def request_available_agent(self, message: Message):
# ensure that the actor system and institution are running...
# message = MessageSpace.create_agent(agent_class)
num_agents = message.get_payload()["num_agents"]
agent_class = message.get_payload()["agent_class"]
for i in range(num_agents):
new_agent = self.createActor(agent_class)
self.agents.append(new_agent)
def initialize_agents(self, message:Message):
'''give the agent its theta and delta'''
payload = message.get_payload()
self.theta = payload["theta"]
self.delta = payload["delta"]
self.epsilon = payload["epsilon"]
def theta_sd(self, message: Message):
'''gives theta and sd to self '''
payload = message.get_payload()
self.theta = payload["theta"]
self.delta = payload["delta"]
self.epsilon = payload["epsilon"]
def returned_response(self, message: Message):
''''records the response of the agent'''
payload = message.get_payload()
response = (payload["choice"][0]) #reset every choice everytime
#print(response) #check here
self._outcome(response) # calculates which payout is chosen
def fill_in_rows(self, message: Message):
''' puts in the rows into the bundles + set period '''
payload = message.get_payload()
self.bundles = payload["bundle"]
self.experiment = payload["experiment"]
self.total_experiments = payload["total_experiments"]
self.run = payload["run"]
self.total_runs = payload["total_runs"]
def returned_response(self, message: Message):
'''records the response of the agent'''
payload = message.get_payload()
response = (payload["choice"][0]) #reset every choice everytime
#print(response)
self.collect_data(response) # collect data and calculate payoff
def make_choice(self, message:Message): # get row from bundle
'''agent making choice'''
#the agents gets the row and sd
payload = message.get_payload()
row = payload["row"]
delta = self.delta
#calculates the theta w/ triangularlly distributed delta
if delta == 0:
self.error = 0
else:
self.error = np.random.triangular(-delta, 0, delta)
theta = self.theta + self.error
OptionA = row[0] # set optionA
OptionB = row[1] # set optionB
if len(OptionA) != len(OptionB): # ensure that there is no error in the row data
raise ValueError("Length of options must be the same")
# the probability and utility of OptionA Choice 1
prob_a1 = (OptionA[0][0])
out_a1 = self.CRRA(OptionA[0][1], theta)
# the probability and utility OptionA Choice 2
prob_a2 = OptionA[1][0]
out_a2 = self.CRRA(OptionA[1][1], theta)
# the probability and utility OptionB Choice 1
prob_b1 = (OptionB[0][0])
out_b1 = self.CRRA(OptionB[0][1], theta)
# the probability and utility OptionB Choice 2
prob_b2 = OptionB[1][0]
out_b2 = self.CRRA(OptionB[1][1], theta)
e = self.epsilon
# calculates the utility w/ triangularlly distributed epsilon
if e == 0:
self.e = 0
else:
self.e = np.random.triangular(-e, 0, e)
# expected utility of OptionA and OptionB
util_a = prob_a1 * out_a1 + prob_a2 * out_a2 + self.e
util_b = prob_b1 * out_b1 + prob_b2 * out_b2 + self.e
# selects and records the option according to agent's preferences
if util_a > util_b:
self.choice.append(OptionA)
else:
self.choice.append(OptionB)
def make_choice(self, message: Message): # get row from bundle
'''agent makes their choice'''
payload = message.get_payload()
row = payload["row"]
OptionA = row[0]
OptionB = row[1]
choice = input("You choose:")
if choice == "A":
self.choice.append(OptionA)
elif choice == "B":
self.choice.append(OptionB)
def make_choice(self, message: Message): # get row from bundle
'''agent makes their choice here'''
#the agents gets the row and sd
payload = message.get_payload()
row = payload["row"]
delta = self.delta
#calculates the theta w/ normal distributed sd
if delta == 0:
error = 0
else:
error = np.random.triangular(-delta, 0, delta)
theta = self.theta + error + self.epsilon
OptionA = row[0] # tuple of option 1
OptionB = row[1] # tuple of optoin 2
if len(OptionA) != len(
OptionB): # ensure that there is no error in the row data
raise ValueError("Length of options must be the same")
# the probability and utility of OptionA Choice 1
prob_a1 = (OptionA[0][0])
out_a1 = self.CRRA(OptionA[0][1], theta)
# the probability and utility OptionA Choice 2
prob_a2 = OptionA[1][0]
out_a2 = self.CRRA(OptionA[1][1], theta)
# the probability and utility OptionB Choice 1
prob_b1 = (OptionB[0][0])
out_b1 = self.CRRA(OptionB[0][1], theta)
# the probability and utility OptionB Choice 2
prob_b2 = OptionB[1][0]
out_b2 = self.CRRA(OptionB[1][1], theta)
# expected utility of OptionA and OptionB
util_a = prob_a1 * out_a1 + prob_a2 * out_a2
util_b = prob_b1 * out_b1 + prob_b2 * out_b2
# selects and records the option with larger expected utility
if util_a > util_b:
self.choice.append(OptionA)
else:
self.choice.append(OptionB)
def simulation_properties(self, message: Message):
self.address_book = AddressBook(self)
self.environment = message.get_sender()
self.log_actor = message.get_payload()["log_actor"]
if "mtree_properties" not in dir(self):
self.mtree_properties = {}
# if "agent_memory" not in dir(self):
# self.agent_memory = {}
if "properties" in message.get_payload().keys():
self.mtree_properties = message.get_payload()["properties"]
if "agent_information" in message.get_payload().keys():
self.short_name = message.get_payload(
)["agent_information"]["short_name"]
self.agent_information = message.get_payload()["agent_information"]
if "subject_id" in message.get_payload().keys():
self.subject_id = message.get_payload()["subject_id"]
def fill_in_institution(self, message: Message):
''' let the agent know the institution '''
payload = message.get_payload()
self.institutions = payload["institutions"]
def initialize_agents(self, message: Message):
'''give the agent its theta and delta'''
payload = message.get_payload()
self.agent_id = payload["agent_id"]
def fill_in_institution(self, message: Message):
''' register agent in the institution '''
payload = message.get_payload()
self.institutions = payload["institutions"]
def fill_in_institution(self, message: Message):
''' gives the agent a list of institutions '''
payload = message.get_payload()
self.institutions = payload["institutions"]
def initialize_agent(self, message: Message):
print("initializing agent")
payload = message.get_payload()
self.institution = payload["institution"]
def fill_in_agents(self, message: Message):
'''give agents to self'''
payload = message.get_payload()
self.agents = payload["agents"]
def fill_in_rows(self, message: Message):
''' puts in the rows into the bundles & set num of experiments/runs '''
payload = message.get_payload()
self.bundles = payload["bundle"]
def initialize_institution(self, message: Message):
print("initializing an institution")
payload = message.get_payload()
self.agents = payload["agent_list"]
def get_payoff(self, message: Message):
'''agent receive payoff after completion of all lotteries'''
payload = message.get_payload()
self.payoff.append(payload["payoff"])
print("You received:", payload["payoff"])
def setup_boxes(self, message: Message):
payload = message.get_payload()
self.box_list = payload["box_list"]
def theta_delta(self, message: Message):
'''keep track of agent's theta, delta'''
payload = message.get_payload()
self.agent_id = payload["agent_id"]
def external_reminder(self, message: Message):
reminder_message = message.get_payload()["reminder_message"]
seconds_to_reminder = message.get_payload()["seconds_to_reminder"]
self.reminder(seconds_to_reminder, reminder_message)
