Goal-Oriented Semantic Resource Allocation with Cumulative Prospect Theoretic Agents

TL;DR

This paper presents a resource allocation framework for goal-oriented semantic networks that models agents with subjective, risk-aware preferences using cumulative prospect theory, enhancing decision-making under uncertainty.

Contribution

It introduces a novel analytical framework incorporating CPT preferences into resource allocation, addressing human-centric decision aspects in semantic networks.

Findings

Framework effectively models subjective agent preferences.

Application to wireless power allocation demonstrates practical relevance.

Framework captures risk perception and loss aversion in decision-making.

Abstract

We introduce a resource allocation framework for goal-oriented semantic networks, where participating agents assess system quality through subjective (e.g., context-dependent) perceptions. To accommodate this, our model accounts for agents whose preferences deviate from traditional expected utility theory (EUT), specifically incorporating cumulative prospect theory (CPT) preferences. We develop a comprehensive analytical framework that captures human-centric aspects of decision-making and risky choices under uncertainty, such as risk perception, loss aversion, and perceptual distortions in probability metrics. By identifying essential modifications in traditional resource allocation design principles required for agents with CPT preferences, we showcase the framework's relevance through its application to the problem of power allocation in multi-channel wireless communication systems.