In my model, I have several different move procedures that an agent can follow. I am trying to use rnd:weighted-one-of-list so that each procedure is followed with a certain probability. I need my probabilities to be assigned via the interface so that I can run BehaviorSpace on the model and test different probabilities for each procedure. However, when trying to create the list for rnd:weighted-one-of-list, I get an error that states I can only use literal values in my list. How could I incorporate interface-defined variables into this procedure?
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I am trying to simulate a finite calling population model in AnyLogic. My population consists of 10 agents and I want them to come back to the Source node after they have been served.
I thought about making conditioning with the SelectOutput node but the Source node does not have any input. The best thing that I came up with is to just limit the number of customers arrivals to 10. However, in this case, the model stops running after 10 arrivals which is not an appropriate result.
What can I do to be able to simulate such a type of model in AnyLogic?
EDIT: I thought that making agents come back to the Source node could be a solution to building the finite calling population model. The main purpose of my question is to understand how can I build such type of model in AnyLogic. Here is the description of the concept of the model.
You cannot send them back to a Source element, as it only acts to create agents.
However, you can send them back to blocks that come after the source as below:
Here, all agents created by the Source block will infinitely loop through the Queue and Delay blocks.
I'm working on a model in Netlogo and I'm having a problem understanding how to set up an "experiment". In my model, I have a matrix that has all of the values that I'm interested in (6 in total) and the matrix is updated whenever a condition is met (every time X turtles are killed off) basically capturing a snapshot of the model at that point. The previous values in the matrix are cleared, so the matrix is a 1x6, not a 10000x6 matrix with only one line being updated for each snapshot.
What I would like to do is to set up an experiment to run my model several hundred times, collecting this matrix each time for the first X number of snapshots or until Y ticks have occurred. But I can't see a way to do that in the experiment setup?
Is this possible to do, or would I have to create the 100x6 (100 snapshots) and then just export that matrix to a CSV somehow?
I've never set up an experiment in Netlogo, so this might be super easy to do or just be completely impossible.
If I understand your question correctly, then you want 6 values reported at specific ticks during the run. Those ticks are chosen by meeting a condition rather than a certain number of ticks. NetLogo has an experiment management tool called BehaviorSpace. It is straightforward to set up your several hundred runs (potentially with different values for any inputs on sliders etc). It's not so straightforward to only output on certain ticks.
The BehaviorSpace dialogue box has a checkmark for every tick or at the end only. If you have it set to every tick, then you can export your six numbers every tick automatically. In your case, it is likely to be easier to do that than to try and only output occasionally. You could add a seventh reporter that is true/false for whether the matrix is being reset this tick. Then all you have to do in post-processing is select the lines where that seventh reporter is true.
If you want to run the model for exactly N snapshots, then you would also need to set up a global variable that is incremented each snapshot point. Your BehaviorSpace settings would then use that counter for the stop condition.
I'm not sure I understand your question, but usually you will have a Setup function and a Run function, correct? So I'm guessing the code structure below should be kind of what you are looking for. I haven't used netlogo in a while so the exact matrix code you'll have to figure out yourself.
globals your-1by6-matrix your-100by6-matrix
to setup
;reset your experiment
end
to run
;run your experiment
end
to run100times
repeat 100[
setup
run
;save your 1by6matrix into your 100by6matrix
]
;use your 100by6matrix to plot or export
end
I am running Netlogo program to simulate if a drunk person manages to cross to another side of the pier safely. I then stored this result in a global variable.
Now I would like to run a repetition of this simulation 100x through BehaviorSpace to find out the percentages of different outcomes.
However, I am unable to extract the value of that variable at the end of each repetition.
I would like to ask if there are any ways I can extract the value of a global variable at the end of each repetition in BehaviorSpace.
Thank you.
You can just at the global to Measure runs using these reporters.
If you only want the last result, uncheck Measure runs at every step.
If you have other things you want to report every step and you insist on not recording the global at every step, you can add to Final commands to write the value to a separate file.
I'm building a cost effectiveness model (infectious disease related) in NetLogo consisting of an intervention and "status quo" cohort that should behave identically except for the influence of the intervention. I can reproduce base case results (default parameter values) using the random-seed statement. However, when I change parameters influence intervention efficacy (e.g., compliance) it changes the random number generation process and then causes the outcomes in the status quo cohort to change. How can I get around this? How do I isolate one cohort from changes taking place in another cohort with respect to the random number generation process? the with-local-randomness statement doesn't really help me ..
Unfortunately, you face two serious problems: NetLogo really does not provide access to random number generators other than the global one, and much of the access to that PRNG is implicit. So for example, whenever you use ask, you are implicitly drawing from the global PRNG.
You may be able to use with-local-randomness. E.g., use it every time you ask turtles outside the status-quo cohort to do something. Or, to use with-local-randomness for the special cohort, you could start the simulation by generating a seed sequence that you then consume as the simulation runs, resetting the random state each time you ask that cohort to do something (or generate random numbers, etc.).
Do the cohorts need to be run simultaneously (ie do they interact)? If not, could you simply run two completely separate experiments - one for the control and one for the intervention scenario.
I have a Perl script that uses rand to generate pseudorandom integers in some range. I want it to be random (i.e. not set the seed by myself to some constant), but also want to be able to reproduce the results of a specific run if needed.
What would you do?
McWafflestix says:
Possibly you want to have a default randomly determined seed, that will give you complete randomness when desired, but which can be set prior to a run manually to give reproducibility.
The obvious way to implement this is to follow your normal seeding process (either manually from a strong random source, or letting perl do it automatically on the first call to rand), then use the first generated random value as the seed, and record it. If you want to reproduce later, just use a recorded value for the seed.
# something like this?
if ( defined $input_rand_seed ) {
srand($input_rand_seed);
} else {
my $seed = rand(); # or something fancier
log_random_seed($seed);
srand($seed);
}
If the purpose is to be able to reproduce simulation paths which incorporate random shocks (say, when you are running an economic model to produce projections, I would give up on the idea of storing the seed, but rather store each sequence alongside the model data.
Note that the built in rand is subject to vagaries of the rand implementation provided by the C runtime. On all Windows machines and across all perl versions I have used, this usually means that rand will only ever produce 32768 unique values.
That is severely limited for any serious purpose. In simulations, a crucial criterion is that random sequences used be independent of each other so that each run can be considered an independent realization.
In fact, if you are going to run a simulation 1,000 times, I would pre-produce 1,000 corresponding random sequences using known-good generators that are consistent across platforms and store them with the model inputs.
You can update the simulations using the same sequences or a new set if parameter estimates change when you get new data.
Log the seed for each run and provide a method to call the script and set the seed?
Why don't you want to set the seed, but at the same time set the seed? As I've said to you before, you need to explain why you don't want to do something so we know what you are actually asking.
You might just set it yourself only in certain conditions:
srand( $ENV{SOME_SEED} ) if defined $ENV{SOME_SEED};
If you don't call srand, rand calls it for you automatically but it doesn't report the seed that it used (at least not until Perl 5.14).
It's really just a simple programming problem. Just turn what you outlined into the code that does what you said.
Your goals are at odds with each other. One one hand, you want a self-seeding, completely random sequence of integers; on the other hand, you want reproducibility. Completely random and reproducibility are at odds with each other.
You can set the seed to something you want. Possibly you want to have a default randomly determined seed, that will give you complete randomness when desired, but which can be set prior to a run manually to give reproducibility.