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@MISC{Oliver:1028485,
      author       = {Oliver, Braganza},
      title        = {{P}roxyeconomics original},
      reportid     = {FZJ-2024-04637},
      year         = {2021},
      abstract     = {Proxyeconomics Agent based model The model is written in
                      Python3.7 using the 'Mesa0.8.6' package. I used 'Anaconda'
                      environment manager and 'spyder3.3.6' editor. All
                      simulations were run on a standard PC (20GB RAM). To install
                      Mesa: in the Anaonda Prompt run: pip install Mesa (note that
                      this is case-sensitive) You will also need the following
                      packages, which can be installed in a similar way or using
                      the anaconda package manager: 'matplotlib, numpy, pandas,
                      scipy, random' The model code is in: ProxyModel1.py It
                      contains two main classes (ProxyAgent, ProxyModel), defining
                      the behavior of the agents and overall model, respectively.
                      Additionally it contains a number of data collector
                      functions to compute model means used by the mesa
                      $batch_runner.$ To run a family of models load and run eg.:
                      $S6_run_ProxyModel_competition.py.$ Within you can set the
                      parameters of interest: The total number of modelsteps can
                      be set under finalStep (line 36). Each
                      $run_ProxyModel_....py$ file is made to display a family of
                      models over one variable parameter (e.g. competition, line
                      37), where the other parameters can be set within lines 38
                      to 47 The number of model repeats for each individual
                      parameter constellation can be set under iterations
                      (line50). Default parameters in $run_ProxyModel_competition$
                      are set to reproduce Fig.3,4. Fig. 5 was produced by running
                      $S6_run_ProxyModel_competition.py$ or
                      $S7_run_ProxyModel_goal_angle.py$ with the indicated
                      parameters. Fig. 6 was produced by increasing the finalStep
                      to 10000 and the $data_collect_interval$ (line38) to 10.
                      Fig. 7 was produced by running families with other variable
                      parameters (e.g.
                      $S11_run_ProxyModel_selection_pressure.py).$ For each
                      parameter, step size was increased until it was clear that
                      equilibrium had been reached. Fig. 8 was produced by
                      modifying the $get_prospect()$ function in the ProxyAgent
                      class in ProxyModel1.py to ''' Step Prospect ''' (A-C). or
                      by setting $angle_agency$ (line 149,154 in
                      $run_ProxyModel_....py)$ to 1 (D-F). Fig. S1 was produced by
                      running $S6_run_ProxyModel_competition.py$ with selection
                      pressure (line 45) set to 0 Fig. S2 was produced by
                      activating (uncommenting)
                      $self.fitness_proportionate_selection()$ in $S5_ProxyModel1$
                      (line 325) and deactivating (commenting) line 324 Fig. S3
                      was produced by returning $own_proxy-survival_threshold$ as
                      prospect (line 155) in addition the changes for S2 Fig. S4
                      was produced by running code as in S3 but for 1000 time
                      steps and p=0.9},
      keywords     = {agent based model mesa (Other)},
      typ          = {PUB:(DE-HGF)33},
      doi          = {10.5281/ZENODO.5669823},
      url          = {https://juser.fz-juelich.de/record/1028485},
}