The rise of large-scale socio-technical systems in which humans interact with
artificial intelligence (AI) systems (including assistants and recommenders, in
short AIs) multiplies the opportunity for the emergence of collective phenomena
and tipping points, with unexpected, possibly unintended, consequences. For
example, navigation systems' suggestions may create chaos if too many drivers
are directed on the same route, and personalised recommendations on social
media may amplify polarisation, filter bubbles, and radicalisation. On the
other hand, we may learn how to foster the "wisdom of crowds" and collective
action effects to face social and environmental challenges. In order to
understand the impact of AI on socio-technical systems and design
next-generation AIs that team with humans to help overcome societal problems
rather than exacerbate them, we propose to build the foundations of Social AI
at the intersection of Complex Systems, Network Science and AI. In this
perspective paper, we discuss the main open questions in Social AI, outlining
possible technical and scientific challenges and suggesting research avenues.

本研究论文旨在借助复杂系统、网络科学和人工智能的交叉领域构建社交人工智能的基础，并探讨了社交人工智能领域中的主要问题、可能出现的技术和科学挑战，以及未来的研究方向。

社交人工智能及人工智能生态系统挑战

Social AI and the Challenges of the Human-AI Ecosystem

We provide a short introduction to the field of topological data analysis and
discuss its possible relevance for the study of complex systems. Topological
data analysis provides a set of tools to characterise the shape of data, in
terms of the presence of holes or cavities between the points. The methods,
based on notion of simplicial complexes, generalise standard network tools by
naturally allowing for many-body interactions and providing results robust
under continuous deformations of the data. We present strengths and weaknesses
of current methods, as well as a range of empirical studies relevant to the
field of complex systems, before identifying future methodological challenges
to help understand the emergence of collective phenomena.

介绍拓扑数据分析及其对于复杂系统研究的潜在意义，讨论当前方法的优缺点并总结相关实证研究，同时指出未来研究中需要解决的方法学挑战。

单纯复合体与复杂系统

Simplicial complexes and complex systems

Statistical physics has proven to be a very fruitful framework to describe
phenomena outside the realm of traditional physics. The last years have
witnessed the attempt by physicists to study collective phenomena emerging from
the interactions of individuals as elementary units in social structures. Here
we review the state of the art by focusing on a wide list of topics ranging
from opinion, cultural and language dynamics to crowd behavior, hierarchy
formation, human dynamics, social spreading. We highlight the connections
between these problems and other, more traditional, topics of statistical
physics. We also emphasize the comparison of model results with empirical data
from social systems.

这篇论文探讨了在社会结构中由个体互动产生的集体现象，从观点、文化和语言动态到群体行为、层次结构形成、人类动态和社会传播等一系列主题进行了综述，并强调了这些问题与统计物理其他更传统的问题之间的联系，并比较了模型结果与社会系统的实证数据。