I also had problems trying to debug my ABM code.  The technique I  
found most useful was to fix the random seed, which makes MASON run  
deterministically.  I could then identify the bug, eliminate it, and  
then try with other random seeds to see if there are other bugs as well.

Liviu.

On Aug 25, 2006, at 6:15 PM, Tony Bigbee wrote:

> Liviu Panait wrote:
>
>> I believe one of the problems with deterministic scheduling is  
>> that  you may start seeing all sorts of artifacts.
>
> You are right, but Mike's point is still an interesting one.   
> Having a means to verify correct code functioning is of very high  
> value and running experiments n times over to smooth out single run  
> outcomes can be time-consuming.  And there is a paucity of easy to  
> use 'tools' to help understand the behavior produced in emergent  
> sims.  When I tried to replicate Growing Artificial Societies  
> (Epstein and Axtell's Sugarscape)  results, I found that I had many  
> kinds of bugs that were hard to track down and that my incorrect  
> implementations may be partially or fully responsible for why I  
> didn't fully replicate the simulation outcomes documented in the book.
>
> For what it's worth, one leading ABM practicioner claimed that he  
> had done experiments to demonstrate that randomizing only 20% of a  
> schedule was sufficient to eliminate artifacts.  I don't think this  
> has been published.
>
> But you would have to turn off other random elements as well, not  
> just the scheduler, to make the simulation deterministic enough,  
> depending on the debugging or verification goal.  From an  
> unpublished paper on my effort to replicate the simulation outcomes  
> described in GAS, here is an attempt at a randomization typology  
> for agent-based models:
>
> Type 1 - Initial distributions of entities and their states, such  
> as  agents' genetic characteristics and initial locations.
>
> Type 2 - Dynamic state assignments from distributions such as the   
> next direction to look in agent visual scan patterns or an agent  
> life  expectancy at birth.
>
> Type 3 - Order of execution among entities (such as agents), and   
> between agents for asynchronous interactions.  In both the classic   
> and MASON models, agents are randomly selected for execution/ 
> behavior  each time step, but the sequence of agent and environment  
> rules execution  is deterministic.
>
> Type 4 - Simulation outcomes that can be characterized by  
> theoretical  tools such as Markov fields.  For example, Culture (K)  
> eventually  reaches equilibrium in one of three absorbing states  
> (all blue,  static levels of the two colors on the sugar mountains,  
> or all red)
>
> A search of the MASON Sugarscape source code indicates that there  
> are  24 statements -- calls to random.next()) -- for Type 1 and  
> Type 2  randomization.  I implemented about 75% of the rules in GAS.
>
> Tony