论文标题
社会系统中基于网络的排名：三个挑战
Network-based ranking in social systems: three challenges
论文作者
论文摘要
排名算法在我们越来越数字化的社会中普遍存在，包括重要的现实应用程序，包括推荐系统，搜索引擎和有影响力的营销实践。从网络科学的角度来看，基于网络的排名算法解决了与复杂系统稳定性和动态的重要节点有关的基本问题。尽管这些算法无处不在且成功地应用，但我们认为我们对它们的绩效和对现实世界问题的应用的理解面临三个基本挑战：（i）排名可能会受到各种因素的偏见； （2）它们的有效性可能仅限于特定问题； （3）由排名驱动的代理商的决定可能会导致潜在的恶性反馈机制和不健康的系统性后果。植根于网络科学和基于代理的建模的方法可以帮助我们理解和克服这些挑战。
Ranking algorithms are pervasive in our increasingly digitized societies, with important real-world applications including recommender systems, search engines, and influencer marketing practices. From a network science perspective, network-based ranking algorithms solve fundamental problems related to the identification of vital nodes for the stability and dynamics of a complex system. Despite the ubiquitous and successful applications of these algorithms, we argue that our understanding of their performance and their applications to real-world problems face three fundamental challenges: (i) Rankings might be biased by various factors; (2) their effectiveness might be limited to specific problems; and (3) agents' decisions driven by rankings might result in potentially vicious feedback mechanisms and unhealthy systemic consequences. Methods rooted in network science and agent-based modeling can help us to understand and overcome these challenges.
