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salary02
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After code review. Fig produced.

This commit is contained in:
He Zhou 2023-01-18 22:21:22 +08:00
parent d6dfe2f6c6
commit 9de2115ce8
15 changed files with 126 additions and 187 deletions
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2
.idea/misc.xml
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@ -1,4 +1,4 @@
<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (salary02)" project-jdk-type="Python SDK" />
<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9 (abm) (3)" project-jdk-type="Python SDK" />
</project>

2
.idea/modules.xml
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@ -2,7 +2,7 @@
<project version="4">
<component name="ProjectModuleManager">
<modules>
<module fileurl="file://$PROJECT_DIR$/.idea/hire.iml" filepath="$PROJECT_DIR$/.idea/hire.iml" />
<module fileurl="file://$PROJECT_DIR$/.idea/salary02.iml" filepath="$PROJECT_DIR$/.idea/salary02.iml" />
</modules>
</component>
</project>

2
.idea/hire.iml → .idea/salary02.iml
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@ -4,7 +4,7 @@
<content url="file://$MODULE_DIR$">
<excludeFolder url="file://$MODULE_DIR$/venv" />
</content>
<orderEntry type="jdk" jdkName="Python 3.8 (salary02)" jdkType="Python SDK" />
<orderEntry type="jdk" jdkName="Python 3.9 (abm) (3)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
</component>
</module>

25
data_analysis.py
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@ -1,10 +1,11 @@
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
num_time_step = 200
num_iter = 10
env_data = pd.DataFrame(pd.read_excel('env_data.xlsx', engine='openpyxl'))
env_data = pd.DataFrame(pd.read_excel('env_data.xlsx', engine='openpyxl', sheet_name=0))
assert env_data.shape[0] == num_iter * (num_time_step + 1)
@ -12,16 +13,16 @@ 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)
lst_column = lst_df[0].columns[1:]
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()
for str_col in lst_column:
x = np.arange(num_time_step+1)
for df in lst_df:
y = np.array(df[str_col]).flatten()
plt.xlabel('t')
plt.ylabel(str_col)
plt.plot(x, y)
# plt.show()
# plt.close()
plt.savefig(f'{str_col}.pdf', bbox_inches="tight")

91
env.py
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@ -3,27 +3,26 @@ 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_rh: float
percent_search: float
n_worker: int
n_firm: int
e_revenue: float
a_lst_worker: ap.AgentList
a_lst_firm: ap.AgentList
a_lst_worker: ap.AgentList[WorkerAgent]
a_lst_firm: ap.AgentList[FirmAgent]
"""
Worker: Mean(s), Gini(s)
Firm: Mean(($$\pi_{j,t}$$)), Gini($$\pi_{j,t}$$)
Firm: Mean((pi_{j,t})), Gini(pi_{j,t})
Env: Percent(IsHired)
"""
out_w_avg_salary: float
@ -44,7 +43,7 @@ class Env(ap.Model):
# 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
# 初始化 worker agent，并把alpha属性传过去
w = WorkerAgent(self, self.p.alpha)
self.a_lst_worker.append(w)
@ -74,13 +73,15 @@ class Env(ap.Model):
self.create_and_destroy_bankrupt_firms()
self.a_lst_worker.update_wd_by_is_hired()
# self.picture_out()
self.update()
if self.t == 200:
self.stop()
# self.picture_out()
pass
# pass
def update(self):
lst_salary = []
@ -94,11 +95,11 @@ class Env(ap.Model):
self.out_w_gini_salary = self.gini(lst_salary)
lst_profit = []
n_w_firm = 0
# 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
# 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)
@ -111,54 +112,37 @@ class Env(ap.Model):
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:
# 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
# 直接淘汰企业，并新增一个企业
for worker in f.l_senior_workers:
worker.s_is_hired = False
worker.working_firm = None
for worker in f.l_junior_workers:
worker.s_is_hired = False
worker.working_firm = None
self.a_lst_firm.remove(f)
del f
new_f = FirmAgent(self, self.p.is_RH_ratio >= uniform(0, 1), self.p.is_FH_ratio >= uniform(0, 1))
self.a_lst_firm.append(new_f)
else:
if f.s_profit < 0:
if f.s_IsFH:
# 第一种方式，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
else:
# 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
# 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)
f.l_all_w = f.l_junior_workers + f.l_senior_workers
min_unit_yield_salary, worst_worker = float('inf'), None
for worker in f.l_all_w:
unit_yield_salary = worker.s_yield if f.s_IsFH else worker.s_yield * 10000 / worker.s_salary
if unit_yield_salary < min_unit_yield_salary:
min_unit_yield_salary = unit_yield_salary
worst_worker = worker
worst_worker.s_is_hired = False
worst_worker.working_firm = None
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
@ -167,7 +151,7 @@ class Env(ap.Model):
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:
@ -194,6 +178,7 @@ class Env(ap.Model):
@staticmethod
def gini(x):
x = [ele for ele in x if ele > 0]
if len(x) == 0:
return 0
if sum(x) == 0:
@ -223,9 +208,8 @@ if __name__ == '__main__':
parameters = {
'n_worker': 1000,
'n_firm': 100,
'percent_search': 0.2,
'alpha': 0.5,
# 'alpha': ap.Range(0, 1, 0.5),
'percent_search': 0.2,
'is_RH_ratio': 0.5,
'is_FH_ratio': 0.5,
}
@ -235,4 +219,3 @@ if __name__ == '__main__':
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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env_data.xlsx
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86
firm.py
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@ -1,46 +1,42 @@
import math
from typing import Union, Any
# from typing import Union, Any
import agentpy as ap
from random import uniform, randint
from typing import TYPE_CHECKING
# from typing import TYPE_CHECKING
import numpy as np
# import numpy as np
if TYPE_CHECKING:
from worker import WorkerAgent
# if TYPE_CHECKING:
# from worker import WorkerAgent
class FirmAgent(ap.Agent):
c_incentive: float
s_IsRH: bool
s_IsFH: bool
s_avg_senior_yield: float
s_avg_junior_yield: float
s_a_yield: float
s_avg_senior_yield: float # updated in self.update_yields
s_avg_junior_yield: float # updated in self.update_yields
s_a_yield: float # updated in self.update_yields
# s_salary: float
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
s_revenue: float # updated in env.provide_logit_share
s_profit: float # updated in self.update_s_profit
s_value: float # updated in self.update_s_profit
# firing_worker: 'WorkerAgent'
l_senior_workers: list # updated in self.update_two_worker_list
l_junior_workers: list # updated in self.update_two_worker_list
l_applied_workers: list # updated in self.apply, self.empty_apply
# def __init__(self, model, *args, **kwargs):
# super().__init__(model, args, kwargs)
# self.l_all_workers = None
def setup(self, is_RH, is_FH):
def setup(self, is_rh, is_fh):
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_all_workers = []
self.l_applied_workers = []
self.s_IsRH = is_RH
self.s_IsFH = is_FH
self.s_IsRH = is_rh
self.s_IsFH = is_fh
self.initial_f_salary = randint(8000, 10000)
self.s_profit = 0
self.s_value = 0
@ -52,13 +48,13 @@ class FirmAgent(ap.Agent):
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
# 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
@ -70,35 +66,21 @@ 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]
# 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
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
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
p_salary = the_worker.s_salary * (1 + self.c_incentive)
unit_yield_salary = the_worker.s_yield * 10000 / p_salary
if unit_yield_salary > max_unit_yield_salary:
max_unit_yield_salary = 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
# 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)
@ -106,7 +88,7 @@ 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_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 the 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)
@ -142,9 +124,9 @@ class FirmAgent(ap.Agent):
self.s_a_yield = 0.8 * self.s_avg_senior_yield + 0.2 * self.s_avg_junior_yield
def get_sum_salary(self):
'''
"""
计算某公司整体的薪金水平
'''
"""
l_all_workers = self.l_senior_workers + self.l_junior_workers
sum_salary = 0.0
for the_worker in l_all_workers:
@ -155,5 +137,5 @@ class FirmAgent(ap.Agent):
self.s_profit = self.s_revenue - self.get_sum_salary()
self.s_value += self.s_profit
def step(self):
pass
# def step(self):
# pass

