A key challenge in responding to public health crises such as COVID-19 is the difficulty of predicting the results of feedback interconnections between the disease and society. As a step towards understanding these interconnections, we pose a simple game-theoretic model of a global pandemic in which individuals can choose where to live, and we investigate the global behavior that may emerge as a result of individuals reacting locally to the competing costs of isolation and infection. We study the game-theoretic equilibria that emerge from this setup when the population is composed of either selfish or altruistic individuals. First, we demonstrate that as is typical in these types of games, selfish equilibria are in general not optimal, but that all stable selfish equilibria are within a constant factor of optimal. Second, there exist infinitely-many stable altruistic equilibria; all but finitely-many of these are worse than the worst selfish equilibrium, and the social cost of altruistic equilibria is unbounded. Our work is in sharp contrast to recent work in network congestion games in which all altruistic equilibria are socially optimal. This suggests that a population without central coordination may react very poorly to a pandemic, and that individual altruism could even exacerbate the problem.
翻译:在应对诸如COVID-19等公共卫生危机方面的一个关键挑战是难以预测疾病与社会之间反馈互联的结果。作为理解这些相互联系的一个步骤,我们提出了一个简单的全球流行病游戏理论模型,在这种流行病中,个人可以选择住在哪里,我们调查个人在当地对孤立和感染的相竞代价作出反应而可能产生的全球行为。我们研究在人口由自私或利己个人组成时,从这一结构中产生的游戏理论平衡。首先,我们证明,自私的平衡总的来说并不是最佳的,但所有稳定的自私的平衡都处在一个不变的最佳因素之中。第二,存在无限的、稳定的利他主义平衡;所有这些现象虽然有限,却比最糟糕的自私平衡还要糟糕,而利他主义平衡的社会成本是没有限制的。我们的工作与最近网络拥堵游戏中的工作形成鲜明对比,而所有利他性平衡和利基平衡都无法很好地恶化了核心人口,这表明,一个没有非常利他性、最坏的社会上最优性的人口问题可能会发生。
React.js(React)是 Facebook 推出的一个用来构建用户界面的 JavaScript 库。
Facebook开源了React,这是该公司用于构建反应式图形界面的JavaScript库,已经应用于构建Instagram网站及 Facebook部分网站。最近出现了AngularJS、MeteorJS 和Polymer中实现的Model-Driven Views等框架,React也顺应了这种趋势。React基于在数据模型之上声明式指定用户界面的理念,用户界面会自动与底层数据保持同步。与前面提及 的框架不同,出于灵活性考虑,React使用JavaScript来构建用户界面,没有选择HTML。Not Rest