def kl_measures(sender_pop, receiver_pop, n=2):
msgs = list(itertools.product(range(n), range(n)))
states = list(itertools.product(range(n), range(n)))
state_probs = [1. / float(len(states))] * len(states)
all_cprobs_msg_given_state = collections.defaultdict(list)
all_cprobs_state_given_msg = collections.defaultdict(list)
information_contents = collections.defaultdict(list)
for i, msg in enumerate(msgs):
cprobs_msg_given_state = []
for j, state in enumerate(states):
pr = 0.
for (sender, sender_prob) in sender_pop:
if simulation.sender_matrix(sender)[j][i] == 1:
pr += sender_prob
cprobs_msg_given_state.append(pr)
all_cprobs_msg_given_state[i] = cprobs_msg_given_state
for j, state in enumerate(states):
if math.fsum(cprobs_msg_given_state) > 0.:
prob_state_given_msg = ((state_probs[j] * cprobs_msg_given_state[j]) /
math.fsum(state_probs[k] * cprobs_msg_given_state[k]
for k in xrange(len(states))))
else:
prob_state_given_msg = float('inf')
all_cprobs_state_given_msg[j].append(prob_state_given_msg)
if prob_state_given_msg > 0. and not math.isinf(prob_state_given_msg):
information_contents[i].append(math.log(prob_state_given_msg / state_probs[j]))
else:
information_contents[i].append(- float('inf'))
return (information_contents, all_cprobs_state_given_msg, all_cprobs_msg_given_state)
def output_klstats(this, out, duplications, r_payoffs, s_payoffs, comparative=False, splitat=None):
options = this.options
times_misinformation = 0
times_sender_hdeception = 0
times_receiver_hdeception = 0
times_full_deception = 0
pcts_senders_deceptive = []
pcts_senders_hdeceptive = []
pcts_interactions_hdeceptive_potential = []
pcts_interactions_deceptive_potential = []
pcts_interactions_hdeceptive = []
pcts_interactions_deceptive = []
n = 2
all_states = list(itertools.product(range(n), range(n)))
state_probs = [1. / float(len(all_states))] * len(all_states)
for dup_i, (final_sender, final_receiver, generations) in enumerate(duplications):
misinfo_msgs = []
(kls, all_cprobs_state_given_msg, all_cprobs_msg_given_state) = kl_measures(final_sender, final_receiver, n)
if not options.quiet:
print >> out, "Conditional Probabilities (msg: [pr(msg | state) for state in states]):"
for msg, cprobs_msg_given_state in all_cprobs_msg_given_state.iteritems():
print >> out, "\t{0:>5}: {1}".format(msg, cprobs_msg_given_state)
print >> out, "Conditional Probabilities (msg: [pr(state | msg) for state in states]):"
for msg, cprobs_state_given_msg in all_cprobs_state_given_msg.iteritems():
print >> out, "\t{0:>5}: {1}".format(msg, cprobs_state_given_msg)
print >> out, "KL Information Measures (msg: I(msg): [I(msg, state) for state in states]):"
for msg, information_content in kls.iteritems():
print >> out, "\t{0:>5}: {1:>15}: {2}".format(msg,
math.fsum(state_content * all_cprobs_state_given_msg[msg][state]
for state, state_content in enumerate(information_content)
if not math.isinf(state_content) and\
not math.isinf(all_cprobs_state_given_msg[msg][state])
),
information_content)
misinfo_msgs = [msg for msg, information_content in kls.iteritems()
if misinfo(msg, information_content)]
if len(misinfo_msgs) > 0:
times_misinformation += 1
# sum of population proportions that are deceptive against some receiver
percent_senders_deceptive = 0.
# sum of population proportions that are almost deceptive against some receiver (no receiver detriment)
percent_senders_halfdeceptive = 0.
# percent of interactions that are deceptive
percent_interactions_deceptive_potential = 0.
# percent of interactions that are almost deceptive
percent_interactions_halfdeceptive_potential = 0.
#
percent_interactions_deceptive = 0.
#
percent_interactions_halfdeceptive = 0.
