Data-Driven Agent-based Modeling of Innovation Diffusion

被引：0
|
作者：
Zhang, Haifeng [1 ]
机构：
[1] Vanderbilt Univ, Elect Engn & Comp Sci, 221 Kirkland Hall, Nashville, TN 37235 USA
来源：
PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS & MULTIAGENT SYSTEMS (AAMAS'15) | 2015年
关键词：
Machine Learning; Agent-based Modeling; Innovation Diffusion; Policy Optimization;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：2009 / 2010
页数：2
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