mirror of
https://github.com/gsi-upm/soil
synced 2024-11-22 11:12:29 +00:00
84 lines
2.6 KiB
Python
84 lines
2.6 KiB
Python
from mesa import Agent, Model
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from mesa.space import MultiGrid
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from mesa.time import RandomActivation
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from mesa.datacollection import DataCollector
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from mesa.batchrunner import BatchRunner
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def compute_gini(model):
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agent_wealths = [agent.wealth for agent in model.schedule.agents]
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x = sorted(agent_wealths)
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N = model.num_agents
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B = sum( xi * (N-i) for i,xi in enumerate(x) ) / (N*sum(x))
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return (1 + (1/N) - 2*B)
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class MoneyAgent(Agent):
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""" An agent with fixed initial wealth."""
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def __init__(self, unique_id, model):
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super().__init__(unique_id, model)
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self.wealth = 1
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def move(self):
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possible_steps = self.model.grid.get_neighborhood(
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self.pos,
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moore=True,
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include_center=False)
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new_position = self.random.choice(possible_steps)
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self.model.grid.move_agent(self, new_position)
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def give_money(self):
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cellmates = self.model.grid.get_cell_list_contents([self.pos])
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if len(cellmates) > 1:
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other = self.random.choice(cellmates)
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other.wealth += 1
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self.wealth -= 1
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def step(self):
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self.move()
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if self.wealth > 0:
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self.give_money()
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class MoneyModel(Model):
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"""A model with some number of agents."""
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def __init__(self, N, width, height):
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self.num_agents = N
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self.grid = MultiGrid(width, height, True)
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self.schedule = RandomActivation(self)
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self.running = True
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# Create agents
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for i in range(self.num_agents):
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a = MoneyAgent(i, self)
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self.schedule.add(a)
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# Add the agent to a random grid cell
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x = self.random.randrange(self.grid.width)
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y = self.random.randrange(self.grid.height)
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self.grid.place_agent(a, (x, y))
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self.datacollector = DataCollector(
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model_reporters={"Gini": compute_gini},
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agent_reporters={"Wealth": "wealth"})
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def step(self):
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self.datacollector.collect(self)
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self.schedule.step()
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if __name__ == '__main__':
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fixed_params = {"width": 10,
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"height": 10}
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variable_params = {"N": range(10, 500, 10)}
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batch_run = BatchRunner(MoneyModel,
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variable_params,
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fixed_params,
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iterations=5,
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max_steps=100,
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model_reporters={"Gini": compute_gini})
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batch_run.run_all()
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run_data = batch_run.get_model_vars_dataframe()
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run_data.head()
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print(run_data.Gini)
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