Visual access to an agent-based simulation model to support political decision making

Abstract

Decision making in the field of policy making is a complex task. On the one hand conflicting objectives influence the availability of alternative solutions for a given problem. On the other hand economic, social, and environmental impacts of the chosen solution have to be considered. In the political context, these solutions are called policy options. To tackle societal problems a thorough analysis of policy options needs to be executed before a policy can be put into practice. Computational simulation is a method considered for measuring the impacts of policy options. However, due to their complexity, the underlying models and their output may be difficult to access by decision makers. In this work, we present a visual-interactive interface for an agent-based simulation model that enables decision makers to evaluate the impacts of alternative policy options in the field of regional energy planning. The decision maker can specify different subsidy strategies for supporting public photovoltaic installations as input and evaluate their impact on the actual adoption via the simulation output. We show the usability and usefulness of the visual interface in a real-world example evolved from the European research project ePolicy

Visualization System Requirements for Data Processing Pipeline Design and Optimization

“…Each colored cell indicates a requirement that is important for a given case study/paper, and an 'x' indicates that the requirement remains unmet by users' current tools. [25] req req req req req Chemical process models [26] x req req x Economic modelling [27] req req xx req req x req Aircraft engine design [28] req req x req req Phylogenetic trees [29,30] x req x x req req Molecular evolution [31,32] x x req x req x * Raidou et al [75] iCoCooN req req req * Ruppert et al [46] --x req x Luboshik et al [38] --x req req req Bruckner et al [10] --x req x req * Beham et al [43] Cupid req req xx Konyha et al [39] --x req x Pretorius et al [12] Paramorama req x req Afzal et al [36] RVF x req req x Bergner et al [13] ParaGlide xx req Torsney-Weir et al [33] Tuner x req x x * Padua et al [11] --x req x * Bögl et al [42] TiMoVA req req req x x req Spence et al [73] --req req Berger et al [9] --req x xx Piringer et al [40] HyperMoVal xx req req req Matkovic et al [17] --req x Coffey et al [34] --x x req Matkovic et al [50] --x Potter et al [49] Ensemble-Vis x req Booshehrian et al [19] Vismon req x req x Brecheisen et al [52] --x req x Unger et al [54] --req req req Amirkhanov et al [74] --x Marks et al [48] Design Galleries req x Waser et al [18] World Lines req x x * Martins et al [35] --x req x * Wu [53] --xx x Guo et al [41] --req…”

“…Alternatively, the pipeline may contain a number of 'what if' branches [42]. Sometimes individual parameters are easy for users to understand (e.g., the number of clusters for k-means), but other parameters are abstractions of physical systems [34] or the parameters are difficult to relate to output consequences [11], [46]. Sometimes, users tend to treat the pipeline as a 'black box' and accept the default settings [47].…”

“…The support ranges from iterative user-defined selection of model parameters [42], [46], to automated parameter selection (e.g., by random or regular sampling [12], [13], [29]). Moreover, hybrid approaches combine user steering and automatic parameter value sampling [63].…”

“…[9] and the phylogenetic trees case study in Sec. 4.7), to enable interactive refinement of parameters to optimize outputs (e.g., [12], [40], [46]), to assess the effect of inputs on outputs ('understanding the black box' [38]) or to interactively analyze the influence of input uncertainty on the output uncertainty through the pipeline [53]. All these cases require that users have the possibility to interactively assess subjective outputs in the context of input parameters.…”

“…Luboschik et al [38] show inputs on a plot X axis and outputs on the Y axis, thereby indicating the influence of inputs on outputs. Both Ruppert et al [46] and Booshehrian et al [19] use a grid-based visualization of outputs. The grid is produced by discretizing the input parameters into a set of intervals, with outputs shown inside the grid cells.…”