# A flocking model using network links

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## WHAT IS IT?

This model is an attempt to mimic the flocking of birds. (The resulting motion also resembles schools of fish.) The flocks that appear in this model are not created or led in any way by special leader birds. Rather, each bird is following exactly the same set of rules, from which flocks emerge.

## HOW IT WORKS

The birds follow three rules: "alignment", "separation", and "cohesion".

"Alignment" means that a bird tends to turn so that it is moving in the same direction that nearby birds are moving.

"Separation" means that a bird will turn to avoid another bird which gets too close.

"Cohesion" means that a bird will move towards other nearby birds (unless another bird is too close).

When two birds are too close, the "separation" rule overrides the other two, which are deactivated until the minimum separation is achieved.

The three rules affect only the bird's heading. Each bird always moves forward at the same constant speed.

## HOW TO USE IT

First, determine the number of birds you want in the simulation and set the POPULATION slider to that value. Press SETUP to create the birds, and press GO to have them start flying around.

The default settings for the sliders will produce reasonably good flocking behavior. However, you can play with them to get variations:

Three TURN-ANGLE sliders control the maximum angle a bird can turn as a result of each rule.

VISION is the distance that each bird can see 360 degrees around it.

## THINGS TO NOTICE

Central to the model is the observation that flocks form without a leader.

There are no random numbers used in this model, except to position the birds initially. The fluid, lifelike behavior of the birds is produced entirely by deterministic rules.

Also, notice that each flock is dynamic. A flock, once together, is not guaranteed to keep all of its members. Why do you think this is?

After running the model for a while, all of the birds have approximately the same heading. Why?

Sometimes a bird breaks away from its flock. How does this happen? You may need to slow down the model or run it step by step in order to observe this phenomenon.

## THINGS TO TRY

Play with the sliders to see if you can get tighter flocks, looser flocks, fewer flocks, more flocks, more or less splitting and joining of flocks, more or less rearranging of birds within flocks, etc.

You can turn off a rule entirely by setting that rule's angle slider to zero. Is one rule by itself enough to produce at least some flocking? What about two rules? What's missing from the resulting behavior when you leave out each rule?

Will running the model for a long time produce a static flock? Or will the birds never settle down to an unchanging formation? Remember, there are no random numbers used in this model.

## EXTENDING THE MODEL

Currently the birds can "see" all around them. What happens if birds can only see in front of them? The `in-cone`

primitive can be used for this.

Is there some way to get V-shaped flocks, like migrating geese?

What happens if you put walls around the edges of the world that the birds can't fly into?

Can you get the birds to fly around obstacles in the middle of the world?

What would happen if you gave the birds different velocities? For example, you could make birds that are not near other birds fly faster to catch up to the flock. Or, you could simulate the diminished air resistance that birds experience when flying together by making them fly faster when in a group.

Are there other interesting ways you can make the birds different from each other? There could be random variation in the population, or you could have distinct "species" of bird.

## NETLOGO FEATURES

Notice the need for the `subtract-headings`

primitive and special procedure for averaging groups of headings. Just subtracting the numbers, or averaging the numbers, doesn't give you the results you'd expect, because of the discontinuity where headings wrap back to 0 once they reach 360.

## RELATED MODELS

- Moths
- Flocking Vee Formation

## CREDITS AND REFERENCES

This model is inspired by the Boids simulation invented by Craig Reynolds. The algorithm we use here is roughly similar to the original Boids algorithm, but it is not the same. The exact details of the algorithm tend not to matter very much -- as long as you have alignment, separation, and cohesion, you will usually get flocking behavior resembling that produced by Reynolds' original model. Information on Boids is available at http://www.red3d.com/cwr/boids/.

## HOW TO CITE

If you mention this model in a publication, we ask that you include these citations for the model itself and for the NetLogo software:

- Wilensky, U. (1998). NetLogo Flocking model. http://ccl.northwestern.edu/netlogo/models/Flocking. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL.
- Wilensky, U. (1999). NetLogo. http://ccl.northwestern.edu/netlogo/. Center for Connected Learning and Computer-Based Modeling, Northwestern Institute on Complex Systems, Northwestern University, Evanston, IL.

## COPYRIGHT AND LICENSE

Copyright 1998 Uri Wilensky.

This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ or send a letter to Creative Commons, 559 Nathan Abbott Way, Stanford, California 94305, USA.

