Root/Additional Metric (RoAM) framework: a guide for goal-centred metric construction

TL;DR

The paper introduces the Root/Additional Metric (RoAM) framework, a flexible, goal-centered method for constructing custom metrics across diverse disciplines using decision analysis and utility theory.

Contribution

It presents a novel workflow for creating customizable performance metrics by dividing criteria into root and additional groups, integrating utility functions for goal achievement evaluation.

Findings

Framework applicable across multiple disciplines

Enables quantitative evaluation of goal achievement

Supports statistical analysis and decision-making

Abstract

The use of metrics underpins the quantification, communication and, ultimately, the functioning of a wide range of disciplines as diverse as labour recruitment, institutional management, economics and science. For application of metrics, customised scores are widely employed to optimise progress monitoring towards a goal, to contribute to decision-making, and to quantify situations under evaluation. However, the development of such metrics in complex and rigorous settings intrinsically relies on mathematical processes which are not always readily accessible. Here, we propose a framework for construction of metrics suitable for a wide range of disciplines, following a specified workflow that combines existing decision analysis and utility theory concepts to create a customisable performance metric (with corresponding uncertainty) that can be used to quantitatively evaluate goal achievement. It involves dividing criteria into two groups (root and additional) to utilise a newly proposed alternative form of utility function designed to build such customised metrics. Once the metrics are produced by this approach, these metrics can be used on a varied set of contexts, including their use in subsequent statistical analysis with the metric values as a response variable, or informing a decision-making process. The flexibility of the metric construction makes it suitable for a wide range of fields and applications, and could provide a valuable first step for monitoring and comparison in many different settings.