Strategic Communication between Prospect Theoretic Agents over a Gaussian Test Channel
Abstract
In this paper, we model a Stackelberg game in a simple Gaussian test channel where a human transmitter (leader) communicates a source message to a human receiver (follower). We model human decision making using prospect theory models proposed for continuous decision spaces. Assuming that the value function is the squared distortion at both the transmitter and the receiver, we analyze the effects of the weight functions at both the transmitter and the receiver on optimal communication strategies, namely encoding at the transmitter and decoding at the receiver, in the Stackelberg sense. We show that the optimal strategies for the behavioral agents in the Stackelberg sense are identical to those designed for unbiased agents. At the same time, we also show that the prospect-theoretic distortions at both the transmitter and the receiver are both larger than the expected distortion, thus making behavioral agents less contended than unbiased agents. Consequently, the presence of cognitive biases increases the need for transmission power in order to achieve a given distortion at both transmitter and receiver.
Recommended Citation
V. S. Nadendla et al., "Strategic Communication between Prospect Theoretic Agents over a Gaussian Test Channel," Proceedings of the 2017 IEEE Military Communications Conference (2017, Baltimore, MD), pp. 109 - 114, Institute of Electrical and Electronics Engineers (IEEE), Oct 2017.
The definitive version is available at https://doi.org/10.1109/MILCOM.2017.8170821
Meeting Name
2017 IEEE Military Communications Conference, MILCOM 2017 (2017: Oct. 23-25, Baltimore, MD)
Department(s)
Computer Science
Keywords and Phrases
Prospect Theory; Strategic Communication
International Standard Book Number (ISBN)
978-153860595-0
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Oct 2017
Comments
This research was supported in part by the Army Research Office (ARO) under Grant W911NF-16-1-0485, and by National Science Foundation (NSF) under Grant 1619339.