Combining Outcome-Based and Preference-Based Matching: A Constrained Priority Mechanism

We introduce a constrained priority mechanism that combines outcome-based matching from machine-learning with preference-based allocation schemes common in market design. Using real-world data, we illustrate how our mechanism could be applied to the assignment of refugee families to host country locations, and kindergarteners to schools. Our mechanism allows a planner to first specify a threshold $\bar g$ for the minimum acceptable average outcome score that should be achieved by the assignment. In the refugee matching context, this score corresponds to the predicted probability of employment, while in the student assignment context it corresponds to standardized test scores. The mechanism is a priority mechanism that considers both outcomes and preferences by assigning agents (refugee families, students) based on their preferences, but subject to meeting the planner's specified threshold. The mechanism is both strategy-proof and constrained efficient in that it always generates a matching that is not Pareto dominated by any other matching that respects the planner's threshold.Comment: This manuscript has been accepted for publication by Political Analysis and will appear in a revised form subject to peer review and/or input from the journal's editor. End-users of this manuscript may only make use of it for private research and study and may not distribute it furthe

An Experimental Investigation of Preference Misrepresentation in the Residency Match

The development and deployment of matching procedures that incentivize truthful preference reporting is considered one of the major successes of market design research. In this study, we test the degree to which these procedures succeed in eliminating preference misrepresentation. We administered an online experiment to 1,714 medical students immediately after their participation in the medical residency match--a leading field application of strategy-proof market design. When placed in an analogous, incentivized matching task, we find that 23% of participants misrepresent their preferences. We explore the factors that predict preference misrepresentation, including cognitive ability, strategic positioning, overconfidence, expectations, advice, and trust. We discuss the implications of this behavior for the design of allocation mechanisms and the social welfare in markets that use them

Just Sort It! A Simple and Effective Approach to Active Preference Learning

We address the problem of learning a ranking by using adaptively chosen pairwise comparisons. Our goal is to recover the ranking accurately but to sample the comparisons sparingly. If all comparison outcomes are consistent with the ranking, the optimal solution is to use an efficient sorting algorithm, such as Quicksort. But how do sorting algorithms behave if some comparison outcomes are inconsistent with the ranking? We give favorable guarantees for Quicksort for the popular Bradley-Terry model, under natural assumptions on the parameters. Furthermore, we empirically demonstrate that sorting algorithms lead to a very simple and effective active learning strategy: repeatedly sort the items. This strategy performs as well as state-of-the-art methods (and much better than random sampling) at a minuscule fraction of the computational cost.Comment: Accepted at ICML 201

The Impacts of Taste, Location of Origin, and Health Information on Market Demand for Sweet Potatoes

Location of product origin is an often-used marketing device by retailers. This approach is based on the assumption that location of origin signals something to consumers about the underlying quality (or other attributes) of the product. This can be an effective strategy if the signal matches the consumer valuation of the product after consumption. In the same vein, health advertising is used to increase demand for a product that exhibits "healthy" dietary attributes. While there have been numerous studies examining the potential impacts of these attributes on demand, there have been few rigorous studies that examine the consistency of consumer valuations of location of origin before and after they have actually consumed the product (or before and after health advertising). Results show that knowledge of location of origin of sweet potatoes does have an impact on consumer valuation. It was also found that both the information from the taste attribute (experience) and the health attribute (credence) played a significant role in participant valuation.Food Consumption/Nutrition/Food Safety,

Hypothetical and Real Choice Differentially Activate Common Valuation Areas

Hypothetical reports of intended behavior are commonly used to draw conclusions about real choices. A fundamental question in decision neuroscience is whether the same type of valuation and choice computations are performed in hypothetical and real decisions. We investigated this question using functional magnetic resonance imaging while human subjects made real and hypothetical choices about purchases of consumer goods. We found that activity in common areas of the orbitofrontal cortex and the ventral striatum correlated with behavioral measures of the stimulus value of the goods in both types of decision. Furthermore, we found that activity in these regions was stronger in response to the stimulus value signals in the real choice condition. The findings suggest that the difference between real and hypothetical choice is primarily attributable to variations in the value computations of the medial orbitofrontal cortex and the ventral striatum, and not attributable to the use of different valuation systems, or to the computation of stronger stimulus value signals in the hypothetical condition

Applying Deep Learning To Airbnb Search

The application to search ranking is one of the biggest machine learning success stories at Airbnb. Much of the initial gains were driven by a gradient boosted decision tree model. The gains, however, plateaued over time. This paper discusses the work done in applying neural networks in an attempt to break out of that plateau. We present our perspective not with the intention of pushing the frontier of new modeling techniques. Instead, ours is a story of the elements we found useful in applying neural networks to a real life product. Deep learning was steep learning for us. To other teams embarking on similar journeys, we hope an account of our struggles and triumphs will provide some useful pointers. Bon voyage!Comment: 8 page