Connections between Relational Event Model and Inverse Reinforcement Learning for Characterizing Group Interaction Sequences

TL;DR

This paper uncovers connections between relational event models and inverse reinforcement learning, demonstrating IRL's utility in analyzing group social interactions through empirical experiments with virtual reality game players.

Contribution

It reveals mathematical analogies between REM and IRL and demonstrates IRL's novel application in characterizing group social behaviors.

Findings

IRL can infer individual preferences from social interaction sequences

Mathematical analogies between REM and IRL are established

Empirical validation with virtual reality game data

Abstract

In this paper we explore previously unidentified connections between relational event model (REM) from the field of network science and inverse reinforcement learning (IRL) from the field of machine learning with respect to their ability to characterize sequences of directed social interaction events in group settings. REM is a conventional approach to tackle such a problem whereas the application of IRL is a largely unbeaten path. We begin by examining the mathematical components of both REM and IRL and find straightforward analogies between the two methods as well as unique characteristics of the IRL approach. We demonstrate the special utility of IRL in characterizing group social interactions with an empirical experiment, in which we use IRL to infer individual behavioral preferences based on a sequence of directed communication events from a group of virtual-reality game players interacting and cooperating to accomplish a shared goal. Our comparison and experiment introduce fresh perspectives for social behavior analytics and help inspire new research opportunities at the nexus of social network analysis and machine learning.