A common strategy in Multi-Criteria Decision Making (MCDM) is to rank alternative solutions by weighted summary scores. Weights, however, are often abstract to the decision maker and can only be set by vague intuition. While previous work supports a point-wise exploration of weight spaces, we argue that MCDM can benefit from a regional and global visual analysis of weight spaces. Our main contribution is WeightLifter, a novel interactive visualization technique for weight-based MCDM that facilitates the exploration of weight spaces with up to ten criteria. Our technique enables users to better understand the sensitivity of a decision to changes of weights, to efficiently localize weight regions where a given solution ranks high, and to filter out solutions which do not rank high enough for any plausible combination of weights. We provide a comprehensive requirement analysis for weight-based MCDM and describe an interactive workflow that meets these requirements. For evaluation, we describe a usage scenario of WeightLifter in automotive engineering and report qualitative feedback from users of a deployed version as well as preliminary feedback from decision makers in multiple domains. This feedback confirms that WeightLifter increases both the efficiency of weight-based MCDM and the awareness of uncertainty in the ultimate decisions.

Abstract In the development process of powertrain systems, 2D function ensembles frequently occur in the context of multi‐run simulations. An analysis has many facets, including distributions of extracted features, comparisons between ensemble members and target functions, and details‐on‐demand. The primary contribution of this paper is a design study of an interactive approach for a comparative visual analysis of 2D function ensembles. The design focuses on a tight integration of domain‐oriented and member‐oriented visualization techniques, and it seeks to preserve the mental model of 2D functions on multiple levels of detail. In this context, we propose a novel focus+context approach for visualizations relying on data‐driven placement which is based on labeling. We also extend work on feature‐preserving downsampling of 2D functions. Our design supports a comparison of 2D functions based on juxtaposition, overlay, and explicit differences. It also enables an analysis in terms of extracted scalar features and 1D aggregations. An evaluation illustrates a workflow in our application context. User feedback indicates a time saving of 70% for common tasks and a qualitative gain for the entire development process.