# Towards a Theory of Systems Engineering Processes: A Principal-Agent Model of a One-Shot, Shallow Process

## TL;DR

This paper develops a principal-agent model for systems engineering processes, analyzing how incentives can align self-interested engineers' efforts with system-level goals in a single, shallow hierarchy.

## Contribution

It introduces a novel principal-agent framework for one-shot, shallow systems engineering, incorporating incomplete information and incentive design, with numerical solutions for optimal contracts.

## Key findings

- Optimal incentives depend on effort costs and skill levels.
- Incentive schemes can mitigate deviations from system goals.
- System complexity influences contract design and effort levels.

## Abstract

Systems engineering processes coordinate the effort of different individuals to generate a product satisfying certain requirements. As the involved engineers are self-interested agents, the goals at different levels of the systems engineering hierarchy may deviate from the system-level goals which may cause budget and schedule overruns. Therefore, there is a need of a systems engineering theory that accounts for the human behavior in systems design. To this end, the objective of this paper is to develop and analyze a principal-agent model of a one-shot (single iteration), shallow (one level of hierarchy) systems engineering process. We assume that the systems engineer maximizes the expected utility of the system, while the subsystem engineers seek to maximize their expected utilities. Furthermore, the systems engineer is unable to monitor the effort of the subsystem engineer and may not have a complete information about their types or the complexity of the design task. However, the systems engineer can incentivize the subsystem engineers by proposing specific contracts. To obtain an optimal incentive, we pose and solve numerically a bi-level optimization problem. Through extensive simulations, we study the optimal incentives arising from different system-level value functions under various combinations of effort costs, problem-solving skills, and task complexities.