Research in cognitive psychology has established that whether people prefer simpler explanations to complex ones is context dependent, but the question of `simple vs. complex' becomes critical when an artificial agent seeks to explain its decisions or predictions to humans. We present a model for abstracting causal reasoning chains for the purpose of explanation. This model uses a set of rules to progressively abstract different types of causal information in causal proof traces. We perform online studies using 123 Amazon MTurk participants and with five industry experts over two domains: maritime patrol and weather prediction. We found participants' satisfaction with generated explanations was based on the consistency of relationships among the causes (coherence) that explain an event; and that the important question is not whether people prefer simple or complex explanations, but what types of causal information are relevant to individuals in specific contexts.

翻译：认知心理学研究已证实，人类对简单与复杂解释的偏好取决于具体情境。当人工智能代理需要向人类解释其决策或预测时，"简单vs复杂"的问题变得尤为关键。我们提出了一种用于解释目的因果推理链抽象模型。该模型通过一组规则，逐步抽象因果证明轨迹中的不同类型因果信息。我们通过亚马逊MTurk平台招募123名参与者开展线上研究，并邀请五位行业专家对海洋巡逻和天气预报两个领域进行评估。研究发现，参与者对生成解释的满意度取决于解释事件成因间关系的一致性（连贯性）；而关键问题并非人类偏爱简单还是复杂解释，而是在特定情境中哪些类型的因果信息与个体具有相关性。