Loss Attitude Aware Energy Management for Signal Detection

TL;DR

This paper develops a Bayesian energy management framework for signal detection that incorporates human loss aversion using prospect theory, revealing how human behavior diverges from rational decision-making in resource consumption.

Contribution

It introduces a novel market-based approach integrating prospect theory to model human loss attitudes in energy management for signal detection.

Findings

Loss attitudes significantly influence resource consumption behavior.

Humans act differently from rational decision makers due to loss aversion.

The model characterizes actual human behavior in energy management scenarios.

Abstract

This work considers a Bayesian signal processing problem where increasing the power of the probing signal may cause risks or undesired consequences. We employ a market based approach to solve energy management problems for signal detection while balancing multiple objectives. In particular, the optimal amount of resource consumption is determined so as to maximize a profit-loss based expected utility function. Next, we study the human behavior of resource consumption while taking individuals' behavioral disparity into account. Unlike rational decision makers who consume the amount of resource to maximize the expected utility function, human decision makers act to maximize their subjective utilities. We employ prospect theory to model humans' loss aversion towards a risky event. The amount of resource consumption that maximizes the humans' subjective utility is derived to characterize the actual behavior of humans. It is shown that loss attitudes may lead the human to behave quite differently from a rational decision maker.