Human dynamics: computation for organizations

The human dynamics group at the MIT Media Laboratory proposes that active pattern analysis of face-to-face interactions within the workplace can radically improve the functioning of the organization. There are several different types of information inherent in such conversations: interaction features, participants, context, and content. By aggregating this information, high-potential collaborations and expertise within the organization can be identified, and information efficiently distributed. Examples of using wearable machine perception to characterize face-to-face interactions and using the results to initiate productive connections are described, and privacy concerns are addressed.