High levels of uncertainty faced by decision makers can be alleviated by characterizing multiple possible ways in which the future might unfold with scenario narratives. Aiming at describing alternative plausible chains of outcomes of key uncertainty factors, scenario narratives are often associated with graphical networks describing the relationships between the outcomes of the factors. We present a participatory framework for bottom-up development of such networks, the PACNAP (PArticipatory development of Consensual narratives through Network Aggregation and Pruning) framework. In this framework, relationships of influence between factor outcomes are judged by a group of scenario process participants. We develop an optimization model for pruning an aggregated graph based on these judgments. The model selects those edges of the aggregate graph that the participants most agree upon and can be tailored to identify compact graphs of varying degrees of cyclicity. As a result, a variety of graphical representations of varying structural richness can be explored to arrive at a succinct representation of a consensus view on the structure of a joint narrative. To this end, the main formal results are the representation of the participants’ agreement lexicographically in a linear objective function of a 0-1 program, and the translation of the requisites of the compactness and cyclicity of the resulting pruned graphs into a set of network flow constraints. The problem of identifying a consensus graphical representation is a general one and our graph pruning method has application potential outside the specific domain of narrative development as well.

中文翻译：

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支持共识情景叙述的参与式开发的结构化框架

决策者面临的高度不确定性可以通过情景叙述来描述未来可能展开的多种可能方式来缓解。为了描述关键不确定性因素的替代合理结果链，情景叙述通常与描述因素结果之间关系的图形网络相关联。我们提出了一个自下而上开发此类网络的参与式框架，即 PACNAP（通过网络聚合和修剪对共识叙事进行 PArticipatory 开发）框架。在这个框架中，因素结果之间的影响关系由一组情景过程参与者来判断。我们开发了一个优化模型，用于根据这些判断修剪聚合图。该模型选择参与者最同意的聚合图的那些边，并且可以对其进行定制以识别具有不同周期性的紧凑图。因此，可以探索具有不同结构丰富性的各种图形表示，以得出对联合叙事结构的共识观点的简洁表示。为此，主要的正式结果是在 0-1 程序的线性目标函数中按字典顺序表示参与者的协议，并将生成的修剪图的紧凑性和循环性的要求转换为一组网络流约束。识别共识图形表示是一个普遍的问题，我们的图形修剪方法在叙事开发的特定领域之外也具有应用潜力。