Individuals in social groups benefit from having accurate information about their group mates. Animals can use different strategies to infer or learn about each other’s underlying quality, such as rules-of-thumb or individual recognition. Few studies have addressed the tradeoffs amongst such strategies. We investigate these tradeoffs using an agent-based model, where animals assess each other using either signal-based assessment strategies or individual recognition. We find that, for all strategies, quality assessments are more accurate when groups are small and memory is long, and that learned signal-based strategies are favored in species that live in large groups, have short memories, and do not have much time to invest in learning. We also find that learning about a signal is as good or better than using an innate rule-of-thumb, except when groups are very large and memory is extremely short, indicating that learned strategies may be much more widespread than commonly assumed. These results generate predictions about when learned strategies may be expected to evolve that can then be tested with empirical data.
