On the Connection between Greedy Algorithms and Imperfect Rationality

TL;DR

This paper characterizes obviously strategyproof mechanisms for single-parameter agents in optimization problems, revealing they operate via greedy or reverse greedy strategies, and applies this to bounds in scheduling problems.

Contribution

It provides a comprehensive characterization of OSP mechanisms for all single-parameter optimization problems, extending previous binary case results.

Findings

OSP mechanisms are essentially greedy, reverse greedy, or split-domain strategies.

The characterization enables deriving bounds on approximation guarantees.

Application to scheduling problems demonstrates practical implications.

Abstract

The design of algorithms or protocols that are able to align the goals of the planner with the selfish interests of the agents involved in these protocols is of paramount importance in almost every decentralized setting (such as, computer networks, markets, etc.) as shown by the rich literature in Mechanism Design. Recently, huge interest has been devoted to the design of mechanisms for imperfectly rational agents, i.e., mechanisms for which agents are able to easily grasp that there is no action different from following the protocol that would satisfy their interests better. This work has culminated in the definition of Obviously Strategyproof (OSP) Mechanisms, that have been shown to capture the incentives of agents without contingent reasoning skills. Without an understanding of the algorithmic nature of OSP mechanisms, it is hard to assess how well these mechanisms can satisfy the goals of the planner. For the case of binary allocation problems and agents whose private type is a single number, recent work has shown that a generalization of greedy completely characterizes OSP. In this work, we strengthen the connection between greedy and OSP by providing a characterization of OSP mechanisms for all optimization problems involving these single-parameter agents. Specifically, we prove that OSP mechanisms must essentially work as follows: they either greedily look for agents with ``better'' types and allocate them larger outcomes; or reverse greedily look for agents with ``worse'' types and allocate them smaller outcomes; or, finally, split the domain of agents in ``good'' and ``bad'' types, and subsequently proceed in a reverse greedy fashion for the former and greedily for the latter. We further demonstrate how to use this characterization to give bounds on the approximation guarantee of OSP mechanisms for the well known scheduling related machines problem.