政治偏见放大了意见极化：一个有界置信模型
Algorithmic bias amplifies opinion polarization: A bounded confidence model
摘要：
通过在线媒体平台到达我们的信息流不是由信息内容或相关性优化的，而是由受欢迎程度和与目标的接近度优化的。这通常是为了最大限度地提高平台使用率而执行的。作为一个副作用，这引入了一种算法偏见，被认为会增强社会辩论的两极分化。为了研究这一现象，我们修改了著名的连续观点动力学模型的有界信心，以占算法的偏见，并调查其后果。在原始模型的最简单版本中，随机选择成对的讨论参与者，如果他们的观点在固定的容忍度范围内，他们的观点就会变得更接近。我们修改的选择规则的讨论伙伴：有一个增强的概率选择个人的意见已经接近对方，从而模仿的行为，建议与类似的同行互动的在线媒体。因此，我们观察到：a）增加的极化趋势，其也出现在原始模型将预测收敛的条件下，以及b）达到渐近状态的收敛的速度的显著减慢，这使得系统高度不稳定。两极分化是由一个支离破碎的初始人口。
英文摘要：
The flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards polarization, which emerges also in conditions where the original model would predict convergence, and b) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Polarization is augmented by a fragmented initial population.
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