Integration of Deep Reinforcement Learning and Agent-based Simulation to Explore Strategies Counteracting Information Disorder

2026-04-16 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Computational Social Science · Depth: Expert, extended

Summary

This research integrates Deep Reinforcement Learning (DRL) with Agent-Based Models (ABM) to explore strategies for counteracting Information Disorder (ID), specifically fake news, on social media. The proposed framework features a Model-Driven Tier (MDT) that simulates complex fake news dynamics using an ABM, and a Data-Driven Tier (DDT) where a DRL-based Super-Agent learns optimal mitigation strategies. Experiments, conducted using NetLogo 6.2.0 and Python 3.10 on an Intel Core i9-11900KF machine, compared fake news "Virality" (V) with and without the Super-Agent under varying network polarization ($P_N$), opinion polarization ($P_O$), and agent credulity ($\theta$). Results indicate that the Super-Agent significantly reduces fake news propagation, particularly in networks with moderate echo chamber effects, with increased intervention frequency leading to greater impact. For instance, with $\theta=0.27$, a Super-Agent action every two ticks was necessary to achieve a V value below 0.5.

Key takeaway

For AI Scientists and Policy Makers developing strategies to combat misinformation, this research demonstrates that integrating Deep Reinforcement Learning with Agent-Based Models offers a powerful framework. You should consider deploying DRL-driven "Super-Agents" within simulated social environments to identify and optimize intervention policies, especially focusing on intervention frequency to maximize impact. This approach can inform the design of more effective real-world countermeasures against information disorder.

Key insights

Integrating DRL with ABM effectively models and mitigates information disorder in social networks.

Principles

• Hybrid data-driven and model-driven approaches enhance social phenomenon analysis.
• Increased intervention frequency improves fake news mitigation by DRL agents.

Method

The method combines an Agent-Based Model (MDT) to simulate fake news spread with a Deep Reinforcement Learning (DDT) Super-Agent that learns optimal actions to mitigate propagation by observing network states and receiving rewards.

In practice

• Simulate social network dynamics using NetLogo and Python.
• Implement a DRL Super-Agent for real-time intervention.
• Adjust intervention frequency to optimize mitigation effectiveness.

Topics

• Deep Reinforcement Learning
• Agent-Based Modeling
• Information Disorder
• Fake News Mitigation
• Social Network Dynamics

Best for: AI Scientist, Research Scientist, Policy Maker

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.