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Semimyopic measurement selection for optimization under uncertainty.

David Tolpin¹, Solomon Eyal Shimony

¹Department of Computer Science, Ben-Gurion University of the Negev, Be'er Sheva 84105, Israel.

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|October 27, 2011

Summary

This summary is machine-generated.

This study introduces a semimyopic value of information (VOI) approach to improve item selection. The novel "blinkered" VOI method enhances measurement policies, outperforming traditional myopic VOI estimates.

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Area of Science:

Decision Analysis
Optimization Theory
Information Economics

Background:

The selection problem involves choosing the best item from a set with unknown utilities.
Measurements of item features can inform selection but incur costs.
Traditional value of information (VOI) methods often use myopic estimates, which can be suboptimal.

Purpose of the Study:

To develop improved measurement policies for sequential decision problems.
To relax the strict myopic assumption in value of information (VOI) estimation.
To introduce and evaluate a novel 'blinkered' VOI method.

Main Methods:

Developed a spectrum of semimyopic VOI methods.
Proposed an efficiently computable 'blinkered' VOI method.
Examined theoretical bounds for special cases and conducted empirical evaluations.

Main Results:

Semimyopic VOI methods offer improved performance over myopic estimates.
The 'blinkered' VOI method is computationally efficient.
Empirical results show 'blinkered' VOI significantly outperforms pure myopic VOI for normally distributed item values.

Conclusions:

Relaxing the myopic assumption in VOI estimation leads to superior measurement policies.
The 'blinkered' VOI method provides a practical and effective enhancement for the selection problem.
This work offers a more robust approach to optimizing sequential decision-making under uncertainty.