Understanding spreading dynamics can help predict how a highly contagious disease can infect an entire population, how ideas propagate in societies, and how successful marketing campaigns emerge. In this study, we develop an agent-based model to highlight the role of individual heterogeneity in defining and shaping spreading dynamics. We select the case study of a virus spreading. The proposed model creates proximity networks in an urban environment, which is based on the city of Brussels. Various implementations of individual features and decision heterogeneity were examined. Our findings highlight the impact of individual irrationality and the size of social networks on emergent spreading and on the efficiency of local interventions.

Robot swarms provide a potential solution to a wide range of collective decision problems due to their large scale, which enables them to explore large environments, and their local intensive communication, which allows information sharing. Despite this, the majority of decision-making processes are designed to solve a single-step decision process. The goal of this study is to design robot swarms that are able to develop proper collective responses to decisions that emerge in parallel in the robot group. We aim to challenge the collective dynamics in order to examine the extent to which it would be possible to allow two decisions correspondingly related to parallel development. We consider both best-of-n and symmetry-breaking decisions and investigate the performance of two well-known voting mechanisms: the majority rule and the voting model. Our results confirm the possibility to build up a proper response to an emerging decision and highlight the key parameters that influence its success.

Social feedback plays a significant role in shaping individual behavior across all types of social communities. When the social network approves or disapproves an individual's behavior, attitudes are formed at individual and group levels. In this paper, we investigate how social feedback can influence an altruistic attitude in the context of resource sharing. We use multi-agent simulations to model static and dynamic interactions through which social feedback is obtained. In particular, we examine how the structure of the interaction network can affect the attitude dynamics and the resource distribution across the group. Our results highlight the key role of network topological features such as degree, directionality and the presence of hubs. Generally, a dominant altruistic behavior leads to a more uniform resource distribution. Surprisingly, for some topologies, such as scale-free networks, individuals with the largest resource count were consistently above-average altruistic.

In a collaborative society, sharing information is advantageous for each individual as well as for the whole community. Maximizing the number of agent-to-agent interactions per time becomes an appealing behavior due to fast information spreading that maximizes the overall amount of shared information. However, if malicious agents are part of society, then the risk of interacting with one of them increases with an increasing number of interactions. In this paper, we investigate the roles of interaction rates and times (aka edge life) in artificial societies of simulated robot swarms. We adapt their social networks to form proper trust sub-networks and to contain attackers. Instead of sophisticated algorithms to build and administrate trust networks, we focus on simple control algorithms that locally adapt interaction times by changing only the robots’ motion patterns. We successfully validate these algorithms in collective decision-making showing improved time to convergence and energy-efficient motion patterns, besides impeding the spread of undesired opinions.