/
Agent.py
executable file
·1573 lines (1284 loc) · 63 KB
/
Agent.py
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# Agent.py
from Tools import *
from agTools import *
from random import *
import graphicDisplayGlobalVarAndFunctions as gvf
import commonVar as common
import numpy.random as npr
import numpy
import pandas as pd
import os
def mySort(ag):
if ag == []:
return []
numAg = []
for a in ag:
numAg.append((a.number, a))
numAg.sort()
agSorted = []
for i in range(len(numAg)):
agSorted.append(numAg[i][1])
return agSorted
def applyRationallyTheRateOfChange(base,rate):
if rate >= 0:
return base*(1+rate)
if rate < 0:
return base/(1+abs(rate))
class Agent(SuperAgent):
def __init__(self, number, myWorldState,
xPos=0, yPos=0, agType=""):
# print xPos,yPos
# the graph
if gvf.getGraph() == 0:
gvf.createGraph()
common.g.add_node(self)
# the environment
self.agOperatingSets = []
self.number = number
self.agType = agType
self.numOfWorkers = 0 # never use it directly to make calculations
self.profit = 0
self.plannedProduction = 0
self.soldProduction = 0
self.revenue = 0
self.consumption = 0
self.consumptionQuantity=0
self.employed = False
self.extraCostsResidualDuration = 0
self.profitStrategyReverseAfterN=0
self.priceSwitchIfProfitFalls=""
if agType == 'workers': #useful in initial creation
common.orderedListOfNodes.append(self)
# use to keep the order
# in output (ex. adjacency matrix)
# colors at http://www.w3schools.com/html/html_colornames.asp
gvf.colors[self] = "OrangeRed"
self.employed = False
self.workTroubles = 0
self.unspentConsumptionCapability = 0
self.jump = 0
if agType == 'entrepreneurs': #useful in initial creation
common.orderedListOfNodes.append(self)
# use to keep the order
# in output (ex. adjacency matrix)
# colors at http://www.w3schools.com/html/html_colornames.asp
gvf.colors[self] = "LawnGreen"
self.employed = True
self.plannedProduction = -100 # not used in plots if -100
self.hasTroubles = 0
self.unspentConsumptionCapability = 0
self.jump = 0
self.myWorldState = myWorldState
self.agType = agType
# the agents
if common.verbose:
print("agent of type", self.agType,
"#", self.number, "has been created at", xPos, ",", yPos)
gvf.pos[self] = (xPos, yPos)
if common.nodeNumbersInGraph:
common.g_labels[self] = str(number)
# to be used to clone (if any)
self.xPos = xPos
self.yPos = yPos
# price memory
self.buyPrice = -1000
self.sellPrice = 1000
self.sellPriceDefined=False
# consumption planning for the current cycle
# if the planning has been made, the variable contains
# the number of the cycle
self.consumptionPlanningInCycleNumber = -1
# status to be used in actOnMarketPlace acting as a buyer
# 0 means never used
# 1 if previous action was a successful buy attempt
# -1 if previous action was an unsuccessful buy attempt
self.statusB = 0
# status to be used in actOnMarketPlace acting as a seller
# 0 means never used
# 1 if previous action was a successful sell attempt
# -1 if previous action was an unsuccessful sell attempt
self.statusS = 0
# talk
def talk(self):
print(self.agType, self.number)
# reset values, redefining the method of agTools.py in $$slapp$$
def setNewCycleValues(self):
# the if is to save time, given that the order is arriving to
# all the agents (in principle, to reset local variables)
if not common.agent1existing:
print("At least one of the agents has to have number==1")
print("Missing that agent, all the agents are resetting common values")
if self.number == 1 or not common.agent1existing:
# introduced with V6
# V6 reset block starts hene
# this part is specific of the first hayekian cycle
# where it replaces the lack of a previous value in
# quantity
# here, if possible, we use the price at t-2
if common.startHayekianMarket > 1:
if common.cycle == common.startHayekianMarket:
if len(common.ts_df.price.values) == 1:
previuosPrice = common.ts_df.price.values[-1] # t=2
if len(common.ts_df.price.values) > 1:
previuosPrice = common.ts_df.price.values[-2] # t>2
