Game theoretic modeling of pilot behavior during mid-air encounters

TL;DR

This paper introduces Semi Network-Form Games, a novel framework combining Bayes nets and game theory to predict pilot behavior during mid-air encounters, accounting for variability in responses to collision avoidance advisories.

Contribution

It develops a new modeling approach that integrates human decision variability into game-theoretic predictions of aircraft pilot behavior.

Findings

Model successfully predicts pilot response variability.

Simulations demonstrate the framework's ability to replicate real-world behaviors.

Provides a foundation for improved collision avoidance system design.

Abstract

We show how to combine Bayes nets and game theory to predict the behavior of hybrid systems involving both humans and automated components. We call this novel framework "Semi Network-Form Games," and illustrate it by predicting aircraft pilot behavior in potential near mid-air collisions. At present, at the beginning of such potential collisions, a collision avoidance system in the aircraft cockpit advises the pilots what to do to avoid the collision. However studies of mid-air encounters have found wide variability in pilot responses to avoidance system advisories. In particular, pilots rarely perfectly execute the recommended maneuvers, despite the fact that the collision avoidance system's effectiveness relies on their doing so. Rather pilots decide their actions based on all information available to them (advisory, instrument readings, visual observations). We show how to build this aspect into a semi network-form game model of the encounter and then present computational simulations of the resultant model.