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agent-based-modeling

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TheBizzle
TheBizzle commented Apr 30, 2018

Steps to reproduce:

  • Open Wolf Sheep Predation
  • Open the plot and note that "wolves" is a line plot
  • Run set-current-plot-pen "wolves"
  • Run set-plot-pen-mode 1 ; bar mode
  • Open the plot again and check the mode of "wolves"; it will still say "Line"
  • As a sanity check, feel free to plot as many points as you want with these settings, while noting that the mode stays "L
AayushSabharwal
AayushSabharwal commented Apr 29, 2022
function add_agent!(A::Type{<:AbstractAgent}, model::ABM, properties...; kwargs...)
    add_agent!(random_position(model), A, model, properties...; kwargs...)
end

It appears this signature of add_agent! (L294 in space_interaction_API.jl) can't be used with nothing space

MWE:

mutable struct Agent1 <: AbstractAgent
    id::Int
end
model = ABM(Agent1)
add_age
bug good first issue
graph-theory

Julia and Python complex system applications in ecology, epidemiology, sociology, economics & finance; network science models including Bianconi-Barabási, Barabási-Albert, Watts-Strogatz, Waxman Model & Erdős-Rényi; graph theory algorithms involving Gillespie, Bron Kerbosch, Ramsey, Bellman Ford, A*, Kruskal, Borůvka, Prim, Dijkstra, DSatur, Randomized Distributed, Vizing, Topological Sort, DFS, BFS

  • Updated Apr 29, 2022
  • Jupyter Notebook
ESL

​The Economic Simulation Library provides an extensive collection of tools to develop, test, analyse and calibrate economic and financial agent-based models. The library is designed to take advantage of different computer architectures. In order to facilitate rapid iteration during model development the library can use parallel computation. Economic models developed using the library can be deployed into large-scale distributed computing environments when working with large model instances and datasets and provides routines to set up large-scale sampling computations during the analysis and calibration process.

  • Updated Oct 28, 2021
  • C++

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