Abstract for: Debiasing Human Response Estimations in Dynamic Models: Exploring the Significance of Delay Structure and Asymmetries

From how public opinion is changed by economic outcomes to inform future voting patterns to impact of risk perception on social interactions during a pandemic, many important social processes include the assimilation of information to shape opinions and perceptions, which then inform individual and societal actions. While such perception delays are well recognized, empirically identifying them is non-trivial. We study this problem in the context of human response to change in the state of a disease mediated by public risk perception. Public risk perception changes through an information diffusion process with significant delays where perception adjustment delay may depend on the direction of the change in risks. Despite these complexities, most models either assume fixed-delay structures or exponential structures with symmetric delay periods. In this paper we show that incorrect delay structures and the assumption of symmetric delay periods can lead to biased and misleading estimates in synthetic data settings. We explore alternative ways of identifying appropriate delay structures that may overcome these challenges. We then apply different delay structures to state-wide US COVID-19 Mobility data to showcase how the estimation of public’s sensitivity to death is influenced by the method used for estimation.