This paper demonstrates a novel approach that combines generative models of organizational dynamics and sensor network data with a stochastic method. Generative models specify how organizational performance is related to who interacts with whom and who performs what. Sensor network data track who interacts with whom and who performs what within an organization, and the stochastic methodology fits multi-agent models to data through the Monte Carlo method. The data set used in this paper documents how employees in a data service center handle tasks with different difficulty levels - tracked with sociometric badges for one month - and documents links between performance and behavior. This paper demonstrates the potential for improving organizational dynamics with body sensor network data, and therefore also shows the need to systematically benchmark differential organizational dynamics models on data sets for different types of organizations.

Sociometric feedback visualizes social signals among group members to increase their awareness of their communication patterns. We deployed the Meeting Mediator, a real-time sociometric feedback system to groups participating in two rounds of a social dilemma task: in one round, all members were co-located and in the other round, the members were geographically distributed. Laboratory results show that the sociometric feedback successfully increases the speaking time and the frequency of turn transitions of groups that are initially distributed and later co-located, and also leads to a higher cooperation rate, increasing the overall earnings of these groups. In addition, the sociometric feedback helps groups have a more consistent pattern of behavior before and after a change in their geographic distribution.

The availability of mobile sociometric sensors allows Computer-Supported Cooperative Work (CSCW) designers the possibility to enhance online meeting support through automatic recognition of conversational context. This paper addresses the task of discriminating one conversational context against another, specifically brainstorming from decision-making interactions using easily computable nonverbal behavioral cues. We hypothesize that the difference in the dynamics between brainstorming and decision-making discussions is significant and measurable using speech activity based nonverbal cues. We employ a set of nonverbal cues to characterize the entire group by the aggregation (both temporal and person-wise) of their nonverbal behavior. Our results on a dataset collected using privacy-sensitive sociometric badges show that the floor-occupation patterns in a brain-storming interaction are different from a decision-making interaction and we can obtain a discrimination accuracy as high as 87.5%.

Creativity is an important ingredient in problem solving, and problem solving is an important activity for both individuals and societies. This paper discusses our novel approach of discovering the structure of problem-solving creativity with statistical methods, and mapping the interaction patterns of group processes to their performances through the discovered creativity structure. Our discussion is based on a lab study data set using the meeting mediator system through which we collected objective quantitative data. We hope our findings and quantitative approach could be applied to many other real-world problem-solving processes and to helping people.

We present the design, implementation, and deployment of a wearable computing platform for measuring and analyzing human behavior in organizational settings. We propose the use of wearable electronic badges capable of automatically measuring the amount of face-to-face interaction, conversational time, physical proximity to other people, and physical activity levels in order to capture individual and collective patterns of behavior. Our goal is to be able to understand how patterns of behavior shape individuals and organizations. By using on-body sensors in large groups of people for extended periods of time in naturalistic settings, we have been able to identify, measure, and quantify social interactions, group behavior, and organizational dynamics. We deployed this wearable computing platform in a group of 22 employees working in a real organization over a period of one month. Using these automatic measurements, we were able to predict employees' self-assessments of job satisfaction and their own perceptions of group interaction quality by combining data collected with our platform and e-mail communication data. In particular, the total amount of communication was predictive of both of these assessments, and betweenness in the social network exhibited a high negative correlation with group interaction satisfaction. We also found that physical proximity and e-mail exchange had a negative correlation of r = -0.55 (p 0.01), which has far-reaching implications for past and future research on social networks.

Mischief is a system to support traditional classroom practices between a remote instructor and a group of collocated students. Meant for developing regions, each student in the classroom is given a mouse and these are connected to a single machine and shared display. We present observations of teaching practices in rural Chinese classrooms that led to Mischief's design. Mischief's user interface, with which scores of collocated students can interact simultaneously, supports anonymous responses, communicates focus of attention, and maintains the role of the instructor. Mischief is an extensible platform in which Microsoft PowerPoint slides, used commonly in developing regions, are made interactive. We setup a controlled environment where Mischief was used by classrooms of children with a remote math instructor. The results from the study provided insight into the usability and capacity of the system to support traditional classroom interactions. These observations were also the impetus for a redesign of several components of Mischief and are also presented. These findings contribute both a novel system for synchronous distance education in an affordable manner and design insights for creators of related systems.

We present the Meeting Mediator (MM), a real-time portable system that detects social interactions and provides persuasive feedback to enhance group collaboration. Social interactions is captured using Sociometric badges [17] and are visualized on mobile phones to promote behavioral change. Particularly in distributed collaborations, MM attempts to bridge the gap among the distributed groups by detecting and communicating social signals. In a study on brainstorming and problem solving meetings, MM had a significant effect on overlapping speaking time and interactivity level without distracting the subjects. The Sociometric badges were also able to detect dominant players in the group and measure their influence on other participants. Most interestingly, in groups with one or more dominant people, MM effectively reduced the dynamical difference between co-located and distributed collaboration as well as the behavioral difference between dominant and non-dominant people. Our system encourages change in group dynamics that may lead to higher performance and satisfaction. We envision that MM will be deployed in real-world organizations to improve interactions across various group collaboration contexts.

As autonomous robots collaborate with people on tasks, the questions "who deserves credit?" and "who is to blame?" are no longer simple. Based on insights from an observational study of a delivery robot in a hospital, this paper deals with how robotic autonomy and transparency affect the attribution of credit and blame. In the study, we conducted a 2times2 experiment to test the effects of autonomy and transparency on attributions. We found that when a robot is more autonomous, people attribute more credit and blame to the robot and less toward themselves and other participants. When the robot explains its behavior (e.g. is transparent), people blame other participants (but not the robot) less. Finally, transparency has a greater effect in decreasing the attribution of blame when the robot is more autonomous