# the code above can act only if t>1
if common.cycle > 1: # if == 1 do nothing
# makeProductionPlan acts
# establishing directly
# self.plannedProduction and the total
# common.totalPlannedProduction
common.totalConsumptionInQuantityInPrevious_TimeStep = \
common.totalPlannedConsumptionInValueInA_TimeStep \
/ previuosPrice
# not in case common.cycle == common.startHayekianMarket == 1
elif common.cycle > common.startHayekianMarket:
common.totalConsumptionInQuantityInPrevious2_TimeStep= \
common.totalConsumptionInQuantityInPrevious1_TimeStep # init. in common
common.totalConsumptionInQuantityInPrevious1_TimeStep = \
common.totalConsumptionInQuantityInA_TimeStep
if common.cycle==common.startHayekianMarket+1:
common.totalConsumptionInQuantityInPrevious_TimeStep = \
common.totalConsumptionInQuantityInPrevious1_TimeStep
if common.cycle > common.startHayekianMarket+1:
common.totalConsumptionInQuantityInPrevious_TimeStep = \
common.Q*common.totalConsumptionInQuantityInPrevious1_TimeStep +\
(1-common.Q)*common.totalConsumptionInQuantityInPrevious2_TimeStep
# !!!! here we can use also delayed values, look at !!!! in
# notesOnHayekianTransformation.md
common.totalConsumptionInQuantityInA_TimeStep = 0
# list of all the transaction prices in a cycle of the
# hayekian market
common.hayekianMarketTransactionPriceList_inACycle=[]
# v6 reset block ends here
common.totalProductionInA_TimeStep = 0
common.totalPlannedConsumptionInValueInA_TimeStep = 0
common.totalProfit = 0
common.totalPlannedProduction = 0
# ratio sellers/buyers
common.ratioSellersBuyersAlreadySet=False
# troubles related idividual variables
if self.agType == "entrepreneurs":
self.hasTroubles = 0
if self.agType == "workers":
self.workTroubles = 0
# hireIfProfit
def hireIfProfit(self):
# workers do not hire
if self.agType == "workers":
return
if self.profit <= common.hiringThreshold:
return
tmpList = []
for ag in self.agentList:
if ag != self:
if ag.agType == "workers" and not ag.employed:
tmpList.append(ag)
if len(tmpList) > 0:
hired = tmpList[randint(0, len(tmpList) - 1)]
hired.employed = True
gvf.colors[hired] = "Aqua"
gvf.createEdge(self, hired) # self, here, is the hiring firm
# count edges (workers) of the firm, after hiring (the values is
# recorded, but not used directly)
self.numOfWorkers = gvf.nx.degree(common.g, nbunch=self)
# nbunch : iterable container, optional (default=all nodes)
# A container of nodes. The container will be iterated through once.
print("entrepreneur", self.number, "has",
self.numOfWorkers, "edge/s after hiring")
def hireFireWithProduction(self):
# workers do not hire/fire
if self.agType == "workers":
return
# to decide to hire/fire we need to know the number of employees
# the value is calcutated on the fly, to be sure of accounting for
# modifications coming from outside
# (nbunch : iterable container, optional (default=all nodes)
# A container of nodes. The container will be iterated through once.)
laborForce0 = gvf.nx.degree(common.g, nbunch=self) + \
1 # +1 to account for the entrepreneur herself
# required labor force
laborForceRequired = int(
self.plannedProduction / common.laborProductivity)
#
# countUnemployed=0
# for ag in self.agentList:
# if not ag.employed: countUnemployed+=1
# print "I'm entrepreneur %d laborForce %d and required %d unemployed are %d" %\
#(self.number, laborForce0, laborForceRequired, countUnemployed)
# no action
if laborForce0 == laborForceRequired:
return
# hire
if laborForce0 < laborForceRequired:
n = laborForceRequired - laborForce0
tmpList = []
for ag in self.agentList:
if ag != self:
if ag.agType == "workers" and not ag.employed:
tmpList.append(ag)
if len(tmpList) > 0:
k = min(n, len(tmpList))
shuffle(tmpList)
for i in range(k):
hired = tmpList[i]
hired.employed = True
gvf.colors[hired] = "Aqua"
gvf.createEdge(self, hired)
# self, here, is the hiring firm
# count edges (workers) of the firm, after hiring (the values is
# recorded, but not used directly)
self.numOfWorkers = gvf.nx.degree(common.g, nbunch=self)
