Setting an effective pricing policy for double auction marketplaces

Shi, Bing, Huang, Yalong, Xiong, Shengwu and Gerding, Enrico H. (2016) Setting an effective pricing policy for double auction marketplaces. In, PRICAI 2016: TRENDS IN ARTIFICIAL INTELLIGENCE: 14th Pacific Rim International Conference on Artificial Intelligence, Phuket, Thailand, August 22-26. (Lecture Notes in Computer Science, 9810) Springer, pp. 457-471. (doi:10.1007/978-3-319-42911-3_38).

Abstract

In this paper, we analyse how double auction marketplaces set an effective pricing policy to determine the transaction prices for matched buyers and sellers. We analyse this problem by considering continuous privately known trader types. Furthermore, we consider two typical pricing policies: equilibrium k pricing policy and discriminatory k pricing policy. We firstly investigate how to determine the transaction prices to reach the maximal allocative efficiency in an isolated marketplace when the traders adopt Bayes-Nash equilibrium bidding strategies. We find that when the marketplace adopts discriminatory k pricing policy, the maximal allocative efficiency is reached by setting k = 0.41 or 0.59. We find that equilibrium k pricing policy provides higher allocative efficiency than discriminatory k pricing policy. We further discuss how different pricing policies can affect traders' Bayes-Nash equilibrium bidding strategies. Furthermore, we extend the analysis to the setting with two marketplaces competing against each other to attract traders. We find that the marketplace using equilibrium k pricing policy is more likely to beat the marketplace using discriminatory k pricing policy, where all traders converge to the marketplace using equilibrium k pricing policy in Bayes-Nash equilibrium. Our analysis can provide meaningful insights for designing an effective pricing policy.

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Contributors

Author: Enrico H. Gerding

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