16
main.py
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@ -1,16 +0,0 @@
# 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/

25
oa25.txt Normal file
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@ -0,0 +1,25 @@
000000
011234
022341
033412
044123
101111
112403
124032
130324
143240
202222
214310
223104
231043
240431
303333
310142
321420
334201
342014
404444
413021
420213
432130
441302

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out_f_avg_profit.pdf Normal file
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out_f_gini_profit.pdf Normal file
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out_w_avg_salary.pdf Normal file
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out_w_gini_salary.pdf Normal file
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out_w_percent_hired.pdf Normal file
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64
worker.py
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@ -12,43 +12,33 @@ if TYPE_CHECKING:
class WorkerAgent(ap.Agent):
select_firm: object
c_effort: float
s_is_hired: bool
# select_firm: object
c_effort: float # used in self.update_yield
s_is_hired: bool # updated in self.update_working_firm_is_hired, env.create_and_destroy_bankrupt_firms
# c_work_months: int
s_work_duration: int
working_firm: 'FirmAgent'
s_salary: float
s_yield: float
s_work_duration: int # updated in self.update_wd_by_is_hired
working_firm: 'FirmAgent' # updated in self.update_working_firm_is_hired, env.create_and_destroy_bankrupt_firms
s_salary: float # updated in self.update_salary
s_yield: float # updated in self.update_yield
# 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.s_work_duration = randint(30, 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)
# n_firms = len(lst_firms)
# find the max incentive and profit among all firms
max_incentive, max_value = 0, 0
for f in lst_firms:
@ -61,22 +51,14 @@ class WorkerAgent(ap.Agent):
return random.choice(lst_firms)
max_utility, best_firm = 0, None
for f in lst_firms:
if self.s_salary < f.s_profit * 1 / 10:
if self.s_salary < f.s_profit / 10:
u = math.pow(f.c_incentive / max_incentive, self.c_alpha) * math.pow(f.s_value / max_value,
1 - self.c_alpha)
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
# self.update_wd_by_is_hired()
def update_wd_by_is_hired(self):
if self.s_is_hired:
self.s_work_duration += 1
@ -84,22 +66,10 @@ class WorkerAgent(ap.Agent):
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:
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
self.s_salary = max(min(self.s_salary * (1 + the_firm.c_incentive), the_firm.s_profit / 10), self.s_salary)
def update_yield(self):
self.s_yield = 2 / (1 + math.exp(-0.01 * self.s_work_duration * self.c_effort)) - 1
@ -108,9 +78,3 @@ class WorkerAgent(ap.Agent):
self.s_is_hired = True
self.working_firm = f
self.update_wd_by_is_hired()
def step(self):
self.update_wd_by_is_hired()
'''
是否更换公司成功的状态转换？
'''