# nbunch : iterable container, optional (default=all nodes)
# A container of nodes. The container will be iterated through
# once.
print(
"entrepreneur",
self.number,
"is applying prod. plan and has",
self.numOfWorkers,
"edge/s after hiring")
# fire
if laborForce0 > laborForceRequired:
n = laborForce0 - laborForceRequired
# the list of the employees of the firm
#entrepreneurWorkers = gvf.nx.neighbors(common.g, self) with nx 2.0
entrepreneurWorkers = list(common.g.neighbors(self))
# print "entrepreneur", self.number, "could fire",
# entrepreneurWorkers
# the list returnes by nx is unstable as order
entrepreneurWorkers = mySort(entrepreneurWorkers)
if len(entrepreneurWorkers) > 0: # has to be, but ...
shuffle(entrepreneurWorkers)
for i in range(n):
fired = entrepreneurWorkers[i]
gvf.colors[fired] = "OrangeRed"
fired.employed = False
# common.g_edge_labels.pop((self,fired)) no labels in edges
common.g.remove_edge(self, fired)
# count edges (workers) after firing (recorded, but not used
# directly)
self.numOfWorkers = gvf.nx.degree(common.g, nbunch=self)
# nbunch : iterable container, optional (default=all nodes)
# A container of nodes. The container will be iterated through
# once.
print(
"entrepreneur",
self.number,
"is applying prod. plan and has",
self.numOfWorkers,
"edge/s after firing")
# fireIfProfit
def fireIfProfit(self):
# workers do not fire
if self.agType == "workers":
return
if self.profit >= common.firingThreshold:
return
# the list of the employees of the firm
#entrepreneurWorkers = gvf.nx.neighbors(common.g, self) with nx 2.0
entrepreneurWorkers = list(common.g.neighbors(self))
# print "entrepreneur", self.number, "could fire", entrepreneurWorkers
# the list returnes by nx is unstable as order
entrepreneurWorkers = mySort(entrepreneurWorkers)
if len(entrepreneurWorkers) > 0:
fired = entrepreneurWorkers[randint(
0, len(entrepreneurWorkers) - 1)]
gvf.colors[fired] = "OrangeRed"
fired.employed = False
# common.g_edge_labels.pop((self,fired)) no label in edges
common.g.remove_edge(self, fired)
# count edges (workers) after firing (recorded, but not used
# directly)
self.numOfWorkers = gvf.nx.degree(common.g, nbunch=self)
# nbunch : iterable container, optional (default=all nodes)
# A container of nodes. The container will be iterated through
# once.
print("entrepreneur", self.number, "has",
self.numOfWorkers, "edge/s after firing")
# produce
def produce(self):
# this is an entrepreneur action
if self.agType == "workers":
return
# to produce we need to know the number of employees
# the value is calcutated on the fly, to be sure of accounting for
# modifications coming from outside
# (nbunch : iterable container, optional (default=all nodes)
# A container of nodes. The container will be iterated through once.)
laborForce = gvf.nx.degree(common.g, nbunch=self) + \
1 # +1 to account for the entrepreneur herself
print("I'm entrepreneur", self.number, "my laborforce is", laborForce)
# productivity is set to 1 in the benginning from common space
self.production = common.laborProductivity * \
laborForce
# totalProductionInA_TimeStep
common.totalProductionInA_TimeStep += self.production
# having a copy, that is update after each agent's action
common.totalProductionInPrevious_TimeStep = common.totalProductionInA_TimeStep
# produce
def produceV5(self):
# this is an entrepreneur action
if self.agType == "workers":
