Understanding the Emergence of Pro-Social Behaviourin Dynamic Social Networks

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In social dilemmas, where individuals make use of collective resources, "co-operators" pay a cost to improve public goods whereas "defectors" free-ride, reaping benefit without incurring any costs. While defection may be the individually rational strategy in one-shot interactions, we know that cooperation can be sustained in repeated games, where complex strategies emerge to counter defectors. In this project we want to understand what happens in dynamic social networks, where individuals facing a social dilemma can choose who to interact with, and can observe(and potentially imitate) others' behaviour. So, when do social networks promote cooperation? is it the ease of making connections, the type of interaction, or the similarity we have with others that motivate us to look beyond our self-interest? In recent theoretical analysis we found that the ratio between the speed of imitation and partner selection is key to promote cooperation. Our results are however still limited to simple partner selection and imitation rules and are still not linked with empirical findings i.e., what humans do in practice. The objective of this project is to devise a theoretical model of learning-based cooperation on social networks and link it to behavioural data we have collected, with the goal of identifying explanations for the patterns of real cooperation and defection we see in the human data. This is an interdisciplinary challenge that uses both analytical and experimental methodologies to come up with a rigorous and empirically backed theoretical model explaining when pro-social behaviour can emerge in dynamic social networks. The project undertaker will employ theoretical analysis, i.e., through the study of dynamical systems and/or their algorithmic complexity, computer-aided analysis, i.e., through agent-based modelling and simulation, and empirical analysis, i.e., through experiments with humans.