Coming together? Social Network Analysis of humanitarian actors in Burkina Faso

The deteriorating security situation in Burkina Faso has meant that humanitarian assistance programs have now been operating in the country for several years. Over the course of the response, emergency education and child protection interventions seeking the wellbeing of children and their rights to quality education have been prioritised. To achieve the best possible results, the humanitarian community has put in place a coordination mechanism and a ‘big deal’ to ensure synergies and maximise impact. The objective of this study is to draw out the operational dynamics between the actors in the response and to reflect on the results. We have found that this push for coordination has had mixed results—only a few organisations in Burkina have extensive networks with significant centrality for state services. Our study indicates that humanitarian organisations in the fields of protection and education must establish more connections with each other, and especially with local organisations, in line with the Grand Bargain’s mission to strengthen andoptimise responses.&nbsp

An Evaluation of Data Sources for Entry Decision Support in Rapid-Onset Disasters

If time-sensitive relief is to be dispatched to a far-away location, the decision to do so – the “entry” decision – has to be taken within hours after the disaster for the relief to make an impact. This paper aims to identify which information sources that become available to the decision maker at what time after a potential disaster, and to establish how the provided information can be best utilized based on its inherent and accumulated quality. The research encompasses 46 case studies in central Asia in the period from 1993 to 2003. The study makes clear that a decision-maker will only benefit from satellite imagery if the time required to deliver a digested product to the decision maker is reduced to a matter of hours or if the area of interest is so remote or widespread that the time necessary for on-site reports exceeds that of acquiring and interpreting remotely sensed imagery. In conclusion, model-based decision support systems are important since they provide an early alert that enables other sources to quicker provide information that is more refined.JRC.G.2-Support to external securit

A Multi-Attribute decision support system for allocation of humanitarian cluster resources , based on decision makers’ perspective

The rush of the humanitarian suppliers into the disaster area proved to be counter-productive. To reduce this proliferation problem, the present research is designed to provide a technique for supplier ranking/selection in disaster response using the principles of utility theory. A resource allocation problem is solved using optimisation based on decision maker’s preferences. Due to the lack of real-time data in the first 72 h after the disaster strike, a Decision Support System (DSS) framework called EDIS is introduced to employ secondary historical data from disaster response in four humanitarian clusters (WASH: Water, Sanitation and Hygiene, Nutrition, Health, and Shelter) to estimate the demand of the affected population. A methodology based on multi-attribute decision-making (MADM), Analytical Hierarchy processing (AHP) and Multi-attribute utility theory (MAUT) provides the following results. First a need estimation technique is put forward to estimate minimum standard requirements for disaster response. Second, a method for optimization of the humanitarian partners selection is provided based on the resources they have available during the response phase. Third, an estimate of resource allocation is provided based on the preferences of the decision makers. This method does not require real-time data from the aftermath of the disasters and provides the need estimation, partner selection and resource allocation based on historical data before the MIRA report is released

An Emerging Decision Support Systems Technology for Disastrous Actions Management

The purpose of the chapter is to introduce a conceptual approach of emerging decision \ud \ud support systems (DSS) development in enhancing contextual support in decision making. \ud \ud We analyse the requirements of outlining a technological solution model for addressing \ud \ud disaster management problem situations in which decision makers at different levels can \ud \ud have the information support to respond effectively

Serious Games as a Validation Tool for Decision Support System in Disaster Management—Case of PREDIS

Validation of Decision Support System (DSS) through simulation games or serious game is one way of validating the cognitive capability models through expert opinion. Here, this technique is used to validate PREDIS as a model for DISaster response supplier selection (PREDIS), previously introduced by authors. This DSS is a combination of a PREDictive component (PRED) for predicting the disaster human impact, an estimation component to Estimate the DISaster (EDIS) needs and optimised for supplier based resource allocation. This paper aims to test the suitability of the PREDIS model further for decision-making in the disaster situation. A quasi-experiment design embedded in a participatory simulation game is conducted to compare the opinion of equal sample of 22 experts and non-experts. The following questions are put forward. First, “Does PREDIS model assists the decision makers to make the same decisions faster”. Second, “Does the PREDIS model assist the non-experts as simulated decision makers to decide like an expert”. Using AHP weights of decision makers’ preference as well as borda counts, the decisions are compared. The result shows that PREDIS helps to reduce the decision making time by experts and non-experts within 6 h after the disaster strike, instead of 72 h. It also assist 71% of the non-expert to make similar de-cision as experts. In summary, the PREDIS model has two major capabilities. It enables the experts and non-experts to predict the disaster results immediately and using the widely available data. It also enables the non-experts to decide almost the same as the experts; either in predicting the human impact of the disaster and estimating the needs or in selecting suitable suppliers

Supporting Humanitarian Relief Distribution Decision-Making under Deep Uncertainty : A System Design Approach

