Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations

Muro, Cristina; Escobedo, Ramón; Spector, Lee; Coppinger, Raymond

doi:10.1016/j.beproc.2011.09.006

Cited by 490 publications

(240 citation statements)

References 13 publications

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“…For instance, wolves (Canis lupus lupus) hunting together a large prey will obtain immediate benefits from the cooperative hunt, and the proximate mechanisms involved do not need to be particularly complex. Although cognition may help make coordination more efficient (Bailey, Myatt, & Wilson, 2013), a recent study suggest that coordinated hunting in wolves could arise as each wolf follows simple rules such as getting the closest safe distance to the prey and getting the best view of the prey (Muro, Escobedo, Spector, & Coppinger, 2011).…”

Section: Partner Choice In Agonistic Supportmentioning

confidence: 99%

Proximate mechanisms underlying cooperation in carnivores

Romero

2015

Japanese Journal of Animal Psychology

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While the evolution of cooperative behaviors has generated an intense debate among evolutionists and animal behaviorists, the proximate mechanisms underlying cooperative relationships have received much less attention. In recent years, it has become clear that an understanding of proximate causation of cooperation is needed in order to obtain a more balanced and complete picture of the phenomenon. The proximate cause of cooperation refers to the immediate situation that triggers behavior, and the role of learning, memory, physiology, and neural processes. Since from an evolutionary point of view cooperative relationships are maintained because of the subsequent benefits they bring, there has been the tendency to erroneously assume that they are also motivated by their future benefits. This assumption would imply that animals engage in social interactions in order to gain future benefits, or that they are able to remember the services given by another individual in order to offer a service in return at a later date. While this "rational" calculation offers a possible explanation, it is currently unclear whether or not some animal species have these cognitive capacities. Here I will argue that, while complex cognitive mechanisms may be present in some species, lesscognitively demanding mechanisms, based on emotions, could be at the basis of the flexibility needed to form complex, enduring cooperative relationships in both human and non-human animals.

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Section: Partner Choice In Agonistic Supportmentioning

confidence: 99%

Proximate mechanisms underlying cooperation in carnivores

Romero

2015

Japanese Journal of Animal Psychology

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“…These interaction functions are a novelty with respect to the piece-wise linear formulation used in [15].…”

Section: Methods: the Modelmentioning

confidence: 99%

“…We use a simple, homogeneous, particle-based computational model where wolves and prey are modelled as particles that interact through smooth attractive and repulsive forces [14][15][16][17][18][19][20][21][22][25][26][27].…”

Section: Methods: the Modelmentioning

confidence: 99%

“…Numerical simulations showed that these patterns can be seen as the emerging result of the combination of two simple rules applied to the behaviour of each individual wolf: (i) move towards the prey until a minimum safe distance to the prey d c is reached, and (ii) when at the safe distance d c , move away from the other wolves that are within the safe area [15]. In this previous work, we assumed for simplicity the particular case where both critical distances are equal (d a ¼ d c ), that is, wolves start to move away from each other only when they are at the safe distance from the prey.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, we presented a simple particle model which reproduces the typical wolf-pack hunting patterns observed in nature [15]: cursorial pursuit, encircling manoeuvre, relay running and ambushing. Numerical simulations showed that these patterns can be seen as the emerging result of the combination of two simple rules applied to the behaviour of each individual wolf: (i) move towards the prey until a minimum safe distance to the prey d c is reached, and (ii) when at the safe distance d c , move away from the other wolves that are within the safe area [15].…”

Section: Introductionmentioning

confidence: 99%

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Group size, individual role differentiation and effectiveness of cooperation in a homogeneous group of hunters

Escobedo¹,

Muro²,

Spector

et al. 2014

J. R. Soc. Interface.

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The emergence of cooperation in wolf-pack hunting is studied using a simple, homogeneous, particle-based computational model. Wolves and prey are modelled as particles that interact through attractive and repulsive forces. Realistic patterns of wolf aggregation readily emerge in numerical simulations, even though the model includes no explicit wolf-wolf attractive forces, showing that the form of cooperation needed for wolf-pack hunting can take place even among strangers. Simulations are used to obtain the stationary states and equilibria of the wolves and prey system and to characterize their stability. Different geometric configurations for different pack sizes arise. In small packs, the stable configuration is a regular polygon centred on the prey, while in large packs, individual behavioural differentiation occurs and induces the emergence of complex behavioural patterns between privileged positions. Stable configurations of large wolf-packs include travelling and rotating formations, periodic oscillatory behaviours and chaotic group behaviours. These findings suggest a possible mechanism by which larger pack sizes can trigger collective behaviours that lead to the reduction and loss of group hunting effectiveness, thus explaining the observed tendency of hunting success to peak at small pack sizes. They also explain how seemingly complex collective behaviours can emerge from simple rules, among agents that need not have significant cognitive skills or social organization.

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Harnessing Disparities in Magnetic Microswarms: From Construction to Collaborative Tasks

Cao,

Mou,

Yang

et al. 2024

Advanced Science

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Individual differences in size, experience, and task specialization in natural swarms often result in heterogeneity and hierarchy, facilitating efficient and coordinated task accomplishment. Drawing inspiration from this phenomenon, a general strategy is proposed for organizing magnetic micro/nanorobots (MNRs) with apparent differences in size, shape, and properties into cohesive microswarms with tunable heterogeneity, controlled spatial hierarchy, and collaborative tasking capability. In this strategy, disparate magnetic MNRs can be manipulated to show reversible transitions between synchronization and desynchronization by elaborately regulating parameter sets of the rotating magnetic field. Utilizing these transitions, alongside local robust hydrodynamic interactions, diverse heterospecific pairings of disparate magnetic MNRs can be organized into heterogeneous microswarms, and their spatial organization can be dynamically adjusted from egalitarian to leader‐follower‐like hierarchies on the fly, both in open space and complex microchannels. Furthermore, when specializing the disparate MNRs with distinct functions (“division of labor”) such as sensing and drug carrying, they can execute precise drug delivery targeting unknown sites in a collaborative sensing‐navigating‐cargo dropping sequence, demonstrating significant potential for precise tumor treatment. These findings highlight the critical roles of attribute differences and hierarchical organization in designing efficient swarming micro/nanorobots for biomedical applications.

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Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations

Cited by 490 publications

References 13 publications

Proximate mechanisms underlying cooperation in carnivores

Proximate mechanisms underlying cooperation in carnivores

Group size, individual role differentiation and effectiveness of cooperation in a homogeneous group of hunters

Harnessing Disparities in Magnetic Microswarms: From Construction to Collaborative Tasks

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