SubsidieMeestersResponsible Link-Recommendations in Dynamic Environments
This project aims to create computational models to assess and redesign link-recommendation algorithms for online social networks to promote cooperation and mitigate misinformation.
Projectdetails
Introduction
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.
Urgency of the Issue
It is urgent to understand how algorithms used in online social media impact human behavioral dynamics given the widespread use of social media platforms and the evidence that they contribute to exacerbating radicalization, misinformation, and inciting hate.
Complexity of Online Platforms
This is a challenging endeavor. 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.
Project Goals
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.
Methodology
This will be achieved by developing agent-based models where the evolution of behaviors 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 behaviors in this setting.
- I will use the modeling results to design new link-recommenders that contribute to stabilize positive social behaviors such as:
- Cooperation
- Collective action
- Misinformation debunking
Evaluation
The developed algorithms will be evaluated with large-scale multi-agent simulations, online experiments, and real-world data.
Financiële details & Tijdlijn
Financiële details
| Subsidiebedrag | € 1.500.000 |
| Totale projectbegroting | € 1.500.000 |
Tijdlijn
| Startdatum | 1-3-2024 |
| Einddatum | 28-2-2029 |
| Subsidiejaar | 2024 |
Partners & Locaties
Projectpartners
- UNIVERSITEIT VAN AMSTERDAMpenvoerder
Land(en)
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