Learning What's going on: reconstructing preferences and priorities from opaque transactions

TL;DR

This paper develops algorithms to reconstruct buyer preferences and seller priorities in an online setting with limited information, enabling accurate future outcome predictions in complex allocation mechanisms.

Contribution

It introduces mistake-bound algorithms for learning buyer preferences and seller priorities in an online, opaque transaction environment with various buyer types.

Findings

Algorithms are efficient in computation and mistake bounds.

Effective for additive, unit-demand, and single-minded buyers.

Handles changing utilities due to observed prices.

Abstract

We consider a setting where $n$ buyers, with combinatorial preferences over $m$ items, and a seller, running a priority-based allocation mechanism, repeatedly interact. Our goal, from observing limited information about the results of these interactions, is to reconstruct both the preferences of the buyers and the mechanism of the seller. More specifically, we consider an online setting where at each stage, a subset of the buyers arrive and are allocated items, according to some unknown priority that the seller has among the buyers. Our learning algorithm observes only which buyers arrive and the allocation produced (or some function of the allocation, such as just which buyers received positive utility and which did not), and its goal is to predict the outcome for future subsets of buyers. For this task, the learning algorithm needs to reconstruct both the priority among the buyers and the preferences of each buyer. We derive mistake bound algorithms for additive, unit-demand and single minded buyers. We also consider the case where buyers' utilities for a fixed bundle can change between stages due to different (observed) prices. Our algorithms are efficient both in computation time and in the maximum number of mistakes (both polynomial in the number of buyers and items).