Solving Graph-based Public Good Games with Tree Search and Imitation Learning

TL;DR

This paper introduces a novel approach combining tree search and imitation learning to efficiently solve complex public goods games modeled on graphs, significantly improving speed while maintaining high solution quality.

Contribution

It proposes a new method that exploits the Maximal Independent Set structure and uses graph neural networks to quickly approximate solutions for public goods games.

Findings

Achieves 99.5% of the planning method's performance.

Evaluates on large graphs, demonstrating three orders of magnitude faster evaluation.

Applicable to various graph-based optimization problems.

Abstract

Public goods games represent insightful settings for studying incentives for individual agents to make contributions that, while costly for each of them, benefit the wider society. In this work, we adopt the perspective of a central planner with a global view of a network of self-interested agents and the goal of maximizing some desired property in the context of a best-shot public goods game. Existing algorithms for this known NP-complete problem find solutions that are sub-optimal and cannot optimize for criteria other than social welfare. In order to efficiently solve public goods games, our proposed method directly exploits the correspondence between equilibria and the Maximal Independent Set (mIS) structural property of graphs. In particular, we define a Markov Decision Process which incrementally generates an mIS, and adopt a planning method to search for equilibria, outperforming existing methods. Furthermore, we devise a graph imitation learning technique that uses demonstrations of the search to obtain a graph neural network parametrized policy which quickly generalizes to unseen game instances. Our evaluation results show that this policy is able to reach 99.5% of the performance of the planning method while being three orders of magnitude faster to evaluate on the largest graphs tested. The methods presented in this work can be applied to a large class of public goods games of potentially high societal impact and more broadly to other graph combinatorial optimization problems.