With respect to copyright, all the papers were excluded from the dissertation.Disasters threaten society with widespread destruction of infrastructure and livelihood. For their survival, affected inhabitants depend on immediate humanitarian assistance from diverse organizations. During quick responses, humanitarian decision- makers (HDMs) act rapidly to distribute necessary relief goods, despite the deep, prevailing uncertainty that arises from scarce, conflicting, and uncertain information. To support HDMs in humanitarian relief distribution (HRD) decision-making, humanitarian logistics (HL) researchers have developed various mathematical models. These models are, however, specific to disaster scenarios, and most of them are detached from the realities of the field since end-users (mainly practitioners) have been absent in the development process. When tested, these decision-making models were found to be capable of producing good results, but they have not been implemented in practice because of operational inconsistency or complexity (i.e., lack of user-friendliness). Therefore, humanitarian responders are still in need of support systems to assist them in determining effective HRD. A computer-based decision support system (DSS) can fill this need by providing necessary recommendations and suggesting decision alternatives. Hence, developing such DSSs is always the priority in HL.publishedVersio

A multi-attribute decision support system for allocation of humanitarian cluster resources based on decision makers’ perspective

The rush of the humanitarian suppliers into the disaster area proved to be counterproductive. To reduce this proliferation problem, the present research is designed to provide a technique for supplier ranking/selection in disaster response using the principles of utility theory. A resource allocation problem is solved using optimisation based on decision maker’s preferences. Due to the lack of real-time data in the first 72 h after the disaster strike, a Decision Support System (DSS) framework called EDIS is introduced to employ secondary historical data from disaster response in four humanitarian clusters (WASH: Water, Sanitation and Hygiene, Nutrition, Health, and Shelter) to estimate the demand of the affected population. A methodology based on multi-attribute decision making (MADM), Analytical Hierarchy processing (AHP) and Multi-attribute utility theory (MAUT) provides the following results. First a need estimation technique is put forward to estimate minimum standard requirements for disaster response. Second, a method for optimization of the humanitarian partners selection is provided based on the resources they have available during the response phase. Third, an estimate of resource allocation is provided based on the preferences of the decision makers. This method does not require real-time data from the aftermath of the disasters and provides the need estimation, partner selection and resource allocation based on historical data before the MIRA report is released

UN Use of Private Military and Security Companies

Although subject to little discussion, the UN has increasingly paid private military and security companies (PMSCs) for a range of services in the areas of humanitarian affairs, peacebuilding and development. However, this practice has rarely translated into coherent policies or guidelines that could guide the UN in setting standards or ensuring responsible contracting procedures. This paper explores UN demand for PMSCs and identifies the need for a more proactive, sensitive and deliberate political approach in order to avoid potential pitfalls associated with involving PMSCs in the delivery of UN tasks

Mapping of risk web-platforms and risk data: collection of good practices

A successful DRR results from the combination of top-down, strategies, with bottom-up, methodological approaches. The top–down approach refers more to administrative directives, organizations, and operational skills linked with the management of the risk and reflects more the policy component. The bottom-up approach is linked to the analyse of the causal factors of disasters, including exposure to hazards, vulnerability, coping capacity, and reflects more the practice component. In the context of disaster science, policy and practice are often disconnected. This is evident in the dominant top-down DRM strategies utilizing global actions on one hand and the context specific nature of the bottom-up approach based on local action and knowledge. A way to bridge the gap between practice and policy is to develop a spatial data infrastructure of the type of GIS web-platforms based on risk mapping. It is a way of linking data information and decision support system (DSS) on a common ground that becomes a “battlefield of knowledge and actions”. This report presents the results of an overview of the risk web-platforms and related risk data used in risk assessment at the level of EU-28. It allows the discovery of the current advancement for risk web infrastructures and capabilities in order to establish a pool of good practices and detection of needs. The outcome of the overview shows the needs in risk web platform developments and tries to recommend capacities that should be prioritized in order to strengthen the link between risk data information and decision support system (DSS). The assessment is based on web search and outcome of diverse disaster risk workshops and conference.JRC.E.1-Disaster Risk Managemen

Mind the gap: state of the art on decision-making related to post-disaster housing assistance

A growing awareness of the long-term impact of disaster relief plans is shifting the focus of post-disaster housing studies towards integrated recovery and development. These would benefit from knowledge about decision-making challenges and dichotomies which determine the success or failure of housing assistance programs, and of methods and tools that can support their holistic resolution. To establish common grounds in this area, this paper systematically reviewed the literature on temporary housing built after sudden natural hazards, from a decision-making perspective, using reflexive thematic analysis methods. This enabled the identification of critical decision-making components (i.e. open challenges, trade-offs, dilemmas and contradictions) and necessary synergies at three levels: the operational, the managerial and the strategic. Results highlight the value of a meta-analysis of the literature to identify decision-making gaps and opportunities for knowledge integration across domains, besides the need of a constructive decision-making alignment at all decision-making levels to enable holistic recovery planning. Additionally, they show the importance of an in-depth examination of decision-making dichotomies for developing novel methods and tools, which respond to contextual needs and local dynamics. Being one of a few studies in a rather underexplored area of research, the primary aim of this review is to offer a broad and structured overview of decision-making issues documented in the literature to date, which connects both theory and practice. The results could be operationalised in future research aimed at supporting Build Back Better efforts towards a truly human-centred housing assistance culture, by investigating the connected decision-making dynamics in specific contexts