Emergence models fish birds-Emergence: Complex behaviour arising from simple roots

The first thing to hit Iain Couzin when he walked into the Oxford lab where he kept his locusts was the smell, like a stale barn full of old hay. The second, third, and fourth things to hit him were locusts. The insects frequently escaped their cages and careened into the faces of scientists and lab techs. The room was hot and humid, and the constant commotion of 20, bugs produced a miasma of aerosolized insect exoskeleton. Many of the staff had to wear respirators to avoid developing severe allergies.

It was also reported that even in Emergence models fish birds generic mean-field approximation of the dynamics, where the interaction does not need to be fully specified, the same results are obtained Typically biologists were working with collectives ranging in number from a few to a few thousand; physicists count groups of a few gazillion. This is another aspect neglected by many previous models assuming a constant speed. Foraging efficiency [94]. Through mutual anticipation, the individual moves to a site where potential transitions among individuals are concentrated. Quick lube loveland coupon present study describes a model based only on mutual anticipation, and not on the alignment rule. Were this an equilibrium problem as in spin system, the explicit symmetry breaking by discrete Emergence models fish birds of the direction as in Ising spin system could lead to an orientationally ordered state which could not occur in the continuous symmetry system e.

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Views Read Edit View history. Swarming is also used to describe groupings of some kinds of bacteria such as myxobacteria. Oxford: Oxford University Press. This is better understood given the following definition of emergence that comes from physics:. Emergence models fish birds further away, in the "zone of alignment", the focal animal will seek to align its direction of motion with its neighbours. ActivMedia Robotics. The remaining particles then move through the problem space following the lead of the optimum Melek an ugly redhead. He also says that living systems like the game of chesswhile emergent, cannot be reduced to underlying Emergence models fish birds of emergence:. A collection of people can also exhibit swarm behaviour, such as pedestrians [] or soldiers swarming the parapets [ dubious — discuss ]. It can be divided into natural swarm research studying biological systems and artificial swarm research studying human artefacts. When all scouts agree on a final location the whole cluster takes off and flies to it.

Collective behavior emerging out of self-organization is one of the most striking properties of an animal group.

  • It is exhilarating to watch a large flock of birds swarming in ever-changing patterns.
  • It is exhilarating to watch a large flock of birds swarming in ever-changing patterns.
  • In philosophy , systems theory , science , and art , emergence occurs when an entity is observed to have properties its parts do not have on their own.
  • Swarm behaviour , or swarming , is a collective behaviour exhibited by entities, particularly animals, of similar size which aggregate together, perhaps milling about the same spot or perhaps moving en masse or migrating in some direction.
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Collective behavior emerging out of self-organization is one of the most striking properties of an animal group. Typically, it is hypothesized that each individual in an animal group tends to align its direction of motion with those of its neighbors. Most previous models for collective behavior assume an explicit alignment rule, by which an agent matches its velocity with that of neighbors in a certain neighborhood, to reproduce a collective order pattern by simple interactions.

Recent empirical studies, however, suggest that there is no evidence for explicit matching of velocity, and that collective polarization arises from interactions other than those that follow the explicit alignment rule. We here propose a new lattice-based computational model that does not incorporate the explicit alignment rule but is based instead on mutual anticipation and asynchronous updating.

Moreover, we show that this model can realize densely collective motion with high polarity. Furthermore, we focus on the behavior of a pair of individuals, and find that the turning response is drastically changed depending on the distance between two individuals rather than the relative heading, and is consistent with the empirical observations.

Therefore, the present results suggest that our approach provides an alternative model for collective behavior. Mobile animal groups such as fish schools and bird flocks exhibit spontaneous polarized movement patterns. Many theoretical models have been proposed on how a collective order pattern would result from local interactions 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8.

The basic assumption incorporated into these models was the alignment rule, according to which an agent matches its velocity with those of others in its neighborhood.

The self-propelled particles model SPP 9 , in particular, which was inspired by the emergent collective properties of the physical system, is commonly used to explain collective behavior. The SPP model exhibits a phase transition by combining the alignment rule with external noise. With the advent of tracking and bio-logging, through the advance of image analysis techniques and global positioning systems, both empirical and observational kinetic data from members of real animal groups have been accumulated.

These data enable the investigation of both the individual 10 , 11 , 12 , 13 , 14 , 15 and group-level 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 behaviors within real groups. Ballerini et al. Cavagna et al.