return
# to produce we need to know the number of employees
# the value is calcutated on the fly, to be sure of accounting for
# modifications coming from outside
# (nbunch : iterable container, optional (default=all nodes)
# A container of nodes. The container will be iterated through once.)
laborForce = gvf.nx.degree(common.g, nbunch=self) + \
1 # +1 to account for the entrepreneur herself
print("I'm entrepreneur", self.number, "my laborforce is", laborForce)
# productivity is set to 1 in the benginning from common space
self.production = common.laborProductivity * \
laborForce
# print "I'm entrepreneur",self.number,"production before correction is",\
# self.production
# correction for work troubles, if any
# self.hasTroubles is 0 if no troubles
self.production *= (1. - self.hasTroubles)
# print "I'm entrepreneur",self.number,"production after correction is",\
# self.production
# totalProductionInA_TimeStep
common.totalProductionInA_TimeStep += self.production
# having a copy, that is update after each agent's action
common.totalProductionInPrevious_TimeStep = common.totalProductionInA_TimeStep
# makeProductionPlan
def makeProductionPlan(self):
# this is an entrepreneur action
if self.agType == "workers":
return
if common.projectVersion >= "3" and common.cycle == 1:
nEntrepreneurs = 0
for ag in self.agentList:
if ag.agType == "entrepreneurs":
nEntrepreneurs += 1
# print nEntrepreneurs
nWorkersPlus_nEntrepreneurs = len(self.agentList)
# print nWorkersPlus_nEntrepreneurs
common.nu = (
common.rho * nWorkersPlus_nEntrepreneurs) / nEntrepreneurs
# print common.rho, common.nu
if (common.projectVersion >= "3" and common.cycle == 1) or \
common.projectVersion < "3":
self.plannedProduction = npr.poisson(
common.nu, 1)[0] # 1 is the number
# of element of the returned matrix (vector)
# print self.plannedProduction
common.totalPlannedProduction += self.plannedProduction
# print "entrepreneur", self.number, "plan", self.plannedProduction,\
# "total", common.totalPlannedProduction
# adaptProductionPlan
def adaptProductionPlan(self):
if common.cycle > 1:
nEntrepreneurs = 0
for ag in self.agentList:
if ag.agType == "entrepreneurs":
nEntrepreneurs += 1
# previous period price
#print ("++++++++++++++++++++++", common.ts_df.price.values[-1])
#print ("&&&&&&&&&&&&&&&&&&&&&&",len(common.ts_df.price.values))
if len(common.ts_df.price.values) == 1:
previuosPrice = common.ts_df.price.values[-1] # t=2
if len(common.ts_df.price.values) > 1:
previuosPrice = common.ts_df.price.values[-2] # t>2
# NB adapt acts from t>1
self.plannedProduction = (common.totalDemandInPrevious_TimeStep /
previuosPrice) \
/ nEntrepreneurs
#self.plannedProduction += common.mg.myGauss(0,self.plannedProduction/10)
shock = uniform(
-common.randomComponentOfPlannedProduction,
common.randomComponentOfPlannedProduction)
if shock >= 0:
self.plannedProduction *= (1. + shock)
if shock < 0:
shock *= -1.
self.plannedProduction /= (1. + shock)
# print self.number, self.plannedProduction
common.totalPlannedProduction += self.plannedProduction
# print "entrepreneur", self.number, "plan", self.plannedProduction,\
# "total", common.totalPlannedProduction
# adaptProductionPlanV6
def adaptProductionPlanV6(self):
# pre hayekian period
if common.cycle > 1 and common.cycle < common.startHayekianMarket:
# count of the entrepreneur number
nEntrepreneurs = 0
for ag in self.agentList:
if ag.agType == "entrepreneurs":
nEntrepreneurs += 1
# with the scheme of prices until V.5c_fd
if len(common.ts_df.price.values) == 1:
previuosPrice = common.ts_df.price.values[-1] # t=2
if len(common.ts_df.price.values) > 1:
previuosPrice = common.ts_df.price.values[-2] # t>2
# NB adapt acts from t>1
self.plannedProduction = (common.totalDemandInPrevious_TimeStep /
previuosPrice) \
/ nEntrepreneurs
shock = uniform(
-common.randomComponentOfPlannedProduction,
common.randomComponentOfPlannedProduction)
if shock >= 0:
self.plannedProduction *= (1. + shock)
if shock < 0:
shock *= -1.
