Collective decision-making for dynamic environments with visual occlusions

For decades, both empirical and theoretical models have been proposed to explain the patterns and mechanisms of collective decision-making (CDM). The most-studied CDM scenario is the best-of-n problem in a static environment. However, natural environments are typically dynamic. In dynamic environments, the visual occlusions produced by other members of a large-scale group are also common. Hence, some agents of a group are less informed than others, and their state uncertainties increase. This paper develops a new model referred to as the generalized Ising model with dynamic confidence (GIM-C) to reduce the state uncertainty induced by visual occlusions. The proposed model first estimates the expected rewards of possible actions with dynamic confidence weighting. It then gives the probability of choosing each action based on the generalized Ising model with an external field defined by the last stage’s results. Numerical simulations demonstrate that GIM-C shares the key feature of social cohesion with previous CDM models. Furthermore, in order to illustrate the efficiency of the proposed GIM-C, the collecting foraging task is considered, where a large-scale group of agents is required to obtain rewards with the presence of a dynamic predator and visual occlusions. The good performance of GIM-C in the collecting foraging task demonstrates that dynamic confidence weighting is efficient in reducing the state uncertainty introduced by visual occlusions. The proposed GIM-C also demonstrates the importance of enhancing the influence of informed agents in CDM problems in a dynamic environment with visual occlusions.