# Leo Breiman, the Rashomon Effect, and the Occam Dilemma

arXiv: https://arxiv.org/abs/2507.03884 · HTML: https://mlsanity.com/paper/2507.03884/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 5 Jul 2025.

## Builds on (most cited first, 16 of 16)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Generalized and Scalable Optimal Sparse Decision Trees](https://mlsanity.com/paper/2006.08690/) (2020)
- [On the Existence of Simpler Machine Learning Models](https://mlsanity.com/paper/1908.01755/) (2019)
- [Amazing Things Come From Having Many Good Models](https://mlsanity.com/paper/2407.04846/) (2024)
- [Learning Optimized Risk Scores](https://mlsanity.com/paper/1610.00168/) (2016)
- [Interpretable Classification Models for Recidivism Prediction](https://mlsanity.com/paper/1503.07810/) (2015)
- [In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction](https://mlsanity.com/paper/2005.04176/) (2020)
- [Fast Sparse Decision Tree Optimization via Reference Ensembles](https://mlsanity.com/paper/2112.00798/) (2021)
- [A Path to Simpler Models Starts With Noise](https://mlsanity.com/paper/2310.19726/) (2023)
- [Exploring and Interacting with the Set of Good Sparse Generalized Additive Models](https://mlsanity.com/paper/2303.16047/) (2023)
- [The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance](https://mlsanity.com/paper/2309.13775/) (2023)
- [FasterRisk: Fast and Accurate Interpretable Risk Scores](https://mlsanity.com/paper/2210.05846/) (2022)
- [Fast Sparse Classification for Generalized Linear and Additive Models](https://mlsanity.com/paper/2202.11389/) (2022)
- [A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations](https://mlsanity.com/paper/2106.02605/) (2021)
- [MALTS: Matching After Learning to Stretch](https://mlsanity.com/paper/1811.07415/) (2018)
- [Variable Importance Matching for Causal Inference](https://mlsanity.com/paper/2302.11715/) (2023)

## Built on by (most cited first, 1 of 1)

- [Analyzing the Temporal Factors for Anxiety and Depression Symptoms with the Rashomon Perspective](https://mlsanity.com/paper/2601.20874/) (2026)

## Abstract (arXiv)

In the famous Two Cultures paper, Leo Breiman provided a visionary perspective on the cultures of ''data models'' (modeling with consideration of data generation) versus ''algorithmic models'' (vanilla machine learning models). I provide a modern perspective on these approaches. One of Breiman's key arguments against data models is the ''Rashomon Effect,'' which is the existence of many different-but-equally-good models. The Rashomon Effect implies that data modelers would not be able to determine which model generated the data. Conversely, one of his core advantages in favor of data models is simplicity, as he claimed there exists an ''Occam Dilemma,'' i.e., an accuracy-simplicity tradeoff. After 25 years of powerful computers, it has become clear that this claim is not generally true, in that algorithmic models do not need to be complex to be accurate; however, there are nuances that help explain Breiman's logic, specifically, that by ''simple,'' he appears to consider only linear models or unoptimized decision trees. Interestingly, the Rashomon Effect is a key tool in proving the nullification of the Occam Dilemma. To his credit though, Breiman did not have the benefit of modern computers, with which my observations are much easier to make. Breiman's goal for interpretability was somewhat intertwined with causality: simpler models can help reveal which variables have a causal relationship with the outcome. However, I argue that causality can be investigated without the use of single models, whether or not they are simple. Interpretability is useful in its own right, and I think Breiman knew that too. Technically, my modern perspective does not belong to either of Breiman's Two Cultures, but shares the goals of both of them - causality, simplicity, accuracy - and shows that these goals can be accomplished in other ways, without the limitations Breiman was concerned about.