Calibration of predictive agent-based models of opinion dynamics

意见动力学是一个快速发展的跨学科研究领域，它结合数据驱动的方法和数学模型来研究对我们的社会至关重要的问题。这一博士学位旨在开发量化方法，旨在模拟和对比在线社交网络(OSN)中“假新闻”和错误信息的传播，这些都是近年来主要研究努力的主题。随着关于这一主题的文献的增长，它的两个主要分支之间出现了明显的鸿沟。一方面，已经提出了越来越复杂的数学模型来理解个体之间不同的通信规则如何导致网络人口中非常不同的状态(例如，共识、两极分化)。另一方面，数据驱动的研究揭示了几个风格化的事实，这些事实表征了最受欢迎的OSN中的信息传播，如Facebook和Twitter。然而，这两个研究方向之间几乎没有重叠，因为目前可用的数学模型难以提出可检验的预测，这反过来又阻止了针对经验数据的任何有意义的模型验证/拒绝。本博士学位的重点将是通过利用基于代理的建模和广泛的现有技术来根据经验数据校准基于代理的模型(ABM)来填补这一空白。

Opinion dynamics is a fast-growing interdisciplinary research area where problems of fundamental importance to our societies are studied with a mix of data-driven approaches and mathematical modelling. This PhD aims to develop quantitative approaches aimed at modelling and contrasting the diffusion of "fake news" and misinformation in online social networks (OSNs), which have been the subject of major research efforts in recent years. As the literature on the subject has grown, an apparent gap has emerged between its two main branches. On the one hand, increasingly sophisticated mathematical models have been put forward to understand how different rules of communication between individuals may lead to very different states (e.g., consensus, polarisation) in networked populations. On the other hand, data-driven studies have revealed several stylised facts that characterise the diffusion of information in the most popular OSNs, such as Facebook and Twitter. Yet, there is very little overlap between these two strands of research, as the currently available mathematical models struggle to come up with testable predictions, which in turn prevents from any meaningful model validation/rejection against empirical data. The focus of this PhD will be that of filling this gap by leveraging agent-based modelling and the wide array of existing techniques to calibrate agent-based models (ABMs) on empirical data.