Authors:
Veronika Kurchyna
1
;
2
;
Stephanie C. Rodermund
1
;
2
;
Ye Eun Bae
1
;
Patrick Mertes
2
;
Philipp Flügger
2
;
Jan Ole Berndt
1
and
Ingo J. Timm
2
;
1
Affiliations:
1
Cognitive Social Simulation, German Research Center for Artificial Intelligence, Behringstr. 31, 54296 Trier, Germany
;
2
Business Informatics, Trier University, Universitaetsring 15, 54296 Trier, Germany
Keyword(s):
Agent Decision-Making, Social Simulation, Protection Motivation, Social Pressure.
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.
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