# Cost-Based Approach to Complexity: A Common Denominator?

## TL;DR

This paper reviews existing complexity measures and proposes a cost-based approach that defines complexity as the sum of modeling and operational costs, aiming to unify various facets of complexity with human relevance.

## Contribution

It introduces a novel cost-based complexity measure that integrates different aspects of complexity and accounts for its subjective and relative nature.

## Key findings

- The cost-based approach effectively captures multiple facets of complexity.
- It varies over time and space, reflecting the dynamic nature of complexity.
- Application examples demonstrate its practical utility in real-data scenarios.

## Abstract

Complexity remains one of the central challenges in science and technology. Although several approaches at defining and/or quantifying complexity have been proposed, at some point each of them seems to run into intrinsic limitations or mutual disagreement. Two are the main objectives of the present work: (i) to review some of the main approaches to complexity; and (ii) to suggest a cost-based approach that, to a great extent, can be understood as an integration of the several facets of complexity while keeping its meaning for humans in mind. More specifically, it is poised that complexity, an inherently relative and subjective concept, can be summarized as the cost of developing a model, plus the cost of its respective operation. As a consequence, complexity can vary along time and space. The proposal is illustrated respectively to several applications examples, including a real-data base situation.