def configure(sim):
"""use this function to configure the given simulation"""
# customize settings
sim.settings.deltaTime = timedelta(days=1)
sim.settings.simStartTime = datetime(2011, 1, 1, 12, 0, 0, 0)
sim.settings.t0 = 0 #startTime
sim.settings.tf = 180 #endTime
# load agents personalities for i, ii, and iii
agent1 = agent(sim.environment)
agent1.state.setPersonality(agent_i.personality())
agent2 = agent(sim.environment)
agent2.state.setPersonality(agent_ii.personality())
agent3 = agent(sim.environment)
agent3.state.setPersonality(agent_iii.personality())
#add disturbances
agent3.state.eta_PA.setFunction(getEta, agent3.inputs.xi_PA,
agent3.state.personality,
agent3.state.zeta_PA)
agent3.state.eta_EB.setFunction(getEta, agent3.inputs.xi_EB,
agent3.state.personality,
agent3.state.zeta_EB)
# customize the CSEL input functions (exogeneous flow vars)
def eatingAttitude(t):
beforeChange = 7
afterChange = 10
changeT = 10
allOthers = 1
return stepOne(t, allOthers, 'behavioralBelief', changeT, beforeChange,
afterChange)
agent1.inputs.attitudeChange_EB.setFunction(
eatingAttitude) #overwrite the default function
agent2.inputs.attitudeChange_EB.setFunction(
eatingAttitude) #overwrite the default function
agent3.inputs.attitudeChange_EB.setFunction(
eatingAttitude) #overwrite the default function
def exerciseAttitude(t):
# stepOne(data,t,value,steppedName,stepTime,beforeStep,afterStep):
beforeChange = 1
afterChange = 3
changeT = 30
allOthers = 1
return stepOne(t, allOthers, 'behavioralBelief', changeT, beforeChange,
afterChange)
agent1.inputs.attitudeChange_PA.setFunction(
exerciseAttitude) #overwrite the default function
agent2.inputs.attitudeChange_PA.setFunction(
exerciseAttitude) #overwrite the default function
agent3.inputs.attitudeChange_PA.setFunction(
exerciseAttitude) #overwrite the default function
def configure(sim): sim.settings = settings() sim.environment = environment() agent(sim.environment)
settings.simStartTime = datetime(2011, 1, 1, 12, 0, 0, 0) print '* simulation start time (sim-time): ' + str(settings.simStartTime) print '* size of time step, deltaTime: ' + str(settings.deltaTime) t0 = 0 #startTime tf = 180 #endTime # === === === === === === AGENT SETUP === === === === === === from behaviorSim.environment.environment import environment envmt = environment() # load default environment # load agents personalities for i, ii, and iii from behaviorSim.PECSagent.agent import agent from behaviorSim.PECSagent.state.CSEL import agent_i, agent_ii, agent_iii agent1 = agent(envmt) agent1.state.setPersonality(agent_i.personality()) agent2 = agent(envmt) agent2.state.setPersonality(agent_ii.personality()) agent3 = agent(envmt) agent3.state.setPersonality(agent_iii.personality()) #add disturbances from behaviorSim.PECSagent.state.CSEL.model_ddeint_firstOrder_withDisturbances import getEta # print 'args='+str(agent3.state.eta_PA.args) #def getEta(data,t,xi_PA,agent,zeta_PA): agent3.state.eta_PA.setFunction(getEta,agent3.inputs.xi_PA,agent3.state.personality,agent3.state.zeta_PA) agent3.state.eta_EB.setFunction(getEta,agent3.inputs.xi_EB,agent3.state.personality,agent3.state.zeta_EB) # customize the CSEL input functions (exogeneous flow vars)
t = 5 #time of interest in simulation timesteps (set in settings) #setup log file for this script import logging from behaviorSim.__util.setupLog import setupLog setupLog() # === === === === === === AGENT SETUP === === === === === === from behaviorSim.environment.environment import environment envmt = environment() #load default environment from behaviorSim.PECSagent.agent import agent myAgent = agent(envmt) #load default agent #TODO: customize the agent #TODO: defaultAgent.tag = 'my very first agent' # you can not reference the agent using 'myAgent' or 'envmt.agent[0]' # add agent to environment print ' === === === === === === SETTINGS === === === === === ===' # load settings of the simulation (just FYI here) from behaviorSim.PECSagent.settings import settings print '* simulation start time (sim-time): ' + str(settings.simStartTime) print '* size of time step, deltaTime: ' + str(settings.deltaTime) print ' === === === === === === DATA === === === === === ===' print envmt.agents[0].inputs(0) print envmt.agents[0].state(0) print envmt.agents[0].motive(0) print envmt.agents[0].behavior(0)
settings.simStartTime = datetime(2011, 1, 1, 12, 0, 0, 0)
print '* simulation start time (sim-time): ' + str(settings.simStartTime)
print '* size of time step, deltaTime: ' + str(settings.deltaTime)
t0 = 0 #startTime
tf = 180 #endTime
# === === === === === === AGENT SETUP === === === === === ===
from behaviorSim.environment.environment import environment
envmt = environment() # load default environment
# load agents personalities for i, ii, and iii
from behaviorSim.PECSagent.agent import agent
from behaviorSim.PECSagent.state.CSEL import agent_i, agent_ii, agent_iii
agent1 = agent(envmt)
agent1.state.setPersonality(agent_i.personality())
agent2 = agent(envmt)
