Gaussian Agency problems with memory and Linear Contracts

TL;DR

This paper demonstrates that in Gaussian principal-agent models with memory, optimal linear contracts in end-of-period outcomes are achievable under certain conditions, even when the process is non-Markovian or non-semimartingale.

Contribution

It introduces a new methodology to handle non-Markovian Gaussian models and proves the optimality of linear contracts in these complex settings.

Findings

Linear contracts are optimal in one-dimensional Gaussian models with memory.

In higher dimensions, linear contracts remain optimal under radial effort costs.

The paper quantifies the gap between linear and optimal contracts for general quadratic effort costs.

Abstract

Can a principal still offer optimal dynamic contracts that are linear in end-of-period outcomes when the agent controls a process that exhibits memory? We provide a positive answer by considering a general Gaussian setting where the output dynamics are not necessarily semi-martingales or Markov processes. We introduce a rich class of principal-agent models that encompasses dynamic agency models with memory. From the mathematical point of view, we develop a methodology to deal with the possible non-Markovianity and non-semimartingality of the control problem, which can no longer be directly solved by means of the usual Hamilton-Jacobi-Bellman equation. Our main contribution is to show that, for one-dimensional models, this setting always allows for optimal linear contracts in end-of-period observable outcomes with a deterministic optimal level of effort. In higher dimension, we show that linear contracts are still optimal when the effort cost function is radial and we quantify the gap between linear contracts and optimal contracts for more general quadratic costs of efforts.