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

该项目将应用一种正在发展的理论，该理论涉及古生态系统作为大规模灭绝期间复杂的适应系统的行为，以及人类和社会经济系统（SES）在COVID-19大流行期间的反应。社会经济系统和生态系统是复杂、适应性系统的例子。这些系统是我们世界的大部分复杂性的基础，包括人类遗传系统和全球经济。系统行为取决于智能体的数量、它们的相互作用、外部影响以及智能体如何组织成子组。在生态系统中，主体是物种，通过捕食或竞争等机制相互作用，当它们具有重叠的相互作用时，就会形成物种群体。由于结构的复杂性，复杂系统的行为很难理解和预测，但如果系统承受极端的压力，则可以学到很多东西。过去，生态系统因小行星撞击等巨大事件而遭受大规模灭绝时，情况就是如此。对于因 COVID-19 大流行及其社会经济影响而受到压力的人类社会经济地位来说也是如此。研究表明，大规模灭绝期间生态系统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演化。该项目将利用生态系统和社会经济体系之间的相似性来模拟大流行病导致的死亡率、发病率和经济的影响。该项目将开发加州和全国社会经济体系的网络模型，将工业部门组织的就业与系统动态联系起来。疫情对选定社会经济体系的影响是根据行业就业人数进行建模的，我们将确定关键行业，预测未来的系统动态。 SES复苏将被建模为就业复苏，比较三种复苏策略：在部门之间随机分布的机会性复苏（随机复苏）、在部门之间公平分布（公平复苏）或不均匀分布以最大化复苏率和幅度（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 NSF 的法定使命，并通过使用基金会的智力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.