Multi-Agent Combinatorial Contracts

TL;DR

This paper introduces novel algorithmic approaches for combinatorial contracts involving multiple agents and actions, providing approximation guarantees and analyzing the welfare-utility gap in complex multi-agent environments.

Contribution

It develops the first constant-factor approximation for submodular multi-agent multi-action problems and introduces an FPTAS for single-agent scenarios with general rewards.

Findings

Constant-factor approximation for submodular multi-agent problems

An FPTAS for single-agent combinatorial actions with general rewards

Welfare-utility gap scales logarithmically for subadditive rewards

Abstract

Combinatorial contracts are emerging as a key paradigm in algorithmic contract design, paralleling the role of combinatorial auctions in algorithmic mechanism design. In this paper we study natural combinatorial contract settings involving teams of agents, each capable of performing multiple actions. This scenario extends two fundamental special cases previously examined in the literature, namely the single-agent combinatorial action model of [Duetting et al., 2021] and the multi-agent binary-action model of [Babaioff et al., 2012, Duetting et al., 2023]. We study the algorithmic and computational aspects of these settings, highlighting the unique challenges posed by the absence of certain monotonicity properties essential for analyzing the previous special cases. To navigate these complexities, we introduce a broad set of novel tools that deepen our understanding of combinatorial contracts environments and yield good approximation guarantees. Our main result is a constant-factor approximation for submodular multi-agent multi-action problems with value and demand oracles access. This result is tight: we show that this problem admits no PTAS (even under binary actions). As a side product of our main result, we devise an FPTAS, with value and demand oracles, for single-agent combinatorial action scenarios with general reward functions, which is of independent interest. We also provide bounds on the gap between the optimal welfare and the principal's utility. We show that, for subadditive rewards, perhaps surprisingly, this gap scales only logarithmically (rather than linearly) in the size of the action space.