Mining and modeling complex leadership–followership dynamics of movement data

Amornbunchornvej, Chainarong; Berger-Wolf, Tanya Y.

doi:10.1007/s13278-019-0600-z

Cited by 10 publications

(9 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other words, initiators do not necessary move first or in a front of a group. In this work, Table 1 suggests that there is 3 The reason that fish datasets have their own table while other datasets are in another table is because of the following reason. To use 10-fold cross validation, we have to be able to learn each individual strategy from one set of coordination events (training datasets) to predict the strategy of the same individual in another set of coordination events (validation datasets).…”

Section: Comparison With the State Of The Art Methodsmentioning

confidence: 93%

Framework for Inferring Following Strategies from Time Series of Movement Data

Amornbunchornvej,

Berger-Wolf

2019

Preprint

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Comparison With the State Of The Art Methodsmentioning

confidence: 93%

Framework for Inferring Following Strategies from Time Series of Movement Data

Amornbunchornvej,

Berger-Wolf

2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Followership is a strategy to deal with social coordination problems, especially those involved in group movement (Pelé et al, 2019). Researchers can know group decision‐making and collective behavior better in social animals, including humans, by understanding how leaders and followers change, emerge, or converge (Amornbunchornvej & Berger‐Wolf, 2019). Shared identity between leaders and group members can bring more willingness to invest time on tasks for members (Slater et al., 2019).…”

Section: Discussionmentioning

confidence: 99%

The Importance of Followership in Human Resources

Chung¹,

Chung²

2021

Perf Improvement Qrtly

View full text Add to dashboard Cite

Leaders have taken the center stage of leadership for many years; researchers start to point out the transition from leadership to followership. When people discuss leadership, they should consider followership because leadership does not occur if followership does not follow. Characteristics and behaviors of leadership and followership are variables of followership, and they can influence followership outcomes. Followership provides a new way for people to think about leadership and helps people understand what a follower should or should not do to be an effective follower. To examine what makes followership important and how followership influences human resources, this literature review conducted searches for relevant followership research within the field of human resources. The details of documents by year, records per year by source, documents by country/territory, and documents by subject area were identified and summarized.

show abstract

“…2019 ), swarm (Shuai et al. 2019 ), leadership (Amornbunchornvej and Berger-Wolf 2019 ) and chasing (de Lucca Siqueira and Bogorny 2011 ). They were also studied as mixed-drove or co-occurrences (Wang et al.…”

Section: Stdm Task-related Challengesmentioning

confidence: 99%

Spatiotemporal data mining: a survey on challenges and open problems

et al. 2021

View full text Add to dashboard Cite

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

show abstract

Mining and modeling complex leadership–followership dynamics of movement data

Cited by 10 publications

References 34 publications

Framework for Inferring Following Strategies from Time Series of Movement Data

Framework for Inferring Following Strategies from Time Series of Movement Data

The Importance of Followership in Human Resources

Spatiotemporal data mining: a survey on challenges and open problems

Contact Info

Product

Resources

About