Generalized Modeling: A survey and guide

TL;DR

This paper reviews generalized modeling as an effective approach for analyzing complex systems with limited data, offering analytical and numerical advantages over traditional models.

Contribution

It provides a comprehensive survey and practical guide for applying generalized modeling to complex, data-scarce systems like ecological models.

Findings

Generalized modeling handles uncertainties effectively.

It enables analytical insights into complex dynamics.

The approach is computationally efficient for large systems.

Abstract

Many current challenges involve understanding the complex dynamical interplay between the constituents of systems. Typically, the number of such constituents is high, but only limited data sources on them are available. Conventional dynamical models of complex systems are rarely mathematically tractable and their numerical exploration suffers both from computational and data limitations. Here we review generalized modeling, an alternative approach to formulating dynamical models. We argue that this approach deals elegantly with the uncertainties that exist in real world data and enables analytical insight or highly efficient numerical investigation. We provide a survey of recent successes of generalized modeling and a guide to the application of this modeling approach in future studies such as complex integrative ecological models.