Conflict transformation and management are complex decision processes with
extremely high stakes at hand and could greatly benefit from formal approaches
to decision support. For this purpose we develop a general framework about how
to use problem structuring methods for such purposes. More precisely we show
how to transform cognitive maps to value trees in order to promote a more
design-oriented approach to decision support aiming at constructing innovative
solutions for conflict management purposes. We show that our findings have a
much wider validity since they allow to move from a descriptive representation
of a problem situation to a more prescriptive one using formal procedures and
models.

利用问题结构化方法将认知图转化为价值树，从而在决策支持中促进以设计为导向的方法，构建创新解决方案，进而实现冲突管理的目的。

冲突转化和管理。从认知地图到价值树

Conflict Transformation and Management. From Cognitive Maps to Value  Trees

Polarization is implicated in the erosion of democracy and the progression to
violence, which makes the polarization properties of large algorithmic content
selection systems (recommender systems) a matter of concern for peace and
security. While algorithm-driven social media does not seem to be a primary
driver of polarization at the country level, it could be a useful intervention
point in polarized societies. This paper examines algorithmic depolarization
interventions with the goal of conflict transformation: not suppressing or
eliminating conflict but moving towards more constructive conflict. Algorithmic
intervention is considered at three stages: which content is available
(moderation), how content is selected and personalized (ranking), and content
presentation and controls (user interface). Empirical studies of online
conflict suggest that the exposure diversity intervention proposed as an
antidote to "filter bubbles" can be improved and can even worsen polarization
under some conditions. Using civility metrics in conjunction with diversity in
content selection may be more effective. However, diversity-based interventions
have not been tested at scale and may not work in the diverse and dynamic
contexts of real platforms. Instead, intervening in platform polarization
dynamics will likely require continuous monitoring of polarization metrics,
such as the widely used "feeling thermometer." These metrics can be used to
evaluate product features, and potentially engineered as algorithmic
objectives. It may further prove necessary to include polarization measures in
the objective functions of recommender algorithms to prevent optimization
processes from creating conflict as a side effect.

本文探讨了三个阶段的算法干预方案，重点在于如何利用推荐系统来解决分裂和冲突问题，以达到更具建设性的冲突转型。实证研究表明，基于多样性的干预措施可能会在某些情况下加剧分裂。因此，本文建议将文本中的温和性度量与多样性相结合，以制定更有效的干预措施。