An evidence-accumulating drift-diffusion model of competing information spread on networks

Julien Corsin, Lorenzo Zino, Mengbin Ye
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Abstract

In this paper, we propose an agent-based model of information spread, grounded on psychological insights on the formation and spread of beliefs. In our model, we consider a network of individuals who share two opposing types of information on a specific topic (e.g., pro- vs. anti-vaccine stances), and the accumulation of evidence supporting either type of information is modelled by means of a drift-diffusion process. After formalising the model, we put forward a campaign of Monte Carlo simulations to identify population-wide behaviours emerging from agents' exposure to different sources of information, investigating the impact of the number and persistence of such sources, and the role of the network structure through which the individuals interact. We find similar emergent behaviours for all network structures considered. When there is a single type of information, the main observed emergent behaviour is consensus. When there are opposing information sources, both consensus or polarisation can result; the latter occurs if the number and persistence of the sources exceeds some threshold values. Importantly, we find the emergent behaviour is mainly influenced by how long the information sources are present for, as opposed to how many sources there are.
网络竞争信息传播的证据积累漂移扩散模型
在本文中,我们提出了一个基于代理的信息传播模型,该模型建立在对信念的形成和传播的心理学见解基础之上。在我们的模型中,我们考虑了一个由个体组成的网络,这些个体在一个特定的话题上分享两种截然相反的信息(例如,支持疫苗与反对疫苗的立场),而支持这两种信息的证据的积累是通过漂移-扩散过程来模拟的。在将模型正规化之后,我们进行了蒙特卡罗模拟,以确定代理人在接触不同信息源时产生的全人群行为,研究这些信息源的数量和持续性的影响,以及个体之间互动的网络结构的作用。我们发现,在所考虑的所有网络结构中,都会出现类似的突发行为。当信息类型单一时,观察到的主要突发行为是达成共识。当存在相互对立的信息源时,就会出现共识或极化现象;如果信息源的数量和持续时间超过某个临界值,就会出现极化现象。重要的是,我们发现突发行为主要受信息源存在时间长短的影响,而不是受信息源数量多少的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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