Jonathan Whetzel

Predicting Guild Membership in Massively Multiplayer Online Games

Massively multiplayer online games (MMOGs) offer a unique laboratory for examining large-scale patterns of human behavior. In particular, the study of guilds in MMOGs has yielded insights about the forces driving the formation of human groups. In this paper, we present a computational model for predicting guild membership in MMOGs and evaluate the relative contribution of 1) social ties, 2) attribute homophily, and 3) existing guild membership toward the accuracy of the predictive model. Our results indicate that existing guild membership is the best predictor of future membership; moreover knowing the identity of a few influential members, as measured by network centrality, is a more powerful predictor than a larger number of less influential members. Based on these results, we propose that community detection algorithms for virtual worlds should exploit publicly available knowledge of guild membership from sources such as profiles, bulletin boards, and chat groups.

Preparing for the aftermath: Using emotional agents in game-based training for disaster response

Ground truth, a training game developed by Sandia National Laboratories in partnership with the University of Southern California GamePipe Lab, puts a player in the role of an incident commander working with teammate agents to respond to urban threats. These agents simulate certain emotions that a responder may feel during this high-stress situation. We construct psychology-plausible models compliant with the Sandia Human Embodiment and Representation Cognitive Architecture (SHERCA) that are run on the sandia cognitive runtime engine with active memory (SCREAM) software. SCREAMs computational representations for modeling human decision-making combine aspects of ANNs and fuzzy logic networks. This paper gives an overview of ground truth and discusses the adaptation of the SHERCA and SCREAM into the game. We include a semiformal description of SCREAM.

Grandmaster: Interactive Text-Based Analytics of Social Media

People use social media resources like Twitter, Facebook, forums etc. to shareand discuss various activities or topics. By aggregating topic trends acrossmany individuals using these services, we seek to construct a richer profileof a persons activities and interests as well as provide a broader context ofthose activities. This profile may then be used in a variety of ways tounderstand groups as a collection of interests and affinities and anindividuals participation in those groups. Our approach considers that muchof these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping ofindividuals with shared interests based on shared conversation, and not onexplicit social software linking them. In this paper, we discuss aproof-of-concept application called Grandmaster built to pull short sections oftext, a persons comments or Twitter posts, together by analysis andvisualization to allow a gestalt understanding of the full collection of allindividuals: how groups are similar and how they differ, based on theirtext inputs.

A Data-Driven Approach to Assessing Team Performance through Team Communication.

For teams working in complex task environments, instilling effective communication between team members is a primary goal during task training. Presently, responsibility for evaluating team communication abilities resides with instructors and outside observers who make qualitative assessments that are shared with the team following a training exercise. Constructing technologies to automate these assessments has historically been prohibitive for two reasons. First, the financial cost of instrumenting the environment to collect team communication data at the necessary fidelity has been too high for an operational setting. Second, past research on using team communication as a proxy for team performance assessment has relied on defining communication through traditional algorithmic design, an approach which does not properly capture the varied nature of communication strategies amongst different teams. Recent scientific research in team dynamics provides a theoretical framework leading to a data-driven solution for analyzing the effectiveness of team communication. By framing team communication as an emergent data stream from a complex system, one may employ machine learning or other statisticalanalysis tools to highlight communication patterns and variance, both shown as effective means for assessing team adaptability to novel scenarios. Furthermore, low-cost wearable computers have opened new possibilities for observing people’s interactions in natural settings to better analyze and improve team performance. We summarize research conducted in developing a data-driven approach to analyzing team communications within the context of Surfaced Piloting and Navigation (SPAN) training for submariners. Using Dynamic Bayesian Networks (DBNs), this approach created predictive models of communication patterns that emerge from the team in different contexts. Based upon data collection conducted in the lab and within live submarine crew training, our results demonstrate the robust nature of DBNs by still identifying key communication events even when teams altered their speaking patterns during these events to accommodate for novel changes in the scenario. Introduction Complex tasks that demand a coordinated effort benefit from the capacity of a team to pool resources via an exchange of information and coordinated action, though the effectiveness of a team may be contingent on a variety of factors [1]. Team effectiveness has particular impact within a military setting, as within combat situations the performance of a group has a direct bearing on the survival of the group and those dependent on them [2], situation that holds true when considering the success of naval operations [3]. In an attempt to determine the critical elements that make up an effective team in a military setting, variables related to team effectiveness have been examined from a variety of perspectives, including team cohesiveness (i.e., shared interpersonal closeness and group goal-orientation) [4], [5] collective orientation [1], shared mental models (i.e., synthesis of input from individual team members) [6], [7], [8], team selection and composition (e.g., the skills possessed by the individual team members, how long the members have been working together) [5], [6], [9], quality of decisions made by commanders [10], [11], cognitive readiness and adaptive decision making at the group level [12], training adequacy [5], the workload involved [13], and even neurophysiologic synchrony between team members, as assessed via electroencephalogram [14]. In the context of naval operations, assessment of the quality of teamwork has proven difficult, with such assessments relying on the observations of subject matter experts, skilled instructors, or a self-evaluation within teams during live or simulated exercises [3]. These judgments are subjective by their very nature, leading to a potential lack of consistency with regard to the quality of assessment. This issue has been recognized, and there have been attempts to resolve it, such as through outcome-based Copyright