Cultural Evolutionary Dynamics Under Structural Uncertainty and the Consequences for Coupled Diffusion Processes
James Holland Jones
Earth Systems Science, Stanford University
The COVID-19 Pandemic has laid bare the social vulnerabilities that make epidemics larger, more deadly, and more difficult to control, both within the US and internationally. Differential vulnerability by social attributes (e.g., race, socioeconomic status, gender) leaves the overall population at greater risk for severe outbreaks than would be the case in less unequal populations. While health researchers have noted the societal vulnerability brought about by structural inequality for years, the COVID-19 pandemic has revealed other surprising sources of structural vulnerability that exacerbate transmission and complicate control. In particular, socio-political polarization has proven to be a pernicious problem for epidemic control. I will present results from a simple model that show how two social processes, homophily and out-group aversion, in a polarized population, can produce complex transmission dynamics that qualitatively resemble the course of the COVID-19 pandemic in the US. I will then present a cultural-evolutionary framework for understanding why such polarization arises in the context of a pandemic. At the outset of a pandemic of a novel pathogen, people are suffused with uncertainty about the nature of the threat, its origin, the severity of disease, the effectiveness of control, timelines, etc. We hypothesize that uncertainty is a key variable underlying increased socio-political polarization on the one hand, and the response to crises such as pandemics on the other. Uncertainty is a fundamental feature not just of epidemics but of any existential crisis facing humanity more generally. Understanding how people respond to uncertainty, and crucially, what the aggregate effects of these responses are is therefore a critical need for research into existential threats. Conventional wisdom tells us that people employ social heuristics when faced with uncertainty. This is important since aggregation itself becomes a major source of structural uncertainty, as the behavior of ensembles of decision-makers is characterized by substantial nonlinearity, feedback, and often surprising threshold effects. I will present new work on modeling decision-making under uncertainty and the aggregate effects for “coupled-contagion” processes of social learning and pathogen diffusion.