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How Do Artificial Agents Make Decisions in Light of Visual Data? — arXiv:2611.05671

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.05671
License (as archived): CC0-1.0
Archived: 2026-11-05T18:16:43.477Z
SHA-256 of the archived text: 859b6863ac7e0660a02e5dea684a708ffb84414aa79f9c34be4bfcb70f3b7c00
Stable URL of this page: https://metraipolis.com/documento/dc_496b2cfac0dc

How to cite

How Do Artificial Agents Make Decisions in Light of Visual Data? — arXiv:2611.05671 — arXiv (en), https://arxiv.org/abs/2611.05671 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_496b2cfac0dc (Metraipolis archive dc_496b2cfac0dc, sha256 859b6863ac7e0660a02e5dea684a708ffb84414aa79f9c34be4bfcb70f3b7c00)

BibTeX

@misc{dc496b2cfac0dc,
  title = {How Do Artificial Agents Make Decisions in Light of Visual Data? — arXiv:2611.05671},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.05671},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_496b2cfac0dc; sha256 859b6863ac7e0660a02e5dea684a708ffb84414aa79f9c34be4bfcb70f3b7c00},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.05671. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Vision-language models (VLMs) have demonstrated significant advances in reasoning with visual data, which is particularly important given that much of human knowledge is encoded visually in scientific articles and other sources. However, a gap remains in understanding how artificial agents, powered by VLMs, perceive and navigate decision-making under uncertainty in the presence of visual data. This study explores this gap by evaluating and analyzing autonomous decision-making processes. Building upon prior work in visualization literacy and decision theory, we conducted two complementary studies: one examining how agents perceive and quantify uncertainty encoded in visualizations, and another investigating how artificial agents make decisions based on visual data in the form of images. We analyzed perception accuracy and decision performance across models and visualization types. Our results show that GPT-4o demonstrates the highest accuracy in perceiving uncertainty in pie and bar charts among the tested models. For decision-making, autonomous agents showed the best alignment with rational decision theory when uncertainty was represented through circle charts, icon arrays, or text.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Human-Computer Interaction (cs.HC).
Pagina del lavoro: https://arxiv.org/abs/2611.05671 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-05T18:12:48.997Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).