The motivation to belong is a fundamental social and psychological need that influences individuals’ well-being and behavior in society. There is currently significant research activity in this field, driven by increasing interest in areas such as social robotics, the Internet of Things (IoT), and complex systems. This article presents a computational model that quantifies a weighted Belonging Score ( B ) as a function of seven socio-cognitive features: group identity, social interaction, acceptance/rejection, social memory, reciprocity, reputation, and utility. The aim is to propose a model that explains how agents’ motivation to belong emerges from their social interactions. The behavior of each model component is formally defined through mathematical functions. The inputs of these functions are normalized and dynamically updated in real time based on agent-agent interactions, ensuring B ∈ − 1 , 1 and enabling comparability across contexts. To validate the proposed model, realistic, nuanced, and context-sensitive scenarios were simulated using a large language model (LLM). In this setup, interactions among agents naturally vary the values of the variables determining each agent’s internal belonging score. Consequently, agents dynamically assess their perception of inclusion or exclusion within the group.
Moreno et al. (Sun,) studied this question.