Rather, individuals move super-diffusively within their group and change their relative position with their neighbors, even though they appear to exhibit cohesive synchronized behavior. Furthermore, Murakami et al. Empirical data obtained from real animals have been analyzed applying theoretical models.

Buhl et al. It was also reported that even in a generic mean-field approximation of the dynamics, where the interaction does not need to be fully specified, the same results are obtained Berdahl et al. Namely, a fish school prefers a darker area similar to their habitat and avoids the light. Larger schools are more sensitive to light.

Thus, previous models showed good agreement with the experimental and observational data. However, a conflict was reported between empirical data and previous models, particularly regarding the alignment rule. Katz et al. They inferred how individuals interact with a single neighbor, without assuming that they coordinate their responses with multiple neighbors. If members of a collective group actually implemented the alignment rule, the turning force would increase when the relative angle between their velocities became larger.

Several researches have reported collective motions resulting from attraction-repulsion interaction without the alignment 25 , 26 , 27 , 28 , 29 , For example, active Brownian agents that interact globally with each other by attraction show noise-induced transition between translational and rotational motions 25 , Moreover, collective rotational or translational motion also results from springlike attraction-repulsion interaction with equilibrium distance without alignment Furthermore, the pursuit-escape interaction motivated by non-cooperative behavior such as cannibalism as a driving force of collective motion in locusts would be an alternative to the direct alignment 28 , This model shows the emergence of global polarization.

But at least if both pursuit and escape interactions are implemented it would be unsuitable for the experimental results with respect to the social turning force in ref. To our knowledge, so far no theoretical models did show direct correspondence to the results with respect to the social turning force in ref.

In the present study, we show a local interaction named by mutual anticipation that is inspired the behavior of a swarm of soldier crabs 31 , 32 , 33 , 34 , We previously proposed a model based both on the alignment and the mutual anticipation with asynchronous position updating 36 , In the model, each individual is assigned a distinct vector called the principal vector and multiple vectors representing potential transitions that are randomly distributed from the principal vector within a maximal angle and maximal length.

Individuals asynchronously move to popular sites with respect to the potential transitions among individuals, follow the antecessors neighbors moved to next sites ahead of them , or move freely by choosing one of the potential transitions. Then, the alignment rule is applied to the principal vector within the neighborhood. We found that this model of a swarm is more robust against external perturbations than previously considered models Moreover, we revealed that an emergent behavior of the soldier crabs could be explained by mutual anticipation In addition, we showed that a robust mobile swarm without collective order could be simulated using only on mutual anticipation without velocity matching and the randomly modified principal vector Recently several researches have also reported collective motions resulting from anticipation.

For example, when anticipation is implemented by angular velocities of neighbors i. The present study describes a model based only on mutual anticipation, and not on the alignment rule. We show that this model forms a dense swarm with high polarity, despite the absence of the explicit alignment rule. Furthermore, we investigate the behavior of individuals in two-individual swarms and observe that their turning force is drastically changed owing to the distance between them rather than the relative angle.

This result is consistent with previous empirical data In this section, we introduce our model based on mutual anticipation and asynchronous updating, inspired by the behavior of soldier crabs.

In our model, position update of each individual at a time step is asynchronously implemented by either of three rules mutual anticipation, following or free moving. The mutual anticipation is the most important mechanism for our model and the other rules following and free moving perform backup roles for the proportion of each rule implemented by individuals in our model, see Supplemental Information , Figure S2.

See also ref. The essence of the mechanism is that i each individual has multiple potential transitions by which it anticipates the movements of other individuals; ii if there are sites where targets of potential transitions are overlapped, one of individuals whose potential transitions reach the site moves there; iii then the other individuals avoid the site and asynchronously move by the remaining potential transitions Fig.

After position updates by mutual anticipation and the other rules i. In the rest of the section, we first describe how these rules are inspired by behavior of a swarm of soldier crabs. Next we describe in due order how asynchronous position update is implemented, explaining about each rule. Each individual in our model has multiple potential transitions.

In this figure there are two individuals who each have three potential transitions dashed arrows. By the potential transitions individuals anticipate the movements each other and thereby intend to move to a site represented by a red circle in this figure where targets of the transitions are overlapped left. Then one of individuals moves to the site, and the other avoids there and asynchronously moves by the remaining potential transitions right.