self.plannedProduction /= (1. + shock)
# print self.number, self.plannedProduction
common.totalPlannedProduction += self.plannedProduction
# print "entrepreneur", self.number, "plan", self.plannedProduction,\
# "total", common.totalPlannedProduction
# hayekian period
if common.cycle >1 and common.cycle >= common.startHayekianMarket:
#the case common.cycle==1, with common.startHayekianMarket==1, is
#absorbed by makeProductionPlan
nEntrepreneurs = 0
for ag in self.agentList:
if ag.agType == "entrepreneurs":
nEntrepreneurs += 1
self.plannedProduction = \
common.totalConsumptionInQuantityInPrevious_TimeStep \
/ nEntrepreneurs
shock = uniform(
-common.randomComponentOfPlannedProduction,
common.randomComponentOfPlannedProduction)
if shock >= 0:
self.plannedProduction *= (1. + shock)
if shock < 0:
shock *= -1.
self.plannedProduction /= (1. + shock)
# print self.number, self.plannedProduction
common.totalPlannedProduction += self.plannedProduction
# print "entrepreneur", self.number, "plan", self.plannedProduction,\
# "total", common.totalPlannedProduction
# to record sold production and revenue in hayekian phase
self.soldProduction=0
self.revenue=0
# set initial sell and buy prices in hayekian market
def setInitialPricesHM(self):
# 1 -----------------------------------------
if common.cycle >= common.startHayekianMarket:
if not common.ratioSellersBuyersAlreadySet:
nEntrepreneurs = 0
for ag in self.agentList:
if ag.agType == "entrepreneurs":
nEntrepreneurs += 1
nSellers=nEntrepreneurs
nBuyers=len(self.agentList)
common.ratioSellersBuyersAlreadySet=True
common.ratioSellersBuyers=nSellers/nBuyers
print("\nRatio sellers/buyers =",common.ratioSellersBuyers,"\n")
# in setNewCycleValues common.ratioSellersBuyersAlreadySet=False
# at the beginning of each cycle
# 2 -----------------------------------------
if common.cycle == common.startHayekianMarket and \
not common.priceWarmingDone:
# setting the basic price uniquely before the first hayekian cycle
common.sellPrice=1000
common.buyPrice=-1000
if common.startHayekianMarket>1:
if len(common.ts_df.price.values) == 1:
common.buyPrice = common.sellPrice = \
common.ts_df.price.values[-1] # the last price
#print("Ag.", self.number,"buying at", self.buyPrice,
# "selling at",self.sellPrice)
# NB the code above can act only if t>1
if len(common.ts_df.price.values) > 1:
common.buyPrice = common.sellPrice = \
common.ts_df.price.values[-2] # the second last price
#print("Ag.", self.number,"buying at", self.buyPrice,
# "selling at",self.sellPrice)
# NB the code above can act only if t>2
# NB NB we set the sellPrice also for workers but we do not
# use it
# when a worker becomes an entreprenuer she copies the
# sell price of the firm she is coming from
else: # case t==1 being common.startHayekianMarket==1
# look at the equilibrium price that would have been created
# at t==1 in the non-hayekian execution
# in the common.startHayekianMarket == 1 case, when
# actOnMaketPlace is activated
# we already have
# common.totalPlannedConsumptionInValueInA_TimeStep and
# common.totalProductionInA_TimeStep
# so, we can calculate
common.buyPrice = common.sellPrice = \
common.totalPlannedConsumptionInValueInA_TimeStep \
/ common.totalProductionInA_TimeStep
# outside WorldState setMarketPriceV3 method, to avoid here
# random shocks
# NB NB we set the sellPrice also for workers but we do not
# use it
# when a worker becomes an entreprenuer she copies the
# sell price of the firm she is coming from
#startingHayekianCommonPrice
print("\n!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
print("starting hayekian common price",common.buyPrice)
