Collaborative Research: Instrumenting Behaviors and Attitudes in Virtual Worlds

合作研究：在虚拟世界中测量行为和态度

• 批准号: 0628072
• 负责人: Daniel Hunter
• 金额: $ 3.81万
• 依托单位: University of Pennsylvania
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-05-01 至 2007-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0628072&HistoricalAwards=false
• 关键词: Collaborative; Research; Instrumenting; Behaviors; Attitudes

This project is a pioneering effort to collect data about Massively Multiplayer Online game players, their actions, attitudes, social networks, and economic activities. People increasingly conduct major parts of their lives in virtual worlds: online spaces where large numbers of users are able to congregate, interact, play, and socialize. Most of these worlds are built as competitive games, and are termed Massively Multiplayer Online games (MMOs). Best estimates indicate in excess of five million North Americans now interact in these worlds, with more than 20 million worldwide. Yet while the serious study of virtual worlds has begun in earnest, it is hamstrung by the absence of systematic data. No research project has been conducted with a representative sample, access to server-side data, or the cooperation of game administrators. However, this project has secured the cooperation of a large game developer, which has agreed to release its in-game data as well as help coordinate a survey instrument of its players. The resulting data set will thus be the first comprehensive study of virtual world behaviors, and will contain a rare combination of both unobtrusive behavioral data with standard attitudinal, demographic and psychographic measures.This data set must be properly collected, cleaned and organized in order to facilitate access by a wide range of researchers. Thus the initial effort will involve programming innovations, cleaning and hosting tasks, as well as the creation of new algorithms for several social science tests. The first three areas to be tested will be social networks, social capital, and economic activity. In each area, the dataset will be a testbed for examining theories in ways that were previously impossible, while also offering several programming and analysis challenges relating to dealing with a massive and comprehensive dataset. For example, social networks tested with multi-theoretical multilevel (MTML) models have generally been small due to the difficulty of collecting complete datasets on groups, yet the current project will offer thousands of complete groups of sizes ranging from two to 2,000, with complete behavioral data over a 16-month time window. For research into the social implications of online worlds, the data offer tests of the cyberbalkanization hypothesis, new media isolation hypotheses and explorations of virtual communities. The dataset also offers the ability to do something never yet done in economic analysis: generate a time series of an entire economy. The wealth of data will require the creation of indices, new metrics and new ways of interpretation.This project has no precedent in social science, both because virtual worlds have not yet been studied systematically, and because it combines attitudinal data with previously unattainable levels of unobtrusive behavioral data. The project allows for both methodological ground breaking as well as innovative theory testing. The research will require the creation of new techniques bridging the domains of both computer science and social science. These new methods will be valuable for future scientific research, for improving the games themselves, and for exploiting the educational potential of virtual worlds.

该项目是一项开创性的工作，旨在收集有关大型多人在线游戏玩家、他们的行为、态度、社交网络和经济活动的数据。 人们越来越多地在虚拟世界中进行生活的主要部分：大量用户能够聚集、互动、玩耍和社交的在线空间。这些世界中的大多数都是作为竞技游戏构建的，被称为大型多人在线游戏（MMO）。最准确的估计表明，目前有超过 500 万北美人在这些世界中互动，全球范围内有超过 2000 万美国人。然而，尽管对虚拟世界的认真研究已经开始，但由于缺乏系统数据而受到阻碍。尚未开展具有代表性样本、服务器端数据访问或游戏管理员合作的研究项目。然而，该项目已经获得了一家大型游戏开发商的合作，该开发商同意发布其游戏内数据并帮助协调其玩家的调查工具。因此，由此产生的数据集将是第一个对虚拟世界行为的全面研究，并将包含不引人注目的行为数据与标准态度、人口统计和心理测量的罕见组合。必须正确收集、清理和组织该数据集，以便于广泛的研究人员访问。因此，最初的工作将涉及编程创新、清理和托管任务，以及为多项社会科学测试创建新算法。前三个测试领域将是社交网络、社会资本和经济活动。在每个领域，该数据集都将成为以以前不可能的方式检验理论的测试平台，同时还提供与处理大量综合数据集相关的一些编程和分析挑战。例如，由于难以收集完整的群体数据集，使用多理论多层次 (MTML) 模型测试的社交网络通常规模较小，但当前项目将提供数千个完整的群体，规模从 2 到 2,000 不等，并提供 16 个月时间窗口内的完整行为数据。为了研究网络世界的社会影响，这些数据提供了网络巴尔干化假设、新媒体隔离假设和虚拟社区探索的检验。该数据集还提供了做经济分析中从未做过的事情的能力：生成整个经济的时间序列。丰富的数据将需要创建指数、新的指标和新的解释方式。这个项目在社会科学中没有先例，既因为虚拟世界尚未得到系统研究，也因为它将态度数据与以前无法达到的不引人注目的行为数据结合起来。该项目既可以实现方法上的突破，也可以进行创新的理论测试。这项研究需要创造连接计算机科学和社会科学领域的新技术。这些新方法对于未来的科学研究、改进游戏本身以及开发虚拟世界的教育潜力都很有价值。