Behavioural Contagion in Human and Artificial Multi-agent Systems: A Computational Modeling Approach

Maryam Karimian, Fabio Reeh, Asieh Daneshi, Marcel Brass, Pawel Romanczuk

Abstract: This study employs a modified formulation of the second-order Drift-Diffusion model to investigate how the interaction between environmental and social cues influences individual decision-making in a binary choice scenario. Environmental information is represented as stochastic cues, often biased towards one of the choices, while social information is conveyed through signals from a group of identical agents making random decisions. The model incorporates simplified human perceptual characteristics via a visual network of social interactions, which considers perceptual limitations due to physical distances and visual occlusions. Model parameters and assumptions are informed by an ongoing behavioural experiment on behavioural contagion, conducted in human and artificial multi-agent systems using virtual reality. The stochastic evolution of decision states in response to environmental and social input mirrors the behavioural choices of human participants, who respond to stimuli presented in the virtual reality environment and social cues from a group of virtual agents. Manipulating the size and density of the group revealed that larger group sizes and lower densities lead to greater alignment of individual decisions with social cues, accompanied by shorter and more homogeneous response times and reduced accuracy. These findings afford preliminary insights into the behavioural experiment. With reciprocal informative exchange from experimental findings, this study would contribute to enhanced realism in future steps.