def central_bank_action(agent, **kwargs):
"""
If exchanges are down "enough", distribute coupons!
Enough is a parameter.
"""
execution_key = get_exec_key(kwargs=kwargs)
CB_interven_pts = get_env_attr("CB_interven_pts",
execution_key=execution_key)
set_env_attr("num_rounds",
get_env_attr("num_rounds", execution_key=execution_key) + 1,
execution_key=execution_key)
co_op_mbrs = get_group(CO_OP_MEMBERS, execution_key=execution_key)
unemp_rate = get_env_attr("last_per_unemp",
execution_key=execution_key,
default_value=0) / len(co_op_mbrs) * 100
unemp_threshold = agent["percent_change"]
if unemp_rate >= unemp_threshold:
user_tell("Unemployment has risen to " + str(unemp_rate) +
" more than default value " + str(unemp_threshold) +
" CB Intervened")
CB_interven_pts.append([
get_env_attr("num_rounds", execution_key=execution_key),
get_env_attr("last_per_exchg", execution_key=execution_key)
])
distribute_coupons(agent, execution_key=execution_key)
return True
def create_non_drinker(name, i, **kwargs):
"""
Create an agent.
"""
execution_key = get_exec_key(kwargs=kwargs)
return Agent(name + str(i), action=drinker_action,
attrs={MOTIV: DEF_MOTIV}, execution_key=execution_key)
def create_resident(name, i, group=BLUE, **kwargs):
"""
Creates agent of specified color type
"""
execution_key = get_exec_key(kwargs=kwargs)
if group == BLUE:
grp_idx = BLUE_GRP_IDX
mean_tol = get_prop('mean_tol',
DEF_TOLERANCE,
execution_key=execution_key)
else:
grp_idx = RED_GRP_IDX
mean_tol = -get_prop(
'mean_tol', DEF_TOLERANCE, execution_key=execution_key)
dev = get_prop('deviation', DEF_SIGMA, execution_key=execution_key)
this_tolerance = get_tolerance(mean_tol, dev)
return Agent(name + str(i),
action=seg_agent_action,
attrs={
TOLERANCE:
this_tolerance,
GRP_INDEX:
grp_idx,
"hood_changed":
True,
"just_moved":
False,
"hood_size":
get_prop('hood_size',
DEF_HOOD_SIZE,
execution_key=execution_key)
},
execution_key=execution_key)
def seek_a_trade(agent, comp=False, **kwargs):
execution_key = get_exec_key(kwargs=kwargs)
nearby_agent = get_env(
execution_key=execution_key).get_closest_agent(agent)
if nearby_agent is not None:
negotiate(agent, nearby_agent, comp)
return MOVE
def tree_action(agent, **kwargs):
"""
This is what trees do each turn in the forest.
"""
execution_key = get_exec_key(kwargs=kwargs)
old_state = agent["state"]
if is_healthy(agent):
if exists_neighbor(agent, pred=is_on_fire,
execution_key=execution_key):
if DEBUG2:
user_log_notif("Setting nearby tree on fire!")
agent["state"] = NF
# if we didn't catch on fire above, do probabilistic transition:
if old_state == agent["state"]:
# we gotta do these str/int shenanigans with state cause
# JSON only allows strings as dict keys
agent["state"] = \
str(prob_state_trans(int(old_state),
get_env_attr(TRANS_TABLE,
execution_key=execution_key)))
if DEBUG2:
if agent["state"] == NF:
user_log_notif("Tree spontaneously catching fire.")
if old_state != agent["state"]:
# if we entered a new state, then...
env = get_env(execution_key=execution_key)
group_map = get_env_attr(GROUP_MAP, execution_key=execution_key)
if group_map is None:
user_log_err("group_map is None!")
return True
agent.has_acted = True
env.add_switch(agent, group_map[old_state], group_map[agent["state"]])
return True
def neighbor_ratio(agent, pred_one, pred_two=None, size=1, region_type=None,
**kwargs):
execution_key = get_exec_key(kwargs=kwargs)
return get_env(execution_key=execution_key) \
.neighbor_ratio(agent, pred_one,
pred_two=pred_two,
size=size,
region_type=region_type)
def tsetter_action(agent, **kwargs):
execution_key = get_exec_key(kwargs=kwargs)
return common_action(agent,
get_group(RED_FOLLOWERS, execution_key=execution_key),
get_group(BLUE_FOLLOWERS,
execution_key=execution_key),
gt,
lt, **kwargs)
def record_exchanges(pop_hist, **kwargs):
"""
This is our hook into the env to record the number of exchanges each
period.
"""
execution_key = get_exec_key(kwargs=kwargs)
pop_hist.record_pop(
"Exchanges", get_env_attr("last_per_exchg",
execution_key=execution_key))
def sell(agent, **kwargs):
execution_key = get_exec_key(kwargs=kwargs)
market_maker = get_env(execution_key=execution_key)[MARKET_MAKER]
price = market_maker["asset_price"] * DEF_NUM_ASSET
if agent["num_stock"] >= DEF_NUM_ASSET:
market_maker["sell"] += 1
agent["capital"] += price
agent["num_stock"] -= DEF_NUM_ASSET
def is_isolated(agent, **kwargs):
'''
Checks if agent is maintaining distancing.
