Framework for Inferring Following Strategies from Time Series of Movement Data

How do groups of individuals achieve consensus in movement decisions? Do individuals follow their friends, the one predetermined leader, or whomever just happens to be nearby? To address these questions computationally, we formalize "Coordination Strategy Inference Problem". In this setting, a group of multiple individuals moves in a coordinated manner towards a target path. Each individual uses a specific strategy to follow others (e.g. nearest neighbors, pre-defined leaders, preferred friends). Given a set of time series that includes coordinated movement and a set of candidate strategies as inputs, we provide the first methodology (to the best of our knowledge) to infer whether each individual uses local-agreement-system or dictatorship-like strategy to achieve movement coordination at the group level. We evaluate and demonstrate the performance of the proposed framework by predicting the direction of movement of an individual in a group in both simulated datasets as well as two real-world datasets: a school of fish and a troop of baboons. Moreover, since there is no prior methodology for inferring individual-level strategies, we compare our framework with the state-of-the-art approach for the task of classification of group-level-coordination models. The results show that our approach is highly accurate in inferring the correct strategy in simulated datasets even in complicated mixed strategy settings, which no existing method can infer. In the task of classification of group-level-coordination models, our framework performs better than the state-of-the-art approach in all datasets. Animal data experiments show that fish, as expected, follow their neighbors, while baboons have a preference to follow specific individuals. Our methodology generalizes to arbitrary time series data of real numbers, beyond movement data.

READ FULL TEXT
research
11/04/2019

Inferring Coordination Strategies from Time Series of Movement Data

How do groups of individuals achieve consensus in movement decisions? Do...
research
04/10/2020

mFLICA: An R package for Inferring Leadership of Coordination From Time Series

Leadership is a process that leaders influence followers to achieve coll...
research
11/06/2019

An Information Theory Approach on Deciding Spectroscopic Follow Ups

Classification and characterization of variable phenomena and transient ...
research
10/04/2020

Mining and modeling complex leadership-followership dynamics of movement data

Leadership and followership are essential parts of collective decision a...
research
04/06/2021

Framework for Inferring Leadership Dynamics of Complex Movement from Time Series

Leadership plays a key role in social animals, including humans, decisio...
research
03/04/2016

FLICA: A Framework for Leader Identification in Coordinated Activity

Leadership is an important aspect of social organization that affects th...
research
01/09/2018

A Local Approach for Information Transfer

In this work, a strategy to estimate the information transfer between th...

Please sign up or login with your details

Forgot password? Click here to reset