Artificial Intelligence and Social Simulation: Studying Group Dynamics on a Massive Scale
Artificial Intelligence and Social Simulation: Studying Group Dynamics on a Massive Scale
复制标题
DOI：
10.1177/1046496418802362
复制
发表时间：
2018-12-01
影响因子：
3.7
通讯作者：
Nagappan, Meiyappan
中科院分区：
文献类型：
--
作者：
Hoey, Jesse;Schroeder, Tobias;Nagappan, Meiyappan
人工智能和计算机科学的最新进展可以被社会科学家用于他们对群体和团队的研究。在这里，我们解释了机器学习和人工智能代理模拟的发展如何帮助团队和团队学者克服他们在研究团队动力学时面临的两个主要问题。首先，由于对群体的实证研究依赖于人工编码，很难研究大量群体（规模问题）。其次，行为科学中的传统统计方法往往无法捕捉到小群体中发生的非线性相互作用动力学（动力学问题）。机器学习有助于解决扩展问题，因为可以利用巨大的计算能力来增加组交互的手动编码。使用人工智能代理的计算机模拟有助于解决动态问题，方法是在数据生成算法中实施社会心理学理论，从而允许复杂的陈述和理论测试。我们描述了一个正在进行的研究项目，旨在计算分析的虚拟软件开发团队。
Recent advances in artificial intelligence and computer science can be used by social scientists in their study of groups and teams. Here, we explain how developments in machine learning and simulations with artificially intelligent agents can help group and team scholars to overcome two major problems they face when studying group dynamics. First, because empirical research on groups relies on manual coding, it is hard to study groups in large numbers (the scaling problem). Second, conventional statistical methods in behavioral science often fail to capture the nonlinear interaction dynamics occurring in small groups (the dynamics problem). Machine learning helps to address the scaling problem, as massive computing power can be harnessed to multiply manual codings of group interactions. Computer simulations with artificially intelligent agents help to address the dynamics problem by implementing social psychological theory in data-generating algorithms that allow for sophisticated statements and tests of theory. We describe an ongoing research project aimed at computational analysis of virtual software development teams.