Vítor V. Vasconcelos

Jeroen F. Uleman, Loes Crielaard, Leonie K. Elsenburg et al.

Causal loop diagrams (CLDs) are widely used in health and environmental research to represent hypothesized causal structures underlying complex problems. However, as qualitative and static representations, CLDs are limited in their ability to support dynamic analysis and inform intervention strategies. We propose Diagrams-to-Dynamics (D2D), a method for converting CLDs into exploratory system dynamics models (SDMs) in the absence of empirical data. With minimal user input - following a protocol to label variables as stocks, flows or auxiliaries, and constants - D2D leverages the structural information already encoded in CLDs, namely, link existence and polarity, to simulate hypothetical interventions and explore potential leverage points under uncertainty. Results suggest that D2D helps distinguish between high- and low-ranked leverage points. We compare D2D to a data-driven SDM constructed from the same CLD and variable labels. D2D showed greater consistency with the data-driven model compared to static network centrality analysis, while providing uncertainty estimates and guidance for future data collection. The D2D method is implemented in an open-source Python package and a web-based application to support further testing and lower the barrier to dynamic modeling for researchers working with CLDs. We expect that additional validation studies will further establish the approach's utility across a broad range of cases and domains.

Gabriella D. Franco, Marta C. Couto, Vítor V. Vasconcelos et al.

Recommendation algorithms, used in online social networks, shape interactions between users. In particular, link-recommendation algorithms suggest new connections and affect how individuals interact and exchange information. These algorithms' efficacy relies on key mechanisms governing the creation of social ties, such as triadic closure and homophily. The first is achieved through structural similarity and represents a heightened chance of recommending users to one another given mutual friends; the second is related to opinion similarity and conveys an increased chance of recommending a connection given similar individual characteristics. These two mechanisms jointly shape the evolution of social networks and behaviors unfolding over them. Their combined effect on the co-evolution of opinion and structure dynamics remains, however, poorly understood. Here, we study how social networks and opinions co-evolve given the joint effect of rewiring based on opinion and structural similarity. We show that both similarity metrics lead to polarized states, but differ in how they impact network fragmentation and opinion diversity. While strongly relying on opinion similarity leads to a higher variation of opinion, rewiring via network similarity leads to a larger number of (dis)connected components, resulting in fragmented networks that lean towards one of the signed opinions. Under strong homophilic settings, introducing a weak dependence on structural similarity prevents network fragmentation and favors moderate opinions. This work can inform the design of new recommender algorithms that explicitly account for interacting social and recommendation mechanisms, with the potential to foster moderate opinion coexistence even in inherently polarizing settings.