Individual exploration and selective social learning: Balancing exploration-exploitation trade-offs in collective foraging

Search requires balancing exploring for more options and exploiting the ones previously found. Individuals foraging in a group face another trade-off: whether to engage in social learning to exploit the solutions found by others or to solitarily search for unexplored solutions. Social learning can decrease the costs of finding new resources, but excessive social learning can decrease the exploration for new solutions. We study how these two trade-offs interact to influence search efficiency in a model of collective foraging under conditions of varying resource abundance, resource density, and group size. We modeled individual search strategies as Lévy walks, where a power-law exponent (μ) controlled the trade-off between exploitative and explorative movements in individual search. We modulated the trade-off between individual search and social learning using a selectivity parameter that determined how agents responded to social cues in terms of distance and likely opportunity costs. Our results show that social learning is favored in rich and clustered environments, but also that the benefits of exploiting social information are maximized by engaging in high levels of individual exploration. We show that selective use of social information can modulate the disadvantages of excessive social learning, especially in larger groups and with limited individual exploration. Finally, we found that the optimal combination of individual exploration and social learning gave rise to trajectories with μ ≈ 2 and provide support for the general optimality such patterns in search. Our work sheds light on the interplay between individual search and social learning, and has broader implications for collective search and problem-solving.

Animate Orientation Based on Visual Landmarks and Scene Recognition

A biologically-inspired approach to robot route following is presented. The ant of the genus Formica rufa (a red forest ant) is used as a model species. These ants actively use collective foraging, unlike many other ant species. The scout ant remembers the route to food and can transmit information about the food location to foraging ants. Foragers can independently reach this place using this data and return home. The basis of the proposed method is the memorization the way by visual landmarks and fuzzy control. The animate path description model consists of a sequence of scenes and includes compass to account for the direction. The behavior of the animate-scout is implemented using an algorithm that simulates the foraging behavior of ants. The animate-forager performs actions to reproduce the route, applying the developed set of rules. The forager behavior is based on the same principles as that of a scout. But the scout remembers the scenes, and the forager recognizes and compares the visible scene and the scene from the route description. The actions of animates are presented in the form of elementary behavioral procedures. Each behavioral procedure is implemented using a finite state machine. The experiments for solving the foraging problem were carried out using a modeling system based on the ROS framework. The simulation results confirm the effectiveness of the proposed method. The method does not require large computing power and advanced sensory capabilities from the robot. It can also be used in reconnaissance and patrol tasks.

Specialization and selective social attention establishes the balance between individual and social learning

A key question individuals face in any social learning environment is when to innovate alone and when to imitate others. Previous simulation results have found that the best performing groups exhibit an intermediate balance, yet it is still largely unknown how individuals collectively negotiate this balance. We use an immersive collective foraging experiment, implemented in the Minecraft game engine, facilitating unprecedented access to spatial trajectories and visual field data. The virtual environment imposes a limited field of view, creating a natural trade-off between allocating visual attention towards individual innovation or to look towards peers for social imitation. By analyzing foraging patterns, social interactions (visual and spatial), and social influence, we shine new light on how groups collectively adapt to the fluctuating demands of the environment through specialization and selective imitation, rather than homogeneity and indiscriminate copying of others.

KINETIC PHASE TRANSITION IN HONEYBEE FORAGING DYNAMICS: SYNERGY OF INDIVIDUAL AND COLLECTIVE

We consider a honeybee colony as a dynamical system gathering information from an environment and accordingly adjusting its behavior. Collective foraging behavior is shown to be triggered by the change of either colony size or profitability of exploited nectar sources. The collective mode provides greater productivity compared to the individual one. The latter does not diminish the importance of individual behavior that ensures the adaptivity of the system. Thus, the transition from the phase of individual behavior to a more complex phase, combining both individual and collective modes, provides the most effective scenario of honeybee colony foraging.

Comparison of solitary and collective foraging strategies of Caenorhabditis elegans in patchy food distributions

Collective foraging has been shown to benefit organisms in environments where food is patchily distributed, but whether this is true in the case where organisms do not rely on long-range communications to coordinate their collective behaviour has been understudied. To address this question, we use the tractable laboratory model organism Caenorhabditis elegans , where a social strain ( npr-1 mutant) and a solitary strain (N2) are available for direct comparison of foraging strategies. We first developed an on-lattice minimal model for comparing collective and solitary foraging strategies, finding that social agents benefit from feeding faster and more efficiently simply owing to group formation. Our laboratory foraging experiments with npr-1 and N2 worm populations, however, show an advantage for solitary N2 in all food distribution environments that we tested. We incorporated additional strain-specific behavioural parameters of npr-1 and N2 worms into our model and computationally identified N2's higher feeding rate to be the key factor underlying its advantage, without which it is possible to recapitulate the advantage of collective foraging in patchy environments. Our work highlights the theoretical advantage of collective foraging owing to group formation alone without long-range interactions and the valuable role of modelling to guide experiments. This article is part of the theme issue ‘Multi-scale analysis and modelling of collective migration in biological systems'.