print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n")
common.priceWarmingDone = True
# 3 -----------------------------------------
# individual starting prices
if common.cycle == common.startHayekianMarket:
#starting sell price
self.sellPrice = \
applyRationallyTheRateOfChange(common.sellPrice,\
uniform(-common.initShift*common.initShock, \
(1-common.initShift)*common.initShock))
if self.agType=="entrepreneurs":
print("entrepreneur", self.number, "has initial sell price",\
self.sellPrice)
self.sellPriceDefined=True
# starting individual buy price
self.buyPrice = \
applyRationallyTheRateOfChange(common.buyPrice,\
uniform((common.initShift-1)*common.initShock, \
common.initShift*common.initShock))
# modify a specific sell price with a jump on the side of the up
# corrections, in full hayekian market
# NB we are at the end of each cycle
def nextSellPriceJumpFHM(self):
if self.agType != "entrepreneurs": return
if common.hParadigm=="quasi": return
if common.pJump != -1 and npr.uniform(0,1)<=common.pJump:
if self.jump == 0:
self.jump=common.jump
self.sellPrice *= 1 + self.jump
print("entrepreur # ", self.number, \
"raises the sell price with a jump")
else:
self.sellPrice /= 1 + self.jump
self.jump=0
print("entrepreur # ", self.number, \
"reduces the sell price with a jump back")
# modify sell prices in quasi hayekian market
# NB we are at the end of each cycle
def nextSellPricesQHM(self):
if self.agType != "entrepreneurs": return
if common.hParadigm=="full": return
# hayekian period, "quasi" hayekian paradigm
# i) considering relative unsold quantity
if common.hParadigm=="quasi" and common.quasiHchoice=="unsold":
if common.cycle >= common.startHayekianMarket:
oldP=self.sellPrice
if common.cycle >1 and \
common.entrepreneursMindIfPlannedProductionFalls and \
common.ts_df.iloc[-1, 3] / common.totalPlannedProduction - 1 >= \
common.thresholdToDecreaseThePriceIfTotalPlannedPFalls:
# indexing Python style, pos. -1 is the last one
self.sellPrice = applyRationallyTheRateOfChange(self.sellPrice,\
uniform(common.decreasingRateRange, 0))
print(("end of t = %d entrepreneur %d initial production"+\
" %.2f sold %.3f \nold price %.3f new price %.3f as "+\
" total plannedProduction falls") %\
(common.cycle,self.number,self.production,\
self.soldProduction,oldP,self.sellPrice))
else:
if self.soldProduction/self.production <= common.soldThreshold1:
self.sellPrice = applyRationallyTheRateOfChange(self.sellPrice,\
uniform(common.decreasingRateRange, 0))
if self.production/self.production>=common.soldThreshold2:
self.sellPrice = applyRationallyTheRateOfChange(self.sellPrice,\
uniform(0, common.increasingRateRange))
print(("end of t = %d entrepreneur %d initial production"+\
" %.2f sold %.3f \nold price %.3f new price %.3f") %\
(common.cycle,self.number,self.production,\
self.soldProduction,oldP,self.sellPrice))
return
# ii) considering randomUp
if common.hParadigm=="quasi" and common.quasiHchoice=="randomUp":
if common.pJump != -1 and npr.uniform(0,1)<=common.pJump:
if self.jump == 0:
self.jump=common.jump
self.sellPrice *= 1 + self.jump
print("entrepreur # ", self.number, \
"raises the sell price with a jump")
else:
self.sellPrice /= 1 + self.jump
self.jump=0
print("entrepreur # ", self.number, \
"reduces the sell price with a jump back")
return
# iii) consideirng profit falls to act on price
if common.hParadigm=="quasi" and common.quasiHchoice=="profit":
if common.cycle >= common.startHayekianMarket:
if self.profitStrategyReverseAfterN==0:
if common.priceSwitchIfProfitFalls=="raise":
if npr.uniform(0,1)<=0.6:
self.priceSwitchIfProfitFalls="raise"
else:
