Integration of Deep Reinforcement Learning and Agent-based Simulation to Explore Strategies Counteracting Information Disorder
Luigi Lomasto, Andrea Camoia, Alfonso Guarino, Nicola Lettieri, Delfina Malandrino, Rocco Zaccagnino

TL;DR
This paper combines agent-based modeling and deep reinforcement learning to simulate and identify effective strategies for mitigating the spread of misinformation on social media.
Contribution
It introduces an integrated framework that merges simulation and AI to explore countermeasures against information disorder, advancing social science simulation and AI application.
Findings
Preliminary results suggest certain policies can effectively reduce misinformation spread.
The integrated approach offers new insights into the conditions for successful containment strategies.
The work highlights promising research directions in social simulation and AI integration.
Abstract
In recent years, the spread of fake news has triggered a growing interest in Information Disorders (ID) on social media, a phenomenon that has become a focal point of research across fields ranging from complexity theory and computer science to cognitive sciences. Overall, such a body of research can be traced back to two main approaches. On the one hand, there are works focused on exploiting data mining to analyze the content of news and related metadata data-driven approach; on the other hand, works are aiming at making sense of the phenomenon at hand and their evolution using explicit simulation models model-driven approach). In this paper, we integrate these approaches to explore strategies for counteracting IDs. Heading in this direction, we put together: i. an Agent-Based model to simulate in a scientifically sound way both complex fake news dynamics and the effects produced by…
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