'''
execution_key = get_exec_key(kwargs=kwargs)
return not exists_neighbor(agent,
size=get_prop('distancing', DEF_DISTANCING,
execution_key=execution_key),
execution_key=execution_key)
def get_num_of_neighbors(agent, exclude_self=False, pred=None, size=1,
region_type=None, **kwargs):
exec_key = get_exec_key(kwargs=kwargs)
return get_env(execution_key=exec_key) \
.get_num_of_neighbors(agent,
exclude_self=True,
pred=None,
size=size,
region_type=region_type)
def exists_neighbor(agent, pred=None, exclude_self=True, size=1,
region_type=None, **kwargs):
execution_key = get_exec_key(kwargs=kwargs)
return get_env(execution_key=execution_key) \
.exists_neighbor(agent,
pred=pred,
exclude_self=exclude_self,
size=size,
region_type=region_type)
def create_tsetter(name, i, props=None, color=RED_SIN, **kwargs):
"""
Create a trendsetter: all RED to start.
"""
execution_key = get_exec_key(kwargs=kwargs)
return Agent(TSETTER_PRENM + str(i),
action=tsetter_action,
attrs={COLOR_PREF: color,
DISPLAY_COLOR: color}, execution_key=execution_key)
def reproduce(agent, create_func, group, **kwargs):
"""
Agents reproduce when TIME_TO_REPR reaches 0
"""
execution_key = get_exec_key(kwargs=kwargs)
if DEBUG:
user_debug(str(agent.name) + " is having a baby!")
get_env(execution_key=execution_key).add_child(group)
agent[TIME_TO_REPR] = agent["orig_repr_time"]
def create_follower(name, i, props=None, color=BLUE_SIN, **kwargs):
"""
Create a follower: all BLUE to start.
"""
execution_key = get_exec_key(kwargs=kwargs)
return Agent(FOLLOWER_PRENM + str(i),
action=follower_action,
attrs={COLOR_PREF: color,
DISPLAY_COLOR: color}, execution_key=execution_key)
def value_investor_action(agent, **kwargs):
# Determine if value investors should buy or sell the stock
execution_key = get_exec_key(kwargs=kwargs)
market_maker = get_env(execution_key=execution_key)[MARKET_MAKER]
if market_maker["asset_price"] >= agent["high_price"]:
sell(agent, **kwargs)
elif market_maker["asset_price"] <= agent["low_price"]:
buy(agent, **kwargs)
return True
def social_distancing(agent, **kwargs):
"""
This function sets a new angle for the agent's movement.
"""
execution_key = get_exec_key(kwargs=kwargs)
curr_region = CircularRegion(get_env(execution_key=execution_key),
agent.get_pos(), DEF_PERSON_MOVE * 2)
agents_in_range = curr_region.get_agents(
get_env(execution_key=execution_key), pred=None)
new_angle = get_move_angle(agent, agents_in_range)
agent["angle"] = new_angle
def create_resident(name, i, state=CALM, **kwargs):
"""
Creates agent of specified color type
"""
execution_key = get_exec_key(kwargs=kwargs)
return Agent(name + str(i),
action=panic_agent_action,
attrs={"state": state,
"hood_size": get_prop('hood_size',
DEF_HOOD_SIZE,
execution_key=execution_key),
"save_neighbours": True}, execution_key=execution_key)
def create_nutrient(name, i, **kwargs):
"""
Create a nutrient.
"""
execution_key = get_exec_key(kwargs=kwargs)
nutrient = Agent(name + str(i),
action=nutrient_action,
execution_key=execution_key)
nutrient["max_move"] = get_prop("nutrient_move",
DEF_NUTRIENT_MOVE,
execution_key=execution_key)
return nutrient
def create_toxin(name, i, **kwargs):
"""
Create a toxin.
"""
execution_key = get_exec_key(kwargs=kwargs)
toxin = Agent(name + str(i),
action=toxin_action,
execution_key=execution_key)
toxin["max_move"] = get_prop("toxin_move",
DEF_TOXIN_MOVE,
execution_key=execution_key)
return toxin
def trend_follower_action(agent, **kwargs):
# Determine if trend followers should buy
# or sell the stock
execution_key = get_exec_key(kwargs=kwargs)
market_maker = get_env(execution_key=execution_key)[MARKET_MAKER]
if trend_direction(agent, market_maker["asset_price"],
market_maker["price_hist"]) == 1:
buy(agent, **kwargs)
else:
sell(agent, **kwargs)
return True
def create_market_maker(name, **kwargs):
"""
Create a market maker.
