Chatroom for the packages of JuliaDynamics and about dynamical systems in general
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Datseris on v2.3.2
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Include RecurrenceAnalysis to t… (compare)
p
".
add_single_agent
, etc. Are there any pitfalls or challenges to look out for? I imagine I will encounter some confusing error messages that I have not seen before while developing the new space 🙂.
model
in the agents' stepping function but that one is internally iterated over instead of just run once. So the model stepping function is much better suited for that purpose.
const
to prevent changing them after construction, providing for greater clarity and optimization ability of these objects (JuliaLang/julia#43305).AgentBasedModel(Agent{T}; properties, scheduler, rng=MersenneTwister(seed))
seed
s ?
model.rng
), there is no way for you to know if the result you see after just one run is an outlier or a common behavior of your model. You’ll want to run it a number of times in order to see the distribution of the results. Running the model once per each seed value is a way to sample from the distribution of all the possible outcomes your model could give you. Now, the number of seed values, and therefore model runs, that you need in order to have a good sample depends on the model itself but there are some heuristics to try to figure it out. Did I understand your question correctly?
[slack] <mcabbott> Right if you observe one number, not a whole trajectory, then that’s obviously lower-dim, and some simple binning may work fine.
But if you don’t know the right summary statistic, you can also compute MI directly between parameters and observed trajectories, which is the high-dim problem. It’s likely that some parameter combinations still won’t matter.
mean
a variance
at each time step with a ribbon. Is there a way to do it without coding too much? Something like DifferentialEquations.jl has for EnsembleSolutions: https://diffeq.sciml.ai/stable/features/ensemble/ (go to the bottom and see the plot)
transform!
to calculate the mean and variance for each time step and then plot the mean column with the variance column as the ribbon.