@inproceedings{e8993ff908764adabe305df3d7f934b4,
title = "Data-driven agent-based modeling of innovation diffusion (doctoral consortium)",
abstract = "We present a novel data-driven agent-based modeling framework to study innovation diffusion. Our first step is to learn a model of individual agent behavior from individual adoption characteristics. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. Finally, we exemplify the proposed method can be used to explore and analyze a broad class of policies aimed at spurring innovation adoption.",
keywords = "Agent-based Modeling, Innovation Diffusion, Machine Learning, Policy Optimization",
author = "Haifeng Zhang and Yevgeniy Vorobeychik",
note = "Publisher Copyright: Copyright {\textcopyright} 2015, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.; 14th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015 ; Conference date: 04-05-2015 Through 08-05-2015",
year = "2015",
language = "English",
series = "Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS",
publisher = "International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)",
pages = "2009--2010",
editor = "Bordini, \{Rafael H.\} and Pinar Yolum and Edith Elkind and Gerhard Weiss",
booktitle = "AAMAS 2015 - Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems",
}