Abstract
Evolving in groups can either enhance or reduce an individual’s task performance. Still, we know little about the factors underlying group performance, which may be reduced to three major dimensions: (a) the individual’s ability to perform a task, (b) the dependency on environmental conditions, and (c) the perception of, and the reaction to, other group members. In our research, we investigated how these dimensions interrelate in simulated evolution experiments using adaptive agents equipped with Markov brains (“animats”). We evolved the animats to perform a spatial-navigation task under various evolutionary setups. The last generation of each evolution simulation was tested across modified conditions to evaluate and compare the animats’ reliability when faced with change. Moreover, the complexity of the evolved Markov brains was assessed based on measures of information integration. We found that, under the right conditions, specialized animats could be as reliable as animats already evolved for the modified tasks, and that reliability across varying group sizes correlated with evolved fitness in most tested evolutionary setups. Our results moreover suggest that balancing the number of individuals in a group may lead to higher reliability but also lower individual performance. Besides, high brain complexity was associated with balanced group sizes and, thus, high reliability under limited sensory capacity. However, additional sensors allowed for even higher reliability across modified environments without a need for complex, integrated Markov brains. Despite complex dependencies between the individual, the group, and the environment, our computational approach provides a way to study reliability in group behavior under controlled conditions. In all, our study revealed that balancing the group size and individual cognitive abilities prevents over-specialization and can help to evolve better reliability under unknown environmental situations.
Highlights
Intelligence is the ability to adapt to changes
Several studies have investigated intelligence and knowledge on the group level, and some have modelled groups of individuals as single agents (e.g., [11,12,13,14,15]). These studies have their origins in a variety of disciplines and have in common that they seek to elucidate the dynamics between group members
We simulated the evolution of artificial organisms (“animats”) with diverse cognitive architectures for 10,000 generations under various conditions
Summary
Intelligence is the ability to adapt to changes. According to this prevalent perspective, possessing general intelligence [1,2] enables one to perform a task correctly under already known conditions, and to perform well under unexpected conditions. In natural environments intelligent behavior is dependent on the (maybe limited) intelligence of the individual organism, and involves interactions with the social and physical environment [3,4,5]. Several studies have investigated intelligence and knowledge on the group level, and some have modelled groups of individuals as single agents (e.g., [11,12,13,14,15]). These studies have their origins in a variety of disciplines and have in common that they seek to elucidate the dynamics between group members. Our understanding of how an individual actor in a group evolves intelligent behavior and reliability is still limited
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