A Game-Theoretic Account of Responsibility Allocation

Abstract

When designing or analyzing multi-agent systems, a fundamental problem is responsibility ascription: to specify which agents are responsible for the joint outcome of their behaviors and to which extent. We model strategic multi-agent interaction as an extensive form game of imperfect information and define notions of forward (prospective) and backward (retrospective) responsibility. Forward responsibility identifies the responsibility of a group of agents for an outcome along all possible plays, whereas backward responsibility identifies the responsibility along a given play. We further distinguish between strategic and causal backward responsibility, where the former captures the epistemic knowledge of players along a play, while the latter formalizes which players – possibly unknowingly – caused the outcome. A formal connection between forward and backward notions is established in the case of perfect recall. We further ascribe quantitative responsibility through cooperative game theory. We show through a number of examples that our approach encompasses several prior formal accounts of responsibility attribution.

Cite

Text

Baier et al. "A Game-Theoretic Account of Responsibility Allocation." International Joint Conference on Artificial Intelligence, 2021. doi:10.24963/IJCAI.2021/244

Markdown

[Baier et al. "A Game-Theoretic Account of Responsibility Allocation." International Joint Conference on Artificial Intelligence, 2021.](https://mlanthology.org/ijcai/2021/baier2021ijcai-game/) doi:10.24963/IJCAI.2021/244

BibTeX

@inproceedings{baier2021ijcai-game,
  title     = {{A Game-Theoretic Account of Responsibility Allocation}},
  author    = {Baier, Christel and Funke, Florian and Majumdar, Rupak},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {1773-1779},
  doi       = {10.24963/IJCAI.2021/244},
  url       = {https://mlanthology.org/ijcai/2021/baier2021ijcai-game/}
}