diff --git a/0829/env.py b/0829/env.py new file mode 100644 index 0000000..b92e811 --- /dev/null +++ b/0829/env.py @@ -0,0 +1,91 @@ +import agentpy as ap + +from random import uniform + +from worker import WorkerAgent +from firm import FirmAgent + + +class Env(ap.Model): + + float_market_size: float + percent_rh: float + percent_search: float + n_worker: int + n_firm: int + e_revenue: float + + a_lst_worker: ap.AgentList + a_lst_firm: ap.AgentList + + def setup(self): + # 工作人员、企业数量、搜寻企业数量赋值 + self.n_worker = self.p.n_worker + self.n_firm = self.p.n_firm + self.percent_search = self.p.percent_search + # 工人、企业列表 + self.a_lst_worker = ap.AgentList(self) + self.a_lst_firm = ap.AgentList(self) + self.e_revenue = 119.3 + + # 在工人列表中添加工人 + for i in range(self.n_worker): + # 初始化 workeragent,并把alpha属性传过去 + w = WorkerAgent(self, self.p.alpha) + self.a_lst_worker.append(w) + + # 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 + for i in range(self.n_firm): + # 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 + f = FirmAgent(self, self.p.is_RH_ratio >= uniform(0, 1)) + self.a_lst_firm.append(f) + + def update_e_revenue(self): + self.e_revenue += 0.01 * self.e_revenue + + def step(self): + self.update_e_revenue() + # 先清空每次的选择列表 + self.a_lst_firm.empty_apply() + # 一开始worker要去选择很多firm + self.a_lst_worker.select_firm() + # 第二步, firm 去选 worker + self.a_lst_firm.select_worker() + + if self.t == 100: + self.stop() + pass + + def provide_lst_random_firms(self, the_worker: WorkerAgent): + '''选择企业数量 = 企业总数*百分比 + 选择企业的列表 = 随机选择的企业的个数 + 如果员工处于被雇佣的状态: + 如果员工工作的企业在随机选定的企业列表中: + 打开列表中的企业 + 移除该企业 + 返回值:移除后,再重新选择随机选择企业 + 否则: + 返回值:选择企业列表 + ''' + n_select_firms = int(self.percent_search * self.n_firm) + a_lst_select_firms = self.a_lst_firm.random(n_select_firms) + if the_worker.s_is_hired: + if the_worker.working_firm in a_lst_select_firms: + # 转换为 list + lst_f = list(self.a_lst_firm) + lst_f.remove(the_worker.working_firm) + return ap.AgentList(self, lst_f).random(n_select_firms) + # 假如以上都不满足, 直接返回 + return ap.AgentList(self, a_lst_select_firms) + + +if __name__ == '__main__': + dict_para = {'n_worker': 100, + 'n_firm': 20, + 'percent_search': 0.2, + 'alpha': 0.5, + 'is_RH_ratio': 0.5} + my_model = Env(dict_para) + my_model.run() + + diff --git a/0829/firm.py b/0829/firm.py new file mode 100644 index 0000000..c535e9c --- /dev/null +++ b/0829/firm.py @@ -0,0 +1,138 @@ +import math +from typing import Union, Any + +import agentpy as ap +from random import uniform, randint + +from typing import TYPE_CHECKING + +import numpy as np + +if TYPE_CHECKING: + from worker import WorkerAgent + + +class FirmAgent(ap.Agent): + c_incentive: float + s_IsRH: bool + s_avg_senior_yield: float + s_avg_junior_yield: float + s_a_yield: float + # s_salary: float + initial_f_salary: float + s_revenue: float + s_profit: float + l_senior_workers: list + l_junior_workers: list + l_applied_workers: list + + def setup(self, is_RH): + self.c_incentive = uniform(0, 1) + # self.s_profit = randint(10, 20) + self.l_applied_workers = [] + self.s_IsRH = is_RH + self.initial_f_salary = randint(8000, 10000) + + def apply(self, the_worker): + self.l_applied_workers.append(the_worker) + + def empty_apply(self): + self.l_applied_workers = [] + + def select_worker(self): + ''' + 企业找到最想招聘的员工 + :return: + ''' + n_workers = len(self.l_applied_workers) + if n_workers > 0: + selected_worker = None + if n_workers > 1: + # 对员工产出进行排队和比较 + # 先判断是什么选择方式 + if self.s_IsRH: + max_s_yield, best_worker = 0.0, None + for the_worker in self.l_applied_workers: + if the_worker.s_yield > max_s_yield: + max_s_yield = the_worker.s_yield + best_worker = the_worker + selected_worker = best_worker + else: + selected_worker = self.l_applied_workers[0] # TODO + # print(f'{self}: my best firm is {best_firm} from {n_firms} firms with utility {max_utility}') + # return best_worker + # 当企业是想要利用产出最高的员工时,从申请的员工中选出产出最高的员工 + # if self.s_IsRH : + # # 计算该名员工的工资水平: 原有工资水平* (1+incentive) + # # bw_salary = self.best_worker.salary() + # + # # 将该名员工的产出与公司原有员工的产出进行对比,如果高于senior列表中的最后一名的产出,就进入senior_list, 否则进入junior_list + # self.l_senior_workers.append(best_worker) + # else: + # self.l_junior_workers.append(best_worker) + # # best_worker.apply(self) + # else: + # # 计算由于改名员工下一期的薪资总数(根据薪资函数更新)以及企业下一期的利润的差值,并选出最大值 + else: + selected_worker = self.l_applied_workers[0] + selected_worker.update_working_firm_is_hired(self) + self.update_two_worker_list(selected_worker) + + def update_two_worker_list(self, new_worker): + lst_all_worker = self.l_senior_workers + self.l_junior_workers + [new_worker] + lst_sorted = lst_all_worker.sort(key=lambda x: x['s_yield'], reverse=True) # from highest yield to lowest + n_all_worker = len(lst_all_worker) + # 向上取整数值 + n_senior_worker = math.ceil(n_all_worker * 0.2) + n_junior_worker = n_all_worker - n_senior_worker + self.l_senior_workers = lst_sorted[:n_senior_worker] + if n_junior_worker == 0: + self.l_junior_workers = [] + else: + self.l_junior_workers = lst_sorted[n_senior_worker:] + + def update_yields(self): # 需要改 + n_sw, n_jw = len(self.l_senior_workers), len(self.l_junior_workers) + # acc_all_yield = 0 + + if n_sw == 0: + self.s_avg_senior_yield = 0 + else: + s_acc_senior_yield = 0 + for sw in self.l_senior_workers: + # 加入工人主体的产出属性 + s_acc_senior_yield += sw.s_yield + # acc_all_yield += sw.s_yield + self.s_avg_senior_yield = s_acc_senior_yield / n_sw + + if n_jw == 0: + self.s_avg_junior_yield = 0 + else: + s_acc_junior_yield = 0 + for jw in self.l_junior_workers: + # 加入工人主体的产出属性 + s_acc_junior_yield += jw.s_yield + self.s_avg_junior_yield = s_acc_junior_yield / n_jw + self.s_a_yield = 0.8 * self.s_avg_senior_yield + 0.2 * self.s_avg_junior_yield + + def logit_share(self): + logit_share = math.exp(self.s_a_yield) / np.sum(math.exp(self.s_a_yield)) + return logit_share + + def sum_salary(self, l_senior_workers, l_junior_workers): + ''' + 计算某公司整体的薪金水平 + ''' + l_all_workers = l_senior_workers + l_junior_workers + sum_salary = self.salary + for the_worker in l_all_workers: + sum_salary += the_worker.salary + return + + def s_profit(self): + # ? + self.s_profit = self.logit_share() * self.p.e_revenue - self.sum_salary() + return + + def step(self): + pass diff --git a/0829/main.py b/0829/main.py new file mode 100644 index 0000000..bf8b9e5 --- /dev/null +++ b/0829/main.py @@ -0,0 +1,16 @@ +# This is a sample Python script. + +# Press Shift+F10 to execute it or replace it with your code. +# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings. + + +def print_hi(name): + # Use a breakpoint in the code line below to debug your script. + print(f'Hi, {name}') # Press Ctrl+F8 to toggle the breakpoint. + + +# Press the green button in the gutter to run the script. +if __name__ == '__main__': + print_hi('PyCharm') + +# See PyCharm help at https://www.jetbrains.com/help/pycharm/ diff --git a/0829/worker.py b/0829/worker.py new file mode 100644 index 0000000..7a5d89b --- /dev/null +++ b/0829/worker.py @@ -0,0 +1,93 @@ +import math + +import agentpy as ap +from random import uniform, randint + +# 编程可以自动补充一些东西,减少报错 +from typing import TYPE_CHECKING +if TYPE_CHECKING: + from firm import FirmAgent + + +class WorkerAgent(ap.Agent): + select_firm: object + c_effort: float + s_is_hired: bool + # c_work_months: int + s_work_duration: int + working_firm: 'FirmAgent' + s_salary: float + s_yield: float + # s_w_applied: bool + c_alpha: float + + def setup(self, alpha): + # super().__init__(unique_id, model) + # self.num_workers = 10000 + self.c_effort = uniform(0, 1) + # self.c_work_months = randint(0, 60) + self.s_is_hired = False + self.s_work_duration = randint(0, 60) + self.c_alpha = alpha + self.update_yield() + self.s_salary = 0 + + # return + + # def A_Utility(self, c_w_weight=0.5): + # a = np.exp(FirmAgent.c_f_incentive, c_w_weight) + # b = np.exp(FirmAgent.s_f_profit/max(FirmAgent.s_f_profit), 1-c_w_weight) + # self.select_firm = a * b + + def select_firm(self): + ''' + 挑选出来的企业列表 + 数量:列表的长度 + ''' + lst_firms = self.model.provide_lst_random_firms(self) + n_firms = len(lst_firms) + # find the max incentive and profit among all firms + max_incentive, max_profit = 0, 0 + for f in lst_firms: + if f.c_incentive > max_incentive: + max_incentive = f.c_incentive + if f.s_profit > max_profit: + max_profit = f.s_profit + # computer the utility for each firm + max_utility, best_firm = 0, None + for f in lst_firms: + u = math.pow(f.c_incentive / max_incentive, self.c_alpha) * math.pow(f.s_profit/max_profit, 1-self.c_alpha) + if u > max_utility: + max_utility = u + best_firm = f + # print(f'{self}: my best firm is {best_firm} from {n_firms} firms with utility {max_utility}') + # 选出能够给自己带来最好的效用的企业,并输出/返回 + best_firm.apply(self) + return best_firm + + def update_wd_by_is_hired(self): + if self.s_is_hired == 1: + self.s_work_duration += 1 + self.update_yield() + self.update_salary() + + def update_salary(self, the_firm: 'FirmAgent'): + if self.s_salary == 0: + self.s_salary = the_firm.initial_f_salary + pass + else: + self.s_salary = self.s_salary * (1 + the_firm.c_incentive) + pass + + def update_yield(self): + self.s_yield = 2 / (1 + math.exp(-0.01 * self.s_work_duration * self.c_effort)) - 1 + + def update_working_firm_is_hired(self, f: 'FirmAgent'): + self.s_is_hired = True + self.working_firm = f + + def step(self): + self.update_wd_by_is_hired() + ''' + 是否更换公司成功的状态转换? + ''' \ No newline at end of file