MIT Media Lab, E14-633
We propose a sensor-based organizational design and engineering approach that combines behavioral sensor data with other sources of information–such as e-mail, surveys, and performance data–in order to design interventions aimed at improving organizational outcomes. The proposed system combines sensor measurements, pattern recognition algorithms, simulation and optimization techniques, social network analysis, and feedback mechanisms that aim at continuously monitoring and improving individual and group performance. We describe the system's general specifications and discuss preliminary studies that we have conducted in several organizations using an experimental sensing platform. We have deployed our system under naturalistic settings in several real organizations. We present several case studies that show that it is possible to automatically capture group dynamics, find the relationship between organizational behavior(s) and individual and group subjective and objective outcomes (such as job satisfaction, quality of group interaction, stress, productivity, and group performance). We propose the use of static and dynamic simulation models of group behavior captured using sensors, in order to optimize group configurations that maximize individual and group outcomes, both in terms of personal satisfaction and organizational performance.
Host/Chair: Alex 'Sandy' Pentland
Irving Wladawsky-Berger, Peter A. Gloor