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      Reproductive skew affects social information use

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          Abstract

          Individuals vary in their propensity to use social learning, the engine of cultural evolution, to acquire information about their environment. The causes of those differences, however, remain largely unclear. Using an agent-based model, we tested the hypothesis that as a result of reproductive skew differences in energetic requirements for reproduction affect the value of social information. We found that social learning is associated with lower variance in yield and is more likely to evolve in risk-averse low-skew populations than in high-skew populations. Reproductive skew may also result in sex differences in social information use, as empirical data suggest that females are often more risk-averse than males. To explore how risk may affect sex differences in learning strategies, we simulated learning in sexually reproducing populations where one sex experiences more reproductive skew than the other. When both sexes compete for the same resources, they tend to adopt extreme strategies: the sex with greater reproductive skew approaches pure individual learning and the other approaches pure social learning. These results provide insight into the conditions that promote individual and species level variation in social learning and so may affect cultural evolution.

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          Most cited references63

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          Social learning strategies.

          In most studies of social learning in animals, no attempt has been made to examine the nature of the strategy adopted by animals when they copy others. Researchers have expended considerable effort in exploring the psychological processes that underlie social learning and amassed extensive data banks recording purported social learning in the field, but the contexts under which animals copy others remain unexplored. Yet, theoretical models used to investigate the adaptive advantages of social learning lead to the conclusion that social learning cannot be indiscriminate and that individuals should adopt strategies that dictate the circumstances under which they copy others and from whom they learn. In this article, I discuss a number of possible strategies that are predicted by theoretical analyses, including copy when uncertain, copy the majority, and copy if better, and consider the empirical evidence in support of each, drawing from both the animal and human social learning literature. Reliance on social learning strategies may be organized hierarchically, their being employed by animals when unlearned and asocially learned strategies prove ineffective but before animals take recourse in innovation.
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            The cultural niche: why social learning is essential for human adaptation.

            In the last 60,000 y humans have expanded across the globe and now occupy a wider range than any other terrestrial species. Our ability to successfully adapt to such a diverse range of habitats is often explained in terms of our cognitive ability. Humans have relatively bigger brains and more computing power than other animals, and this allows us to figure out how to live in a wide range of environments. Here we argue that humans may be smarter than other creatures, but none of us is nearly smart enough to acquire all of the information necessary to survive in any single habitat. In even the simplest foraging societies, people depend on a vast array of tools, detailed bodies of local knowledge, and complex social arrangements and often do not understand why these tools, beliefs, and behaviors are adaptive. We owe our success to our uniquely developed ability to learn from others. This capacity enables humans to gradually accumulate information across generations and develop well-adapted tools, beliefs, and practices that are too complex for any single individual to invent during their lifetime.
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              Demography and Cultural Evolution: How Adaptive Cultural Processes can Produce Maladaptive Losses: The Tasmanian Case

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                Author and article information

                Journal
                R Soc Open Sci
                R Soc Open Sci
                RSOS
                royopensci
                Royal Society Open Science
                The Royal Society
                2054-5703
                July 2019
                3 July 2019
                3 July 2019
                : 6
                : 7
                : 182084
                Affiliations
                [1 ]Department of Biology, University of Pennsylvania , Philadelphia, PA, USA
                [2 ]School of Earth and Environmental Sciences, Faculty of Science and Engineering, University of Manchester , Manchester, UK
                [3 ]Department of Ecology & Genetics, Uppsala University , Uppsala, Sweden
                Author notes
                Author for correspondence: Marco Smolla e-mail: smolla@ 123456sas.upenn.edu

                Electronic supplementary material is available online at https://dx.doi.org/10.6084/m9.figshare.c.4542083.

                Author information
                http://orcid.org/0000-0001-6367-8765
                http://orcid.org/0000-0003-2000-7966
                http://orcid.org/0000-0002-7135-4880
                Article
                rsos182084
                10.1098/rsos.182084
                6689588
                31417699
                3d3a11f0-b403-40d4-a47a-5084725cf15d
                © 2019 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 11 December 2018
                : 6 June 2019
                Funding
                Funded by: Army Research Office, http://dx.doi.org/10.13039/100000183;
                Award ID: W911NF-12-R-0012-03
                Categories
                1001
                70
                60
                14
                Biology (Whole Organism)
                Research Article
                Custom metadata
                July, 2019

                cultural evolution,reproductive skew,social learning,competition,agent-based model

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