Abstract:

Previous studies suggest that social learning strategies can be adaptive, promoting collective intelligence because it filters out seemingly unfavourable behavioural options from individual exploration. Such adaptive filtering functions well, as long as individuals share the same fitness landscape or are tasked with the same decision problems. However, this is rarely the case in real life, with human populations having diverse needs, circumstances, and goals. In such heterogeneous populations, exactly copying others may not guarantee acquiring adaptive behaviours, and socially-guided exploration may backfire. To address this issue, we added social correlations to a spatially-correlated multi-armed bandit task, which lets us operationalize differences in taste while maintaining a common ground truth that applies to all individuals. We introduce a novel model, Social Generalization (SG), which generalises social observations as noisier information than privately obtained payoff information. Evolutionary simulations show that SG out-competes existing simpler social learning models including both the decision biassing and value shaping. The results from online experiments suggest that humans are best fit by the SG model, and benefited from socially-guided exploration despite the individual differences. Our study shows that social learning is adaptive in a wider range of circumstances than previously assumed, even in a heterogeneous population.

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