arXiv:2604.19791v3 Announce Type: replace Abstract: Attitude change - the process by which individuals revise their evaluative stances - has been explained by a set of influential but competing verbal theories. These accounts often function as mechanism sketches: rich in conceptual detail, yet lacking the technical specifications and operational constraints required to run as executable systems. We present a generative actor-based modelling workflow for "rendering" these sketches as runnable actor - environment simulations using the Concordia simulation library. In Concordia, actors operate by
This research is emerging as AI systems are increasingly tasked with complex social modeling, requiring more robust and explainable mechanisms for simulating human behavior and attitude dynamics.
Improving the accuracy and stability of generative models for attitude change is critical for applications ranging from social science research to influence campaigns and policy simulations.
The ability to 'render' abstract social theories into executable simulations provides a new methodology for testing and refining our understanding of how attitudes evolve, moving from verbal accounts to quantitative models.
- · Social scientists
- · AI ethicists
- · Computational social science platforms
- · Policy makers
- · Opaquely black-box AI models
- · Static social theories
- · Traditional survey research
More sophisticated and verifiable simulations of social influence and attitude formation become possible.
These simulations could inform more effective and potentially manipulative strategies for persuasion or public opinion management.
The enhanced capability to model and predict attitude shifts could reshape political campaigns, marketing, and the overall dynamics of public discourse, raising significant ethical and regulatory questions.
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Read at arXiv cs.AI