agent2.state.setPersonality(agent_ii.personality())
agent3 = agent(envmt)
agent3.state.setPersonality(agent_iii.personality())
#add disturbances
from behaviorSim.PECSagent.state.CSEL.model_ddeint_firstOrder_withDisturbances import getEta
# print 'args='+str(agent3.state.eta_PA.args)
#def getEta(data,t,xi_PA,agent,zeta_PA):
agent3.state.eta_PA.setFunction(getEta, agent3.inputs.xi_PA,
agent3.state.personality, agent3.state.zeta_PA)
agent3.state.eta_EB.setFunction(getEta, agent3.inputs.xi_EB,
agent3.state.personality, agent3.state.zeta_EB)
from behaviorSim.environment.environment import environment envmt = environment() #load default environment from behaviorSim.PECSagent.agent import agent agent1 = agent(envmt) #load default agent t = 3 # === how the raw data is handled (don't do this) === # attempted direct access shows data in memory (don't do this) print ' initTime: ' + str(agent1.inputs.initTime.data) # note that it is empty, something likke _inputs__initTime[t] here would cause out-of-bounds exception # function call ensures that value is present and up-to-date (this is what you should be using) print 'initTime(t): ' + str(agent1.inputs.initTime(t)) # and updates the raw data (as shown) (do not do this) print ' initTime: ' + str(agent1.inputs.initTime.data)
def setupAgent(self, sim):
print 'setting up default agent...'
sim.environment.addAgent(agent(sim.environment)) #add a default agent
print 'done'
def configure(sim):
# set up the question(s)
#TODO: question should include prospect of outcomes, ... MORE
class questionIterator(object):
'''
This class returns the current question being posed.
'''
def __init__(self):
self.currentQuestionN = 0
self.currentQuestion = None
self.questions = [[2500,.33,2400,.66,0,.01],
[2500,.33,0 ,.67],
[4000,.8 ,3000],
]
def __call__(self,t,agent):
# update question if agent has competed last question
if agent.behavior.choice(t) != None:
self.currentQuestionN += 1
self.currentQuestion = self.getQuestion()
return self.currentQuestion
def getQuestion(self):
n = self.currentQuestionN
if n<1:
raise ValueError('question # cannot be negative')
elif n == 1:
self.currentQuestion = []
elif n == 2:
self.currentQuestion = []
else:
raise ValueError('question #'+str(n)+' not found')
return self.currentQuestion
currentQuestionProspect = API.getConstruct('currentQuestionProspect'
,questionIterator)
# this should be None when there is no prospect question posed,
# if there is a question it is a list
API.addFeature(environment,time,)
#sim.environment.???
API.addEvent(environment,time, gamblePrompt)
# create rational agent
rationalAgent = agent(sim.environment)
class event:
if time == timeTrigger:
triggerEvent(environment)
prospect = API.getConstruct('prospect'
,getProspectFromEnvironment
,environment.currentQuestionProspect)
API.addConstruct(rationalAgent.context,prospect)
def expectation(prospect):
'''
returns expectation value for given set of probabilities
t = time
prospect = list of outcome and probability pairs (x_1,p_1,x_2,p_2...x_n,p_n)
'''
sum = 0
for i in range(int(len(prospect)/2)):
sum += prospect[i*2]*prospect[i*2+1]
return sum
#TODO: def utilityOfAssets(assetPosition)
#TODO: def assetIntegration(prospect,assetPosition):
#TODO: def riskAversionAssetUtility(assetPosition):
# ''' this is a concave function of assetPosition '''
expectation = API.getConstruct('expectation'
,calculateExpectationValue
,rationalAgent.context.prospect)
API.addConstruct(rationalAgent.state,expectationValue)
# create a classically risk-averted agent
riskAvertedAgent = copy.deepcopy(rationalAgent)
# create a more realistic human agent by extending the rational agent
prospectTheoryAgent = copy.deepcopy(rationalAgent)
def getDefaultAgent(envmt):
from behaviorSim.PECSagent.agent import agent
return agent(envmt)
t = 5 #time of interest in simulation timesteps (set in settings) #setup log file for this script import logging from behaviorSim.__util.setupLog import setupLog setupLog() # === === === === === === AGENT SETUP === === === === === === from behaviorSim.environment.environment import environment envmt = environment() #load default environment from behaviorSim.PECSagent.agent import agent myAgent = agent(envmt) #load default agent #TODO: customize the agent #TODO: defaultAgent.tag = 'my very first agent' # you can not reference the agent using 'myAgent' or 'envmt.agent[0]' # add agent to environment print ' === === === === === === SETTINGS === === === === === ===' # load settings of the simulation (just FYI here) from behaviorSim.PECSagent.settings import settings print '* simulation start time (sim-time): ' + str(settings.simStartTime) print '* size of time step, deltaTime: ' + str(settings.deltaTime) print ' === === === === === === DATA === === === === === ===' print envmt.agents[0].inputs(0)
def configure(sim):
# set up the question(s)
#TODO: question should include prospect of outcomes, ... MORE
class questionIterator(object):
'''
This class returns the current question being posed.
