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Abstract Regional Psychologically Valid Agents (R-PVAs) are computational models representing cognition and behavior of regional populations. R-PVAs are developed using ACT-R—a computational implementation of the Common Model of Cognition. We developed R-PVAs to model mask-wearing behavior in the U.S. over the pre-vaccination phase of COVID-19 using regionally organized demographic, psychographic, epidemiological, information diet, and behavioral data. An R-PVA using a set of five regional predictors selected by stepwise regression, a psychological self-efficacy process, and context-awareness of the effective transmission number,R_t, yields good fits to the observed proportion of the population wearing masks in 50 U.S. states [R²= 0.92]. An R-PVA based on regional Big 5 personality traits yields strong fits [R²= 0.83]. R-PVAs can be probed with combinations of population traits and time-varying context to predict behavior. R-PVAs are a novel technique to understand dynamical, nonlinear relations amongst context, traits, states, and behavior based on cognitive modeling. 

In this paper, we investigate whether symbolic semantic representations, extracted from deep semantic parsers, can help to reason over the states of involved entities in a procedural text. We consider a deep semantic parser (TRIPS) and semantic role labeling as two sources of semantic parsing knowledge. First, we propose PROPOLIS, a symbolic parsing-based procedural reasoning framework. Second, we integrate semantic parsing information into state-of-the-art neural models for procedural reasoning. Our experiments indicate that explicitly incorporating such semantic knowledge improves procedural understanding. This paper presents new metrics for evaluating procedural reasoning tasks that clarify the challenges and identify differences among neural, symbolic, and integrated models.