The resulting moves are represented by solid arrows. Distribution of potential transitions upper left , Mutual anticipation upper second left , following and free move upper right , and the resulting distribution bottom. Individuals are represented by black lattices. The popular sites and the created empty sites are represented by pale red and pale blue lattices, respectively. After an individual updates its position from a pale blue square to a black square its velocity represented with a pale red arrow , its principal vector is updated from a pale blue arrow to a blue arrow.

Here we describe how our model is inspired by behavior of a swarm of soldier crabs. Through numerical field observations and experimental results 31 , we identified the following characteristics of general swarming behavior in soldier crabs: i a swarm moving in the tidal zone has inherent noise, i. In contrast, if the swarm becomes bigger and forms a dense region, this part of the swarm rushes into the pool without pausing due to the effect of the group.

Characteristic i suggests perpetual negotiation among individuals with respect to direction. Characteristic ii reveals that density affects the mechanism that generates a swarm. Such an inherent noise has been found not only in the swarming of soldier crabs but also in other animal groups.

Considering i combined with ii suggests that inherent noise positively contributes to the generation and maintenance of a swarm. To incorporate soldier crab swarm behaviors into a model, we introduce several potential transitions for each individual that allow the individual to anticipate the movements of other individuals within the swarm. Here we introduce asynchronous position update in our model by mutual anticipation mechanism that is inspired by above characteristics of swarming behavior in soldier crabs, and that is implemented by potential transitions assigned to each individual.

In this model Fig. We denote the number of the potential transitions by P. Here, we briefly describe the mutual anticipation rules for more detailed description see Supplemental Information , Appendix S1. In the upper left in Fig. First, the number of overlapped potential transition targets among individuals is counted as the site popularity regardless of whose potential transition targets these are.

If some potential transitions reach a site with popularity greater than one popular site , an individual moves to the site with the highest popularity. In the upper second left in Fig. This rule represents the mutual anticipation of the individuals. For example, people often manage to avoid collisions and walk in a crowd with others using anticipation 40 , 41 , Therefore, we implement this type of behavior in our model.

If more than one individual intends to move to the same site, then one individual whose direction of potential transition reaching the site is the closest to that of its principal vector, is randomly chosen to occupy the site, and the other individuals move to the site with the second highest popularity. The rule of one individual per site is referred to as the repulsion rule due to the asynchronous update.

We note that while peoples avoid collisions using anticipation, individuals in our model both intend to move to popular sites and avoid the sites.

In other words, if there is no individual that moved to a popular site, individuals whose potential transitions reach the site intend to move there. However, because only one individual is allowed to move to a popular site, after it occupied there, the others avoid moving to the site. In this way, individuals asynchronously both intend to move to and avoid popular sites. Position update by following is implemented by individuals who have not updated their positions because of absence of popular site.

In other words, if there is no longer any popular site but is some new empty sites resulting from the flockmate moving to the popular site by mutual anticipation, randomly chosen individuals among those with potential transitions toward the empty sites move to them.

Note that the number of individuals who implement following is always limited equal to or smaller than the number of those who implement mutual anticipation.

In the right diagram in Fig. This behavior mimics followers and is referred to as the attraction rule. Finally, if there is no popular site and no new empty site in the neighborhood, an individual freely moves randomly assuming one of the potential transitions.

The game of chess is inescapably historical, even though it is also constrained and shaped by a set of rules, not to mention the laws of physics. The mathematical modelling of flocking behaviour is a common technology, and has found uses in animation. Animal migration altitudinal tracking coded wire tag Bird migration flyways reverse migration Cell migration Fish migration diel vertical lessepsian salmon run sardine run Homing natal philopatry Insect migration butterflies monarch Sea turtle migration. Animal Migration. It remains to be seen whether this applies to other animals.

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Swarm behaviour , or swarming , is a collective behaviour exhibited by entities, particularly animals, of similar size which aggregate together, perhaps milling about the same spot or perhaps moving en masse or migrating in some direction. It is a highly interdisciplinary topic. The term flocking or murmuration can refer specifically to swarm behaviour in birds, herding to refer to swarm behaviour in tetrapods , and shoaling or schooling to refer to swarm behaviour in fish.

