Evolving conformity: Conditions favouring conformist social learning over random copying. (May 2019)

• Record Type: Journal Article
• Authors: Grove, Matt
• Is Part Of: Cognitive systems research. Volume 54(2019)
• Page Start: 232
• Page End: 245
• Publication Date: 2019-05
• Subjects: Social learning -- Asocial learning -- Conformity -- Random copying -- Reproductive rate -- Environmental change; Cognition -- Periodicals; Cognitive engineering (System design) -- Periodicals; Artificial intelligence -- Periodicals; 153.05
• DOI: 10.1016/j.cogsys.2018.10.012
• Languages: English
• ISSNs: 1389-0417

Abstract

Abstract: There is a growing interest in the relative benefits to the learner of the different social learning strategies used to transmit information between conspecifics, and in the extent to which they require input from individual learning. To date, theoretical models have tended to examine the success of particular strategies in relation to specific parameters or circumstances. This study employs individual-based simulations to derive the optimal proportion of individual learning that co-exists with random copying and conformist social learning strategies in populations experiencing wide-ranging variation in levels of environmental change, reproductive turnover, learning error, and individual learning costs. Predictions derived from the literature – that optimal levels of individual learning will be higher for both strategies when the rate of environmental change is higher, and when reproductive turnover and individual learning costs are lower, are supported. Contrary to the theoretical prediction, optimal levels of individual learning are sometimes higher under higher levels of learning error, particularly when reproductive rates are low. Results for the two strategies are qualitatively similar, but demonstrate numerous parameter combinations under which random copying is fitter than conformist social learning. Contrary to established expectations, the strategy employing the lesser proportion of individual learning is not always the fittest.