arXiv subjects

Matt Shum

Publications and source records attributed to Matt Shum.

4 recordsLinked to original sources

arXiv2026

One-Shot Pricing for Hands-Off-the-Wheel Advertising Markets

Per-impression auctions have long served as the allocation mechanism in online advertising. We argue that in ``hands-off-the-wheel'' (HOTW) markets, where advertisers declare budgets and ``return-on-investment'' (ROI) targets and the exchange's ML models predict click values, auctions are no longer necessary: all information required for optimal pricing is already known to the exchange, which occupies the position of a monopolist pricing against a downward-sloping demand curve. A HOTW market is a Fisher market, whose competitive equilibrium can be computed via the convex program of Eisenberg and Gale, yielding market-clearing prices and allocations satisfying all budget and ROI constraints simultaneously. This competitive-equilibrium price is revenue-optimal for the exchange among all uniform-price mechanisms: avoiding the demand reduction problem in typical uniform-price multi-unit auctions. The resulting competitive equilibrium is moreover outcome-equivalent to sequential first-price auctions with pacing, with pacing multipliers computable ex-ante by the exchange. This one-shot approach replaces millions of individual auctions with one convex program, which is not only operationally simpler than dynamically evolving bidding strategies, but revenue-optimal for the exchange, while delivering the same equilibrium outcome.

econ.TH↗

arXiv2021

Yogurts Choose Consumers? Estimation of Random-Utility Models via Two-Sided Matching

The problem of demand inversion - a crucial step in the estimation of random utility discrete-choice models - is equivalent to the determination of stable outcomes in two-sided matching models. This equivalence applies to random utility models that are not necessarily additive, smooth, nor even invertible. Based on this equivalence, algorithms for the determination of stable matchings provide effective computational methods for estimating these models. For non-invertible models, the identified set of utility vectors is a lattice, and the matching algorithms recover sharp upper and lower bounds on the utilities. Our matching approach facilitates estimation of models that were previously difficult to estimate, such as the pure characteristics model. An empirical application to voting data from the 1999 European Parliament elections illustrates the good performance of our matching-based demand inversion algorithms in practice.

econ.EM↗

arXiv2021

Duality in dynamic discrete-choice models

Using results from convex analysis, we investigate a novel approach to identification and estimation of discrete choice models which we call the Mass Transport Approach (MTA). We show that the conditional choice probabilities and the choice-specific payoffs in these models are related in the sense of conjugate duality, and that the identification problem is a mass transport problem. Based on this, we propose a new two-step estimator for these models; interestingly, the first step of our estimator involves solving a linear program which is identical to the classic assignment (two-sided matching) game of Shapley and Shubik (1971). The application of convex-analytic tools to dynamic discrete choice models, and the connection with two-sided matching models, is new in the literature.

econ.EM↗

arXiv2019

Discovery of Bias and Strategic Behavior in Crowdsourced Performance Assessment

With the industry trend of shifting from a traditional hierarchical approach to flatter management structure, crowdsourced performance assessment gained mainstream popularity. One fundamental challenge of crowdsourced performance assessment is the risks that personal interest can introduce distortions of facts, especially when the system is used to determine merit pay or promotion. In this paper, we developed a method to identify bias and strategic behavior in crowdsourced performance assessment, using a rich dataset collected from a professional service firm in China. We find a pattern of "discriminatory generosity" on the part of peer evaluation, where raters downgrade their peer coworkers who have passed objective promotion requirements while overrating their peer coworkers who have not yet passed. This introduces two types of biases: the first aimed against more competent competitors, and the other favoring less eligible peers which can serve as a mask of the first bias. This paper also aims to bring angles of fairness-aware data mining to talent and management computing. Historical decision records, such as performance ratings, often contain subjective judgment which is prone to bias and strategic behavior. For practitioners of predictive talent analytics, it is important to investigate potential bias and strategic behavior underlying historical decision records.

cs.LG↗