The term collective behavior refers to social processes in which the coordinated actions of a group of agents result in the emergence of global structures. This term encompasses a series of fascinating biological phenomena ranging from flocking in birds and schooling in fish, to collective decision-making in social insects, and patterns of collective alignment observed in human crowds or in cancerous cells. The operational principles underpinning collective behavior remain largely unknown due to the intrinsic complexity of such systems in which the population level phenomena have to be explained by understanding the behavior of the system components and how they interact. However difficult this scientific challenge may be, it remains crucial to understand various manifestations of life, as well as to design and manage various distributed, decentralized ICT systems such as sensor networks, smart grids, and multi-robot systems. In support of the understanding of collective behaviors, novel methods and technologies have been recently introduced, which can pinpoint the relevant patterns of interactions among agents, as well as behavioral heterogeneities within groups, which are at the basis of the emergence of the system-level collective dynamics.
This Research Topic calls for contributions that illustrate and discuss innovative methods and technologies that crucially contribute to shed new light on collective behaviors, enhancing its understanding. For example, the observation of collective behaviors in natural setting has been made more informative through the use of technologies that facilitated tracking of the individual behavior in large groups. New computational modeling techniques and machine learning methods have been used to account for the effect produced by the level of connectivity among the system components, and to uncover hidden patterns and correlations in massive data about social behavior. Virtual reality environments have been used to observe the responses of single individuals in an experimentally controlled crowd. Moreover, new insights into the principles of collective behavior have been generated through the development of techniques to control and guide the collective response of a group of natural and artificial agents. Multi-robot systems and simulations of embodied multi-agent systems are methodological platforms that have been used to generate and test new hypotheses on the cognitive mechanisms required by agents engaged in complex collective dynamics. These tools have also been used to look at the effects of genetic/behavioral heterogeneities on the emergence of global responses. The use of mixed societies made of animals and artificial agents have facilitated the setup of controlled experimental conditions in which the behavior of single system components can be experimentally manipulated to look at their effects on the population-level response.
In conclusion, this Research Topic welcomes cross-disciplinary contributions that—through the use of innovative technologies and methods like those mentioned above—contribute to push forward our understanding of collective behavior in natural and artificial systems.
The term collective behavior refers to social processes in which the coordinated actions of a group of agents result in the emergence of global structures. This term encompasses a series of fascinating biological phenomena ranging from flocking in birds and schooling in fish, to collective decision-making in social insects, and patterns of collective alignment observed in human crowds or in cancerous cells. The operational principles underpinning collective behavior remain largely unknown due to the intrinsic complexity of such systems in which the population level phenomena have to be explained by understanding the behavior of the system components and how they interact. However difficult this scientific challenge may be, it remains crucial to understand various manifestations of life, as well as to design and manage various distributed, decentralized ICT systems such as sensor networks, smart grids, and multi-robot systems. In support of the understanding of collective behaviors, novel methods and technologies have been recently introduced, which can pinpoint the relevant patterns of interactions among agents, as well as behavioral heterogeneities within groups, which are at the basis of the emergence of the system-level collective dynamics.
This Research Topic calls for contributions that illustrate and discuss innovative methods and technologies that crucially contribute to shed new light on collective behaviors, enhancing its understanding. For example, the observation of collective behaviors in natural setting has been made more informative through the use of technologies that facilitated tracking of the individual behavior in large groups. New computational modeling techniques and machine learning methods have been used to account for the effect produced by the level of connectivity among the system components, and to uncover hidden patterns and correlations in massive data about social behavior. Virtual reality environments have been used to observe the responses of single individuals in an experimentally controlled crowd. Moreover, new insights into the principles of collective behavior have been generated through the development of techniques to control and guide the collective response of a group of natural and artificial agents. Multi-robot systems and simulations of embodied multi-agent systems are methodological platforms that have been used to generate and test new hypotheses on the cognitive mechanisms required by agents engaged in complex collective dynamics. These tools have also been used to look at the effects of genetic/behavioral heterogeneities on the emergence of global responses. The use of mixed societies made of animals and artificial agents have facilitated the setup of controlled experimental conditions in which the behavior of single system components can be experimentally manipulated to look at their effects on the population-level response.
In conclusion, this Research Topic welcomes cross-disciplinary contributions that—through the use of innovative technologies and methods like those mentioned above—contribute to push forward our understanding of collective behavior in natural and artificial systems.
