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

点击翻译按钮获取中文摘要

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.