def generate_name(sex, race):
name = random_choice(name_given_sex[sex.name].items())
race = race_map[race.name]
if isinstance(race, list):
race = random.choice(race)
surname = random_choice(surname_given_race[race].items())
return '{} {}'.format(name, surname).title()
def generate_name(sex, race):
name = random_choice(name_given_sex[sex.name].items())
race = race_map[race.name]
if isinstance(race, list):
race = random.choice(race)
surname = random_choice(surname_given_race[race].items())
return '{} {}'.format(name, surname).title()
def start_business(self, world, buildings):
# can only have one business
if self._state['firm_owner']:
return False, None, None
# must be able to find a place with affordable rent
buildings = [b for b in buildings if b.available_space]
if not buildings:
return False, None, None
denom = sum(1/b.rent for b in buildings)
building = random_choice([(b, 1/(b.rent*denom)) for b in buildings])
# must be able to hire at least one employee
min_cost = self.min_business_capital + building.rent + world['mean_wage']
if self._state['cash'] < min_cost:
return False, None, None
industries = ['equip', 'material', 'consumer_good', 'healthcare']
total_mean_profit = sum(world['mean_{}_profit'.format(name)] for name in industries)
industry_dist = [(name, world['mean_{}_profit'.format(name)]/total_mean_profit) for name in industries]
industry = random_choice(industry_dist)
# choose an industry (based on highest EWMA profit)
self.twoot('i\'m starting a BUSINESS in {}!'.format(industry), world)
logger.info('person:{}'.format(json.dumps({
'event': 'started_firm',
'id': self.id
})))
return True, industry, building
def hire(self, applicants, wage, world):
hired = []
while self.worker_change > 0 and applicants:
# based on employment prob
apps = []
for a in applicants:
ref = 'friend' if set(a.friends).intersection(self.workers) else 'ad_or_cold_call'
p = offer_prob(world['year'], world['month'], a.sex, a.race, ref, precomputed_emp_dist=emp_dist)
apps.append((a, p))
apps_mass = sum(p for a, p in apps)
apps = [(a, pr/apps_mass) for a, pr in apps]
worker = random_choice(apps)
if worker.employer is not None:
worker.employer.fire(worker)
worker.wage = wage
worker.employer = self
applicants.remove(worker)
self.workers.append(worker)
hired.append(worker)
logger.info('person:{}'.format(json.dumps({
'event': 'hired',
'id': worker.id
})))
self.worker_change -= 1
# increase wage to attract more employees
if self.worker_change > 0:
wage += self.config['wage_increment'] * (1.1+self.owner.altruism)
return hired, self.worker_change, wage
def healthcare_market(self):
sold = []
hospitals = [h for h in self.hospitals]
for person in shuffle(self.people):
if person._state['health'] < 1.:
affordable = [
h for h in hospitals if h.price <= person._state['cash']
]
if not affordable:
continue
utilities = [
person.health_change_utility(1 - person._state['health']) +
person.cash_change_utility(-h.price) for h in affordable
]
u_t = sum(utilities)
choices = [(h, u / u_t) for h, u in zip(hospitals, utilities)]
hospital = random_choice(choices)
hospital.sell(1)
person._state['cash'] -= hospital.price
person._state['health'] = 1
person._state['sick'] = False if random.random(
) < self.state['recovery_prob'] else True
sold.append(hospital.price)
if hospital.supply == 0:
hospitals.remove(hospital)
if not hospitals:
break
profits = [f.revenue - f.costs for f in self.hospitals]
return sold, profits
def labor_market(self, jobs):
tasks = [p.get('employer') for p in self.people]
applicants = {f: [] for (_, __), f in jobs}
employers = yield from asyncio.gather(*tasks)
job_seekers = [p for p, e in zip(self.people, employers) if e is None]
while job_seekers and jobs:
job_dist = self.job_distribution(jobs)
for p in shuffle(job_seekers):
(n_vacancies, wage), firm = random_choice(job_dist)
applicants[firm].append(p)
# firms select from their applicants
_jobs = []
for job in shuffle(jobs):
# filter down to valid applicants
(n_vacancies, wage), firm = job
apps = [a for a in applicants[firm] if a in job_seekers]
hired, n_vacancies, wage = yield from firm.call('hire', apps, wage)
# remove hired people from the job seeker pool
for p in hired:
job_seekers.remove(p)
if not job_seekers:
break
# if vacancies remain, post the new jobs with the new wage
if n_vacancies:
_jobs.append(((n_vacancies, wage), firm))
jobs = _jobs
def consumer_good_market(self):
firm_dist = self.firm_distribution(self.consumer_good_firms)
sold = []
households = [h for h in self.households]
rounds = 0
while households and firm_dist and rounds < MAX_ROUNDS:
for household in shuffle(households):
supplier = random_choice(firm_dist)
desired, purchased = household.purchase_goods(supplier)
sold.append((purchased, supplier.price))
if desired == 0:
households.remove(household)
# if supplier sold out, update firm distribution
if supplier.supply == 0:
firm_dist = self.firm_distribution(self.consumer_good_firms)
