Cognitive Social Learners: An Architecture for Modeling Normative Behavior

Abstract

In many cases, creating long-term solutions to sustainability issues requires not only innovative technology, but also large-scale public adoption of the proposed solutions. Social simulations are a valuable but underutilized tool that can help public policy researchers understand when sustainable practices are likely to make the delicate transition from being an individual choice to becoming a social norm. In this paper, we introduce a new normative multi-agent architecture, Cognitive Social Learners (CSL), that models bottom-up norm emergence through a social learning mechanism, while using BDI (Belief/Desire/Intention) reasoning to handle adoption and compliance. CSL preserves a greater sense of cognitive realism than influence propagation or infectious transmission approaches, enabling the modeling of complex beliefs and contradictory objectives within an agent-based simulation. In this paper, we demonstrate the use of CSL for modeling norm emergence of recycling practices and public participation in a smoke-free campus initiative.

Cite

Text

Beheshti et al. "Cognitive Social Learners: An Architecture for Modeling Normative Behavior." AAAI Conference on Artificial Intelligence, 2015. doi:10.1609/AAAI.V29I1.9441

Markdown

[Beheshti et al. "Cognitive Social Learners: An Architecture for Modeling Normative Behavior." AAAI Conference on Artificial Intelligence, 2015.](https://mlanthology.org/aaai/2015/beheshti2015aaai-cognitive/) doi:10.1609/AAAI.V29I1.9441

BibTeX

@inproceedings{beheshti2015aaai-cognitive,
  title     = {{Cognitive Social Learners: An Architecture for Modeling Normative Behavior}},
  author    = {Beheshti, Rahmatollah and Ali, Awrad Mohammed and Sukthankar, Gita Reese},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2015},
  pages     = {2017-2023},
  doi       = {10.1609/AAAI.V29I1.9441},
  url       = {https://mlanthology.org/aaai/2015/beheshti2015aaai-cognitive/}
}