self.priceSwitchIfProfitFalls="lower"
if common.priceSwitchIfProfitFalls=="lower":
if npr.uniform(0,1)<=0.4:
self.priceSwitchIfProfitFalls="raise"
else:
self.priceSwitchIfProfitFalls="lower"
if common.pJump != -1 and self.profit <0 and \
npr.uniform(0,1)<=common.pJump:
if self.priceSwitchIfProfitFalls=="raise":
self.sellPrice *= 1 + common.jump
print("entrepreur # ", self.number, \
"with profit<0, is raising the sell price")
self.profitStrategyReverseAfterN=\
common.profitStrategyReverseAfterN
# 0 means: acting again always possible
# a value > the number of cycles means:
# acting again never possible
if self.priceSwitchIfProfitFalls=="lower":
self.sellPrice /= 1 + common.jump
print("entrepreur # ", self.number, \
"with profit<0, is lowering the sell price")
self.profitStrategyReverseAfterN=\
common.profitStrategyReverseAfterN
else:
self.profitStrategyReverseAfterN-=1
if self.profitStrategyReverseAfterN==0:
if self.priceSwitchIfProfitFalls=="raise":
self.sellPrice /= 1 + common.jump
print("entrepreur # ", self.number, \
"lowering back the sell price")
if self.priceSwitchIfProfitFalls=="lower":
self.sellPrice *= 1 + common.jump
print("entrepreur # ", self.number, \
"raising back the sell price")
return
# here in error
print("Using the 'quasi' option in hayekian market:\n",\
"the",common.quasiHchoice, "value is not one of the\n",
"valid option (unsold, randomUp, profit)")
os.sys.exit(1)
# all acting as consumers on the market place
def actOnMarketPlace(self):
if common.cycle < common.startHayekianMarket: return
# in each sub step, we show residual consumption and production; the
# code operates on different agents, but consistently (in each call,
# the elaboration jumps from an instance of agent to another one)
if common.checkResConsUnsoldProd:
#print(self.number)
if common.withinASubstep:
common.internalSubStepAgentCounter+=1
#print('*',common.internalSubStepAgentCounter)
if common.internalSubStepAgentCounter==len(self.agentList):
common.withinASubstep=False
else: # not withinASubstep
common.withinASubstep=True
common.internalSubStepAgentCounter=1
if common.currentCycle != common.cycle:
common.currentCycle = common.cycle
common.subStepCounter=0
common.readySellerList=False
print()
common.subStepCounter+=1
residualConsumptionCapabilityInValue=0
residualUnsoldProduction=0
for anAgent in self.agentList:
residualConsumptionCapabilityInValue += anAgent.consumption
if anAgent.agType=="entrepreneurs":
residualUnsoldProduction+= \
anAgent.production - anAgent.soldProduction
print(\
"subc. %2d.%3d starts with cons. capab. (v) %.1f and uns. p. (q) %.1f"\
% (common.cycle, common.subStepCounter, residualConsumptionCapabilityInValue,\
residualUnsoldProduction))
try: common.wr.writerow
except:
print("The file firstStepOutputInHayekianMarket.csv was not"+\
" created in mActions.py")
os.sys.exit(1)
# first call in each cycle, preparing action (only once per cycle)
#if self.currentCycle != common.cycle:
if not common.readySellerList:
#self.currentCycle = common.cycle
common.readySellerList=True
# we check that the planning of the consumption has been
# made for the current cycle
if self.consumptionPlanningInCycleNumber != common.cycle:
print('Attempt of using actOnMarketPlace method before'+\
' consumption planning')
os.sys.exit(1) # to stop the execution, in the calling module
# we have multiple except, with 'SystemExit' case
# create a temporary list of sellers, starting each step (cycle)
common.sellerList=[]
for anAg in self.agentList:
if anAg.getType() == "entrepreneurs":
if not anAg.sellPriceDefined:
print("Inconsistent situation, an active selles"\
+" has no sell price defined.")
