Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment

TL;DR

This paper introduces monotone-value neural networks (MVNNs) that incorporate preference monotonicity to improve combinatorial valuation predictions, leading to better auction efficiency and faster winner determination.

Contribution

The paper presents MVNNs that enforce monotonicity and normality, proving their universality and providing a MILP formulation for practical auction applications.

Findings

MVNNs outperform existing models in prediction accuracy.

MVNNs achieve state-of-the-art efficiency in spectrum auctions.

MVNNs reduce computational time for winner determination.

Abstract

Many important resource allocation problems involve the combinatorial assignment of items, e.g., auctions or course allocation. Because the bundle space grows exponentially in the number of items, preference elicitation is a key challenge in these domains. Recently, researchers have proposed ML-based mechanisms that outperform traditional mechanisms while reducing preference elicitation costs for agents. However, one major shortcoming of the ML algorithms that were used is their disregard of important prior knowledge about agents' preferences. To address this, we introduce monotone-value neural networks (MVNNs), which are designed to capture combinatorial valuations, while enforcing monotonicity and normality. On a technical level, we prove that our MVNNs are universal in the class of monotone and normalized value functions, and we provide a mixed-integer linear program (MILP) formulation to make solving MVNN-based winner determination problems (WDPs) practically feasible. We evaluate our MVNNs experimentally in spectrum auction domains. Our results show that MVNNs improve the prediction performance, they yield state-of-the-art allocative efficiency in the auction, and they also reduce the run-time of the WDPs. Our code is available on GitHub: https://github.com/marketdesignresearch/MVNN.