A Quantum Decision Approach for Human-AI Decision-Making

15 pages•Published: July 12, 2024

Scott Humr, Mustafa Canan and Mustafa Demir

Abstract

Artificial Intelligence is set to encompass additional decision space that has traditionally been the purview of humans. However, this decision space remains contested. Incongruencies between artificial intelligence and human rationalization processes introduce uncertainties in human decision-making, which require new conceptualizations that capture these distinct types of uncertainties. Hence, developing new ways to model human and artificial intelligence interactions are necessary to account for such uncertainties and improve situation awareness and decision-making. In this paper, we outline current conceptualizations of human and machine rationalities. Next, we offer the concept of rational prediction deviations (via quantum probability theory) for capturing uncertainty in situational awareness. Lastly, we propose a human-in-the-loop construct to explicate how applications of quantum probability theory in decision science can ameliorate situation awareness models by providing a novel way to capture distinct dynamics of decision making.

Keyphrases: artificial intelligence, decision making, human in the loop, quantum probability

In: Kenneth Baclawski, Michael Kozak, Kirstie Bellman, Giuseppe D'Aniello, Alicia Ruvinsky and Candida Da Silva Ferreira Barreto (editors). Proceedings of Conference on Cognitive and Computational Aspects of Situation Management 2023, vol 102, pages 111-125.

Links:	https://easychair.org/publications/paper/B3zp
	https://doi.org/10.29007/6f6j

BibTeX entry

@inproceedings{CogSIMA2023:Quantum_Decision_Approach_Human,
  author    = {Scott Humr and Mustafa Canan and Mustafa Demir},
  title     = {A Quantum Decision Approach for Human-AI Decision-Making},
  booktitle = {Proceedings of Conference on Cognitive and Computational Aspects of Situation Management 2023},
  editor    = {Kenneth Baclawski and Michael Kozak and Kirstie Bellman and Giuseppe D'Aniello and Alicia Ruvinsky and Candida Da Silva Ferreira Barreto},
  series    = {EPiC Series in Computing},
  volume    = {102},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/B3zp},
  doi       = {10.29007/6f6j},
  pages     = {111-125},
  year      = {2024}}

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