Multiple-Agent Logics as Drivers of Rural Transformation: A Complex Adaptive Systems Analysis of Lin’an, Zhejiang, China

The global countryside constitutes a complex social–ecological system undergoing profound transformation. Understanding how such systems navigate transitions and achieve resilient, sustainable outcomes requires examining the interactions and adaptive behaviors of multiple actors. This study investigates the restructuring of rural China through a complex adaptive systems lens, focusing on the county of Lin’an in Zhejiang Province. We employ a middle-range theory and process-tracing approach to analyze the co-evolutionary pathways shaped by the interactions among three key agents: local governments, enterprises, and village communities. Our findings reveal distinct yet interdependent behavioral logics—local governments and enterprises primarily exhibit instrumental rationality, driven by political performance and profit maximization, respectively, while villages demonstrate value-rational behavior anchored in communal well-being and territorial identity. Crucially, this study identifies the emergence of a vital integrative mechanism, the “village operator” model, underpinned by the collective economy. This institutional innovation facilitates the synergistic linkage of interests and the integration of endogenous and exogenous resources, thereby mitigating conflicts and alienation. We argue that this multi-agent collaboration drives a synergistic restructuring of spatial, economic, and social subsystems. The case demonstrates that sustainable rural revitalization hinges not on the dominance of a single logic, but on the emergence of adaptive governance structures that effectively coordinate diverse actor logics. This process fosters systemic resilience, enabling the rural system to adapt to external pressures and internal changes. The Lin’an experience offers a transferable framework for understanding how coordinated multi-agent interactions can guide complex social–ecological systems toward sustainable transitions.