A chain game for distributed trading and negotiation

This position paper introduces a simulation gaming environment for enacting a production network. The environment aims to be an integrative laboratory for investigating supply networks, as well as being a versatile training tool. The primary focus is on food production networks. The environment enables a number of teams of participants, each representing one actor in a food chain, to conduct business together. The teams can have the role of auction, co-operation, wholesaler, factory, retail chain, and retail outlet. Producers and consumers are either enacted or simulated. The game leaders freely determine the products and production methods in each run of the game. The gaming environment takes performance, process and institutional aspects of chains into account. It is particularly suited for investigating issues of sustainability and trust. Currently the gaming environment is under development. The paper presents version 1B. This version can be found at http://www.chaingame.org. It runs on the Web, enabling to model distributed chains.     

Loss of Life Caused by the Flooding of New Orleans After Hurricane Katrina: Analysis of the Relationship Between Flood Characteristics and Mortality

In this article a preliminary analysis of the loss of life caused by Hurricane Katrina in the New Orleans metropolitan area is presented. The hurricane caused more than 1,100 fatalities in the state of Louisiana. A preliminary data set that gives information on the recovery locations and individual characteristics for 771 fatalities has been analyzed. One-third of the analyzed fatalities occurred outside the flooded areas or in hospitals and shelters in the flooded area. These fatalities were due to the adverse public health situation that developed after the floods. Two-thirds of the analyzed fatalities were most likely associated with the direct physical impacts of the flood and mostly caused by drowning. The majority of victims were elderly: nearly 60% of fatalities were over 65 years old. Similar to historical flood events, mortality rates were highest in areas near severe breaches and in areas with large water depths. An empirical relationship has been derived between the water depth and mortality and this has been compared with similar mortality functions proposed based on data for other flood events. The overall mortality among the exposed population for this event was approximately 1%, which is similar to findings for historical flood events. Despite the fact that the presented results are preliminary they give important insights into the determinants of loss of life and the relationship between mortality and flood characteristics.     

Flood Risk Assessment in the Netherlands: A Case Study for Dike Ring South Holland

Large parts of the Netherlands are below sea level. Therefore, it is important to have insight into the possible consequences and risks of flooding. In this article, an analysis of the risks due to flooding of the dike ring area South Holland in the Netherlands is presented. For different flood scenarios the potential number of fatalities is estimated. Results indicate that a flood event in this area can expose large and densely populated areas and result in hundreds to thousands of fatalities. Evacuation of South Holland before a coastal flood will be difficult due to the large amount of time required for evacuation and the limited time available. By combination with available information regarding the probability of occurrence of different flood scenarios, the flood risks have been quantified. The probability of death for a person in South Holland due to flooding, the so‐called individual risk, is small. The probability of a flood disaster with many fatalities, the so‐called societal risk, is relatively large in comparison with the societal risks in other sectors in the Netherlands, such as the chemical sector and aviation. The societal risk of flooding appears to be unacceptable according to some of the existing risk limits that have been proposed in literature. These results indicate the necessity of a further societal discussion on the acceptable level of flood risk in the Netherlands and the need for additional risk reducing measures.     

robROSE: A robust approach for dealing with imbalanced data in fraud detection

Statistical Methods & Applications - A major challenge when trying to detect fraud is that the fraudulent activities form a minority class which make up a very small proportion of the data set....     

Family planning and resilience: associations found in a Population,Health, and Environment (PHE) project in Western Tanzania

Using 2016 household survey data from Tanzania, we define and measure resilience within the context of Population, Health, and Environment programming and quantify the link between resilience and family planning. We created a multicomponent model using confirmatory factor analysis in a structural equation modeling context. Factor loadings for eight defined latent factors of resilience were statistically significant (p?<?0.001). We created a factor called “FP-MCH” reflecting awareness, attitudes, and access to family planning (FP) and health care services and use of maternal and child health care (MCH) facilities. Analysis, with controls, shows that a 1 standard deviation (SD) increase in FP/MCH was associated with a 0.68 SD increase in resilience (p?<?0.01), suggesting that the association between FP/MCH and resilience is robust across a range of factors. Analyses showed that the association between FP/MCH is broadly related to the construct of resilience and not through any single component. This study supports the importance of including FP/MCH as part of integrated projects to enhance resilience.     

Outlyingness: Which variables contribute most?

Outlier detection is an inevitable step to most statistical data analyses. However, the mere detection of an outlying case does not always answer all scientific questions associated with that data point. Outlier detection techniques, classical and robust alike, will typically flag the entire case as outlying, or attribute a specific case weight to the entire case. In practice, particularly in high dimensional data, the outlier will most likely not be outlying along all of its variables, but just along a subset of them. If so, the scientific question why the case has been flagged as an outlier becomes of interest. In this article, a fast and efficient method is proposed to detect variables that contribute most to an outlier’s outlyingness. Thereby, it helps the analyst understand in which way an outlier lies out. The approach pursued in this work is to estimate the univariate direction of maximal outlyingness. It is shown that the problem of estimating that direction can be rewritten as the normed solution of a classical least squares regression problem. Identifying the subset of variables contributing most to outlyingness, can thus be achieved by estimating the associated least squares problem in a sparse manner. From a practical perspective, sparse partial least squares (SPLS) regression, preferably by the fast sparse NIPALS (SNIPLS) algorithm, is suggested to tackle that problem. The performed method is demonstrated to perform well both on simulated data and real life examples.