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Patrick Shepherd

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2 papers

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AAAI Conference 2021 System Paper

Software for Agent-based Network Simulation and Visualization

Patrick Shepherd
Isaac Batts
Judy Goldsmith
Emory Hufbauer
Mia Weaver
Angela Zhang

We present a network software suite that can model contagions or opinion manipulation in social networks, that combines features from the standard packages and extends them to allow complex, interacting, dynamic topologies, and dynamic heterogeneous agent types, with individual interaction policies. The framework allows for the easy implementation of new agent types, and provides flexible visualization tools to elucidate network behavior over time.

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AAAI Conference 2020 Short Paper

A Reinforcement Learning Approach to Strategic Belief Revelation with Social Influence

Patrick Shepherd
Judy Goldsmith

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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