Framework for Inferring Leadership Dynamics of Complex Movement from Time Series

TL;DR

This paper introduces a novel framework for inferring leadership roles in coordinated group activities from time series data, applicable to various biological and social systems, without assuming specific leader traits.

Contribution

It formalizes the Faction Initiator Inference Problem and proposes a leadership inference framework that outperforms baseline methods on simulated and real biological datasets.

Findings

Framework performs better than baseline in simulated data

Effective in analyzing fish school and baboon movement datasets

Enables scientific hypothesis generation on collective behaviors

Abstract

Leadership plays a key role in social animals, including humans, decision-making and coalescence in coordinated activities such as hunting, migration, sport, diplomatic negotiation etc. In these coordinated activities, leadership is a process that organizes interactions among members to make a group achieve collective goals. Understanding initiation of coordinated activities allows scientists to gain more insight into social species behaviors. However, by using only time series of activities data, inferring leadership as manifested by the initiation of coordinated activities faces many challenging issues. First, coordinated activities are dynamic and are changing over time. Second, several different coordinated activities might occur simultaneously among subgroups. Third, there is no fundamental concept to describe these activities computationally. In this paper, we formalize Faction Initiator Inference Problem and propose a leadership inference framework as a solution of this problem. The framework makes no assumption about the characteristics of a leader or the parameters of the coordination process. The framework performs better than our non-trivial baseline in both simulated and biological datasets (schools of fish). Moreover, we demonstrate the application of our framework as a tool to study group merging and splitting dynamics on another biological dataset of trajectories of wild baboons. In addition, our problem formalization and framework enable opportunities for scientists to analyze coordinated activities and generate scientific hypotheses about collective behaviors that can be tested statistically and in the field.