  Given the escalating intricacy and multifaceted nature of contemporary social
systems, manually generating solutions to address pertinent social issues has
become a formidable task. In response to this challenge, the rapid development
of artificial intelligence has spurred the exploration of computational
methodologies aimed at automatically generating solutions. However, current
methods for auto-generation of solutions mainly concentrate on local social
regulations that pertain to specific scenarios. Here, we report an automatic
social operating system (ASOS) designed for general social solution generation,
which is built upon agent-based models, enabling both global and local analyses
and regulations of social problems across spatial and temporal dimensions. ASOS
adopts a hypergraph with extensible social semantics for a comprehensive and
structured representation of social dynamics. It also incorporates a
generalized protocol for standardized hypergraph operations and a symbolic
hybrid framework that delivers interpretable solutions, yielding a balance
between regulatory efficacy and function viability. To demonstrate the
effectiveness of ASOS, we apply it to the domain of averting extreme events
within international oil futures markets. By generating a new trading role
supplemented by new mechanisms, ASOS can adeptly discern precarious market
conditions and make front-running interventions for non-profit purposes. This
study demonstrates that ASOS provides an efficient and systematic approach for
generating solutions for enhancing our society.
