# A Mood Value for Fair Resource Allocations

## TL;DR

This paper introduces the 'Mood Value', a new fairness measure for resource allocation in networks, addressing limitations of classical methods by focusing on user satisfaction within cooperative game frameworks.

## Contribution

It proposes a novel fairness index based on user satisfaction and a new allocation rule called 'Mood Value' that better supports fairness in cooperative resource sharing.

## Key findings

- Mood Value outperforms classical fairness measures in simulations.
- The new fairness index provides a more accurate fairness assessment.
- The approach better supports cooperative resource allocation scenarios.

## Abstract

In networking and computing, resource allocation is typically addressed using classical sharing protocols as, for instance, the proportional division rule, the max-min fair allocation , or other solutions inspired by cooperative game theory. In this paper, we argue that, describing the resource allocation problem as a cooperative game, such classical resource allocation approaches, as well as associated notions of fairness, show important limitations. We identify in the individual satisfaction rate the key aspect of the challenge of defining a new notion of fairness and, consequently, a resource allocation algorithm more appropriate for the cooperative context. We generalize the concept of user satisfaction considering the set of admissible solutions for bankruptcy games. We adapt the Jain's fairness index to include the new user satisfaction rate. Accordingly, we propose a new allocation rule we call 'Mood Value'. For each user it equalizes our novel game-theoretic definition of user satisfaction with respect to a distribution of the resource. We test the mood value and the new fairness index through extensive simulations showing how they better support the fairness analysis.