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A Myopic Allocation that Learns Its Variance

Authors

  • Ryan Turner Author
  • Raymond Nelson Author
  • Daniel Barrett Author

Abstract

Myopic choice and unknown-variance ranking create a feedback between estimated precision and future measurement. A misleading pilot can reduce follow-up on a competitive alternative. Evaluation should vary initial evidence and preserve complete paths, then independently check terminal decisions. Long-run allocation quality remains separate from the reliability of the finite variance-learning process. This focused synthesis develops an empirical comparison that preserves the terminal action, observation process, and total verification cost. Its proposed tests are prospective; no new simulation run, model-training result, or intervention effect is reported.

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Published

2026-10-12

Versions

Issue

Vol. 2 No. 10 (2026): October 2026

Section

Articles