fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) method analyzes the structure of complex cause-effect relationships between the sub-indicators based on perceived direct influences. The results provide insight into the composite indicators and can be used to correct the sub-indicator weighting for relations between the sub-indicators and allow the identification of cause- and effect-group sub-indicators which is an important information for selecting mitigation measures in risk management. The fuzzy DEMATEL method is generalized to take into account trapezoidal membership functions. A composite indicator originally developed to determine the disaster resilience in US counties is adapted, operationalized and used to assess the resilience of Germany at county level using corrected weights. Resilience is highest in urban areas and in southern Germany and lowest in rural areas, in particular in eastern Germany.

Adapting rail and road networks to weather extremes: case studies for southern Germany and Austria

Abstract The assessment of the current impacts of extreme weather conditions on transport systems reveals high costs in specific locations. Prominent examples for Europe are the economic consequences of the harsh winter periods 2009/2010 and 2010/2011 and the floods in Austria, Eastern Europe, Germany and the United Kingdom in 2005 and 2007. Departing from the EC-funded project WEATHER, this paper delves into the subject of adaptation strategies by revisiting the project’s general findings on adaptation strategies and by adding two specific cases: (1) advanced winter maintenance on roads in southwest Germany and (2) technical and organizational measures in Alpine rail transport. For these two cases, feasible adaptation strategies are elaborated and their potential is discussed in light of damage cost forecasts up to 2050. For the road sector, we find a high potential to mitigate weather-related costs, although damages here are expected to decline. In contrast, rail systems face strongly increasing damages and the mitigation options offered by improved information and communication systems seem to be largely exploited. Consequently, it is easier to justify expensive adaptation measures for high-cost rail infrastructures than for road transport. A generic analysis of 14 damage cases worldwide, however, revealed that generally awareness raising, cooperation and communication strategies are sufficient to mitigate the most severe damages by natural disasters.

Risk Accelerators in Disasters

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.

Enhancing Robustness in Multi-criteria Decision-Making: A Scenario-Based Approach

This paper presents a distributed system facilitating robust decision-making under uncertainty in complex situations often found in strategic emergency management. The construction of decision-relevant scenarios, each being a consistent, coherent and plausible description of a situation and its future development, is used as a rationale for collecting, organizing, filtering and processing information for decision-making. The construction of scenarios is targeted at assessing decision alternatives avoiding time-consuming processing of irrelevant information. The scenarios are constructed in a distributed setting ensuring that each decision can be founded on a coherent and consistent set of assessments and assumptions provided by the best (human or artificial) experts available within limited time. Our theoretical framework is illustrated by means of an emergency management example.

Resource Planning in Disaster Response

Managing the response to natural, man-made, and technical disasters is becoming increasingly important in the light of climate change, globalization, urbanization, and growing conflicts. Sudden onset disasters are typically characterized by high stakes, time pressure, and uncertain, conflicting or lacking information. Since the planning and management of response is a complex task, decision makers of aid organizations can thus benefit from decision support methods and tools. A key task is the joint allocation of rescue units and the scheduling of incidents under different conditions of collaboration. The authors present an approach to support decision makers who coordinate response units by (a) suggesting mathematical formulations of decision models, (b) providing heuristic solution procedures, and (c) evaluating the heuristics against both current best practice behavior and optimal solutions. The computational experiments show that, for the generated problem instances, (1) current best practice behavior can be improved substantially by our heuristics, (2) the gap between heuristic and optimal solutions is very narrow for instances without collaboration, and (3) the described heuristics are capable of providing solutions for all generated instances in less than a second on a state-of-the-art PC.

Cognitive biases in humanitarian sensemaking and decision-making lessons from field research

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.

The Impacts of ICT Support on Information Distribution, Task Assignment for Gaining Teams’ Situational Awareness in Search and Rescue Operations

Information and Communication Technology (ICT) has changed the way we communicate and work. To study the effects of ICT for Information Distribution (ID) and Task Assignment (TA) for gaining Teams’ Situational Awareness (TSA) across and within rescue teams, an indoor fire game was played with students. We used two settings (smartphone-enabled support vs. traditional walkietalkies) to analyze the impact of technology on ID and TA for gaining TSA in a simulated Search and Rescue operation. The results presented in this paper combine observations and quantitative data from a survey conducted after the game. The results indicate that the use of the ICT was good in second scenario than first scenario for ID and TA for gaining TSA. This might be explained as technology is more preferable and effective for information sharing, for gaining TSA and also for clear tasks assignment.

Decision Support for Disaster Risk Management: Integrating Vulnerabilities into Early-Warning Systems

Despite the potential of new technologies and the improvements of early-warning systems since the 2004 Tsunami, damage and harm caused by disasters do not stop to increase. There is a clear need for better integrating the fragmented landscape of researchers and practitioners working on different aspects of decision support for disaster risk reduction and response. To demonstrate and discuss the advantages of integrated systems, we will focus in this paper on vulnerabilities and early-warning systems. While vulnerabilities are mostly used to allocate risk management resources (preparedness), early-warning systems are designed to initiate the response phase. Indicator models have been used as a part of disaster risk reduction frameworks, and in the design of early-warning systems. In this paper we analyse the commonalities and differences between both, and outline how an integrated system that understands vulnerability assessments as part of both risk reduction programs and early-warning shall be designed in future.