A robust learning approach to repeated auctions with monitoring and entry fees

Amir Danak, Shie Mannor

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we present a strategic bidding framework for repeated auctions with monitoring and entry fees. We motivate and formally define the desired properties of our framework and present a recursive bidding algorithm, according to which buyers learn to avoid submitting bids in stages where they have a relatively low chance of winning the auctioned item. The proposed bidding strategies are computationally simple as players do not need to recompute the sequential strategies from the data collected to date. Pursuing the proposed efficient bidding (EB) algorithm, players monitor their relative performance in the course of the game and submit their bids based on their current estimate of the market condition. We prove the stability and robustness of the proposed strategies and show that they dominate myopic and random bidding strategies using an experiment in search engine marketing.

Original languageEnglish
Article number5936110
Pages (from-to)302-315
Number of pages14
JournalIEEE Transactions on Computational Intelligence and AI in Games
Volume3
Issue number4
DOIs
StatePublished - Dec 2011
Externally publishedYes

Keywords

  • Auction theory
  • Dynamic game theory
  • Repeated games
  • Resource allocation

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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