  We present a novel framework to learn functions that estimate decisions of
sellers and buyers simultaneously in an oligopoly market for a price-sensitive
product. In this setting, the aim of the seller network is to come up with a
price for a given context such that the expected revenue is maximized by
considering the buyer's satisfaction as well. On the other hand, the aim of the
buyer network is to assign probability of purchase to the offered price to
mimic the real world buyers' responses while also showing price sensitivity
through its action. In other words, rejecting the unnecessarily high priced
products. Similar to generative adversarial networks, this framework
corresponds to a minimax two-player game. In our experiments with simulated and
real-world transaction data, we compared our framework with the baseline model
and demonstrated its potential through proposed evaluation metrics.
