Deciphering Shortcut Learning from an Evolutionary Game Theory Perspective

📰 ArXiv cs.AI

Learn how evolutionary game theory helps understand shortcut learning in deep neural networks and why it matters for more robust models

advanced Published 5 May 2026

Action Steps

Define core and shortcut features in a dataset using evolutionary game theory principles
Model data samples as players and neural tangent features as strategies to analyze shortcut bias
Apply game theory to understand the origins of shortcut learning in deep neural network training
Analyze the impact of shortcut features on model performance and robustness
Develop strategies to mitigate shortcut bias in deep learning models using insights from evolutionary game theory

Who Needs to Know This

Machine learning researchers and engineers can benefit from this perspective to develop more reliable models, while data scientists can use it to identify potential biases in their datasets

Key Insight

💡 Evolutionary game theory can help explain the formation of shortcut bias in deep learning models by modeling data samples and neural features as players and strategies

Key Takeaways

Learn how evolutionary game theory helps understand shortcut learning in deep neural networks and why it matters for more robust models

Full Article

Title: Deciphering Shortcut Learning from an Evolutionary Game Theory Perspective

Abstract:
arXiv:2605.02658v2 Announce Type: new Abstract: Shortcut learning causes deep learning models to rely on non-essential features within the data. However, its formation in deep neural network training still lacks theoretical understanding. In this paper, we provide a formal definition of core and shortcut features and employ evolutionary game theory to analyze the origins of shortcut bias by modeling data samples as players and their corresponding neural tangent features as strategies, assuming t

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