An Iot-Based Experimental Framework for Studying Continuous Social Dynamics in a Game-Theoretical and Face-to-Face Situation With Human Participants

Abstract

COVID-19 studies reveal negative correlations between computer-mediated communication and well-being, highlighting the role of face-to-face interaction in understanding human behavior. We present an IoT-based framework for studying face-to-face interactions in which participants use microcomputers to interact via Bluetooth signals that represent their social closeness. Participants adjust their interaction strategy in real time via a button on the device, which is displayed on the screen along with the accumulated score. We simulated a situation similar to the Social Particle Swarm model and its online (web-based) experimental variant, where formation and collapse of cooperative clusters emerged. This paper presents a framework and validates its performance through three experiments: one focused on capturing social relationship dynamics, the second comparing behavioral patterns between face-to-face and web-based conditions, and the final experiment testing the framework with a larger group. Implications for the importance of information visibility of others for active and cooperative social dynamics will be discussed.

Editorial for the Special Issue: Predictive Mind: From Neuroscience to Humanities

“…Their article shows that the collective behaviors of agents (particles) acting on the basis of game theory with minimal predictions approximate real human social phenomena well. Elhamer et al (2023) validate and extend the implications from this computational model through experiments with human participants.…”