Abstract

As part of the DARPA SocialSim challenge, we address the problem of predicting behavioral phenomena including information spread involving hundreds of thousands of users across three major linked social networks: Twitter, Reddit and GitHub. Our approach develops a framework for data-driven agent simulation that begins with a discrete-event simulation of the environment populated with generic, flexible agents, then optimizes the decision model of the agents by combining a number of machine learning classification problems. The ML problems predict when an agent will take a certain action in its world and are designed to combine aspects of the agents, gathered from historical data, with dynamic aspects of the environment including the resources, such as tweets, that agents interact with at a given point in time. In this way, each of the agents makes individualized decisions based on their environment …

Date

January 1, 1970

Authors

Goran Murić, Alexey Tregubov, Jim Blythe, Andrés Abeliuk, Divya Choudhary, Kristina Lerman, Emilio Ferrara

Journal

Autonomous Agents and Multi-Agent Systems

Volume

36

Issue

2

Pages

38

Publisher

Springer US