Intentional Community Building from Local to Global


The typology of sociotechnical transition pathways, as developed by Geels and Schot, provides a theoretical foundation for understanding how transitions within sociotechnical systems occur. This framework outlines various pathways through which these transitions can unfold, including transformation, reconfiguration, technological substitution, and de-alignment and re-alignment. Each pathway describes different mechanisms of change driven by interactions between social actors, technological innovations, and institutional dynamics .

Building a future society in line with a specific vision requires a strategic approach that leverages these pathways. By embedding intentional communities within existing regional contexts, a niche network of key innovators and change-makers can be formed. Despite criticism of being utopian, these communities, through sustained interaction and long-term engagement with the regional community, can lead to significant transformation. Initially perceived as isolated innovative practices, these efforts can gradually evolve, aligning the community with envisioned societal goals. This process fosters localized changes that can expand to broader societal contexts . 


Our research methodology combines the strengths of detailed case studies and multiple case studies while leveraging the capabilities of Large Language Models (LLMs) to address the challenges of scaling social innovation from local to global levels. Traditional anthropological methods provide rich, context-specific insights but often face transferability issues, while multiple case studies offer broader applicability but lack specificity. By utilizing LLMs, we aim to replicate the successful processes of community formation and development observed in intentional communities across diverse contexts. This approach focuses on understanding and scaling the mechanisms of building social connections, developing new prototypes, and transforming individual roles within communities, thereby creating a robust framework for sociotechnical transitions on a global scale.

Core Elements of Community Dynamics

Social Connections

Community building emphasizes the development of strong social connections through networks of trust, collaboration, and mutual support. Regular interactions, such as workshops and dialogues, are essential for maintaining these social bonds. Additionally, promoting cultural exchange through the sharing of ideas and practices fosters inclusivity within the community.

New Prototypes

Innovative practices involve developing prototypes that address local issues while aligning with broader societal goals. Encouraging experimentation and adaptation allows communities to test new ideas and share outcomes. The focus is on designing context-specific prototypes that are scalable and transferable to other communities, ensuring broader impact.

Transformation of Individual Roles

Active participation empowers individuals to become innovators and change-makers within their communities. Capacity building through training and resources enhances individuals’ skills, enabling them to contribute more effectively. Encouraging continuous personal and collective growth allows individuals to evolve from learners to leaders, fostering a dynamic community culture.

Embedding Community Dynamics with LLMs

LLMs can integrate detailed, context-rich case studies into a Retrieval-Augmented Generation (RAG) database, combining anthropological insights with implicit guidelines embedded within LLMs. This approach enables the adaptive transfer of successful community formation processes to new contexts. By utilizing RAG databases with detailed case studies and leveraging generalizable guidelines from diverse datasets, LLMs can effectively embed community dynamics into new environments.

LLMs can generate potential connection ideas, simulate interaction scenarios, and facilitate collaborative creativity, enhancing the innovation capacity of communities. They can help communities envision potential futures through scenario simulations, and enhance brainstorming sessions with suggestions and inspiration.

Limitations and Further Study

The dataset size may not be large enough to capture all necessary nuances. Ignoring cultural contexts could deepen biases within the model. Additionally, there is an ongoing debate on whether reference communities are worth replicating, questioning the replicability of successful community dynamics.

Future research should focus on assessing the practicality of model recommendations and simulations. Defining key performance indicators for social cohesion, role transitions, innovative prototype creation, and shared responsibility is crucial. Optimizing training paths for human-in-the-loop systems and obtaining reliable datasets while protecting community privacy are also important areas for further investigation.