Discrete Choice, Permutations, and Reconstruction
Abstract
In this paper we study the well-known family of Random Utility Models, developed over 50 years ago to codify rational user behavior in choosing one item from a finite set of options. In this setting each user draws i.i.d. from some distribution a utility function mapping each item in the universe to a real-valued utility. The user is then offered a subset of the items, and selects the one of maximum utility. A Max-Dist oracle for this choice model takes any subset of items and returns the probability (over the distribution of utility functions) that each will be selected. A discrete choice algorithm, given access to a Max-Dist oracle, must return a function that approximates the oracle.
We show three primary results. First, we show that any algorithm exactly reproducing the oracle must make exponentially many queries. Second, we show an equivalent representation of the distribution over utility functions, based on permutations, and show that if this distribution has support size k, then it is possible to approximate the oracle using O(nk) queries. Finally, we consider settings in which the subset of items is always small. We give an algorithm that makes less than n^{(1–∊/2)K} queries, each to sets of size at most (1–∊/2)K, in order to approximate the Max-Dist oracle on every set of size |T| ≤ K with statistical error at most ∊. In contrast, we show that any algorithm that queries for subsets of size 2^{O(sqrt{log n})} must make maximal statistical error on some large sets.
We show three primary results. First, we show that any algorithm exactly reproducing the oracle must make exponentially many queries. Second, we show an equivalent representation of the distribution over utility functions, based on permutations, and show that if this distribution has support size k, then it is possible to approximate the oracle using O(nk) queries. Finally, we consider settings in which the subset of items is always small. We give an algorithm that makes less than n^{(1–∊/2)K} queries, each to sets of size at most (1–∊/2)K, in order to approximate the Max-Dist oracle on every set of size |T| ≤ K with statistical error at most ∊. In contrast, we show that any algorithm that queries for subsets of size 2^{O(sqrt{log n})} must make maximal statistical error on some large sets.
Research Areas