if not firm_dist:
break
rounds += 1
profits = [f.revenue - f.costs for f in self.consumer_good_firms]
return sold, profits
def consumer_good_market(self):
firm_dist = self.firm_distribution(self.consumer_good_firms)
sold = []
households = [h for h in self.households]
rounds = 0
while households and firm_dist and rounds < MAX_ROUNDS:
for household in shuffle(households):
supplier = random_choice(firm_dist)
desired, purchased = household.purchase_goods(supplier)
sold.append((purchased, supplier.price))
if desired == 0:
households.remove(household)
# if supplier sold out, update firm distribution
if supplier.supply == 0:
firm_dist = self.firm_distribution(
self.consumer_good_firms)
if not firm_dist:
break
rounds += 1
profits = [f.revenue - f.costs for f in self.consumer_good_firms]
return sold, profits
def set_production_target(self, world):
"""firm decides on how much supply they want to produce this step,
and what they need to do to accomplish that"""
# assess previous day's results
self.prev_profit = self.profit
self.leftover = self.supply
# adjust production
action = self.learner.choose_action(self.current_state)
action = self.actions[action]
self.desired_supply = max(1, self.desired_supply + action.get('supply', 0))
self.profit_margin += action.get('profit_margin', 0)
# supply expires every day
self.supply = 0
# unused materials expire every day
self.materials = 0
# resets every day
self.n_sold = 0
self.revenue = 0
self.costs = 0
# fire workers that are being paid too much
for worker in self.workers:
if worker.wage >= world['mean_wage'] + (self.config['extravagant_wage_range'] * (1.1+self.owner.altruism)):
self.fire(worker)
# figure out labor goal
required_labor = self.desired_supply * self.config['labor_cost_per_good']
n_workers, wage, n_equip = self.assess_assets(required_labor, world['mean_wage'], world['mean_equip_price'])
# sometimes optimization function returns a huge negative value for
# workers, need to look into that further
if n_workers < 0:
n_workers = 0
self.worker_change = n_workers - len(self.workers)
self.desired_equipment = self.equipment + max(0, n_equip - self.equipment)
# fire workers if necessary
while self.worker_change < 0:
# weighted random choice by unemployment prob
ws = []
for w in self.workers:
pu = emp_dist[world['year']][world['month'] - 1][w.race.name][w.sex.name]
ws.append((w, pu['unemployed']))
ws_mass = sum(p for w, p in ws)
ws = [(w, p/ws_mass) for w, p in ws]
worker = random_choice(ws)
self.fire(worker)
self.worker_change += 1
# job vacancies
return self.worker_change, wage
def sample_rent(year, puma):
"""generates a plausible rent amount (here, rent can mean a monthly mortgage payment)
given a year and a PUMA"""
rent = 0
rent_info = rent_dists[year][puma]
if random.random() < rent_info['p_owned']:
ownership = random_choice(rent_info['p_ownership'].items())
if ownership == 'free':
rent = 0
elif ownership == 'mortgage1':
rent = rent_info['mortgage1_dist'].resample(1)[0][0]
elif ownership == 'mortgage2':
try:
rent += rent_info['mortgage2_dist'].resample(1)[0][0]
except AttributeError:
rent += rent_info['mortgage2_dist']
else:
rent = rent_info['rent_dist'].resample(1)[0][0]
return rent
def market(self, sellers, buyers, purchase_func):
sold = []
seller_dist = yield from self.firm_distribution(sellers)
while buyers and seller_dist:
for buyer in shuffle(buyers):
supplier = random_choice(seller_dist)
required, purchased = yield from buyer.call(purchase_func, supplier)
supply, price = yield from supplier.get('supply', 'price')
sold.append((purchased, price))
if required == 0:
buyers.remove(buyer)
# if supplier sold out, update firm distribution
if supply == 0:
seller_dist = yield from self.firm_distribution(sellers)
if not seller_dist:
break
return sold
def sample_node(self, n, sampled):
"""sample an individual node,
samples from parents as necessary"""
parents = self.g.predecessors(n)
if not parents:
# if no parents, use p(n)
try:
prob_dist = self._cache[n.name]['_']
except KeyError:
prob_dist = self.p_n(n)
else:
# if parents, use prod(p(n|x_i) for x_i in parents)
for parent in parents:
if parent not in sampled:
sampled = self.sample_node(parent, sampled)
given = {k: v for k, v in sampled.items() if k in parents}
prob_dist = self.probs_given(n, given=given)
sampled[n] = random_choice(prob_dist.items())
return sampled
def sample_node(self, n, sampled):
"""sample an individual node,
samples from parents as necessary"""
parents = self.g.predecessors(n)
if not parents:
# if no parents, use p(n)
try:
prob_dist = self._cache[n.name]['_']
except KeyError:
prob_dist = self.p_n(n)
else:
# if parents, use prod(p(n|x_i) for x_i in parents)
for parent in parents:
if parent not in sampled:
sampled = self.sample_node(parent, sampled)
given = {k:v for k,v in sampled.items() if k in parents}
prob_dist = self.probs_given(n, given=given)
sampled[n] = random_choice(prob_dist.items())
return sampled
