Control problems in online advertising and benefits of randomized bidding strategies

2016 ◽  
Vol 30 ◽  
pp. 31-49 ◽  
Author(s):  
Niklas Karlsson
Keyword(s):  
Online Advertising ◽  
Control Problems ◽  
Bidding Strategies

Learning in Repeated Auctions with Budgets: Regret Minimization and Equilibrium

2019 ◽  
Vol 65 (9) ◽  
pp. 3952-3968 ◽  
Author(s):  
Santiago R. Balseiro ◽  
Yonatan Gur
Keyword(s):  
Stochastic Models ◽  
Online Advertising ◽  
Asymptotic Optimality ◽  
Sample Path ◽  
Complete Information ◽  
Regret Minimization ◽  
Bidding Strategies ◽  
Approximate Nash Equilibrium ◽  
Pacing Strategies ◽  
Equilibrium Bidding

In online advertising markets, advertisers often purchase ad placements through bidding in repeated auctions based on realized viewer information. We study how budget-constrained advertisers may compete in such sequential auctions in the presence of uncertainty about future bidding opportunities and competition. We formulate this problem as a sequential game of incomplete information, in which bidders know neither their own valuation distribution nor the budgets and valuation distributions of their competitors. We introduce a family of practical bidding strategies we refer to as adaptive pacing strategies, in which advertisers adjust their bids according to the sample path of expenditures they exhibit, and analyze the performance of these strategies in different competitive settings. We establish the asymptotic optimality of these strategies when competitors’ bids are independent and identically distributed over auctions, but also when competing bids are arbitrary. When all the bidders adopt these strategies, we establish the convergence of the induced dynamics and characterize a regime (well motivated in the context of online advertising markets) under which these strategies constitute an approximate Nash equilibrium in dynamic strategies: the benefit from unilaterally deviating to other strategies, including ones with access to complete information, becomes negligible as the number of auctions and competitors grows large. This establishes a connection between regret minimization and market stability, by which advertisers can essentially follow approximate equilibrium bidding strategies that also ensure the best performance that can be guaranteed off equilibrium. This paper was accepted by Noah Gans, stochastic models and simulation.

Self-Control Problems Measure

2011 ◽  
Author(s):  
Kevin M. King ◽  
Charles B. Fleming ◽  
Kathryn C. Monahan ◽  
Richard F. Catalano
Keyword(s):  
Self Control ◽  
Control Problems

On closed-loop equilibrium strategies for mean-field stochastic linear quadratic problems

2020 ◽  
Vol 26 ◽  
pp. 41
Author(s):  
Tianxiao Wang
Keyword(s):  
Optimal Control ◽  
Closed Loop ◽  
Optimal Control Problems ◽  
Mean Field ◽  
Open Loop ◽  
Time Inconsistency ◽  
Control Problems ◽  
Linear Quadratic ◽  
Linear Quadratic Optimal Control ◽  
Equilibrium Strategies

This article is concerned with linear quadratic optimal control problems of mean-field stochastic differential equations (MF-SDE) with deterministic coefficients. To treat the time inconsistency of the optimal control problems, linear closed-loop equilibrium strategies are introduced and characterized by variational approach. Our developed methodology drops the delicate convergence procedures in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. When the MF-SDE reduces to SDE, our Riccati system coincides with the analogue in Yong [Trans. Amer. Math. Soc. 369 (2017) 5467–5523]. However, these two systems are in general different from each other due to the conditional mean-field terms in the MF-SDE. Eventually, the comparisons with pre-committed optimal strategies, open-loop equilibrium strategies are given in details.

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