Collaborative Proposal: DHB Virtual Worlds: An Exploratorium for Theorizing and Modeling the Dynamics of Group Behavior

协作提案：DHB 虚拟世界：群体行为动态理论化和建模的探索馆

• 批准号: 0729421
• 负责人: Jaideep Srivastava
• 金额: $ 14.52万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0729421&HistoricalAwards=false
• 关键词: Collaborative; Proposal; DHB; Virtual; Worlds

Abstract This major inter-disciplinary research effort will use virtual worlds as an exploratorium to theoretically extend and empirically model the dynamics of group behavior. In the process it will develop novel computational techniques for analyzing large-scale networks, which will have applicability across a wide variety of domains. The most important and complex decisions made by governments and organizations occur in group contexts. A central challenge, spurred by new developments in information technologies (IT), is that the nature of groups and how they operate has changed radically. Today, many groups ? in social, political, and economic contexts - are ad hoc, agile, transient entities that emerge from a larger primordial network of relationships. For a short time, these groups accomplish a variety of tasks, and then they dissolve, only to be reconstituted later with a different configuration. While there is growing awareness of the socio-economic consequences of these groups, our understanding of how they form and their impact on effectiveness is severely limited. This project will address this limitation by developing a theoretical framework that reflects the contemporary conceptualizations of groups. It proposes a network approach to modeling the eco-system of overlapping and constantly changing groups that constitute the fabric of contemporary society. It recognizes that empirically testing such a model poses formidable data collection challenges. However, a unique resource available to the research team is access to all behavioral traces (server logs) from one of the world''s largest Massively Multiplayer Online (MMO) games, EverQuest 2, which is particularly well-suited to theorize and empirically model the dynamics of group behavior. MMOs comprise tens of thousands of players who are at any one point in time coalescing in thousands of groups to accomplish """"quests"""" and """"raids"""" that involve a variety of activities similar to tasks we undertake in real life ? finding information or materials, making, selling or buying products and services. Beyond the data collection challenge, the scale of the proposed research enterprise also poses significant computational challenges in uncovering and analyzing the complexities that govern the dynamics of group behavior in these virtual worlds. Using advanced computing applications and technologies, this project seeks to capture, infer, and model the networks that explain how groups emerge and how they function. Specifically, the researchers will use temporally evolving graphs to model such networks, and develop scalable algorithms to compute metrics of group behavior on them. Tying these complex and shifting individual and networked behaviors to traditional forms of analyses represents a novel interdisciplinary challenge in both scope and complexity. The project will expand our knowledge of how groups form and operate in larger ecosystems of groups, individuals, and organizations. The analysis of logs generated from Virtual Worlds poses novel challenges from a computational perspective. This interdisciplinary investigation will result in new (1) information models for modeling the Virtual World, (2) data structuring and algorithmic techniques for data access and analysis, and (3) techniques for computational efficiency. The knowledge and tools developed in this research will allow researchers to understand more fully, and practitioners to cultivate more effectively, the emergence and performance of ad hoc groups in contemporary society. It will also provide other disciplines with new computational and statistical modeling methodologies and tools, which should have considerable positive implications for future research in other disciplinary areas. The findings and deliverables of the proposed research will be immediately generalizable to training and education related to groups (beyond just MMOs or Virtual Worlds), social networks, and online games.

摘要这一跨学科的研究工作将使用虚拟世界作为一个探索中心，从理论上扩展和经验模型的动态群体行为。在这个过程中，它将开发用于分析大规模网络的新计算技术，这些技术将适用于各种各样的领域。政府和组织做出的最重要和最复杂的决策都是在群体环境中做出的。在信息技术新发展的推动下，一个核心挑战是群体的性质及其运作方式发生了根本变化。今天，许多团体？在社会、政治和经济背景下，它们是从一个更大的原始关系网络中出现的临时、敏捷、短暂的实体。在很短的时间内，这些群体完成各种任务，然后他们解散，只是后来以不同的配置进行重组。虽然人们日益认识到这些群体的社会经济后果，但我们对这些群体如何形成及其对效力的影响的了解非常有限。本项目将通过发展一个反映当代群体概念化的理论框架来解决这一限制。它提出了一种网络方法来模拟构成当代社会结构的重叠和不断变化的群体的生态系统。它认识到，从经验上测试这种模式带来了巨大的数据收集挑战。然而，研究团队可用的一个独特资源是访问世界上最大的大型多人在线（MMO）游戏之一《无尽的任务2》的所有行为轨迹（服务器日志），该游戏特别适合理论化和经验建模群体行为的动态。MMO由成千上万的玩家组成，他们在任何一个时间点上都聚集在数千个小组中完成“，这些任务涉及各种各样的活动，类似于我们在真实的生活中承担的任务？寻找信息或材料，制造，销售或购买产品和服务。除了数据收集的挑战，拟议的研究企业的规模也构成了重大的计算挑战，揭示和分析的复杂性，在这些虚拟世界中的群体行为的动态。使用先进的计算应用程序和技术，该项目旨在捕捉，推断和建模的网络，解释群体如何出现，以及他们如何运作。具体来说，研究人员将使用时间演化图来建模这样的网络，并开发可扩展的算法来计算群体行为的指标。将这些复杂和不断变化的个人和网络行为与传统形式的分析联系起来，在范围和复杂性方面都是一个新的跨学科挑战。该项目将扩大我们对群体如何在群体，个人和组织的更大生态系统中形成和运作的知识。从计算的角度来看，从虚拟世界生成的日志的分析提出了新的挑战。这种跨学科的研究将导致新的（1）信息模型建模的虚拟世界，（2）数据结构和算法技术的数据访问和分析，以及（3）计算效率的技术。在这项研究中开发的知识和工具将使研究人员更充分地了解，和从业人员更有效地培养，在当代社会的特设小组的出现和表现。它还将为其他学科提供新的计算和统计建模方法和工具，这将对其他学科领域的未来研究产生相当大的积极影响。拟议研究的结果和可交付成果将立即推广到与团体（不仅仅是MMO或虚拟世界），社交网络和在线游戏相关的培训和教育。