Negotiating Networks in Oligopoly Markets for Price-Sensitive Products

TL;DR

This paper introduces a novel adversarial framework for modeling seller and buyer decision-making in oligopoly markets, capturing price sensitivity and maximizing seller revenue.

Contribution

The paper proposes a new minimax learning framework that simultaneously models seller pricing strategies and buyer purchase responses in oligopoly markets.

Findings

Framework outperforms baseline models in simulated data

Effective in real-world transaction data

Captures buyer price sensitivity accurately

Abstract

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.