Phytoplankton also gather in huge swarms called blooms , although these organisms are algae and are not self-propelled the way animals are. From a more abstract point of view, swarm behaviour is the collective motion of a large number of self-propelled entities. Swarm behaviour is also studied by active matter physicists as a phenomenon which is not in thermodynamic equilibrium , and as such requires the development of tools beyond those available from the statistical physics of systems in thermodynamic equilibrium.

Swarm behaviour was first simulated on a computer in with the simulation program boids. The model was originally designed to mimic the flocking behaviour of birds, but it can be applied also to schooling fish and other swarming entities.

In recent decades, scientists have turned to modeling swarm behaviour to gain a deeper understanding of the behaviour. Early studies of swarm behaviour employed mathematical models to simulate and understand the behaviour. The simplest mathematical models of animal swarms generally represent individual animals as following three rules:.

The boids computer program, created by Craig Reynolds in , simulates swarm behaviour following the above rules. In the "zone of repulsion", very close to the animal, the focal animal will seek to distance itself from its neighbours to avoid collision. Slightly further away, in the "zone of alignment", the focal animal will seek to align its direction of motion with its neighbours.

In the outermost "zone of attraction", which extends as far away from the focal animal as it is able to sense, the focal animal will seek to move towards a neighbour.

The shape of these zones will necessarily be affected by the sensory capabilities of a given animal. For example, the visual field of a bird does not extend behind its body. Fish rely on both vision and on hydrodynamic perceptions relayed through their lateral lines , while Antarctic krill rely both on vision and hydrodynamic signals relayed through antennae.

However recent studies of starling flocks have shown that each bird modifies its position, relative to the six or seven animals directly surrounding it, no matter how close or how far away those animals are. It remains to be seen whether this applies to other animals. Another recent study, based on an analysis of high-speed camera footage of flocks above Rome and assuming minimal behavioural rules, has convincingly simulated a number of aspects of flock behaviour.

In order to gain insight into why animals evolve swarming behaviours, scientists have turned to evolutionary models that simulate populations of evolving animals. Typically these studies use a genetic algorithm to simulate evolution over many generations. These studies have investigated a number of hypotheses attempting to explain why animals evolve swarming behaviours, such as the selfish herd theory [9] [10] [11] [12] the predator confusion effect, [13] [14] the dilution effect, [15] [16] and the many eyes theory.

The concept of emergence—that the properties and functions found at a hierarchical level are not present and are irrelevant at the lower levels—is often a basic principle behind self-organizing systems.

The queen does not give direct orders and does not tell the ants what to do. Here each ant is an autonomous unit that reacts depending only on its local environment and the genetically encoded rules for its variety. Despite the lack of centralized decision making, ant colonies exhibit complex behaviours and have even been able to demonstrate the ability to solve geometric problems.

For example, colonies routinely find the maximum distance from all colony entrances to dispose of dead bodies. A further key concept in the field of swarm intelligence is stigmergy. The principle is that the trace left in the environment by an action stimulates the performance of a next action, by the same or a different agent.

In that way, subsequent actions tend to reinforce and build on each other, leading to the spontaneous emergence of coherent, apparently systematic activity. Stigmergy is a form of self-organization. It produces complex, seemingly intelligent structures, without need for any planning, control, or even direct communication between the agents. As such it supports efficient collaboration between extremely simple agents, who lack any memory, intelligence or even awareness of each other.

Swarm intelligence is the collective behaviour of decentralized , self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence.

The expression was introduced by Gerardo Beni and Jing Wang in , in the context of cellular robotic systems. Swarm intelligence systems are typically made up of a population of simple agents such as boids interacting locally with one another and with their environment. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of intelligent global behaviour, unknown to the individual agents.

Swarm intelligence research is multidisciplinary. It can be divided into natural swarm research studying biological systems and artificial swarm research studying human artefacts.

There is also a scientific stream attempting to model the swarm systems themselves and understand their underlying mechanisms, and an engineering stream focused on applying the insights developed by the scientific stream to solve practical problems in other areas.

Swarm algorithms follow a Lagrangian approach or an Eulerian approach. It is a hydrodynamic approach, and can be useful for modelling the overall dynamics of large swarms. Individual particle models can follow information on heading and spacing that is lost in the Eulerian approach.

Ant colony optimization is a widely used algorithm which was inspired by the behaviours of ants, and has been effective solving discrete optimization problems related to swarming. Species that have multiple queens may have a queen leaving the nest along with some workers to found a colony at a new site, a process akin to swarming in honeybees.

