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SongQi 2022-10-31 15:05:22 +08:00
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commit 4e1084b517
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.idea/.name Normal file
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worker.py

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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="VcsDirectoryMappings">
<mapping directory="$PROJECT_DIR$" vcs="Git" />
</component>
</project>

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0829/env.py
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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()

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0829/firm.py
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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

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0829/main.py
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# 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/

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0829/worker.py
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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？
'''

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data_analysis.py Normal file
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import numpy as np
import pandas as pd
num_time_step = 20
num_iter = 10
env_data = pd.DataFrame(pd.read_excel('env_data.xlsx', engine='openpyxl'))
assert env_data.shape[0] == num_iter * (num_time_step + 1)
lst_df = []
for i in range(num_iter):
df_tmp = env_data.iloc[i * (num_time_step + 1): (i + 1) * (num_time_step + 1), 1:]
lst_df.append(df_tmp)
# df_tmp = env_data.iloc[1: 21]
# lst_df.append(df_tmp)
x = np.array(env_data[['t']])
y = np.array(env_data[['out_w_avg_salary']])
import matplotlib.pyplot as plt
plt.xlabel('t')
plt.ylabel('out_w_avg_salary')
plt.plot(x, y)
plt.show()

163
env.py
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@ -1,13 +1,16 @@
import agentpy as ap
import numpy as np
from random import uniform
import math
from traits.trait_types import self
from worker import WorkerAgent
from firm import FirmAgent
# plt.ion()
class Env(ap.Model):
float_market_size: float
percent_rh: float
percent_search: float
@ -18,6 +21,17 @@ class Env(ap.Model):
a_lst_worker: ap.AgentList
a_lst_firm: ap.AgentList
"""
Worker: Mean(s), Gini(s)
Firm: Mean(($$\pi_{j,t}$$)), Gini($$\pi_{j,t}$$)
Env: Percent(IsHired)
"""
out_w_avg_salary: float
out_w_gini_salary: float
out_f_avg_profit: float
out_f_gini_profit: float
out_w_percent_hired: float
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
@ -26,7 +40,7 @@ class Env(ap.Model):
# 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
self.e_revenue = 1193e+7
# 在工人列表中添加工人
for i in range(self.n_worker):
@ -52,15 +66,103 @@ class Env(ap.Model):
# 第二步， firm 去选 worker
self.a_lst_firm.select_worker()
if self.t == 100:
self.a_lst_firm.update_yields()
self.provide_logit_share()
self.a_lst_firm.update_s_profit()
self.create_and_destroy_bankrupt_firms()
# self.picture_out()
self.update()
if self.t == 200:
self.stop()
# self.picture_out()
pass
def update(self):
lst_salary = []
n_hired = 0
for w in self.a_lst_worker:
lst_salary.append(w.s_salary)
if w.s_is_hired:
n_hired += 1
n_workers = len(lst_salary)
self.out_w_avg_salary = sum(lst_salary) / n_workers
self.out_w_gini_salary = self.gini(lst_salary)
