This paper looks at the dynamics of information flows in social networks using multi-agent reinforcement learning. I haven’t read it, but am impressed by the work of the second author. Abstract:
We model the spread of news as a social learning game on a network. Agents can either endorse or oppose a claim made in a piece of news, which itself may be either true or false. Agents base their decision on a private signal and their neighbors’ past actions. Given these inputs, agents follow strategies derived via multi-agent deep reinforcement learning and receive utility from acting in accordance with the veracity of claims. Our framework yields strategies with agent utility close to a theoretical, Bayes optimal benchmark, while remaining flexible to model re-specification. Optimized strategies allow agents to correctly identify most false claims, when all agents receive unbiased private signals. However, an adversary’s attempt to spread fake news by targeting a subset of agents with a biased private signal can be successful. Even more so when the adversary has information about agents’ network position or private signal. When agents are aware of the presence of an adversary they re-optimize their strategies in the training stage and the adversary’s attack is less effective. Hence, exposing agents to the possibility of fake news can be an effective way to curtail the spread of fake news in social networks. Our results also highlight that information about the users’ private beliefs and their social network structure can be extremely valuable to adversaries and should be well protected.
There’s better, simpler results that I recall but cannot locate right now on doing local updating that is algebraic, rather than deep learning. I did find this, which is related in that it models this type of information flow and shows it works even without fully Bayesian reasoning; Jadbabaie, A., Molavi, P., Sandroni, A., & Tahbaz-Salehi, A. (2012). Non-Bayesian social learning. Games and Economic Behavior, 76(1), 210–225. https://doi.org/https://doi.org/10.1016/j.geb.2012.06.001
Given those types of results, the fact that RL agents can learn to do this should be obvious. (Though the social game dynamic result in the paper is cool, and relevant to other things I’m working on, so thanks!)
This paper looks at the dynamics of information flows in social networks using multi-agent reinforcement learning. I haven’t read it, but am impressed by the work of the second author. Abstract:
There’s better, simpler results that I recall but cannot locate right now on doing local updating that is algebraic, rather than deep learning. I did find this, which is related in that it models this type of information flow and shows it works even without fully Bayesian reasoning; Jadbabaie, A., Molavi, P., Sandroni, A., & Tahbaz-Salehi, A. (2012). Non-Bayesian social learning. Games and Economic Behavior, 76(1), 210–225. https://doi.org/https://doi.org/10.1016/j.geb.2012.06.001
Given those types of results, the fact that RL agents can learn to do this should be obvious. (Though the social game dynamic result in the paper is cool, and relevant to other things I’m working on, so thanks!)