Attributes: Selective Learning and Influence

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United States Simons Institute for the Theory of Computing
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Published on ● Video Link: https://www.youtube.com/watch?v=_l6csmBKYXs


Duration: 50:20
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Arjada Bardhi (Duke University)
https://simons.berkeley.edu/talks/tbd-477
Quantifying Uncertainty: Stochastic, Adversarial, and Beyond

Two players who disagree on the relevance of the attributes of a complex project eval- uate it based on a select few. The optimal attribute sample is characterized in a general framework in which the correlation across attributes is modeled through a Gaussian process. Two sufficient statistics inform optimal sampling: (i) the resulting alignment between the players’ estimates, and (ii) the variability of the decision. We identify an intuitive property of the correlation structure—the nearest-attribute property—that is critical for the pattern of optimal sampling. Under such a property, all optimal attributes are relevant for some player: at most two are idiosyncratic and the rest are common. The fewer and more peripheral the common attributes and the stronger the attribute correlation, the more skewed and redundant the sample. We draw testable implications for attribute-based product evaluation and strategic selection of pilot sites.



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Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Quantifying Uncertainty: Stochastic Adversarial and Beyond
Arjada Bardhi