Agent-based design research to explore the effectiveness of bottom-up organizational design in shaping sustainable vernacular landscapes: A case in Hailar, China

Most vernacular landscapes today are under inevitable transformations. To manage these transforming landscapes, previous research has highlighted “bottom-up planning” that integrates local knowledge and willingness into decision-making, and “organizational design” that manipulates the self-organizing process based on local actions. However, indeterminacy exists in the effectiveness of bottom-up organizational design in shaping sustainability at the landscape scale. Addressing this, this paper aims to explore (1) whether bottom-up organizational designs can result in durable and robust improvements in the overall landscape performance, and (2) whether prioritizing local-level willingness or upper-level goals in bottom-up organizational designs can bring about better performance. A case study was carried out in the Hailar District of Hulunbuir (China), a vernacular landscape with significant complexity. The study method involves integrating agent-based modeling and a design research process in three iterative phases: intelligence, design, and choice. In the “intelligence” phase, local knowledge and preferences in land-use activities were identified through interviews and discussions. In the “design” phase, bottom-up processes were designed, scenarios combining different goals were developed, and an agent-based model was constructed. In the “choice” phase, the scenarios were simulated, evaluated with multiple indices, and compared at different time-scales. The results show that bottom-up organizational design based on local knowledge is effective in shaping sustainable transformations of vernacular landscapes; local willingness taking precedence over the government’s goals could result in better overall benefits through appropriate design. Suggestions were made for policy-making to cherish local traditions, recognize informal adaptations, and prioritize local interests.