Minimal Selection Principle

The Minimal Selection Principle (MSP) broadly addresses the problem of optimally selecting subsets from larger datasets, considering factors like fairness, efficiency, and robustness to manipulation. Current research focuses on developing algorithms and models that achieve these objectives in various contexts, ranging from compressed sensing and sequential data analysis to resource allocation and political representation (e.g., sortition). This work emphasizes both theoretical guarantees, such as proving the identifiability of selection structures or establishing bounds on fairness and manipulation, and practical applications, including improved screening processes and more efficient machine learning techniques. The impact of MSP research spans diverse fields, offering improvements in data analysis, resource management, and the design of fair and effective selection procedures.