Mark Orr’s research while affiliated with University of Virginia and other places

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, Rt, 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.

There is little significant work at the intersection of mathematical and computational epidemiology and detailed psychological processes, representations, and mechanisms. This is true despite general agreement in the scientific community and the general public that human behavior in its seemingly infinite variation and heterogeneity, susceptibility to bias, context, and habit is an integral if not fundamental component of what drives the dynamics of infectious disease. The COVID-19 pandemic serves as a close and poignant reminder. We offer a 10-year prospectus of kinds that centers around an unprecedented scientific approach: the integration of detailed psychological models into rigorous mathematical and computational epidemiological frameworks in a way that pushes the boundaries of both psychological science and population models of behavior.

Organizations are complex systems comprised of many dynamic and evolving interaction patterns among individuals and groups. Understanding these interactions and how patterns, such as informal structures and knowledge sharing behavior, emerge are crucial to creating effective and efficient organizations. Studying organizations as complex systems is a challenge as we must account for hierarchically nested structures, multi-level processes, and changes over time. Informal structures interact with individual attitudes to influence organizational processes such as knowledge sharing, a process vital to organizational performance and innovation. To explore such organizational dynamics, we integrate dynamic social networks, a cognitive model of attitude formation and change, and a physical environment into an agent-based model, the combination of which represents a novel way to study organizations. We use a hospital in southwest Virginia as our case study. The agents in the model are the healthcare workers within the hospital and agent movement occurs over the physical environment of the hospital. Results show that the simulated hospital is resilient to impacts from employee attrition but that communication approaches must be thought through strategically so as not to hinder knowledge sharing. For managers, this type of modeling approach can provide resource and planning guidance in regards to attrition-based strategies and communication approaches.

We present a computational cognitive model that incorporates and formalizes aspects of theories of individual-level behavior change and present simulations of COVID-19 behavioral response that modulates transmission rates. This formalization includes addressing the psychological constructs of attitudes, self-efficacy, and motivational intensity. The model yields signature phenomena that appear in the oscillating dynamics of mask wearing and the effective reproduction number, as well as the overall increase of rates of mask-wearing in response to awareness of an ongoing pandemic.

There is little significant work at the intersection of mathematical and computational epidemiology and detailed psychological processes, representations and mechanisms. This is true despite general agreement in the scientific community and the general public that human behavior–in its seemingly infinite variation and heterogeneity, susceptibility to bias, context and habit–is an integral if not fundamental component of what drives the dynamics of infectious disease. The COVID-19 pandemic serves as a close and poignant reminder. We offer a ten-year prospectus of kinds that centers around an unprecedented scientific approach: the integration of detailed psychological models into rigorous mathematical and computational epidemiological frameworks in a way that pushes the boundaries of both psychological science and population models of behavior.

Exchange is a foundational form of human interaction underlying more complex forms of cooperation and collaboration. Exchange scholars have demonstrated that both the structure of exchange relationships, and the cultural logics that govern them influence the benefits that exchange partners contribute and receive. These factors influence behavior by shaping expectations; but, the cognitive process involved in forming expectations over the course of repeated exchanges is less well understood. We introduce a cognitive model of social exchange implemented in ACT-UP, employing instance-based learning (Gonzalez et al. 2003). In this paper, we focus on how the logical structure of exchange relationships influences exchange behaviors by comparing simulation outcomes to experimental data collected by (Molm et al. 2013). We find that the cognitive model is able to replicate well the outcomes of negotiated exchanges but that reciprocal exchanges pose a greater challenge.

Understanding how humans respond to an ongoing pandemic and interventions is crucial to monitoring and forecasting the dynamics of viral transmission. Heterogeneous response over time and geographical regions may depend on the individual beliefs and information consumption patterns of populations. To address the need for more precise and accurate epidemiological models we are researching Psychologically Valid Agent models of human responses to epidemic information and non-pharmaceutical interventions during the COVID-19 global pandemic with input drivers induced from sources including online media that provide indicators of pandemic awareness, beliefs, and attitudes.

We recently put forth a thesis, the Resolution Thesis, that suggests that cognitive science and generative social science are interdependent and should thus be mutually informative. The thesis invokes a paradigm, the reciprocal constraints paradigm, that was designed to leverage the interdependence between the social and cognitive levels of scale for the purpose of building cognitive and social simulations with better resolution. We review our thesis here, provide the current research context, address a set of issues with the thesis, and provide some parting thoughts to provoke discussion. We see this work as an initial step to motivate both social and cognitive sciences in a new direction, one that represents unity of purpose, an interdependence of theory and methods, and a call for the careful development of new approaches for understanding human social systems, broadly construed.

In this chapter, we expand on our Resolution Thesis, a thesis that makes claims about the interdependence between cognitive systems (individual minds) and social systems in terms of how well we understand and simulate each. Along with the thesis comes a methodological paradigm, the Reciprocal Constraints Paradigm, in which information from the cognitive and social levels of scales constrain one another in a principled way. We work through some hypothetical examples of what the application of the Reciprocal Constraints Paradigm would look like and close by framing it as a way of moving social simulation forward in a new direction.

We put forth a thesis, the Resolution Thesis, that suggests that cognitive science and generative social science are interdependent and should thus be mutually informative. The thesis invokes a paradigm, the reciprocal constraints paradigm, that was designed to leverage the interdependence between the social and cognitive levels of scale for the purpose of building cognitive and social simulations with better resolution. In addition to explaining our thesis, we provide the current research context, a set of issues with the thesis and some parting thoughts to provoke discussion. We see this work as an initial step to motivate both social and cognitive sciences in a new direction, one that represents some unity of purpose and interdependence of theory and methods.