Negotiating Networks in Oligopoly Markets for Price-Sensitive Products
This work addresses pricing optimization in oligopoly markets, which is an incremental advancement for sellers and buyers in economic modeling.
The paper tackles the problem of modeling seller pricing and buyer purchase decisions in oligopoly markets for price-sensitive products by proposing a novel minimax game framework, and demonstrates its potential through experiments on simulated and real-world data with comparisons to a baseline model.
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.