lst_profit = []
n_w_firm = 0
for f in self.a_lst_firm:
lst_profit.append(f.s_profit)
if f.s_profit > 0:
n_w_firm += 1
n_firms = len(lst_profit)
self.out_f_avg_profit = sum(lst_profit) / n_firms
self.out_f_gini_profit = self.gini(lst_profit)
self.out_w_percent_hired = n_hired / n_workers
self.record('out_w_avg_salary')
self.record('out_w_gini_salary')
self.record('out_f_avg_profit')
self.record('out_f_gini_profit')
self.record('out_w_percent_hired')
def create_and_destroy_bankrupt_firms(self):
n_bankrupt_firms = 0
for f in self.a_lst_firm:
if f.s_value < 0:
# 两种方式，第一种是直接淘汰企业（此处设定为清空企业员工，清空企业利润值和价值，相当于重新添加了一个新的企业？）
n_bankrupt_firms += 1
# for work in f.l_senior_workers:
# work.s_is_hired = False
# for work in f.l_junior_workers:
# work.s_is_hired = False
# f.s_profit = 0
# f.s_value = 0
# 第二种方式，末位淘汰制，淘汰所有员工中单位产值(产值/工资)价值最低的
f.l_all_w = f.l_junior_workers + f.l_senior_workers
for work in f.l_all_w:
work.unit_yield_salary = work.s_yield * 10000 / work.s_salary
f.l_all_w.sort(key=lambda x: x['unit_yield_salary'], reverse=True)
if work == f.l_all_w[-1]:
work.s_is_hired = False
# 第二种方式，末位淘汰制，淘汰所有员工中生产价值最低的
# f.l_junior_workers.sort(key=lambda x: x['s_yield'], reverse=True)
# for work in f.l_junior_workers:
# # f.l_junior_workers.sort(key=lambda x: x['s_yield'], reverse=True)
# if work == f.l_junior_workers[-1]:
# work.s_is_hired = False
# for f in self.a_lst_firm:
# if f.s_profit < 0: # TODO
# n_bankrupt_firms += 1
# for work in f.l_senior_workers:
# work.s_is_hired = False
# for work in f.l_junior_workers:
# work.s_is_hired = False
# self.a_lst_firm.remove(f)
# del f
# if n_bankrupt_firms > 0:
# for _ in range(n_bankrupt_firms):
# f = FirmAgent(self, self.p.is_RH_ratio >= uniform(0, 1))
# self.a_lst_firm.append(f)
assert len(self.a_lst_firm) == self.n_firm, \
f'current num firm {len(self.a_lst_firm)} != expected num firm {self.n_firm}'
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：
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
@ -78,14 +180,59 @@ class Env(ap.Model):
# 假如以上都不满足， 直接返回
return ap.AgentList(self, a_lst_select_firms)
def provide_logit_share(self):
fen_mu = 0
for f in self.a_lst_firm:
fen_mu += math.exp(f.s_a_yield)
for f in self.a_lst_firm:
f.s_revenue = self.e_revenue * math.exp(f.s_a_yield) / fen_mu
# def picture_out(self, the_worker: WorkerAgent):
# a = self.t
# b = len(the_worker.s_salary)
# plt.plot(a, b, 'ro')
# return plt.show()
@staticmethod
def gini(x):
if len(x) == 0:
return 0
if sum(x) == 0:
return 0
x = np.array(x)
mad = np.abs(np.subtract.outer(x, x)).mean() # Mean absolute difference
r_mad = mad / np.mean(x) # Relative mean absolute difference
return 0.5 * r_mad
if __name__ == '__main__':
dict_para = {'n_worker': 100,
'n_firm': 20,
dict_para = {'n_worker': 1000,
'n_firm': 100,
'percent_search': 0.2,
'alpha': 0.5,
'is_RH_ratio': 0.5}
my_model = Env(dict_para)
my_model.run()
#
# print(my_model.gini([10000, 0, 0, 0, 0, 0, 0]))
# my_model.run()
# # a = range(0, 101)
# # b = len(WorkerAgent.s_yield)
# # plt.plot(a,b)
# # plt.show()
# plt.show()
parameters = {
'n_worker': 500,
'n_firm': 10,
'percent_search': 0.2,
'alpha': 0.5,
# 'alpha': ap.Range(0, 1, 0.5),
'is_RH_ratio': 0.5,
}
sample = ap.Sample(parameters)
# sample = ap.Sample(parameters, n=3)
#
exp = ap.Experiment(Env, sample, iterations=10, record=True)
results = exp.run()
results['variables']['Env'].to_excel('env_data.xlsx', engine='openpyxl')