Simulations demonstrate that a suitable "nearest neighbour rule" eventually results in all the particles swarming together, or moving in the same direction. This emerges, even though there is no centralized coordination, and even though the neighbours for each particle constantly change over time.

It has become a challenge in theoretical physics to find minimal statistical models that capture these behaviours. Particle swarm optimization is another algorithm widely used to solve problems related to swarms.

It was developed in by Kennedy and Eberhart and was first aimed at simulating the social behaviour and choreography of bird flocks and fish schools. The system initially seeds a population with random solutions.

It then searches in the problem space through successive generations using stochastic optimization to find the best solutions. The solutions it finds are called particles. Each particle stores its position as well as the best solution it has achieved so far. The particle swarm optimizer tracks the best local value obtained so far by any particle in the local neighbourhood.

The remaining particles then move through the problem space following the lead of the optimum particles. At each time iteration, the particle swarm optimiser accelerates each particle toward its optimum locations according to simple mathematical rules.

Particle swarm optimization has been applied in many areas. It has few parameters to adjust, and a version that works well for a specific applications can also work well with minor modifications across a range of related applications. Researchers in Switzerland have developed an algorithm based on Hamilton's rule of kin selection.

The algorithm shows how altruism in a swarm of entities can, over time, evolve and result in more effective swarm behaviour. The earliest evidence of swarm behaviour in animals dates back about million years. Fossils of the trilobite Ampyx priscus have been recently described as clustered in lines along the ocean floor. The animals were all mature adults, and were all facing the same direction as though they had formed a conga line or a peloton.

It has been suggested they line up in this manner to migrate, much as spiny lobsters migrate in single-file queues. Examples of biological swarming are found in bird flocks , [51] fish schools , [52] [53] insect swarms , [54] bacteria swarms , [55] [56] molds, [57] molecular motors , [58] quadruped herds [59] and people. The behaviour of insects that live in colonies , such as ants, bees, wasps and termites, has always been a source of fascination for children, naturalists and artists. Individual insects seem to do their own thing without any central control, yet the colony as a whole behaves in a highly coordinated manner.

The group coordination that emerges is often just a consequence of the way individuals in the colony interact. These interactions can be remarkably simple, such as one ant merely following the trail left by another ant. Yet put together, the cumulative effect of such behaviours can solve highly complex problems, such as locating the shortest route in a network of possible paths to a food source.

The organised behaviour that emerges in this way is sometimes called swarm intelligence. Individual ants do not exhibit complex behaviours, yet a colony of ants collectively achieves complex tasks such as constructing nests, taking care of their young, building bridges and foraging for food. A colony of ants can collectively select i. Selection of the best food source is achieved by ants following two simple rules. First, ants which find food return to the nest depositing a pheromone chemical.

More pheromone is laid for higher quality food sources. Ants in the nest follow another simple rule, to favor stronger trails, on average. More ants then follow the stronger trail, so more ants arrive at the high quality food source, and a positive feedback cycle ensures, resulting in a collective decision for the best food source.

If there are two paths from the ant nest to a food source, then the colony usually selects the shorter path. This is because the ants that first return to the nest from the food source are more likely to be those that took the shorter path. More ants then retrace the shorter path, reinforcing the pheromone trail. The successful techniques used by ant colonies have been studied in computer science and robotics to produce distributed and fault-tolerant systems for solving problems.

This area of biomimetics has led to studies of ant locomotion, search engines that make use of "foraging trails", fault-tolerant storage and networking algorithms. When a honey bee swarm emerges from a hive they do not fly far at first. They may gather in a tree or on a branch only a few meters from the hive. In this new location, the bees cluster about the queen and send 20 scout bees out to find a suitable new nest locations.

The scout bees are the most experienced foragers in the cluster. An individual scout returning to the cluster promotes a location she has found. She uses a dance similar to the waggle dance to indicate direction and distance to others in the cluster. The more excited she is about her findings the more excitedly she dances. If she can convince other scouts to check out the location she found, they may take off, check out the proposed site and promote the site further upon their return.

Several different sites may be promoted by different scouts at first. After several hours and sometimes days, slowly a favourite location emerges from this decision making process. When all scouts agree on a final location the whole cluster takes off and flies to it. Sometimes, if no decision is reached, the swarm will separate, some bees going in one direction; others, going in another.