Using agent-based computational modeling to stimulate the mechanic stress responses of specific communities
Session Number
Project ID: CMPS 12
Advisor(s)
Dr. Kiarri Kershaw; Northwestern University, Feinberg School of Medicine
Dr. Katharine Harrington; Northwestern University, Feinberg School of Medicine
Discipline
Computer Science
Start Date
20-4-2022 8:50 AM
End Date
20-4-2022 9:05 AM
Abstract
Research shows that people with low incomes and racial/ethnic minority populations experience greater levels of stress than their more affluent, white counterparts. This can lead to significant disparities in both mental and physical health that ultimately affect life expectancy and shows correlation with a reduction in household SES as paired with mental and physical health barriers. To learn more about stress responses and their direct implications in diverse communities, a microscale model was built to stimulate the simultaneous operations and interactions of multiple agents to re-create the predict the appearance of this complex phenomena. This model analyzed and visualized the complex dynamic systems of stress to understand how individual environment interactions influence decisions with the mesa documentation in python. The adaptive behavior of the model and its complex systems allows for non-linear causality: an environmental variation prompts the agents’ behavioral responses, which then feed back into additional environmental variations, and so on.
This project anticipated to be part of a step in a larger research agenda enabling local Chicago families to make healthier decisions, and enforcing public health departments across the country to give communities the resources to make positive changes.
Using agent-based computational modeling to stimulate the mechanic stress responses of specific communities
Research shows that people with low incomes and racial/ethnic minority populations experience greater levels of stress than their more affluent, white counterparts. This can lead to significant disparities in both mental and physical health that ultimately affect life expectancy and shows correlation with a reduction in household SES as paired with mental and physical health barriers. To learn more about stress responses and their direct implications in diverse communities, a microscale model was built to stimulate the simultaneous operations and interactions of multiple agents to re-create the predict the appearance of this complex phenomena. This model analyzed and visualized the complex dynamic systems of stress to understand how individual environment interactions influence decisions with the mesa documentation in python. The adaptive behavior of the model and its complex systems allows for non-linear causality: an environmental variation prompts the agents’ behavioral responses, which then feed back into additional environmental variations, and so on.
This project anticipated to be part of a step in a larger research agenda enabling local Chicago families to make healthier decisions, and enforcing public health departments across the country to give communities the resources to make positive changes.