New Analysis and Modeling Directions in Social Interaction Science (Hardcover)

There is much scientific literature on multimodal social interaction between human and/or virtual agents, and related multimodal synchrony analysis and behavioural adaptivity within the interaction such as affiliation and bonding. However, due to its strongly cyclical character, complexity, and adaptivity, mathematical formalisation and computational simulation of it is a nontrivial topic. Moreover, the subjective, agent-oriented perspective on synchrony analysis has not yet received much attention in the literature. This book provides from an agent-oriented perspective an overview of recent work on mathematical formalisation and computational simulation of multimodal interaction, subjective multimodal synchrony analysis, and related behavioural adaptivity underlying, for example, affiliation, liking and bonding. It does so by exploiting the possibilities of multi-time-scale, multi-adaptive, multi-level self-modeling network models for agents to analyse and model these dynamic and adaptive processes formally. In this way it contributes to the development of a solid basis for social interaction science.

There is much scientific literature on multimodal social interaction between human and/or virtual agents, and related multimodal synchrony analysis and behavioural adaptivity within the interaction such as affiliation and bonding. However, due to its strongly cyclical character, complexity, and adaptivity, mathematical formalisation and computational simulation of it is a nontrivial topic. Moreover, the subjective, agent-oriented perspective on synchrony analysis has not yet received much attention in the literature. This book provides from an agent-oriented perspective an overview of recent work on mathematical formalisation and computational simulation of multimodal interaction, subjective multimodal synchrony analysis, and related behavioural adaptivity underlying, for example, affiliation, liking and bonding. It does so by exploiting the possibilities of multi-time-scale, multi-adaptive, multi-level self-modeling network models for agents to analyse and model these dynamic and adaptive processes formally. In this way it contributes to the development of a solid basis for social interaction science.