s_decept = False
r_decept = False
f_decept = False
for (sender, sender_prob) in final_sender:
s_matrix = simulation.sender_matrix(sender)
s_hdeceptive = False
s_fdeceptive = False
for msg in misinfo_msgs:
states_msg_sent = [state for state, row in enumerate(s_matrix) if row[msg] == 1]
for (receiver, receiver_prob) in final_receiver:
sri_hdeceptive = False
sri_fdeceptive = False
if comparative and receiver < splitat:
r_matrix = simulation.sender_matrix(receiver)
elif comparative:
r_matrix = simulation.receiver_matrix(receiver - splitat)
elif this._result_options.combinatorial:
r_matrix = simulation.receiver_matrix(receiver)
else:
r_matrix = simulation.sender_matrix(receiver)
action = r_matrix[msg].index(1)
for state in states_msg_sent:
if comparative and receiver < splitat:
receiver_payoff_actual = r_payoffs[0][state][action]
receiver_payoff_ifknew = max(r_payoffs[0][state])
actions_ifknew = [act for act, payoff in enumerate(r_payoffs[0][state])
if payoff == receiver_payoff_ifknew]
elif comparative:
receiver_payoff_actual = r_payoffs[1][state][action]
receiver_payoff_ifknew = max(r_payoffs[1][state])
actions_ifknew = [act for act, payoff in enumerate(r_payoffs[1][state])
if payoff == receiver_payoff_ifknew]
else:
receiver_payoff_actual = r_payoffs[state][action]
receiver_payoff_ifknew = max(r_payoffs[state])
actions_ifknew = [act for act, payoff in enumerate(r_payoffs[state])
if payoff == receiver_payoff_ifknew]
sender_payoff_actual = s_payoffs[state][action]
sender_payoffs_ifknew = [s_payoffs[state][act] for act in actions_ifknew]
if any(sender_payoff_actual > spk for spk in sender_payoffs_ifknew):
percent_interactions_halfdeceptive += sender_prob * receiver_prob * state_probs[state]
sender_benefit = True
if not s_decept:
times_sender_hdeception += 1
s_decept = True
if not s_hdeceptive:
percent_senders_halfdeceptive += sender_prob
s_hdeceptive = True
if not sri_hdeceptive:
percent_interactions_halfdeceptive_potential += sender_prob * receiver_prob
sri_hdeceptive = True
else:
sender_benefit = False
if receiver_payoff_actual < receiver_payoff_ifknew:
receiver_detriment = True
if not r_decept:
times_receiver_hdeception += 1
r_decept = True
else:
receiver_detriment = False
if sender_benefit and receiver_detriment:
percent_interactions_deceptive += sender_prob * receiver_prob * state_probs[state]
if not f_decept:
times_full_deception += 1
f_decept = True
if not s_fdeceptive:
percent_senders_deceptive += sender_prob
s_fdeceptive = True
if not sri_fdeceptive:
percent_interactions_deceptive_potential += sender_prob * receiver_prob
sri_fdeceptive = True
pcts_senders_deceptive.append(percent_senders_deceptive)
pcts_senders_hdeceptive.append(percent_senders_halfdeceptive)
pcts_interactions_hdeceptive_potential.append(percent_interactions_halfdeceptive_potential)
pcts_interactions_deceptive_potential.append(percent_interactions_deceptive_potential)
pcts_interactions_hdeceptive.append(percent_interactions_halfdeceptive)
pcts_interactions_deceptive.append(percent_interactions_deceptive)
print >> out, "Number of duplications with misinformation: {0}".format(times_misinformation)
print >> out, "Number of duplications with senders benefitting: {0}".format(times_sender_hdeception)
print >> out, "Number of duplications with receivers losing out: {0}".format(times_receiver_hdeception)
print >> out, "Number of duplications with deception: {0}".format(times_full_deception)
print >> out
if options.gphx is not None:
fname_s_hdecept = options.gphx.format("s_hdecept")
fname_s_decept = options.gphx.format("s_decept")
fname_i_hdecept_pos = options.gphx.format("i_hdecept_pos")
fname_i_decept_pos = options.gphx.format("i_decept_pos")
fname_i_hdecept = options.gphx.format("i_hdecept")
fname_i_decept = options.gphx.format("i_decept")
else:
fname_s_hdecept = None
fname_s_decept = None
fname_i_hdecept_pos = None
fname_i_decept_pos = None
fname_i_hdecept = None
fname_i_decept = None
print >> out, "Percent of Senders Benefitting:"
print >> out, format_stats(pcts_senders_hdeceptive, "\t", fname_s_hdecept)
print >> out, "Percent of Senders Deceptive:"
print >> out, format_stats(pcts_senders_deceptive, "\t", fname_s_decept)
print >> out, "Percent of Interactions with Potential Sender Benefit:"
print >> out, format_stats(pcts_interactions_hdeceptive_potential, "\t", fname_i_hdecept_pos)
print >> out, "Percent of Interactions with Potential Deception:"
print >> out, format_stats(pcts_interactions_deceptive_potential, "\t", fname_i_decept_pos)
print >> out, "Percent of Interactions with Sender Benefit:"
print >> out, format_stats(pcts_interactions_hdeceptive, "\t", fname_i_hdecept)
print >> out, "Percent of Interactions with Deception:"
print >> out, format_stats(pcts_interactions_deceptive, "\t", fname_i_decept)
print >> out, "=" * 72
