TY - GEN
T1 - Detection in Human-Sensor Systems Under Quantum Prospect Theory Using Bayesian Persuasion Frameworks
AU - Hu, Yinan
AU - Zhu, Quanyan
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Human-sensor systems have a wide range of applications in fields such as robotics, healthcare, and finance. These systems utilize sensors to observe the true state of nature and generate strategically designed signals, aiding humans in making more accurate decisions regarding the state of nature. We adopt a Bayesian persuasion framework that is integrated with quantum prospect theories. In this framework, we develop a detection scheme where humans aim to determine the true state by observing the realization of quantum states from the sensor. We derive the optimal signaling rule for the sensor and the optimal decision rule for humans. We discover that this scenario violates the total law of probability. Furthermore, we examine how such violation can influence the human detection performance and the signaling rules employed by the sensor.
AB - Human-sensor systems have a wide range of applications in fields such as robotics, healthcare, and finance. These systems utilize sensors to observe the true state of nature and generate strategically designed signals, aiding humans in making more accurate decisions regarding the state of nature. We adopt a Bayesian persuasion framework that is integrated with quantum prospect theories. In this framework, we develop a detection scheme where humans aim to determine the true state by observing the realization of quantum states from the sensor. We derive the optimal signaling rule for the sensor and the optimal decision rule for humans. We discover that this scenario violates the total law of probability. Furthermore, we examine how such violation can influence the human detection performance and the signaling rules employed by the sensor.
KW - Bayesian Persuasion
KW - Quantum Detection
KW - Quantum Signal Processing
KW - game theory
UR - http://www.scopus.com/inward/record.url?scp=85168875111&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85168875111&partnerID=8YFLogxK
U2 - 10.1109/SSP53291.2023.10208035
DO - 10.1109/SSP53291.2023.10208035
M3 - Conference contribution
AN - SCOPUS:85168875111
T3 - IEEE Workshop on Statistical Signal Processing Proceedings
SP - 36
EP - 40
BT - Proceedings of the 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
PB - IEEE Computer Society
T2 - 22nd IEEE Statistical Signal Processing Workshop, SSP 2023
Y2 - 2 July 2023 through 5 July 2023
ER -