If they were able to predict precisely how many customers would visit their shop and accept this price, they would know exactly how much to supply. But, when their forecasts are not perfect they may not have the appropriate quantity, given the number of possible buyers they actually face at the close of the market. It might therefore, in this case, be better for them to sell at a lower price rather than to keep goods that they are not, by assumption, able to sell the next day. We ran the simulations with a constant afternoon price which is the morning price lowered by a factor . A more intelligent choice for the sellers would be to monitor previous fluctuations of the number of buyers and to decrease afternoon price in proportion.
Buyers visit one shop in the morning.
During the afternoon they sell at a lower price which reduces losses from unsold quantities. Buyers visit one shop in the afternoon. Sellers arrive in the morning with a quantity Q of the good corresponding to the number of customers they expect times q , plus some extra quantity of that good in case they have more customers than expected. The profit they expect from this additional amount is that obtained by satisfying new customers or unexpected former customers who might appear. Buyers have to decide every morning whether to buy at the morning price or to wait for a better price in the afternoon.