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55
firm.py
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@ -22,16 +22,26 @@ class FirmAgent(ap.Agent):
initial_f_salary: float
s_revenue: float
s_profit: float
s_value: float
firing_worker: 'WorkerAgent'
l_senior_workers: list
l_junior_workers: list
l_applied_workers: list
# def __init__(self, model, *args, **kwargs):
# super().__init__(model, args, kwargs)
# self.l_all_workers = None
def setup(self, is_RH):
self.c_incentive = uniform(0, 1)
# self.s_profit = randint(10, 20)
self.l_senior_workers, self.l_junior_workers = [], []
self.l_all_workers = []
self.l_applied_workers = []
self.s_IsRH = is_RH
self.initial_f_salary = randint(8000, 10000)
self.s_profit = 0
self.s_value = 0
def apply(self, the_worker):
self.l_applied_workers.append(the_worker)
@ -58,7 +68,21 @@ class FirmAgent(ap.Agent):
best_worker = the_worker
selected_worker = best_worker
else:
selected_worker = self.l_applied_workers[0] # TODO
# selected_worker = self.l_applied_workers[0] # TODO
# 加入worker进入到某个企业，企业更新了自己的利润，利润差值作为该名员工能带来的单位利润，排序利润
# 但是代码中对于员工产出是（0，1）值，需要先更新员工所在列表，然后更新企业产出、利润、工资总额，再用假设利润-上期利润
# 问题是，当期的估算不能考虑到有员工会在下一期离指的问题，或许考虑换成？max_单位产出成本？
max_unit_yield_salary, p_salary, best_worker= 0.0,0.0, None
for the_worker in self.l_applied_workers:
if the_worker.s_salary == 0:
the_worker.p_salary = self.initial_f_salary
else:
the_worker.p_salary = the_worker.s_salary*(1+self.c_incentive)
the_worker.unit_yield_salary = the_worker.s_yield * 10000 / the_worker.p_salary
if the_worker.unit_yield_salary > max_unit_yield_salary:
max_unit_yield_salary = the_worker.unit_yield_salary
best_worker = the_worker
selected_worker = best_worker
# 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
@ -80,18 +104,18 @@ class FirmAgent(ap.Agent):
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
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]
self.l_senior_workers = lst_all_worker[:n_senior_worker]
if n_junior_worker == 0:
self.l_junior_workers = []
else:
self.l_junior_workers = lst_sorted[n_senior_worker:]
self.l_junior_workers = lst_all_worker[n_senior_worker:]
def update_yields(self): # 需要改
def update_yields(self):
n_sw, n_jw = len(self.l_senior_workers), len(self.l_junior_workers)
# acc_all_yield = 0
@ -115,24 +139,19 @@ class FirmAgent(ap.Agent):
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):
def get_sum_salary(self):
'''
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
l_all_workers = self.l_senior_workers + self.l_junior_workers
sum_salary = 0.0
for the_worker in l_all_workers:
sum_salary += the_worker.salary
return
sum_salary += the_worker.s_salary
return sum_salary
def s_profit(self):
# ?
self.s_profit = self.logit_share() * self.p.e_revenue - self.sum_salary()
return
def update_s_profit(self):
self.s_profit = self.s_revenue - self.get_sum_salary()
self.s_value += self.s_profit
def step(self):
pass

49
worker.py
View File

@ -1,10 +1,12 @@
import math
import random
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
@ -30,6 +32,7 @@ class WorkerAgent(ap.Agent):
self.s_work_duration = randint(0, 60)
self.c_alpha = alpha
self.update_yield()
self.s_salary = 0
# return
@ -47,37 +50,56 @@ class WorkerAgent(ap.Agent):
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
max_incentive, max_value = 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
if f.s_value > max_value:
max_value = f.s_value
# computer the utility for each firm
if max_value == 0:
return random.choice(lst_firms)
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
if self.s_salary < f.s_profit * 1 / 10:
u = math.pow(f.c_incentive / max_incentive, self.c_alpha) * math.pow(f.s_value / max_value,
1 - self.c_alpha)
if u > max_utility:
max_utility = u
best_firm = f
# if self.s_salary < best_firm.s_profit * 5 / 100:
best_firm.apply(self)
# 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
# best_firm.apply(self)
# return best_firm
# self.update_wd_by_is_hired()
def update_wd_by_is_hired(self):
if self.s_is_hired == 1:
if self.s_is_hired:
self.s_work_duration += 1
self.update_yield()
self.update_salary()
self.update_salary(self.working_firm)
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
if self.s_salary <= the_firm.s_profit*1/10:
if self.s_salary * (1 + the_firm.c_incentive) <= the_firm.s_profit*1/10:
self.s_salary = self.s_salary * (1 + the_firm.c_incentive)
pass
else:
self.s_salary = the_firm.s_profit*1/10
pass
pass
else:
self.s_salary = the_firm.s_profit*1/10
def update_yield(self):
self.s_yield = 2 / (1 + math.exp(-0.01 * self.s_work_duration * self.c_effort)) - 1
@ -85,6 +107,7 @@ class WorkerAgent(ap.Agent):
def update_working_firm_is_hired(self, f: 'FirmAgent'):
self.s_is_hired = True
self.working_firm = f
self.update_wd_by_is_hired()
def step(self):
self.update_wd_by_is_hired()