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Presentation . 2025
License: CC BY SA
Data sources: Datacite
ZENODO
Presentation . 2025
License: CC BY SA
Data sources: Datacite
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From Ethnography to Agent-Based Modeling: Tracing Transdisciplinary Knowledge Flows in European Large Scientific Infrastructures

Authors: Zhu, Anqi;

From Ethnography to Agent-Based Modeling: Tracing Transdisciplinary Knowledge Flows in European Large Scientific Infrastructures

Abstract

Las Grandes Infraestructuras de Investigación (LRIs, por sus siglas en inglés) están inmersas en amplias redes de partes interesadas, que incluyen agencias gubernamentales, ONG, socios de la industria, diversos grupos públicos, científicos, ingenieros y responsables de políticas. Estas coproducen tanto descubrimientos fundamentales como resultados de desarrollo sostenible. Proponemos un marco integrador que combina una descripción antropológica rica y "densa" con el modelado basado en agentes (ABM, por sus siglas en inglés) para rastrear la circulación de habilidades tácitas, principios de diseño y el planteamiento de problemas a través de las fronteras disciplinarias, organizativas y geográficas en torno a las LRIs. La investigación etnográfica revela modalidades interaccionales críticas a través de las cuales se arraigan las culturas de colaboración y los objetivos orientados a los Objetivos de Desarrollo Sostenible (ODS). Estos conocimientos cualitativos se formalizan en un ABM que conceptualiza las LRIs dentro de redes múltiples de partes interesadas, permitiendo una exploración sistemática de cómo las decisiones de las partes interesadas, los arreglos institucionales y las normas de colaboración moldean de manera conjunta las dinámicas de aprendizaje y la transferencia de conocimiento. Al combinar la etnografía con la simulación ABM, nuestro marco ofrece una metodología novedosa para probar intervenciones políticas y diseños institucionales que fortalezcan el intercambio de conocimientos y promuevan la consecución de los ODS dentro de ecosistemas de investigación a gran escala.

Large Research Infrastructures (LRIs) are embedded within expansive stakeholder networks, including government agencies, NGOs, industry partners, diverse public groups, scientists, engineers, and policymakers. They co-produce both fundamental discoveries and sustainable-development outcomes. We propose an integrative framework that combines rich, “thick” anthropological description with agent-based modeling (ABM) to trace the circulation of tacit skills, design principles, and problem framings across disciplinary, organizational, and geographical boundaries around LIRs. Ethnographic inquiry uncovers critical interactional modalities, through which collaboration cultures and Sustainable Development Goal–oriented objectives become embedded. These qualitative insights are formalized into an ABM that conceptualizes LRIs within multiplex stakeholder networks, enabling systematic exploration of how stakeholder choices, institutional arrangements, and collaboration norms jointly shape learning dynamics and knowledge transfer. By combining ethnography with ABM simulation, our framework offers a novel methodology for testing policy interventions and institutional designs that strengthen knowledge exchange and advance SDG realization within large-scale research ecosystems.

Keywords

Large Research Infrastructures, Agent-Based Modeling, Social anthropology, Anthropology, Ethnography, Cultural anthropology, Stakeholder Networks, Knowledge Transfer, FOS: Sociology

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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