Replicating and Extending Iain Couzin’s Model of Collective Animal Behavior Project Overview
What, Why, and How?
What?
I am attempting to write my own implementation of the flocking model of collective animal behavior recently presented by Iain Couzin.
Why?
Firstly, as a personal challenge - can I do it? Beyond that, to lay the groundwork for new additions to the model - and possible research.
How?
I am writing the model in Processing, a simplified graphics and animation toolkit based on Java. See processing.org for more info and examples.
Reference Papers 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.
Introduction and description of the model.
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.
Addition of individual goal-oriented behavior.
Implementation The key feature of my implementation is support for multiple behaviors and agent types. Entity
Behavior
Sense
Type
Sense
Field of View
Location
Target Type
Range
Velocity
Weight
Interference
Agent
Collective
Behaviors
Zone of Repulsion
Turning Rate
Zone of Orientation
Speed
Zone of Attraction
Goal
Goal-Oriented
Static, scripted, or manually controlled.
Attracted or repelled to target type entities.
At each time step in the animation, every entity in the simulation is instructed to update its location and velocity. Agents’ updates are controlled by the behaviors they exhibit. The initial goal is the simple case of one agent type exhibiting one collective behavior.
Status
•
Basic agent and behavior logic is nearly complete
•
No graphics or initialization yet
•
Taking the approach of getting the procedure right before worrying about efficiency
Empirical Verification All models are abstractions, but an abstraction is not useful unless it is representative of the modeled system.
Whirligig beetles are an appealing model species because they exhibit swarming behavior in two dimensions on the surface of the water, minimizing the procedural challenges associated with tracking schools of fish or flocks of birds in three dimensions. Romey 1995
To pursue this work in the long term, I would want to identify and obtain lab or field access to a real “collectivist� population in order to test modeled predictions and model observed behavior. Terrestrial vertebrate herds present practical problems of their own - namely, size.
Future Directions Possibilities for the Model
Research for Me
•
More realistic agent motion mechanics. (Speed is currently a constant and environmental substrate is assumed uniform.)
•
Familiarization with related work as well as familiarization with general animal behavior literature to guide model development.
•
Developmental loss or acquisition of behaviors.
•
Familiarization with different modeling and empirical approaches.
•
Agent health: attaining “food” goals increases resource status while activity depletes it. Failure to avoid “predator” goals also threatens status. Death at zero; behavioral changes at other threshold status levels.
•
Evolutionary component: reproduction as a behavioral incentive. Mate choice/access in collective contexts.
The appeal of modeling is as a controlled means of testing hypothesized links between proximate and ultimate mechanisms: do particular high-level outcomes indeed arise from predicted causative behaviors and characteristics?