Evolutionary Data Theory: On the Similarities between Data Problems and Evolutionary Games

Problem
This preprint addresses a gap in the intersection of data science and evolutionary game theory (EGT). While EGT has been extensively applied to biological and social systems, its application to data problems remains underexplored. The paper introduces the concept of Evolutionary Data Theory (EDT), proposing a framework that interprets data as evolutionary entities, thereby providing a novel perspective on data representation and optimization. The authors aim to bridge the theoretical gap by demonstrating how data can be analyzed through the lens of evolutionary strategies, which could lead to new methodologies in data analysis and machine learning.

Method
The core technical contribution of this work is the formulation of data records and features as participants in an evolutionary game, where input data is represented in matrix form analogous to genes and organisms. The authors define two evolutionary strategies: Dominant-Balanced and Altruistic-Selfish. The fitness of data records is determined by their ability to contribute to the overall performance of the dataset, akin to biological fitness. The paper proves the convergence of this evolutionary game to a unique rest point, ensuring that all data features persist in the population. Additionally, the authors present a basic example of multi-objective optimization within this framework, illustrating how the evolutionary dynamics can be applied to solve distribution problems in data.

Results
The authors demonstrate that the proposed EDT framework converges to a stable state where all features are retained, which is a significant finding in the context of feature selection and data optimization. While specific numerical results or comparisons against established baselines are not provided in the abstract, the theoretical implications suggest that the evolutionary approach could yield superior performance in multi-objective optimization tasks compared to traditional methods. The paper invites further empirical validation on standard benchmarks, which could substantiate the effectiveness of the proposed framework.

Limitations
The authors acknowledge that the theoretical nature of their work requires empirical validation to assess practical applicability. They do not provide extensive experimental results or comparisons with existing data optimization techniques, which limits the immediate applicability of their findings. Additionally, the framework’s reliance on the assumptions of EGT may not hold in all data scenarios, particularly in high-dimensional or sparse datasets where evolutionary dynamics could behave unpredictably. The lack of detailed computational complexity analysis is another potential limitation that could affect scalability in real-world applications.

Why it matters
The introduction of Evolutionary Data Theory has significant implications for downstream work in data science and machine learning. By framing data problems within an evolutionary context, researchers may develop new algorithms that leverage the principles of natural selection to enhance feature selection, optimization, and data representation. This approach could lead to more robust models that are better equipped to handle complex, multi-objective tasks. Furthermore, the theoretical foundation laid by this work could inspire future research that integrates EGT with other areas of machine learning, potentially leading to innovative solutions for longstanding challenges in the field.

Authors: Philipp Wissgott
Source: arXiv:2605.26685
URL: https://arxiv.org/abs/2605.26685v1