arXiv subjects

Rakesh V. Vohra

Publications and source records attributed to Rakesh V. Vohra.

3 recordsLinked to original sources

arXiv2025

Sharing with Frictions: Limited Transfers and Costly Inspections

The radio spectrum suitable for commercial wireless services is limited. A portion of the radio spectrum has been reserved for institutions using it for non-commercial purposes such as federal agencies, defense, public safety bodies and scientific institutions. In order to operate efficiently, these incumbents need clean spectrum access. However, commercial users also want access, and granting them access may materially interfere with the existing activity of the incumbents. Conventional market based mechanisms for allocating scarce resources in this context are problematic. Allowing direct monetary transfers to and from public or scientific institutions risks distorting their non-commercial mission. Moreover, often only the incumbent knows the exact value of the interference it experiences, and, likewise, only commercial users can predict accurately the expected monetary outcome from sharing the resource. Thus, our problem is to determine the efficient allocation of resources in the presence of private information without the use of direct monetary transfers. The problem is not unique to spectrum. Other resources that governments hold in trust share the same feature. We propose a novel mechanism design formulation of the problem, characterize the optimal mechanism and describe some of its qualitative properties.

econ.TH↗

arXiv2020

Matroidal Approximations of Independence Systems

Milgrom (2017) has proposed a heuristic for determining a maximum weight basis of an independence system ${\mathcal I}$ given that we want an approximation guarantee only for sets in a prescribed ${\mathcal O}\subseteq {\mathcal I}$. This ${\mathcal O}$ reflects prior knowledge of the designer about the location of the optimal basis. The heuristic is based on finding an `inner matroid', one contained in the independence system. We show that even in the case ${\mathcal O}={\mathcal I}$ of zero additional knowledge the worst-case performance of this new heuristic can be better than that of the classical greedy algorithm.

cs.DM↗

arXiv2019

Algorithmic and Economic Perspectives on Fairness

Algorithmic systems have been used to inform consequential decisions for at least a century. Recidivism prediction dates back to the 1920s. Automated credit scoring dates began in the middle of the last century, but the last decade has witnessed an acceleration in the adoption of prediction algorithms. They are deployed to screen job applicants for the recommendation of products, people, and content, as well as in medicine (diagnostics and decision aids), criminal justice, facial recognition, lending and insurance, and the allocation of public services. The prominence of algorithmic methods has led to concerns regarding their systematic unfairness in their treatment of those whose behavior they are predicting. These concerns have found their way into the popular imagination through news accounts and general interest books. Even when these algorithms are deployed in domains subject to regulation, it appears that existing regulation is poorly equipped to deal with this issue. The word 'fairness' in this context is a placeholder for three related equity concerns. First, such algorithms may systematically discriminate against individuals with a common ethnicity, religion, or gender, irrespective of whether the relevant group enjoys legal protections. The second is that these algorithms fail to treat people as individuals. Third, who gets to decide how algorithms are designed and deployed. These concerns are present when humans, unaided, make predictions.

cs.CY↗