The use of multilayer network analysis in animal behaviour

Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, known as \emph{multilayer network analysis}, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour as connected 'layers' of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population, and evolutionary levels of organisation. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.Comment: Thoroughly revised; title changed slightl

Adaptability, Cooperation and Reconfiguration in Very Complex Multiregional Network Organizations

There seems to be a general trend that the development of technologies which interact with human beings also enhances the knowledge of human functions. For example, with the development of color television systems progress in the knowledge of human color vision was also recorded. In return this new knowledge then helped in the design of even more efficient color television system. A similar situation seems to reign in computer systems and computer networks. Managing different resources in computer systems by operational systems resembles somewhat the management of resources in an organization. The inference block in 5th generation computers may resemble human inference and is pursued by an artificial intelligence discipline. The study of cooperative features in computer systems and networks may bring us closer to understanding these processes in organizations or even in human societies at large. This happens because many causal relations are present in computer systems in clearer and sometimes more primitive forms, stripped of many of the accompanying but irrelevant (emotional) ingredients. This Collaborative Paper is the continuation of an activity that started when Dr. Cifersky joined the Management and Technology Area of IIASA in 1882 as a participant in the Young Scientists Summer Program, under the supervision of Dr. R. Lee. The paper scans those problems in organizations which are evoked by the environment. It attempts to describe some of those processes which are taking place in complex organizations as a response to external influences, and identifies some of the impacts this may have on the organization's performance objectives. The paper has not been edited and supplemented by a vocabulary, therefore it does not make easy reading. It uses terms common in organization research, computer systems (for example, communication protocol), or principles used in fail-safe computer systems (reconfiguration). The topic is interesting and stimulating and can contribute to further research at the Institute in this field

Deductive and Inductive Stream Reasoning for Semantic Social Media Analytics

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The use of multilayer network analysis in animal behaviour

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.We gratefully acknowledge the 806 supporters of MX16: the UC Davis Institute for Social Sciences, the U.S. Army Research Office 807 under Multidisciplinary University Research Initiative Award No. W911NF-13-1-0340, the UC 808 Davis Complexity Sciences Center, the UC Davis Anthropology Department, the UC Davis 809 Graduate Student Association, the UC Davis Department of Engineering, and the UC Davis 810 Office of Research.Network analysis has driven key developments in research on animal behaviour by providing quantitative methods to study the social structures of animal groups and populations. A recent formalism, known as multilayer network analysis, has advanced the study of multifaceted networked systems in many disciplines. It offers novel ways to study and quantify animal behaviour through connected ‘layers’ of interactions. In this article, we review common questions in animal behaviour that can be studied using a multilayer approach, and we link these questions to specific analyses. We outline the types of behavioural data and questions that may be suitable to study using multilayer network analysis. We detail several multilayer methods, which can provide new insights into questions about animal sociality at individual, group, population and evolutionary levels of organization. We give examples for how to implement multilayer methods to demonstrate how taking a multilayer approach can alter inferences about social structure and the positions of individuals within such a structure. Finally, we discuss caveats to undertaking multilayer network analysis in the study of animal social networks, and we call attention to methodological challenges for the application of these approaches. Our aim is to instigate the study of new questions about animal sociality using the new toolbox of multilayer network analysis.Natural Environment Research Council (NERC)National Science Foundation (NSF) Graduate Research FellowshipNFS IOS grantNIH R01NERC standard gran