The content of what we learn shapes the evolution of human culture and society. In this thesis, we have quantified the influence of content-dependent biases in social learning strategies. Our theoretical framework combines agent-based models and Bayesian inference to measure content-dependent biases in large-scale social learning strategies. Our first empirical study measures the impact of social transmission biases in Twitter. The novelty of the second study is two-fold: ours is one of the rare uses of computational modelling in historical Roman Studies and one of the first tests of the impact of success bias across large spatial and temporal scales.
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