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Reasoning about Human-Friendly Strategies in Repeated Keyword Auctions

Abstract : In online advertising, search engines sell ad placements for keywords continuously through auctions. This problem can be seen as an infinitely repeated game since the auction is executed whenever a user performs a query with the keyword. As advertisers may frequently change their bids, the game will have a large set of equilibria with potentially complex strategies. In this paper, we propose the use of natural strategies for reasoning in such setting as they are processable by artificial agents with limited memory and/or computational power as well as understandable by human users. To reach this goal, we introduce a quantitative version of Strategy Logic with natural strategies in the setting of imperfect information. In a first step, we show how to model strategies for repeated keyword auctions and take advantage of the model for proving properties evaluating this game. In a second step, we study the logic in relation to the distinguishing power, expressivity, and model-checking complexity for strategies with and without recall.
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Contributor : Munyque Mittelmann Connect in order to contact the contributor
Submitted on : Thursday, May 12, 2022 - 2:11:11 PM
Last modification on : Monday, July 4, 2022 - 9:59:27 PM

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  • HAL Id : hal-03666430, version 1
  • ARXIV : 2201.09616


Francesco Belardinelli, Wojciech Jamroga, Vadim Malvone, Munyque Mittelmann, Aniello Murano, et al.. Reasoning about Human-Friendly Strategies in Repeated Keyword Auctions. 21st International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2022), International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), May 2022, Auckland (virtual), New Zealand. ⟨hal-03666430⟩



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