def target_slot(self, t, history, reserve):
"""Figure out the best slot to target, assuming that everyone else
keeps their bids constant from the previous rounds.
Returns (slot_id, min_bid, max_bid), where min_bid is the bid needed to tie
the other-agent bid for that slot in the last round. If slot_id = 0,
max_bid is min_bid * 2
"""
i = argmax_index(self.expected_utils(t, history, reserve))
info = self.slot_info(t, history, reserve)
try:
return info[i]
except:
i = argmax_index(self.expected_utils(t, history, reserve)[:-1])
return info[i]
def target_slot(self, t, history, reserve):
"""Figure out the best slot to target, assuming that everyone else
keeps their bids constant from the previous rounds.
Returns (slot_id, min_bid, max_bid), where min_bid is the bid needed to tie
the other-agent bid for that slot in the last round. If slot_id = 0,
max_bid is min_bid * 2
"""
i = argmax_index(self.expected_utils(t, history, reserve))
info = self.slot_info(t, history, reserve)
return info[i]
def estimate_values(self, t, history,
reserve): # this is assuming other players play BB
# the values are drawn in U[25, 175]
prev_round = history.round(t - 1)
other_bids = filter(lambda (a_id, b): a_id != self.id, prev_round.bids)
# check that bids are sorted by agent id
if t == 1: # at second round, estimate with the mean 100, to refine
self.estimated_values = [(other_bids[k][0], 100)
for k in range(len(other_bids))]
# assume the agents are playing balanced bidding
else:
prevprev_round = history.round(t - 2)
clicks = prevprev_round.clicks
for i in range(len(other_bids)):
# assume agent i is playing balanced bids in round t-1
id_i = other_bids[i][0]
bid_i = other_bids[i][1]
other_bids_i = filter(lambda (a_id, b): a_id != id_i,
prevprev_round.bids)
other_bids_i = sorted([x[1] for x in other_bids_i],
reverse=True)
num_slots = len(clicks)
for _ in range(len(other_bids_i), num_slots):
other_bids_i.append(0)
other_bids_i = [max(x, reserve) for x in other_bids_i]
expected_utilities_i = [
clicks[k] * (self.estimated_values[i][1] -
max(other_bids_i[k], reserve))
for k in range(len(other_bids_i))
]
target_slot_i = argmax_index(expected_utilities_i)
if target_slot_i == 0:
value_i = bid_i
elif expected_utilities_i[target_slot_i] < 0:
value_i = bid_i
else:
t_star_i = other_bids_i[target_slot_i]
value_i = (clicks[target_slot_i] * t_star_i - bid_i * clicks[target_slot_i - 1]) / \
(clicks[target_slot_i] - clicks[target_slot_i-1])
if value_i < 25:
value_i = 25
if value_i > 175:
value_i = 175
if t == 2:
self.estimated_values[i] = (id_i, value_i)
else:
self.estimated_values[i] = (id_i,
(self.estimated_values[i][1] *
(t - 2) + value_i) / (t - 1))
self.estimated_values[i] = (id_i, value_i)
