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socialWelfareSrc.py
179 lines (142 loc) · 7.31 KB
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socialWelfareSrc.py
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import numpy as np
import random
import itertools
def deal_design_not_yet_defined(self,tick) :
raise Exception("The deal_design kind of agent "+str(self.name)+" has not been defined yet !")
def random_friend_and_bundle(self,tick) :
if (len(self.accointances)==0 or len(self.bag)==0) : return
actualSocialWelfare = self.sma.socialWelfare()
actualMyWelfare = self.individual_welfare()
friend = self.sma.agentList[np.random.choice(self.accointances)]
actualFriendWelfare = friend.individual_welfare()
r_given = np.random.choice(self.bag, min(np.random.randint(1,self.nb_ressources+1),len(self.bag)), False)
r_received = np.random.choice(friend.bag, min(np.random.randint(0,friend.nb_ressources+1),len(friend.bag)), False)
self.exchange(friend,r_given,r_received)
if(self.acceptability(self,actualMyWelfare,actualSocialWelfare) and friend.acceptability(friend,actualFriendWelfare,actualSocialWelfare)) : return
else :
self.exchange(friend,r_received,r_given)
return
#### POURQUOI Y A T IL DES RETURN PARTOUT, C'EST SALE !!!!
def priority_friend(self,tick) :
if (len(self.accointances)==0 or len(self.bag)==0) : return
r_possible_given = self.possible_bundles(1)
actualSocialWelfare = self.sma.socialWelfare()
actualMyWelfare = self.individual_welfare()
for f in self.accointances :
friend = self.sma.agentList[f]
actualFriendWelfare = friend.individual_welfare()
r_possible_received = friend.possible_bundles(0)
for r in r_possible_given :
for p in r_possible_received :
self.exchange(friend,r,p)
if(self.acceptability(self,actualMyWelfare,actualSocialWelfare) and friend.acceptability(friend,actualFriendWelfare,actualSocialWelfare)) : return
else : self.exchange(friend,p,r)
def priority_bundle(self,tick) :
if (len(self.accointances)==0 or len(self.bag)==0) : return
r_possible_given = self.possible_bundles(1)
actualSocialWelfare = self.sma.socialWelfare()
actualMyWelfare = self.individual_welfare()
for r in r_possible_given :
for f in self.accointances :
friend = self.sma.agentList[f]
actualFriendWelfare = friend.individual_welfare()
r_possible_received = friend.possible_bundles(0)
for p in r_possible_received :
self.exchange(friend,r,p)
if(self.acceptability(self,actualMyWelfare,actualSocialWelfare) and friend.acceptability(friend,actualFriendWelfare,actualSocialWelfare)) : return
else : self.exchange(friend,p,r)
def acceptability_not_yet_defined(self, old_self, old_social) :
raise Exception("The acceptability criterion of agent "+str(self.name)+" has not been defined yet !")
def irrational(self, old_self, old_social) :
return True
def social(self, old_self, old_social) :
return (old_social < self.sma.socialWelfare())
def selfish(self, old_self, old_social) :
return (old_self < self.individual_welfare())
class Agent:
def __init__(self, name, own_sma, deal_design_kind, swap_kind, resources, valeurs,maxEx) :
self.name=name
self.sma = own_sma
self.fUtil = {r : random.choice(valeurs) for r in resources}
self.bag = []
self.accointances = []
self.nb_ressources = maxEx # Nombre de ressources max que l'agent va échanger.
self.acceptability = eval(swap_kind or 'acceptability_not_yet_defined') # Va chercher la fonction d'acceptabilité correspondant a swap_kind
self.decision = eval(deal_design_kind or 'dealDesign_not_yet_defined') # Va chercher la fonction de comportement correspondant a deal_design_kind
def __str__(self) :
return "agent "+str(self.name)+" Welfare :"+ str(self.individual_welfare())+"\tbag :"+ str(self.bag)
def individual_welfare(self):
return sum([self.fUtil.get(r) for r in self.bag])
def getSortedBag(self):
bagval = [ (r,self.fUtil.get(r)) for r in self.bag]
return [r for r,_ in sorted(bagval, key=lambda x: x[1])]
def exchange(self,agent2,given,received) :
for g in given :
self.bag.remove(g)
agent2.bag.append(g)
for r in received :
agent2.bag.remove(r)
self.bag.append(r)
#print(str(self.name) + " echange " + str(given) + " contre " + str(received) + " avec " + str(agent2.name))
return
def possible_bundles(self,minimum) :
r = []
for i in range (minimum, min(len(self.bag)+1,self.nb_ressources+1)) :
r = r + list(itertools.combinations(self.getSortedBag(), i))
return r
class SMA:
def __init__(self, nb_agents, welfare, deal_design_kind , swap_kind, resources, valeurs, maxEx):
self.agentList = [Agent(i, self, deal_design_kind, swap_kind, resources, valeurs, maxEx) for i in range(nb_agents)]
self.welfare_type=welfare
self.tick=0
self.history=[]
def __str__(self) :
s=""
for i in range(len(self.agentList)):
s=s+(self.agentList[i].__str__()+"\n")
s=s+("Social Welfare : " + str(self.socialWelfare()))
return(s)
def redefineSMA(self) :
for a in self.agentList :
a.sma = self
def set_resources(self,resources) :
for r in resources :
random.choice(self.agentList).bag.append(r)
def setGlobalSwapKind(self,swap_kind) :
for a in self.agentList :
a.acceptability = eval(swap_kind)
def setGlobalMaxEx(self,maxEx) :
for a in self.agentList :
a.nb_ressources =maxEx
def setGlobaldealDesignKind(self,deal_design_kind) :
for a in self.agentList :
a.decision = eval(deal_design_kind)
def set_accointances(self,adjacency_matrix) :
if (len(adjacency_matrix[0]) != len(self.agentList)) :
raise ValueError("Problem of size in setAccointances :", len(adjacency_matrix[0]) , len(agentList))
for i in range(len(self.agentList)) :
self.agentList[i].accointances = np.nonzero(adjacency_matrix[i])[0]
def run(self, rounds):
self.tick=0
self.history=[]
self.history.append(self.socialWelfare())
for i in range(0,rounds):
self.runOnce()
def runOnce(self):
self.tick += 1
for agent in self.agentList:
agent.decision(agent,self.tick)
#print("tick " + str(self.tick) + " ended")
self.history.append(self.socialWelfare())
#print("Le Welfare actuel est de ", self.socialWelfare())
def socialWelfare(self):
if self.welfare_type.upper()=='UTILITARIST':
return sum([a.individual_welfare() for a in self.agentList])
elif self.welfare_type.upper()=='EGALITARIST':
return min([a.individual_welfare() for a in self.agentList])
elif self.welfare_type.upper()=='ELISTIST':
return max([a.individual_welfare() for a in self.agentList])
elif self.welfare_type.upper()=='NASH':
return np.prod([a.individual_welfare() for a in self.agentList])
else :
raise ValueError("Unknown method in socialWelfare")