# Utilizing Information Optimally to Influence Distributed Network Routing

## TL;DR

This paper explores how system designers can leverage detailed network and user information to design optimal pricing mechanisms that influence social routing behavior, improving efficiency over traditional toll strategies.

## Contribution

It introduces optimal scaled marginal-cost pricing mechanisms for parallel-network routing games, highlighting the value of network structure knowledge over user distribution data.

## Key findings

- Optimal pricing mechanisms improve routing efficiency.
- Network structure knowledge is more valuable than user distribution info.
- Performance guarantees are derived for different information scenarios.

## Abstract

How can a system designer exploit system-level knowledge to derive incentives to optimally influence social behavior? The literature on network routing contains many results studying the application of monetary tolls to influence behavior and improve the efficiency of self-interested network traffic routing. These results typically fall into two categories: (1) optimal tolls which incentivize socially-optimal behavior for a known realization of the network and population, or (2) robust tolls which provably reduce congestion given uncertainty regarding networks and user types, but may fail to optimize routing in general. This paper advances the study of robust influencing, mechanisms asking how a system designer can optimally exploit additional information regarding the network structure and user price sensitivities to design pricing mechanisms which influence behavior. We design optimal scaled marginal-cost pricing mechanisms for a class of parallel-network routing games and derive the tight performance guarantees when the network structure and/or the average user price-sensitivity is known. Our results demonstrate that from the standpoint of the system operator, in general it is more important to know the structure of the network than it is to know distributional information regarding the user population.