Generative Models: An Interdisciplinary Perspective

TL;DR

This paper provides an interdisciplinary overview of generative models, detailing their conceptual foundations, applications across fields, and practical implementation, emphasizing modularity and cross-disciplinary opportunities.

Contribution

It offers a comprehensive synthesis of generative model concepts, applications, and modular design, highlighting interdisciplinary connections and practical coding resources.

Findings

Generative models support complex reasoning and experimental design.

Modular recombination enhances flexibility in problem-solving.

Research is fragmented across disciplines, with opportunities for integration.

Abstract

By linking conceptual theories with observed data, generative models can support reasoning in complex situations. They have come to play a central role both within and beyond statistics, providing the basis for power analysis in molecular biology, theory building in particle physics, and resource allocation in epidemiology, for example. We introduce the probabilistic and computational concepts underlying modern generative models and then analyze how they can be used to inform experimental design, iterative model refinement, goodness-of-fit evaluation, and agent-based simulation. We emphasize a modular view of generative mechanisms and discuss how they can be flexibly recombined in new problem contexts. We provide practical illustrations throughout, and code for reproducing all examples is available at https://github.com/krisrs1128/generative_review. Finally, we observe how research in generative models is currently split across several islands of activity, and we highlight opportunities lying at disciplinary intersections.