222 lines
7.7 KiB
Python
222 lines
7.7 KiB
Python
import agentpy as ap
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import numpy as np
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from random import uniform
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import math
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from worker import WorkerAgent
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from firm import FirmAgent
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# plt.ion()
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class Env(ap.Model):
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float_market_size: float
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# percent_rh: float
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percent_search: float
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n_worker: int
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n_firm: int
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e_revenue: float
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a_lst_worker: ap.AgentList[WorkerAgent]
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a_lst_firm: ap.AgentList[FirmAgent]
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"""
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Worker: Mean(s), Gini(s)
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Firm: Mean((pi_{j,t})), Gini(pi_{j,t})
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Env: Percent(IsHired)
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"""
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out_w_avg_salary: float
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out_w_gini_salary: float
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out_f_avg_profit: float
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out_f_gini_profit: float
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out_w_percent_hired: float
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def setup(self):
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# 工作人员、企业数量、搜寻企业数量赋值
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self.n_worker = self.p.n_worker
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self.n_firm = self.p.n_firm
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self.percent_search = self.p.percent_search
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# 工人、企业列表
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self.a_lst_worker = ap.AgentList(self)
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self.a_lst_firm = ap.AgentList(self)
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self.e_revenue = 5815100000000
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# 在工人列表中添加工人
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for i in range(self.n_worker):
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# 初始化 worker agent，并把alpha属性传过去
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w = WorkerAgent(self, self.p.alpha)
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self.a_lst_worker.append(w)
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# 在企业列表中添加企业，放入一个is_RH_ratio, 即有多大比例的企业是属于RH类型的
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for i in range(self.n_firm):
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# 对于企业属性true or false 的判断， 影响到firm 板块下， self.s_IsRH = is_RH 语句的判断
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f = FirmAgent(self, self.p.is_RH_ratio >= uniform(0, 1), self.p.is_FH_ratio >= uniform(0, 1))
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self.a_lst_firm.append(f)
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def update_e_revenue(self):
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self.e_revenue += 0.01 * self.e_revenue
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def step(self):
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self.update_e_revenue()
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# 先清空每次的选择列表
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self.a_lst_firm.empty_apply()
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# 一开始worker要去选择很多firm
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self.a_lst_worker.select_firm()
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# 第二步， firm 去选 worker
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self.a_lst_firm.select_worker()
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self.a_lst_firm.update_yields()
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self.provide_logit_share()
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self.a_lst_firm.update_s_profit()
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self.create_and_destroy_bankrupt_firms()
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self.a_lst_worker.update_wd_by_is_hired()
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# self.picture_out()
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self.update()
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if self.t == 200:
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self.stop()
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# self.picture_out()
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# pass
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def update(self):
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lst_salary = []
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n_hired = 0
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for w in self.a_lst_worker:
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lst_salary.append(w.s_salary)
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if w.s_is_hired:
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n_hired += 1
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n_workers = len(lst_salary)
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self.out_w_avg_salary = sum(lst_salary) / n_workers
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self.out_w_gini_salary = self.gini(lst_salary)
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lst_profit = []
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# n_w_firm = 0
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for f in self.a_lst_firm:
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lst_profit.append(f.s_profit)
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# if f.s_profit > 0:
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# n_w_firm += 1
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n_firms = len(lst_profit)
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self.out_f_avg_profit = sum(lst_profit) / n_firms
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self.out_f_gini_profit = self.gini(lst_profit)
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self.out_w_percent_hired = n_hired / n_workers
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self.record('out_w_avg_salary')
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self.record('out_w_gini_salary')
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self.record('out_f_avg_profit')
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self.record('out_f_gini_profit')
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self.record('out_w_percent_hired')
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def create_and_destroy_bankrupt_firms(self):
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for f in self.a_lst_firm:
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if f.s_value < 0:
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# 直接淘汰企业，并新增一个企业
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for worker in f.l_senior_workers:
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worker.s_is_hired = False
