A Game-Theoretic Framework for Approximation with Soft Sets

Addressing uncertainty issues is a significant challenge in decision-making. Soft set theory is designed to assist in complex decision-making scenarios where multiple approximations are involved. Those approximations, represented as parametrized sets in soft sets, could provide decision-makers with more informed choices when integrated effectively. However, con-flicts among distinct approximations make the integration chal-lenging. To address this issue, we propose a game-theoretic soft set model based on a set-oriented perception. This model effectively manages the merging of approximation sets by dividing the universe into overlapping and conflicting regions and employing tailored strategies for each. Experimental results indicate that the model not only can achieve a balance among various parameters or conflicting decision goals, but also improves approximation performance across accuracy, precision, recall, and Fl-score.