Commercial licenses are also available. To inquire about commercial licenses, please contact Uri Wilensky at uri@northwestern.edu.

This model was created as part of the project: CONNECTED MATHEMATICS: MAKING SENSE OF COMPLEX PHENOMENA THROUGH BUILDING OBJECT-BASED PARALLEL MODELS (OBPML). The project gratefully acknowledges the support of the National Science Foundation (Applications of Advanced Technologies Program) -- grant numbers RED #9552950 and REC #9632612.

This model was converted to NetLogo as part of the projects: PARTICIPATORY SIMULATIONS: NETWORK-BASED DESIGN FOR SYSTEMS LEARNING IN CLASSROOMS and/or INTEGRATED SIMULATION AND MODELING ENVIRONMENT. The project gratefully acknowledges the support of the National Science Foundation (REPP & ROLE programs) -- grant numbers REC #9814682 and REC-0126227. Converted from StarLogoT to NetLogo, 2002.

## Comments and Questions

extensions [nw] turtles-own [ positiv-neighbors ;; the neighbors with "positive-weighted-links" negativ-neighbors ;; the neighbors with "negative-weighted-links" avg-head-pos ;; average heading difference with the positiv-neighbors avg-head-neg ;; average heading difference with the negativ-neighbors ] undirected-link-breed [ positiv-links positiv-link ] ;; "positive-weighted-links" undirected-link-breed [ negativ-links negativ-link ] ;; "negative-weighted-links" positiv-links-own [ weight ] negativ-links-own [ weight ] to setup ;; setup: create the turtles and set simulation time to 0 clear-all crt population [ setxy random-xcor random-ycor ; find-negativ-neighbors ;; find the turtles that are too close from me ; find-positiv-neighbors ;; find the turtles that are not too close and not too far ; link-negativ-neighbors ;; create links between neighbors and add weight ; link-positiv-neighbors ;; create links between neighbors and add weight ] reset-ticks end to go ;; delete old links ask positiv-links [ die ] ask negativ-links [ die ] ;; compute the new direction ask turtles [ flock ] if not showlink [ask links [ hide-link ]] ;; move ask turtles [ fd 1 ] ;; increase simulation time tick end to flock find-negativ-neighbors ;; find the turtles that are too close from me find-positiv-neighbors ;; find the turtles that are not too close and not too far link-negativ-neighbors ;; create links between neighbors and add weight link-positiv-neighbors ;; create links between neighbors and add weight ifelse showlabel [ set label word count positiv-neighbors "," set label word label count negativ-neighbors ][ set label "" ] ;; heading toward weighted average of neighbors directions set avg-head-pos 0 set avg-head-neg 0 if any? positiv-neighbors [ set avg-head-pos mean [[heading] of self] of positiv-neighbors ] if any? negativ-neighbors [ set avg-head-neg mean [[heading] of self] of negativ-neighbors ] set heading heading + coupling-strength * ( coherence-strength * (avg-head-pos - heading) + separation-strength * ( 180 + avg-head-neg - heading ) + alignment-strength * ( (0.5 * (avg-head-neg + avg-head-pos)) - heading)) end to find-negativ-neighbors ;; find all turtles that are too close ( distance < minimum-separation ) set negativ-neighbors other turtles in-radius minimum-separation end to find-positiv-neighbors ;; find turtle that are not too close and not too far ( minimum-separation < distance < maximum-separation ) let my-neg-neigh negativ-neighbors ifelse any? negativ-neighbors [ ;; if we already have some "negativ-neighbors", get all the turtles within a specified radius minus the "negativ-neighbors" set positiv-neighbors other turtles in-radius maximum-separation with [ not member? self my-neg-neigh ] ] [ set positiv-neighbors other turtles in-radius maximum-separation ] end to link-negativ-neighbors ;; create links with color red and weight < 0 if any? negativ-neighbors [ create-negativ-links-with negativ-neighbors [ set color red set weight separation-strength ] ] end to link-positiv-neighbors ;; create links with color blue and weight > 0 if any? positiv-neighbors [ create-positiv-links-with positiv-neighbors [ set color blue set weight coherence-strength ] ] end

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A flocking model using network links.png | preview | Preview for 'A flocking model using network links' | over 9 years ago, by julien siebert | Download |

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