This paper describes the design, implementation, and early experiences with a novel agent-based simulator of online media streams, developed under DARPA’s SocialSim Program to extract and predict trends in information dissemination on online media. A hallmark of the simulator is its self-configuring property. Instead of requiring initial set-up, the input to the simulator constitutes data traces collected from the medium to be simulated. The simulator automatically learns from the data such elements as the number of agents involved, the number of objects involved, and the rate of introduction of new agents and objects. It also develops behavior models of simulated agents and objects, and their dependencies. These models are then used to run simulations allowing future extrapolation and “what if” analysis. An interesting property of the simulator is its multi-level abstraction capability that allows modeling social systems at various degrees of abstraction by lumping similar agents into larger categories. Preliminary experiences are discussed with using this system to simulate multiple social media platforms, including Twitter, Reddit, and Github.

中文翻译：

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使用SocialCube进行多尺度在线媒体模拟

本文介绍了在DARPA的SocialSim计划下开发的，基于代理的新型基于代理的在线媒体流的设计，实现和早期经验，以提取和预测在线媒体上的信息传播趋势。模拟器的标志是其自配置属性。不需要初始设置，模拟器的输入就构成了从要模拟的介质中收集的数据迹线。模拟器会从数据中自动学习诸如所涉及的代理数量，所涉及的对象数量以及新代理和对象的引入率之类的元素。它还开发了模拟代理和对象的行为模型及其依赖关系。然后，这些模型将用于运行模拟，以便将来进行推断和“假设分析”。模拟器的一个有趣的特性是它的多级抽象功能，该功能允许通过将相似的主体归入更大的类别来对社交系统进行各种抽象程度的建模。讨论了使用此系统模拟多个社交媒体平台（包括Twitter，Reddit和Github）的初步经验。