"""
execution_key = get_exec_key(kwargs=kwargs)
market_maker = Agent(name,
action=market_maker_action,
execution_key=execution_key)
market_maker["buy"] = 0
market_maker["sell"] = 0
market_maker["asset_price"] = DEF_PRICE
market_maker["prev_asset_price"] = DEF_PRICE
market_maker["price_hist"] = [DEF_PRICE]
return market_maker
def plant_tree(name, i, state=HE, **kwargs):
"""
Plant a new tree!
By default, they start out healthy.
"""
execution_key = get_exec_key(kwargs=kwargs)
name = TREE_PREFIX
return Agent(name + str(i),
action=tree_action,
attrs={
"state": state,
"save_neighbors": True
},
execution_key=execution_key)
def create_sheep(name, i, **kwargs):
"""
Method to create sheep
"""
exec_key = get_exec_key(kwargs=kwargs)
time_to_repro = randint(1, SHEEP_REPRO_PERIOD)
return Agent(AGT_SHEEP_NAME + str(i),
duration=SHEEP_LIFESPAN,
action=sheep_action,
attrs={
TIME_TO_REPR: time_to_repro,
"orig_repr_time": SHEEP_REPRO_PERIOD
},
execution_key=exec_key)
def get_prey(agent, sheep, **kwargs):
"""
Wolves eat active sheep from the neighbourhood
"""
exec_key = get_exec_key(kwargs=kwargs)
if len(get_group(SHEEP_GROUP, exec_key)) <= 0:
raise NoSheep("All out of sheep!")
return get_env(execution_key=exec_key) \
.get_neighbor_of_groupX(agent,
SHEEP_GROUP,
hood_size=get_env_attr(
"prey_dist",
execution_key=exec_key))
def create_trader(name, i, **kwargs):
execution_key = get_exec_key(kwargs=kwargs)
return Agent(name + str(i),
action=seek_a_trade_w_comp,
attrs={
"goods": {
"truck": {
AMT_AVAIL: 0,
UTIL_FUNC: "steep_util_func",
"incr": 0,
COMPLEMENTS: ["fuel", "land"]
},
"penguin": {
AMT_AVAIL: 0,
UTIL_FUNC: "steep_util_func",
"incr": 0,
COMPLEMENTS: ["pet_food", "meat"]
},
"pet_food": {
AMT_AVAIL: 0,
UTIL_FUNC: "steep_util_func",
"incr": 0,
COMPLEMENTS: ["penguin", "meat"]
},
"fuel": {
AMT_AVAIL: 0,
UTIL_FUNC: "steep_util_func",
"incr": 0,
COMPLEMENTS: ["truck", "land"]
},
"land": {
AMT_AVAIL: 0,
UTIL_FUNC: "steep_util_func",
"incr": 0,
COMPLEMENTS: ["truck", "fuel"]
},
"meat": {
AMT_AVAIL: 0,
UTIL_FUNC: "steep_util_func",
"incr": 0,
COMPLEMENTS: ["penguin", "pet_food"]
}
},
"graph": create_graph(),
"util": 0,
"pre_trade_util": 0,
"trades_with": "trader"
},
execution_key=execution_key)
def __init__(self, space=None, center=None, radius=None, agents_move=True,
**kwargs):
self.execution_key = get_exec_key(kwargs)
if (space is None):
space = get_env(execution_key=self.execution_key)
self.space = space
self.center = center
self.radius = radius
self.name = "Circle"
self.agents_move = agents_move
self.my_sub_regs = []
if self.agents_move:
self.my_agents = []
else:
self.my_agents = self._load_agents()
def create_wolf(name, i, **kwargs):
"""
Method to create wolf
"""
execution_key = get_exec_key(kwargs=kwargs)
time_to_repro = randint(1, WOLF_REPRO_PERIOD)
return Agent(AGT_WOLF_NAME + str(i),
duration=WOLF_LIFESPAN,
action=wolf_action,
attrs={
TIME_TO_REPR: time_to_repro,
"orig_repr_time": WOLF_REPRO_PERIOD
},
execution_key=execution_key)
def create_bacterium(name, i, **kwargs):
"""
Create a baterium.
"""
execution_key = get_exec_key(kwargs=kwargs)
bacterium = Agent(name + str(i),
action=bacterium_action,
execution_key=execution_key)
bacterium["prev_toxicity"] = None
bacterium["prev_nutricity"] = None
bacterium["angle"] = None
bacterium["max_move"] = get_prop("bacterium_move",
DEF_BACTERIUM_MOVE,
execution_key=execution_key)
return bacterium
def seg_agent_action(agent, **kwargs):
"""
If the agent is surrounded by more "others" than it
is comfortable with, the agent will move.
The whole idea here is to count those in other group
and those in my group, and get the ratio.
"""
execution_key = get_exec_key(kwargs=kwargs)
agent_group = agent.group_name()
ratio_num = neighbor_ratio(agent,
lambda agent: agent.group_name() == agent_group,
size=agent['hood_size'],
execution_key=execution_key)
if DEBUG2:
print("ratio test" + str(ratio_num))
return env_favorable(ratio_num, agent[TOLERANCE])