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worker.working_firm = None
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for worker in f.l_junior_workers:
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worker.s_is_hired = False
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worker.working_firm = None
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self.a_lst_firm.remove(f)
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del f
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new_f = FirmAgent(self, self.p.is_RH_ratio >= uniform(0, 1), self.p.is_FH_ratio >= uniform(0, 1))
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self.a_lst_firm.append(new_f)
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else:
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if f.s_profit < 0:
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f.l_all_w = f.l_junior_workers + f.l_senior_workers
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min_unit_yield_salary, worst_worker = float('inf'), None
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for worker in f.l_all_w:
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unit_yield_salary = worker.s_yield if f.s_IsFH else worker.s_yield * 10000 / worker.s_salary
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if unit_yield_salary < min_unit_yield_salary:
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min_unit_yield_salary = unit_yield_salary
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worst_worker = worker
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worst_worker.s_is_hired = False
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worst_worker.working_firm = None
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assert len(self.a_lst_firm) == self.n_firm, \
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f'current num firm {len(self.a_lst_firm)} != expected num firm {self.n_firm}'
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def provide_lst_random_firms(self, the_worker: WorkerAgent):
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"""
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选择企业数量 = 企业总数*百分比
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选择企业的列表 = 随机选择的企业的个数
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如果员工处于被雇佣的状态：
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如果员工工作的企业在随机选定的企业列表中
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打开列表中的企业
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移除该企业
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返回值：移除后，再重新选择随机选择企业
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否则：
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返回值：选择企业列表
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"""
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n_select_firms = int(self.percent_search * self.n_firm)
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a_lst_select_firms = self.a_lst_firm.random(n_select_firms)
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if the_worker.s_is_hired:
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if the_worker.working_firm in a_lst_select_firms:
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# 转换为 list
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lst_f = list(self.a_lst_firm)
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lst_f.remove(the_worker.working_firm)
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return ap.AgentList(self, lst_f).random(n_select_firms)
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# 假如以上都不满足， 直接返回
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return ap.AgentList(self, a_lst_select_firms)
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def provide_logit_share(self):
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fen_mu = 0
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for f in self.a_lst_firm:
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fen_mu += math.exp(f.s_a_yield)
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for f in self.a_lst_firm:
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f.s_revenue = self.e_revenue * math.exp(f.s_a_yield) / fen_mu
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# def picture_out(self, the_worker: WorkerAgent):
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# a = self.t
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# b = len(the_worker.s_salary)
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# plt.plot(a, b, 'ro')
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# return plt.show()
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@staticmethod
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def gini(x):
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x = [ele for ele in x if ele > 0]
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if len(x) == 0:
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return 0
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if sum(x) == 0:
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return 0
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x = np.array(x)
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mad = np.abs(np.subtract.outer(x, x)).mean() # Mean absolute difference
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r_mad = mad / np.mean(x) # Relative mean absolute difference
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return 0.5 * r_mad
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if __name__ == '__main__':
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# dict_para = {'n_worker': 1000,
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# 'n_firm': 100,
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# 'percent_search': 0.2,
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# 'alpha': 0.5,
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# 'is_RH_ratio': 0.5}
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# my_model = Env(dict_para)
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#
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# print(my_model.gini([10000, 0, 0, 0, 0, 0, 0]))
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# my_model.run()
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# # a = range(0, 101)
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# # b = len(WorkerAgent.s_yield)
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# # plt.plot(a,b)
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# # plt.show()
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# plt.show()
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parameters = {
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'n_worker': 1000,
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'n_firm': 100,
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'alpha': 0.5,
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'percent_search': 0.2,
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'is_RH_ratio': 0.5,
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'is_FH_ratio': 0.5,
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}
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sample = ap.Sample(parameters)
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# sample = ap.Sample(parameters, n=3)
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#
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exp = ap.Experiment(Env, sample, iterations=10, record=True)
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results = exp.run()
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results['variables']['Env'].to_excel('env_data.xlsx', engine='openpyxl')
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