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

Abstract

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 better exploit learned information and decrease the costs of finding new resources, but excessive social learning can lead to over-exploitation and too little 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 modelled 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 favoured 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 when individual exploration is limited. 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 of 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.

Evolution of explorative and exploitative search strategies in collective foraging

“…5b) close to the theoretical optimum (shown in dashed lines in Fig. 5b) as estimated from the model reported by Garg et al 2022.…”

“…Previous studies have shown that with little or no social learning (i.e, solitary foraging), individual search strategies that balance explorative and exploitative movements with the Lévy exponent, µ ≈ 2 are optimal for individual and group-level search efficiencies [Viswanathan et al, 2008, Garg et al, 2022, Bartumeus et al, 2016. We similarly In the presence of social information and high levels of overall social learning, our previous model [Garg et al, 2022] showed that explorative individual search strategies can maximize group-level search efficiencies. The model also showed that exploratory search can increase the rate at which resources are discovered while decreasing excessive aggregation at patches.…”

“…Such amplifying effects of large group sizes could intensify if the foraging group is composed of slow, exploitative foragers that are likely to stay near previously found patches for longer durations [Garg et al, 2022].…”

“…For example, an increase in group size can amplify social information [Detrain and Deneubourg, 2008], especially in rich environments, and accelerate the rate at which patches are depleted. Such amplifying effects of large group sizes could intensify if the foraging group is composed of slow, exploitative foragers that are likely to stay near previously found patches for longer durations [Garg et al, 2022].…”

Evolutionary emergence of collective intelligence in large groups of students

“… Mao et al (2016) point out the need of investigating the behavior of such large groups in experiments featuring a limited number of variables, and taking place in natural contexts that reflects the complexity of real-life situations. Analyses have thus been conducted on simulations that study a group’s degree of heterogeneity ( Dai et al, 2020 ), as well as a group’s tendency to consensus ( De Vincenzo et al, 2017 ; Massari et al, 2019 ) the social learning and the group size ( Garg et al, 2022 ). In experimental situations and natural contexts, Tinati et al (2014) studied the problem of collaborative creation in their analysis of citizen science projects and the different levels of involvement of volunteers.…”