Abstract

[Doug Donalson]

Swarmites,

 

Here is my tentative abstract for Swarmfest.

 

 

Cell-based and discrete (synchronous) time models by definition introduce

quantization errors into simulations. In the case of cell-based space, individuals

are required to reside in the center of a cell. This requires that individuals or

subpopulations must be separated by a minimum distance. (In population

ecology this might represent model imposed competition). In the case of

synchronous time models, if the time step is longer than rates of change of

the state variables, estimates must be calculated for the number of state

changes for each state variable in the interval. The maximum number of state

changes in the interval and the ordering of those changes can have a strong

effect on the system dynamics.

 

I will talk about an alternate formulation for IBM’s that allows for a continuous time

and continuous space representation of the dynamics. Asynchronous schedules

(also called event driven schedules) are based on the idea that no two events

(state changes) happen simultaneously. It should therefore be possible to order

events in such a way that there is no ambiguity. I will provide a handout on

asynchronous schedules with my talk that is part of a chapter in my dissertation,

comments are encouraged. (Please, beat me up before my committee does!)

 

The use of asynchronous schedules allows development of the Stochastic

Birth-Death (SBD) model. When trying to link equation based models to IBMs,

there are a number of factors changed in the dynamics. One of these is the

change from a continuous (often density based) representation of model dynamics

 to one where state changes are discrete. For instance, in a density based model,

a population can change from 100 to 100.01, in an individual representation, the

change (increase) must be to 101. An SBD model can be used two ways. First, if

the results of an IBM are significantly different than its equation based counterpart,

 an SBD model can factor out the changes resulting from demographic

stochasticity (requiring individuals to reproduce in whole units) from other factors in

 the IBM such as space and behavior. SBD’s can also be used to analyze equation

 based models for sensitivity to demographic stochasticity.

 

Finally, and if there is time, I will introduce the some tricks I use in my latest model

 to allow a continuous space representation of a mussel bed while still using

Swarm’s great graphic potential. They also allow significant speed up of global

search procedures.

 

For those of you interested, a good (but non-Swarm) example of a simple

continuous space/time simulation is my Heuristic Asynchronous Discrete Event

Simulation (HADES) in the following paper.

 

(Paul J., your reprint is in the mail tomorrow.)

 

Donalson & Nisbet

Population dynamics and spatial scale:

Effects of system size on population persistence.

Ecology, 80(8) : 2492-2507 (December 1999)

 

Cheers,

 

   D3

 

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* Ecology, Evolution,           Home:   (805) 961-4447
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