def capital_equipment_market(self):
sold = []
firm_dist = self.firm_distribution(self.capital_equipment_firms)
firms = self.consumer_good_firms + self.raw_material_firms
rounds = 0
while firms and firm_dist and rounds < MAX_ROUNDS:
for firm in shuffle(firms):
supplier = random_choice(firm_dist)
required, purchased = firm.purchase_equipment(supplier)
sold.append((purchased, supplier.price))
if required == 0:
firms.remove(firm)
# if supplier sold out, update firm distribution
if supplier.supply == 0:
firm_dist = self.firm_distribution(self.capital_equipment_firms)
if not firm_dist:
break
rounds += 1
profits = [f.revenue - f.costs for f in self.capital_equipment_firms]
return sold, profits
def raw_material_market(self):
sold = []
firm_dist = self.firm_distribution(self.raw_material_firms)
firms = self.consumer_good_firms + self.capital_equipment_firms
rounds = 0
while firms and firm_dist and rounds < MAX_ROUNDS:
for firm in shuffle(firms):
supplier = random_choice(firm_dist)
required, purchased = firm.purchase_materials(supplier)
sold.append((purchased, supplier.price))
if required == 0:
firms.remove(firm)
# if supplier sold out, update firm distribution
if supplier.supply == 0:
firm_dist = self.firm_distribution(self.raw_material_firms)
if not firm_dist:
break
rounds += 1
profits = [f.revenue - f.costs for f in self.raw_material_firms]
return sold, profits
def hire(self, applicants, wage, world):
hired = []
while self.worker_change > 0 and applicants:
# based on employment prob
apps = []
for a in applicants:
ref = 'friend' if set(a.friends).intersection(
self.workers) else 'ad_or_cold_call'
p = offer_prob(world['year'],
world['month'],
a.sex,
a.race,
ref,
precomputed_emp_dist=emp_dist)
apps.append((a, p))
apps_mass = sum(p for a, p in apps)
apps = [(a, pr / apps_mass) for a, pr in apps]
worker = random_choice(apps)
if worker.employer is not None:
worker.employer.fire(worker)
worker.wage = wage
worker.employer = self
applicants.remove(worker)
self.workers.append(worker)
hired.append(worker)
logger.info('person:{}'.format(
json.dumps({
'event': 'hired',
'id': worker.id
})))
self.worker_change -= 1
# increase wage to attract more employees
if self.worker_change > 0:
wage += self.config['wage_increment'] * (1.1 + self.owner.altruism)
return hired, self.worker_change, wage
def healthcare_market(self):
sold = []
hospitals = [h for h in self.hospitals]
for person in shuffle(self.people):
if person._state['health'] < 1.:
affordable = [h for h in hospitals if h.price <= person._state['cash']]
if not affordable:
continue
utilities = [person.health_change_utility(1 - person._state['health']) + person.cash_change_utility(-h.price) for h in affordable]
u_t = sum(utilities)
choices = [(h, u/u_t) for h, u in zip(hospitals, utilities)]
hospital = random_choice(choices)
hospital.sell(1)
person._state['cash'] -= hospital.price
person._state['health'] = 1
person._state['sick'] = False if random.random() < self.state['recovery_prob'] else True
sold.append(hospital.price)
if hospital.supply == 0:
hospitals.remove(hospital)
if not hospitals:
break
profits = [f.revenue - f.costs for f in self.hospitals]
return sold, profits
def set_production_target(self, world):
"""firm decides on how much supply they want to produce this step,
and what they need to do to accomplish that"""
# assess previous day's results
self.prev_profit = self.profit
self.leftover = self.supply
# adjust production
action = self.learner.choose_action(self.current_state)
action = self.actions[action]
self.desired_supply = max(
1, self.desired_supply + action.get('supply', 0))
self.profit_margin += action.get('profit_margin', 0)
# supply expires every day
self.supply = 0
# unused materials expire every day
self.materials = 0
# resets every day
self.n_sold = 0
self.revenue = 0
self.costs = 0
# fire workers that are being paid too much
for worker in self.workers:
if worker.wage >= world['mean_wage'] + (
self.config['extravagant_wage_range'] *
(1.1 + self.owner.altruism)):
self.fire(worker)
# figure out labor goal
required_labor = self.desired_supply * self.config[
'labor_cost_per_good']
n_workers, wage, n_equip = self.assess_assets(
required_labor, world['mean_wage'], world['mean_equip_price'])
# sometimes optimization function returns a huge negative value for
# workers, need to look into that further
if n_workers < 0:
n_workers = 0
self.worker_change = n_workers - len(self.workers)
self.desired_equipment = self.equipment + max(0,
n_equip - self.equipment)
# fire workers if necessary
while self.worker_change < 0:
# weighted random choice by unemployment prob
ws = []
for w in self.workers:
pu = emp_dist[world['year']][world['month'] -
1][w.race.name][w.sex.name]
ws.append((w, pu['unemployed']))
ws_mass = sum(p for w, p in ws)
ws = [(w, p / ws_mass) for w, p in ws]
worker = random_choice(ws)
self.fire(worker)
self.worker_change += 1
# job vacancies
return self.worker_change, wage
