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Summary on collective behaviour
Topic: Collective behaviour
by Morgan, 2018 Cohort
Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on.
Overview#
Collective behaviour is a concept central to the idea of complexity which describes the relationship between the actions of the individual parts or components of a system and the large-scale behaviour of the system itself. Because collective behaviour is typically produced from the interaction of a very large number of components, it gives rise to characteristically complex and dynamic patterns of behaviour that are difficult to predict. Stated more precisely, the study of collective behaviour in complex systems involves attempting to explain how the individual actions of the parts of a system, which typically follow relatively simple rules of behaviour, interact to produce the sophisticated and co-ordinated large-scale behaviour that is observed at the level of the system even in the absence of central control or leadership. As is perhaps evident from the description above, collective behaviour is closely related to other concepts utilised in the study of complexity such systems thinking, emergence, and adaptation.
Collective Behaviour in Ant Colonies#
If you’ve ever casually observed a group of ants, you’re likely to have seen this process in action. Individual ants who each perform relatively simple actions (driven by genetic imperatives such as the need to gather food) and who only communicate with a small fraction of other ants in the colony, co-ordinate their behaviour to accomplish sophisticated tasks such as the construction and maintenance of the nest. Moreover, despite the simplicity of the behaviour of each ant and the limited range over which they can communicate, ant colonies appear to able to efficiently allocate ants to different task in response to changes in the environment. In this example, collective behaviour is the behaviour observed at the level of the colony; behaviour that is produced from the collective actions of each ant.
One possible way to think about the role of collective behaviour in complex systems relates to information processing. In this conceptual model, statistical and time-varying patterns in the collective behaviour of low-level components encodes information about the environment in which a system is situated (see the diagram below). This idea is demonstrated simply in the way many species of ants forage for food.
Initially, forager ants set out randomly in different directions in search of food. When a forager finds a source of food, they return to the nest leaving a trail of signalling chemicals called pheromones. When another ant encounters this trail they are likely to follow it. The likelihood that an ant will follow a given pheromone trail is proportional to the concentration of pheromones on the that trail. When an ant following the original pheromone trail encounters the food source, it too will return to the nest reinforcing the pheromone trail. Other pheromone trails that are not reinforced in this manner evaporate. By this process, ants collaboratively generate and communicate information about the location and quality of different sources of food in the environment. At any point in time, the information that has been accumulated is represented by the existing trails and their relative chemical concentration. This behaviour has been modelled in computer algorithms and applied to solve optimisation problems which involve finding the best possible route or solution from all feasible optionsin areas such as transportation planning and network routing.
Resources Used#
This summary was produced using information from several chapters of Melanie Mitchells Complexity: A Guided Tour, especially Chapters 1 and 12. This text is very accessible and provides an excellent account of the origins of complexity science and its development.
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This content has been contributed by a student as part of a learning activity.
If there are inaccuracies, or opportunities for significant improvement on this topic, feedback is welcome on how to improve the resource.
You can improve articles on this topic as a student in "Unravelling Complexity", or by including the amendments in an email to: Chris.Browne@anu.edu.au
Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on.
Overview#
Collective behaviour is a concept central to the idea of complexity which describes the relationship between the actions of the individual parts or components of a system and the large-scale behaviour of the system itself. Because collective behaviour is typically produced from the interaction of a very large number of components, it gives rise to characteristically complex and dynamic patterns of behaviour that are difficult to predict. Stated more precisely, the study of collective behaviour in complex systems involves attempting to explain how the individual actions of the parts of a system, which typically follow relatively simple rules of behaviour, interact to produce the sophisticated and co-ordinated large-scale behaviour that is observed at the level of the system even in the absence of central control or leadership. As is perhaps evident from the description above, collective behaviour is closely related to other concepts utilised in the study of complexity such systems thinking, emergence, and adaptation.
Collective Behaviour in Ant Colonies#
If you’ve ever casually observed a group of ants, you’re likely to have seen this process in action. Individual ants who each perform relatively simple actions (driven by genetic imperatives such as the need to gather food) and who only communicate with a small fraction of other ants in the colony, co-ordinate their behaviour to accomplish sophisticated tasks such as the construction and maintenance of the nest. Moreover, despite the simplicity of the behaviour of each ant and the limited range over which they can communicate, ant colonies appear to able to efficiently allocate ants to different task in response to changes in the environment. In this example, collective behaviour is the behaviour observed at the level of the colony; behaviour that is produced from the collective actions of each ant.
One possible way to think about the role of collective behaviour in complex systems relates to information processing. In this conceptual model, statistical and time-varying patterns in the collective behaviour of low-level components encodes information about the environment in which a system is situated (see the diagram below). This idea is demonstrated simply in the way many species of ants forage for food.
Initially, forager ants set out randomly in different directions in search of food. When a forager finds a source of food, they return to the nest leaving a trail of signalling chemicals called pheromones. When another ant encounters this trail they are likely to follow it. The likelihood that an ant will follow a given pheromone trail is proportional to the concentration of pheromones on the that trail. When an ant following the original pheromone trail encounters the food source, it too will return to the nest reinforcing the pheromone trail. Other pheromone trails that are not reinforced in this manner evaporate. By this process, ants collaboratively generate and communicate information about the location and quality of different sources of food in the environment. At any point in time, the information that has been accumulated is represented by the existing trails and their relative chemical concentration. This behaviour has been modelled in computer algorithms and applied to solve optimisation problems which involve finding the best possible route or solution from all feasible optionsin areas such as transportation planning and network routing.
Resources Used#
This summary was produced using information from several chapters of Melanie Mitchells Complexity: A Guided Tour, especially Chapters 1 and 12. This text is very accessible and provides an excellent account of the origins of complexity science and its development.