Reasoning about Human-Friendly Strategies in Repeated Keyword Auctions
This work addresses the challenge of designing understandable and computationally feasible strategies for advertisers and agents in online advertising auctions, though it appears incremental as it builds on existing logic frameworks.
The paper tackles the problem of reasoning about strategies in repeated keyword auctions, which are complex due to frequent bid changes and many equilibria, by introducing a quantitative version of Strategy Logic with natural strategies to model and analyze these games, showing results in terms of distinguishing power, expressivity, and model-checking complexity.
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.