Understanding the Emergence of Pro-Social Behaviourin Dynamic Social Networks

了解动态社交网络中亲社会行为的出现

• 批准号: 2741067
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2741067
• 关键词: Understanding; Emergence; Pro; Social; Behaviourin

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.

在个人利用集体资源的社会困境中，“合作者”为改善公共产品付出了代价，而“背叛者”则搭了便车，在不付出任何代价的情况下获得了利益。虽然在一次性互动中，背叛可能是个体的理性策略，但我们知道，在重复博弈中，合作可以持续下去，在重复博弈中，出现复杂的策略来对抗背叛者。在这个项目中，我们想了解动态社交网络中发生了什么，在这个网络中，面临社交困境的个人可以选择与谁互动，并可以观察（并可能模仿）他人的行为。那么，社交网络在什么时候促进了合作呢？是建立联系的容易程度、互动的类型，还是我们与他人的相似性促使我们超越自身利益？在最近的理论分析中，我们发现模仿速度和伙伴选择之间的比例是促进合作的关键。然而，我们的研究结果仍然局限于简单的合作伙伴选择和模仿规则，仍然没有与实证研究结果相联系，即，人类在实践中所做的事情。该项目的目标是设计一个基于学习的社交网络合作的理论模型，并将其与我们收集的行为数据联系起来，目的是为我们在人类数据中看到的真实的合作和背叛模式找到解释。这是一个跨学科的挑战，使用分析和实验方法来提出一个严格的和经验支持的理论模型，解释什么时候亲社会行为可以出现在动态的社交网络。该项目将采用理论分析，即，通过对动力系统和/或其算法复杂性的研究，计算机辅助分析，即，通过基于代理的建模和仿真，以及经验分析，即，通过人体实验。