An experimental framework for exploring the social dynamics of a real-time physical network composed of human participants using beacon technology

Abstract

We communicate information by displaying a set of social behaviors conveyed through facial expressions, audible inputs, etc. Our purpose is to develop an experimental framework for understanding human social dynamics in a physically-situated and real-time decision-making environment using IoT. Each participant has a beacon integrated in a Raspberry Pi Zero device, which uses Bluetooth Low Energy technology to exchange signals between other devices. Each participant displaces and shares their current game-theoretical strategy with the neighboring participants on a small LCD screen mounted on the device. They are asked to maximize the accumulated score which is measured from all the payoffs received from the neighbors, scaled by how far apart they are, meaning that the distance between participants reflects their social closeness. We used a zone-based positioning approach which involved identifying the neighboring zone in which a participant is currently present based on the received signal strength (RSSI) from the beacon, and can be classified as immediate, near or far from it, which gives us an estimate of the social network of the participants. We show the applicability of this framework by conducting preliminary experiments with a few participants which confirmed the interactivity feature we seek.