# Predictive Multiplicity in Survival Models: A Method for Quantifying Model Uncertainty in Predictive Maintenance Applications

arXiv: https://arxiv.org/abs/2504.12156 · HTML: https://mlsanity.com/paper/2504.12156/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 45 references that are in the map.
- First posted on arXiv 16 Apr 2025.

## Builds on (most cited first, 21 of 21)

- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [Predictive Multiplicity in Classification](https://mlsanity.com/paper/1909.06677/) (2019)
- [Amazing Things Come From Having Many Good Models](https://mlsanity.com/paper/2407.04846/) (2024)
- [Predictive Multiplicity in Probabilistic Classification](https://mlsanity.com/paper/2206.01131/) (2022)
- [Arbitrariness and Social Prediction: The Confounding Role of Variance in Fair Classification](https://mlsanity.com/paper/2301.11562/) (2023)
- [Position: Explain to Question not to Justify](https://mlsanity.com/paper/2402.13914/) (2024)
- [Loss-Aversively Fair Classification](https://mlsanity.com/paper/2105.04273/) (2021)
- [One Model Many Scores: Using Multiverse Analysis to Prevent Fairness Hacking and Evaluate the Influence of Model Design Decisions](https://mlsanity.com/paper/2308.16681/) (2023)
- [Multi-Target Multiplicity: Flexibility and Fairness in Target Specification under Resource Constraints](https://mlsanity.com/paper/2306.13738/) (2023)
- [An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification](https://mlsanity.com/paper/2405.01557/) (2024)
- [Cross-model Fairness: Empirical Study of Fairness and Ethics Under Model Multiplicity](https://mlsanity.com/paper/2203.07139/) (2022)
- [Interpretable machine learning for time-to-event prediction in medicine and healthcare](https://mlsanity.com/paper/2303.09817/) (2023)
- [Reconciling Model Multiplicity for Downstream Decision Making](https://mlsanity.com/paper/2405.19667/) (2024)
- [Energy-Based Survival Models for Predictive Maintenance](https://mlsanity.com/paper/2302.00629/) (2023)
- [Identifying Drivers of Predictive Aleatoric Uncertainty](https://mlsanity.com/paper/2312.07252/) (2023)
- [The Cost of Arbitrariness for Individuals: Examining the Legal and Technical Challenges of Model Multiplicity](https://mlsanity.com/paper/2407.13070/) (2024)
- [Predictive Maintenance using Machine Learning](https://mlsanity.com/paper/2205.09402/) (2022)
- [Practical Attribution Guidance for Rashomon Sets](https://mlsanity.com/paper/2407.18482/) (2024)
- [Predictive maintenance on event logs: Application on an ATM fleet](https://mlsanity.com/paper/2011.10996/) (2020)
- [Rashomon perspective for measuring uncertainty in the survival predictive maintenance models](https://mlsanity.com/paper/2502.15772/) (2025)

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

- [On the Tunability of Random Survival Forests Model for Predictive Maintenance](https://mlsanity.com/paper/2504.14744/) (2025)

## Abstract (arXiv)

In many applications, especially those involving prediction, models may yield near-optimal performance yet significantly disagree on individual-level outcomes. This phenomenon, known as predictive multiplicity, has been formally defined in binary, probabilistic, and multi-target classification, and undermines the reliability of predictive systems. However, its implications remain unexplored in the context of survival analysis, which involves estimating the time until a failure or similar event while properly handling censored data. We frame predictive multiplicity as a critical concern in survival-based models and introduce formal measures -- ambiguity, discrepancy, and obscurity -- to quantify it. This is particularly relevant for downstream tasks such as maintenance scheduling, where precise individual risk estimates are essential. Understanding and reporting predictive multiplicity helps build trust in models deployed in high-stakes environments. We apply our methodology to benchmark datasets from predictive maintenance, extending the notion of multiplicity to survival models. Our findings show that ambiguity steadily increases, reaching up to 40-45% of observations; discrepancy is lower but exhibits a similar trend; and obscurity remains mild and concentrated in a few models. These results demonstrate that multiple accurate survival models may yield conflicting estimations of failure risk and degradation progression for the same equipment. This highlights the need to explicitly measure and communicate predictive multiplicity to ensure reliable decision-making in process health management.