def bid(self, t, history, reserve):
# The Balanced bidding strategy (BB) is the strategy for a player j that, given
# bids b_{-j},
# - targets the slot s*_j which maximizes his utility, that is,
# s*_j = argmax_s {clicks_s (v_j - p_s(j))}.
# - chooses his bid b' for the next round so as to
# satisfy the following equation:
# clicks_{s*_j} (v_j - p_{s*_j}(j)) = clicks_{s*_j-1}(v_j - b')
# (p_x is the price/click in slot x)
# If s*_j is the top slot, we (arbitrarily) choose
# b' = (v_j + p_0(j)) / 2. We can
# thus deal with all slots uniformly by defining clicks_{-1} = 2 clicks_0.
#
# initialize parameters
if self.NUMBER_OF_PLAYERS == 0 or self.TOTAL_CLICKS == 0 or self.NUMBER_OF_SLOTS == 0:
self.initialize_parameters(t, history)
# print "Number of players: ", self.NUMBER_OF_PLAYERS
# print "Number of slots: ", self.NUMBER_OF_SLOTS
# print "Total clicks", self.TOTAL_CLICKS
prev_round = history.round(t-1)
(slot, min_bid, max_bid) = self.target_slot(t, history, reserve)
num_default = self.defaults(t, history, reserve)
bid = 0
if num_default == 0:
bid = (min_bid + max_bid)/2
elif num_default > 0 and num_default <= 1:
bid = (.25*min_bid + .75*max_bid)
elif num_default > 1:
bid = max_bid
budget_effect = self.budget_factor(t, history, reserve)
click_effect = self.clicks_factor(t)
# print "defaults: ", num_default
# print "bid (pre factors): ", bid, min_bid, max_bid
# print "slot: ", slot
# print "budget: ", budget_effect
# print "click: ", click_effect
bid = bid*budget_effect*click_effect
if bid > max_bid:
bid = max_bid
if bid > self.value:
bid = self.value
if bid < reserve+1.01 and reserve+1.01 < self.value:
return reserve+1.01
if self.value < 75 and budget_effect > 1:
return self.value
return iround(bid)-0.01
def clicks_slot(self, t, slot):
return iround(iround(30*math.cos(math.pi*t/24) + 50)*(.75**slot))