os.sys.exit(1)
else: common.sellerList.append(anAg)
# acting (NB self.consumption comes from planConsumptionInValueV6)
# if buying action is possible
#print("cycle",common.cycle,"ag",self.number,"cons val",self.consumption)
if self.consumption > 0:
if common.sellerList != []:
# chose a seller
mySeller=common.sellerList[randint(0,len(common.sellerList)-1)]
sellerQ=mySeller.production - mySeller.soldProduction
if sellerQ>0:
# try a deal
if self.buyPrice < mySeller.sellPrice:
self.statusB=mySeller.statusS=-1
if self.buyPrice >= mySeller.sellPrice:
self.statusB=mySeller.statusS= 1
#print(common.cycle,"entr.",mySeller.number,\
# mySeller.production,mySeller.soldProduction,\
# mySeller.sellPrice)
# NB production can be < plannedProduction due to lack of workers
# consumption in value cannot exceed self.maxConsumptionInAStep
buyerQ=min(self.consumption/mySeller.sellPrice, sellerQ,\
self.maxConsumptionInAStep/mySeller.sellPrice)
mySeller.soldProduction+=buyerQ
mySeller.revenue+=buyerQ*mySeller.sellPrice
self.consumption-=buyerQ*mySeller.sellPrice
self.unspentConsumptionCapability=self.consumption
#print("cycle",common.cycle,"ag",self.number,"deal: cons val",\
# buyerQ*mySeller.sellPrice,"price",mySeller.sellPrice)
# saving the price of the transaction
common.hayekianMarketTransactionPriceList_inACycle.\
append(mySeller.sellPrice)
common.totalConsumptionInQuantityInA_TimeStep += buyerQ
#ouput - seller has no goods to sell
elif common.cycle==common.startHayekianMarket:
common.wr.writerow\
(["nogoods", "buy", numpy.nan, self.consumption, self.number,\
"sell", numpy.nan,mySeller.number])
#output - deal vs. nodeal
if common.cycle==common.startHayekianMarket:
if mySeller.statusS==1:
common.wr.writerow\
(["deal", "buy", self.buyPrice, self.consumption, self.number,\
"sell", mySeller.sellPrice,mySeller.number])
if mySeller.statusS==-1 and mySeller.sellPriceDefined:
common.wr.writerow\
(["nodeal", "buy", self.buyPrice, self.consumption, self.number,\
"sell", mySeller.sellPrice,mySeller.number])
# correct running prices
# if the status is != 0 the agent has already been acting
if self.statusB == 1: # buyer case (statusB 1, successful buy attempt,
# acting mostly to decrease the reservation price)
self.buyPrice = applyRationallyTheRateOfChange(self.buyPrice,\
uniform(-(1-common.runningShiftB)* \
common.runningShockB, \
common.runningShiftB* \
common.runningShockB))
if self.statusB == -1: # buyer case (statusB -1, unsuccessful buy attempt,
# acting mostly to increase the reservation price)
self.buyPrice = applyRationallyTheRateOfChange(self.buyPrice,\
uniform(-common.runningShiftB* \
common.runningShockB, \
(1-common.runningShiftB)* \
common.runningShockB))
if mySeller.statusS == 1 and common.hParadigm=="full" or \
(common.hParadigm=="quasi" and \
common.cycle==common.startHayekianMarket):
# seller case (statusS 1, successful sell attempt,
mySeller.sellPrice = applyRationallyTheRateOfChange(mySeller.sellPrice,\
common.ratioSellersBuyers*\
uniform(-common.runningShiftS* \
common.runningShockS,
(1-common.runningShiftS)* \
common.runningShockS))
if mySeller.statusS == -1 and common.hParadigm=="full" or \
(common.hParadigm=="quasi" and \
common.cycle==common.startHayekianMarket):
# seller case (statusS -1, unsuccess. s. attempt,
# acting mostly to decrease the reservation price)
mySeller.sellPrice = applyRationallyTheRateOfChange(mySeller.sellPrice,\
common.ratioSellersBuyers*\
uniform(-(1-common.runningShiftS)* \
common.runningShockS, \
common.runningShiftS* \
common.runningShockS))
#print("ag.", self.number, "new prices", self.buyPrice, mySeller.sellPrice)
# cleaning the situation (redundant)\\
self.statusB=mySeller.statusS=0
#output - common.sellerList==[]
elif common.cycle==common.startHayekianMarket:
common.wr.writerow\
(["nosellers", "buy", self.buyPrice, self.consumption, self.number,\
"sell", numpy.nan,numpy.nan])
#output - self.consumption<=0
elif common.cycle==common.startHayekianMarket:
common.wr.writerow\
(["noconsumption", "buy", numpy.nan, self.consumption, self.number,\
"sell", numpy.nan,numpy.nan])
#output close
if common.cycle==common.startHayekianMarket+1 and not common.closed:
common.csvf.close()
common.closed=True
# calculateProfit V0