Working on a Model of Flocking Behavior

A month or so ago I started working on a simple model of “flocking” behavior, based on work presented at a seminar I attended. I implemented most of the math, but left the visualization for later. It lay fallow for a few weeks. Over the past two days, I dusted off the project and added some simple graphics. It works!

Try it out for yourself right here (Java plugin required). The current camera controls are designed with a three-button mouse in mind, but support for the center button (scroll wheel) seems inconsistent across browsers. Hey, it’s just a demo.

Here are a few slides from a brief presentation I gave to my colleagues when I began this project.

Iain Couzin gave a talk in March as part of the EvoS seminar series. Iain’s research program involves computer modeling and experimental observation of collective animal behavior systems (schools of fish, flocks of birds, herds of ungulates). Rigorous feedback between theory and observation was evident in his work. It was inspiring.

Iain’s presentation catalyzed interest in agent-based modeling in our lab (I am an affiliated staff member). Others jumped in with NetLogo, but I decided to see what I could do with Processing due mainly to my recently rekindled interest in the language. The Obsessive Camera Direction library makes it easy to navigate the visualization.

My implementation is derived from two of the publications which formed the basis for much of the work displayed during Iain’s EvoS seminar. The first paper describes the basic rules from which a variety of flocking behaviors can be elicited, depending on particular parameter values:

Couzin, I. D., Krause, J., James, R., Ruxton, G. D., & Franks, N. R. (2002). Collective Memory and Spatial Sorting in Animal Groups. Journal of Theoretical Biology, 218(1), 1-11. [PDF]

The second paper adds goal-oriented behavior to the model and explores the effect of different proportions of “leaders” in a group (how many cooks constitute too many cooks in the kitchen?):

Couzin, I. D., Krause, J., Franks, N. R., & Levin, S. A. (2005). Effective leadership and decision-making in animal groups on the move. Nature, 433(7025), 513-516. [PDF]

My implementation does not yet include any goal-oriented behavior, but it is designed with the addition of this and other extensions in mind:

Here’s a conceptual diagram of how the pieces of the simulation are arranged. Every speck you see flying about is an Agent, and every agent may exhibit one or more Behaviors. A Collective behavior dictates the direction an agent will move based on its proximity to other visible agents. This design may be be extended to model the interaction of multiple agent types.

Whether I proceed with all or any of the potential improvements to this model is an open question. I may decide to apply this approach to other problems more closely matched to existing research programs at BU. However, I do think it would be swell to develop and test this implementation sufficiently to replicate the varied patterns presented by Iain Couzin.

Posted on Tuesday, May 11th, 2010 in Development, Evolution. 2 Comments