def plot_qs_pcs_M(game, num_steps=10000, **kwargs):
"""Plot qs, pcs, and M together."""
game = game.copy_with_agents(hist_agent("qs_hist", "qs"),
hist_agent("pcs_hist", "pcs"),
hist_agent("M_hist", "M"))
game.run(num_steps)
fig, axs = plt.subplots(3, 2)
plot_qs(game, num_steps=None, axs=axs[0], **kwargs)
plot_pcs(game, num_steps=None, axs=axs[1], **kwargs)
plot_M(game, num_steps=None, axs=axs[2], **kwargs)
plt.show()
def plot_M(game, num_steps=10000, axs=None, **kwargs):
if num_steps is not None:
game = game.copy_with_agents(hist_agent("M_hist", "M"))
game.run(num_steps)
show = axs is None
if axs is None:
fig, axs = plt.subplots(1, 2)
MCs = (list)((starmap)(lambda i, M: M / (i + 2), (enumerate)(
(map)(_coconut.operator.itemgetter((C)), game.env["M_hist"]))))
MDs = (list)((starmap)(lambda i, M: M / (i + 2), (enumerate)(
(map)(_coconut.operator.itemgetter((D)), game.env["M_hist"]))))
xs = range(1, len(game.env["M_hist"]) + 1)
game.plot(axs[0], xs, MCs, label="M(C)/M", **kwargs)
game.plot(axs[0], xs, MDs, label="M(D)/M", **kwargs)
axs[0].set(xlabel="t")
axs[0].legend()
log_xs = (list)((map)(log, xs))
game.plot(axs[1], log_xs, MCs, label="M(C)/M", **kwargs)
game.plot(axs[1], log_xs, MDs, label="M(D)/M", **kwargs)
axs[1].set(xlabel="log(t)")
axs[1].legend()
if show:
plt.show()
def plot_pcs(game, num_steps=10000, axs=None, **kwargs):
"""Plot pcs over time in the given game."""
if num_steps is not None:
game = game.copy_with_agents(hist_agent("pcs_hist", "pcs"))
game.run(num_steps)
show = axs is None
if axs is None:
fig, axs = plt.subplots(1, 2)
xs = range(1, len(game.env["pcs_hist"]) + 1)
game.plot(axs[0],
xs,
lambda env: (map)(_coconut.operator.itemgetter(
(C)), env["pcs_hist"]),
label="P(C|C)",
**kwargs)
game.plot(axs[0],
xs,
lambda env: (map)(_coconut.operator.itemgetter(
(D)), env["pcs_hist"]),
label="P(C|D)",
**kwargs)
axs[0].set(xlabel="t")
axs[0].legend()
log_xs = (list)((map)(log, xs))
game.plot(axs[1],
log_xs,
lambda env: (map)(_coconut.operator.itemgetter(
(C)), env["pcs_hist"]),
label="P(C|C)",
**kwargs)
game.plot(axs[1],
log_xs,
lambda env: (map)(_coconut.operator.itemgetter(
(D)), env["pcs_hist"]),
label="P(C|D)",
**kwargs)
axs[1].set(xlabel="log(t)")
axs[1].legend()
if show:
plt.show()
def run_experiment(game,
num_iters=500,
num_steps=5000,
bucket_size=0.01,
pc_calc_steps=500):
"""Measure limiting behavior for the given game."""
game = game.copy_with_agents(
hist_agent("a_hist", "a", maxhist=pc_calc_steps))
buckets = [0] * int(1 / bucket_size)
coop_props = []
print("Running experiment for {_coconut_format_0}...".format(
_coconut_format_0=(game.name)))
for _ in tqdm(range(num_iters)):
game.run(num_steps, use_tqdm=False)
prop_coop = sum((a == C for a in game.env["a_hist"])) / pc_calc_steps
coop_props.append(prop_coop)
bucket = int(prop_coop // bucket_size)
if bucket == len(buckets):
bucket -= 1
buckets[bucket] += 1
game.reset()
for i in range(len(buckets)):
buckets[i] /= num_iters
return buckets, sum(coop_props) / len(coop_props)
])
PREV_DEFECT_PENALTY = PREV_COOP_REWARD - 2
SELF_PD_DEL_C = COOP_PENALTY, 3 * PREV_COOP_REWARD
PAY_FORWARD_DEL_C = COOP_PENALTY, 2 * PREV_COOP_REWARD
COOKIE_DEL_C = DEFECT_REWARD, 2 * PREV_DEFECT_PENALTY
BUTTON_DEL_C = NO_REWARD, PREV_COOP_REWARD
def coop_with_prob(p):
return np.random.binomial(1, 1 - p)
common_params = dict(INIT_C_PROB=0.5, DEL_C=SELF_PD_DEL_C, USE_STATE=False)
a_hist_1step = hist_agent("a_hist_1step", "a", maxhist=1)
def get_prev_a(env):
if not env["a_hist_1step"]:
env["a_hist_1step"].append(coop_with_prob(env["INIT_C_PROB"]))
return env["a_hist_1step"][-1]
@agent(name="r")
def get_corrupted_feedback(env, s=None, a=None, k=None):
s = get_prev_a(env) if s is None else s
a = env["a"] if a is None else a
k = a if k is None else k
DEL, C = env["DEL_C"]
feedback = DEL[s][k]