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Negotiating Networks in Oligopoly Markets for Price-Sensitive Products

Naman Shukla, Kartik Yellepeddi

arXiv 25 Oct 2021 · Machine Learning

arXiv:2110.13303 · PDF · DOI · OpenAlex · Extracted main text

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.

Citation extraction

39
references
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in-text mentions
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distinct cited
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self-citations
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appendix boundary found by appendix_command · 82% of the source is main text. Read the extracted text to check this.

Most heavily cited references

The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.

ReferenceIntensityMentionsSectionsMain text
1Peng Ye, Julian Qian, Jieying Chen, Chen-hung Wu, Yitong Zhou, Spenc… (2018) Customized regression model for airbnb dynamic pricing0.92843100%
2Akhil Gupta, Naman Shukla, Lavanya Marla, Arinbjörn Kolbeinsson, and… (1909) How to Incorporate Monotonicity in Deep Networks While Preserving Flexibility? self0.64422100%
3Naman Shukla, Arinbjörn Kolbeinsson, Ken Otwell, Lavanya Marla, and… (2019) Dynamic Pricing for Airline Ancillaries with Customer Context self0.64422100%
4M. Ben-Akiva and S. Lerman (1985) Discrete choice analysis0.40511100%
5X. Gabaix and D. Laibson (2006) Shrouded attributes, consumer myopia, and information suppression in competitive markets0.40511100%
6D. Kahneman and A. Tversky (1979) Prospect theory: an analysis of decision under risk0.40511100%
7J.D. Shulman and X. Geng (2013) Management Science, 59:0 899–917, 20130.40511100%
8Diederik P. Kingma and Jimmy Ba (2015) Adam: A method for stochastic optimization0.40511100%
9Alankrita Aggarwal, Mamta Mittal, and Gopi Battineni (2021) Generative adversarial network: An overview of theory and applications0.40511100%
10George J Avlonitis and Kostis A Indounas (2005) Pricing objectives and pricing methods in the services sector0.40511100%

Showing the top 10 of 39 scored citations.