RE-LINK
Financiado
Responsible Link-Recommendations in Dynamic Environments
This proposal aims to develop, for the first time, new computational models to systematically evaluate 1) the long-term societal impacts of link-recommendation algorithms in online social networks and 2) design a new paradigm of l...
This proposal aims to develop, for the first time, new computational models to systematically evaluate 1) the long-term societal impacts of link-recommendation algorithms in online social networks and 2) design a new paradigm of link-recommenders that incentivize cooperation, collective action, and misinformation control.It is urgent to understand how algorithms used in online social media impact human behavioural dynamics given the widespread use of social media platforms and the evidence that they contribute to exacerbate radicalization, misinformation, and incite hate. This is a challenging endeavour. Online platforms are nowadays complex ecosystems where millions of humans influence each other while co-existing with AI algorithms. In this context, link-recommendation algorithms, used to recommend new connections to users, are ubiquitous. Such algorithms fundamentally affect how new connections are formed and the information users are exposed to. Governing online social networks requires understanding the impact of link-recommenders and how to adapt them to ensure long-term benefits.With RE-LINK, I aim to develop a new class of models to understand the impact of link-recommendations on social dynamics and, in turn, design a new paradigm of algorithms that balance short-term performance and long-term societal benefits. This will be achieved by developing agent-based models where the evolution of behaviours occurs over adaptive networks whose growth, in turn, follows the heuristics used by link-recommenders. I will resort to evolutionary game theory and stochastic population dynamics to formally study the stability of behaviours in this setting. I will use the modelling results to design new link-recommenders that contribute to stabilize positive social behaviours such as cooperation, collective action, and misinformation debunking. The developed algorithms will be evaluated with large-scale multi-agent simulations, online experiments, and real-world data.
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Duración del proyecto: 59 meses
Fecha Inicio: 2024-03-01
Fecha Fin: 2029-02-28
Fecha Fin: 2029-02-28
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-03-01
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2023-STG:
ERC STARTING GRANTS
Cerrada
hace 2 años
Presupuesto
El presupuesto total del proyecto asciende a
2M€
Líder del proyecto
UNIVERSITEIT VAN AMSTERDAM
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5