Publication
Seeing Through the Smoke: An Agent Architecture for Representing Health Protection Motivation Under Social Pressure
Veronika Kurchyna; Stephanie Fendrich; Ye Eun Bae; Patrick Mertes; Philipp Flügger; Jan Ole Berndt; Ingo Timm
In: Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART. International Conference on Agents and Artificial Intelligence (ICAART-2024), February 24-26, Rome, Italy, Pages 315-325, ISBN 978-989-758-680-4, SciTePress, 2024.
Abstract
Representing and emulating human decision-making processes in artificial intelligence systems is a challenging task. This is because both internal (such as attitude, perceived health or motivation) and external factors (such as the opinions of others) and their mutual interactions affect decision-making. Modelling agents capable of human-like behavior, including undesirable actions, is an interesting use case for designing different AI-systems when it comes to human-AI-interactions and similar scenarios. However, agent-based decision-models in this domain tend to reflect the complex interplay of these factors only to a limited extent. To overcome this, we enrich these approaches with an agent architecture inspired by theories from psychology and sociology. Using human health behavior, specifically smoking, as a case study, we propose an agent-based approach to combine social pressure within Protection Motivation Theory (PMT) to allow for a theory-based representation of potentially h armful behavior including both internal and external factors. Based on smoking in social settings, we present experiments to demonstrate the model’s capability to simulate human health behavior and the mutual influences between the selected concepts. In this use case, the resulting model has shown that social pressure is a driving influence in the observable system dynamics.