A Reinforcement Learning Approach to Strategic Belief Revelation with Social Influence

AAAI 2020 pp. 13734-13735

doi:10.1609/AAAI.V34I10.7139 /aaai/2020/shepherd2020aaai-reinforcement/

Abstract

The study of social networks has increased rapidly in the past few decades. Of recent interest are the dynamics of changing opinions over a network. Some research has investigated how interpersonal influence can affect opinion change, how to maximize/minimize the spread of opinion change over a network, and recently, if/how agents can act strategically to effect some outcome in the network's opinion distribution. This latter problem can be modeled and addressed as a reinforcement learning problem; we introduce an approach to help network agents find strategies that outperform hand-crafted policies. Our preliminary results show that our approach is promising in networks with dynamic topologies.

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Cite

Text

Shepherd and Goldsmith. "A Reinforcement Learning Approach to Strategic Belief Revelation with Social Influence." AAAI Conference on Artificial Intelligence, 2020. doi:10.1609/AAAI.V34I10.7139

Markdown

[Shepherd and Goldsmith. "A Reinforcement Learning Approach to Strategic Belief Revelation with Social Influence." AAAI Conference on Artificial Intelligence, 2020.](https://mlanthology.org/aaai/2020/shepherd2020aaai-reinforcement/) doi:10.1609/AAAI.V34I10.7139

BibTeX

@inproceedings{shepherd2020aaai-reinforcement,
  title     = {{A Reinforcement Learning Approach to Strategic Belief Revelation with Social Influence}},
  author    = {Shepherd, Patrick and Goldsmith, Judy},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2020},
  pages     = {13734-13735},
  doi       = {10.1609/AAAI.V34I10.7139},
  url       = {https://mlanthology.org/aaai/2020/shepherd2020aaai-reinforcement/}
}