Recent minimal modeling work, following a dynamical approach to the phenomenology of body memory and the en-active approach to cognitive science, has served as a computational proof of concept in support of conceiving body memory as a relational property that arises from the history of interactions of a whole brain-body-environment system, rather than as contents within the brain. Particularly, some of these studies have been focused on investigating the minimal type of social memory, i.e., dyadic body memory, using the so-called embodied dyadic interaction models. Here, we expand the related work on dyadic body memory by employing a sample of the embodied dyadic interaction models, which has demonstrated, in line with previous related work in social interaction, that by evolving agent pairs to maximize their neural complexity, they consistently display mutually coordinated behavior, which cannot be possible to achieve in isolation. We aim to investigate the emergent behavioral patterns during the encounters between agents with “different” (i.e., because of proceeding from interactive or isolated primary environments) minimal social ontogenies. For this purpose, we propose a re-definition of the concept of social ontogeny as the shaping of “being social”, which involves body memory, as being arisen from shared histories of social interactions, and present three simulation experiments. Our results revealed the emergence of three core behavioral patterns: (1) mutually coordinated dyads, (2) “exaggerated-shy” dyads, and (3) limited-coordination dyads. An analysis of agents’ neural and behavioral complexity is also performed. We then draw loose analogies between our findings and real-world examples.

Social network analysis and agent-based modeling are two approaches used to study biological and artificial multi-agent systems. However, so far there is little work integrating these two approaches. Here we present a first step toward integration. We developed a novel approach that allows the creation of a social network on the basis of measures of interactions in an agent-based model for purposes of social network analysis. We illustrate this approach by applying it to a minimalist case study in swarm robotics loosely inspired by ant foraging behavior. For simplicity, we measured a network’s inter-agent connection weights as the total number of interactions between mobile agents. This measure allowed us to construct weighted directed networks from the simulation results. We then applied standard methods from social network analysis, specifically focusing on node centralities, to find out which are the most influential nodes in the network. This revealed that task allocation emerges and induces two classes of agents, namely foragers and loafers, and that their relative frequency depends on food availability. This finding is consistent with the behavioral analysis, thereby showing the compatibility of these two approaches.