'''
def __init__(self):
self.currentQuestionN = 0
self.currentQuestion = None
self.questions = [
[2500, .33, 2400, .66, 0, .01],
[2500, .33, 0, .67],
[4000, .8, 3000],
]
def __call__(self, t, agent):
# update question if agent has competed last question
if agent.behavior.choice(t) != None:
self.currentQuestionN += 1
self.currentQuestion = self.getQuestion()
return self.currentQuestion
def getQuestion(self):
n = self.currentQuestionN
if n < 1:
raise ValueError('question # cannot be negative')
elif n == 1:
self.currentQuestion = []
elif n == 2:
self.currentQuestion = []
else:
raise ValueError('question #' + str(n) + ' not found')
return self.currentQuestion
currentQuestionProspect = API.getConstruct('currentQuestionProspect',
questionIterator)
# this should be None when there is no prospect question posed,
# if there is a question it is a list
API.addFeature(
environment,
time,
)
#sim.environment.???
API.addEvent(environment, time, gamblePrompt)
# create rational agent
rationalAgent = agent(sim.environment)
class event:
if time == timeTrigger:
triggerEvent(environment)
prospect = API.getConstruct('prospect', getProspectFromEnvironment,
environment.currentQuestionProspect)
API.addConstruct(rationalAgent.context, prospect)
def expectation(prospect):
'''
returns expectation value for given set of probabilities
t = time
prospect = list of outcome and probability pairs (x_1,p_1,x_2,p_2...x_n,p_n)
'''
sum = 0
for i in range(int(len(prospect) / 2)):
sum += prospect[i * 2] * prospect[i * 2 + 1]
return sum
#TODO: def utilityOfAssets(assetPosition)
#TODO: def assetIntegration(prospect,assetPosition):
#TODO: def riskAversionAssetUtility(assetPosition):
# ''' this is a concave function of assetPosition '''
expectation = API.getConstruct('expectation', calculateExpectationValue,
rationalAgent.context.prospect)
API.addConstruct(rationalAgent.state, expectationValue)
# create a classically risk-averted agent
riskAvertedAgent = copy.deepcopy(rationalAgent)
# create a more realistic human agent by extending the rational agent
prospectTheoryAgent = copy.deepcopy(rationalAgent)
def setupAgent(self,sim): print 'setting up default agent...' sim.environment.addAgent(agent(sim.environment)) #add a default agent print 'done'
def getDefaultAgent(envmt): from behaviorSim.PECSagent.agent import agent return agent(envmt)
import pylab
# === === === === === === AGENT SETUP === === === === === ===
from behaviorSim.environment.environment import environment
envmt = environment() #load default environment
from behaviorSim.PECSagent.agent import agent
agent1 = agent(envmt) #load default agent
# === === === === === === PLOTS === === === === === ===
from behaviorSim.PECSplotter.plot import plotAll, plotInputs, plotState #load the plotter
print 'plot using plotAll? (y/n)'
choice = raw_input()
while choice != 'n' and choice != 'y':
print choice + '? please enter y or n.'
choice = raw_input()
if choice == 'y':
plotAll(agent1,0,10)
# and show the plots:
pylab.show()
print 'plot using individual plotters? (y/n)'
choice = raw_input()
while choice != 'n' and choice != 'y':
print choice + '? please enter y or n.'
choice = raw_input()
if choice == 'y':
#using the built-in plotters:
plotInputs(agent1,0,10) #plot inputs t={0->10}
plotState(agent1,0,10)