Efficient Multi-Agent Delegated Search

TL;DR

This paper studies how a principal can effectively delegate a stochastic search problem to multiple agents without payments, achieving near-optimal utility as the number of agents grows, with insights into the underlying mechanisms.

Contribution

It extends delegated search models to multiple agents and provides nearly tight bounds on the principal's utility approximation, revealing that increased agents improve outcomes beyond competition effects.

Findings

Principal's utility approaches optimal with more agents.

Approximation bounds are nearly tight and improve with agent number.

Improvement is not primarily due to agent competition.

Abstract

Consider a principal who wants to search through a space of stochastic solutions for one maximizing their utility. If the principal cannot conduct this search on their own, they may instead delegate this problem to an agent with distinct and potentially misaligned utilities. This is called delegated search, and the principal in such problems faces a mechanism design problem in which they must incentivize the agent to find and propose a solution maximizing the principal's expected utility. Following prior work in this area, we consider mechanisms without payments and aim to achieve a multiplicative approximation of the principal's utility when they solve the problem without delegation. In this work, we investigate a natural and recently studied generalization of this model to multiple agents and find nearly tight bounds on the principal's approximation as the number of agents increases. As one might expect, this approximation approaches 1 with increasing numbers of agents, but, somewhat surprisingly, we show that this is largely not due to direct competition among agents.