EAGER: Applying Paleoecosystem-Mass Extinction Theory to Socio-Economic Systems During COVID-19

该项目将把古生态系统作为大灭绝期间复杂适应系统的行为这一发展中的理论应用于2019冠状病毒病大流行期间人类和社会经济系统（SESs）的反应。社会经济系统和生态系统是复杂的适应性系统的例子。这些系统构成了我们这个世界的复杂性，包括人类遗传系统和全球经济。系统行为取决于代理的数量、它们之间的相互作用、外部影响以及代理如何组织成子组。在生态系统中，代理人是物种，通过捕食或竞争等机制相互作用，当它们有重叠的相互作用时，物种群体就形成了。由于结构的复杂性，复杂系统的行为是难以理解和预测的，但如果系统受到极端应力，则可以学到很多东西。过去，由于小行星撞击等巨大事件，生态系统遭受了大规模灭绝，情况就是如此。COVID-19大流行及其社会经济影响所带来的人类社会经济压力也是如此。研究表明，生态系统在大灭绝期间的恢复能力是由结构复杂性决定的，而适当的恢复取决于结构复杂性如何重新进化。该项目将利用生态系统和社会经济效益之间的相似性来模拟流行病导致的死亡率、发病率和经济影响。该项目将开发加州和全国SESs的网络模型，将工业部门组织的就业与系统动力学联系起来。大流行对选定的社会经济部门的影响以部门就业人数为模型，我们将确定关键部门，预测未来的系统动态。SES复苏将以就业复苏为模型，比较三种复苏策略：随机分布在部门之间的机会主义复苏（随机复苏）、公平分布在部门之间的机会主义复苏（公平复苏）、不均匀分布以最大化恢复率和规模的复苏（战略复苏）。后一种策略将使用马尔可夫链蒙特卡洛大都会-黑斯廷斯机器学习方法进行建模。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The project will apply a developing theory regarding the behavior of paleoecosystems as complex adaptive systems during mass extinctions, to the response of human and socio-economic systems (SESs) during the COVID-19 pandemic. Socio-economic systems and ecosystems are examples of complex, adaptive systems. Such systems underlie much of our world’s complexity, including human genetic systems, and the global economy. System behavior depends on the number of agents, their interactions, external influences, and how agents are organized into sub-groups. In ecosystems, the agents are species, interacting through mechanisms like predation or competition, and groups of species form when they have overlapping interactions. The behavior of a complex system is difficult to understand and forecast because of structural complexity, but a lot may be learned if the system is subjected to extreme stress. This has been the case when ecosystems in the past suffered mass extinctions, driven by enormous events such as asteroid impact. It is also the case for human SESs stressed by the COVID-19 pandemic and its socio-economic fallout. Studies have shown that ecosystem resilience during mass extinctions was determined by structural complexity, and that proper recovery depended on how structural complexity was re-evolved. This project will use similarities between ecosystems and SESs to model the impact of pandemic-driven mortality, morbidity, and economics.The project will develop network models of Californian and national SESs, relating employment organized by industrial sectors to system dynamics. The pandemic’s impact on selected SESs are modeled with numbers of persons employed in sectors, and we will identify critical sectors, forecasting future system dynamics. SES recovery will be modeled as employment recovery, comparing three recovery strategies: opportunistic recovery distributed randomly among sectors (random recovery), distributed fairly among sectors (equitable recovery), or distributed unevenly to maximize recovery rate and magnitude (strategic recovery). The latter strategy will be modeled using a Markov Chain Monte Carlo Metropolis-Hastings machine learning method.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.