Kevin A. Bryan
University of Toronto
Institutional Affiliation: University of Toronto
Information about this author at RePEc
NBER Working Papers and Publications
|January 2020||The Allocation of Decision Authority to Human and Artificial Intelligence|
with Susan C. Athey, Joshua S. Gans: w26673
The allocation of decision authority by a principal to either a human agent or an artificial intelligence (AI) is examined. The principal trades off an AI’s more aligned choice with the need to motivate the human agent to expend effort in learning choice payoffs. When agent effort is desired, it is shown that the principal is more likely to give that agent decision authority, reduce investment in AI reliability and adopt an AI that may be biased. Organizational design considerations are likely to impact on how AI’s are trained.
Published: Susan C. Athey & Kevin A. Bryan & Joshua S. Gans, 2020. "The Allocation of Decision Authority to Human and Artificial Intelligence," AEA Papers and Proceedings, vol 110, pages 80-84.
|April 2019||In-Text Patent Citations: A User’s Guide|
with Yasin Ozcan, Bhaven N. Sampat: w25742
We introduce, validate, and provide a public database of a new measure of the knowledge inventors draw on: scientific references in patent specifications. These references are common and algorithmically extractable. Critically, they are very different from the “front page” prior art commonly used to proxy for inventor knowledge. Only 24% of front page citations to academic articles are in the patent text, and 31% of in-text citations are on the front page. We explain these differences by describing the legal rules and practice governing citation. Empirical validations suggest that in-text citations appear to more accurately measure real knowledge flows, consistent with their legal role.
Published: Kevin A. Bryan & Yasin Ozcan & Bhaven Sampat, 2020. "In-text patent citations: A user's guide," Research Policy, vol 49(4).
|July 2018||A Theory of Multihoming in Rideshare Competition|
with Joshua S. Gans: w24806
We examine competition amongst ridesharing platforms where firms compete by choosing both the price of rides and the extent of idleness. Idleness means drivers who are compensated without picking up passengers, instead acting to reduce passenger wait time. We show that when consumers are the only agents who multihome, idleness falls compared with when they face a monopoly ridesharing platform. When drivers and consumers multihome, idleness further falls to zero as it involves costs for each platform that are appropriated, in part, by their rival. Interestingly, socially superior outcomes may involve monopoly or competition under various multihoming regimes, depending on the density of the city, and the relative costs of idleness versus consumer disutility of waiting.
Published: Kevin A. Bryan & Joshua S. Gans, 2019. "A theory of multihoming in rideshare competition," Journal of Economics & Management Strategy, vol 28(1), pages 89-96. citation courtesy of