Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets

• URL: http://arxiv.org/abs/2306.07709v1
• Date: Tue, 13 Jun 2023 11:55:04 GMT
• Title: Coordinated Dynamic Bidding in Repeated Second-Price Auctions with Budgets
• Authors: Yurong Chen, Qian Wang, Zhijian Duan, Haoran Sun, Zhaohua Chen, Xiang Yan, Xiaotie Deng
• Abstract summary: We study coordinated online bidding algorithms in repeated second-price auctions with budgets. We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding.
• Score: 17.937079224726073
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In online ad markets, a rising number of advertisers are employing bidding agencies to participate in ad auctions. These agencies are specialized in designing online algorithms and bidding on behalf of their clients. Typically, an agency usually has information on multiple advertisers, so she can potentially coordinate bids to help her clients achieve higher utilities than those under independent bidding. In this paper, we study coordinated online bidding algorithms in repeated second-price auctions with budgets. We propose algorithms that guarantee every client a higher utility than the best she can get under independent bidding. We show that these algorithms achieve maximal coalition welfare and discuss bidders' incentives to misreport their budgets, in symmetric cases. Our proofs combine the techniques of online learning and equilibrium analysis, overcoming the difficulty of competing with a multi-dimensional benchmark. The performance of our algorithms is further evaluated by experiments on both synthetic and real data. To the best of our knowledge, we are the first to consider bidder coordination in online repeated auctions with constraints.

Related papers

• Learning in Repeated Multi-Unit Pay-As-Bid Auctions [3.6294895527930504]
  We consider the problem of learning how to bid in repeated multi-unit pay-as-bid auctions. The problem of learning how to bid in pay-as-bid auctions is challenging according to the nature of the action space. We show that the optimal solution to the offline problem can be obtained using a time dynamic programming scheme.
  arXiv  Detail & Related papers  (2023-07-27T20:49:28Z)
• Multi-Platform Budget Management in Ad Markets with Non-IC Auctions [6.037383467521294]
  In online advertising markets, budget-constrained advertisers acquire ad placements through repeated bidding in auctions on various platforms. We present a strategy for bidding optimally in a set of auctions that may or may not be incentive-compatible under the presence of budget constraints. Our strategy maximizes the expected total utility across auctions while satisfying the advertiser's budget constraints in expectation.
  arXiv  Detail & Related papers  (2023-06-12T18:21:10Z)
• Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics [53.62091043347035]
  We study a game between autobidding algorithms that compete in an online advertising platform. We propose a gradient-based learning algorithm that is guaranteed to satisfy all constraints and achieves vanishing individual regret.
  arXiv  Detail & Related papers  (2023-01-30T21:59:30Z)
• Incentive-Aware Recommender Systems in Two-Sided Markets [49.692453629365204]
  We propose a novel recommender system that aligns with agents' incentives while achieving myopically optimal performance. Our framework models this incentive-aware system as a multi-agent bandit problem in two-sided markets. Both algorithms satisfy an ex-post fairness criterion, which protects agents from over-exploitation.
  arXiv  Detail & Related papers  (2022-11-23T22:20:12Z)
• No-regret Learning in Repeated First-Price Auctions with Budget Constraints [5.834615090865286]
  We propose an RL-based bidding algorithm against the optimal non-anticipating strategy under stationary competition. Our algorithm obtains $widetilde O(sqrt T)$-regret if the bids are all revealed at the end of each round.
  arXiv  Detail & Related papers  (2022-05-29T04:32:05Z)
• Bidding via Clustering Ads Intentions: an Efficient Search Engine Marketing System for E-commerce [13.601308818833301]
  This paper introduces the end-to-end structure of the bidding system for search engine marketing for Walmart e-commerce. We exploit the natural language signals from the users' query and the contextual knowledge from the products to mitigate the sparsity issue. We analyze the online and offline performances of our approach and discuss how we find it as a production-efficient solution.
  arXiv  Detail & Related papers  (2021-06-24T00:12:07Z)
• A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising [53.636153252400945]
  We propose a general Multi-Agent reinforcement learning framework for Auto-Bidding, namely MAAB, to learn the auto-bidding strategies. Our approach outperforms several baseline methods in terms of social welfare and guarantees the ad platform's revenue.
  arXiv  Detail & Related papers  (2021-06-11T08:07:14Z)
• A novel auction system for selecting advertisements in Real-Time bidding [68.8204255655161]
  Real-Time Bidding is a new Internet advertising system that has become very popular in recent years. We propose an alternative betting system with a new approach that not only considers the economic aspect but also other relevant factors for the functioning of the advertising system.
  arXiv  Detail & Related papers  (2020-10-22T18:36:41Z)
• Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions [40.30925727499806]
  We develop the first minimax optimal online bidding algorithm that achieves an $widetildeO(sqrtT)$ regret. We test our algorithm on three real-world first-price auction datasets obtained from Verizon Media.
  arXiv  Detail & Related papers  (2020-07-09T05:40:39Z)
• Online Joint Bid/Daily Budget Optimization of Internet Advertising Campaigns [115.96295568115251]
  We study the problem of automating the online joint bid/daily budget optimization of pay-per-click advertising campaigns over multiple channels. For every campaign, we capture the dependency of the number of clicks on the bid and daily budget by Gaussian Processes. We design four algorithms and show that they suffer from a regret that is upper bounded with high probability as O(sqrtT) We present the results of the adoption of our algorithms in a real-world application with a daily average spent of 1,000 Euros for more than one year.
  arXiv  Detail & Related papers  (2020-03-03T11:07:38Z)
• Adversarial Attacks on Linear Contextual Bandits [87.08004581867537]
  Malicious agents may have incentives to attack the bandit algorithm to induce it to perform a desired behavior. We show that a malicious agent can force a linear contextual bandit algorithm to pull any desired arm $T - o(T)$ times over a horizon of $T$ steps. We also investigate the case when a malicious agent is interested in affecting the behavior of the bandit algorithm in a single context.
  arXiv  Detail & Related papers  (2020-02-10T15:04:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.