# Modelling Like an Experimentalist

## TL;DR

This paper suggests that modeling in ecology and evolution can be better understood as a form of experimentation, improving design and communication.

## Contribution

The novel contribution is framing modeling as experimentation, emphasizing treatments, responses, and replication to enhance rigor and clarity.

## Key findings

- Modeling involves treatments like parameter regimes and data manipulations.
- Replicated model runs yield summaries and comparisons similar to empirical experiments.
- Thinking like an experimentalist improves modeling design and communication.

## Abstract

Modelling has become a routine part of ecological and evolutionary research, yet its practice often lacks a clear conceptual framework. I propose that modelling can be fruitfully understood as experimentation. Like empirical studies, modelling projects involve treatments, levels and responses: parameter regimes or data manipulations serve as treatments, replicated runs yield summaries and comparisons across conditions reveal main effects and interactions. This framing sharpens design, reduces mission creep and clarifies communication. I outline common layers of abstraction, highlight good modelling habits and argue that thinking like an experimentalist fosters rigour, reliability and credibility without constraining creativity.

Dahlin et al. (2024) apply experimental thinking to a model of mosquito‐borne disease transmissions.