Response Prediction for Low-Regret Agents

@article{Alaei2019ResponsePF,
  title={Response Prediction for Low-Regret Agents},
  author={Saeed Alaei and Ashwinkumar Badanidiyuru and Mohammad Mahdian and Sadra Yazdanbod},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.02056}
}
  • Saeed Alaei, Ashwinkumar Badanidiyuru, +1 author Sadra Yazdanbod
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • Companies like Google and Microsoft run billions of auctions every day to sell advertising opportunities. Any change to the rules of these auctions can have a tremendous effect on the revenue of the company and the welfare of the advertisers and the users. Therefore, any change requires careful evaluation of its potential impacts. Currently, such impacts are often evaluated by running simulations or small controlled experiments. This, however, misses the important factor that the advertisers… CONTINUE READING

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    References

    Publications referenced by this paper.
    SHOWING 1-10 OF 22 REFERENCES
    Predicting advertiser bidding behaviors in sponsored search by rationality modeling
    21
    Econometrics for Learning Agents
    44
    Stochastic variability in sponsored search auctions: observations and models
    44
    Agent-based simulation of dynamic online auctions
    54
    Greedy bidding strategies for keyword auctions
    162
    A Structural Model of Sponsored Search Advertising Auctions
    131
    Selling to a No-Regret Buyer
    18
    Estimation of a Dynamic Auction Game
    358
    Bid generation for advanced match in sponsored search
    18
    Semiparametric Estimation of First-Price Auctions with Risk Averse Bidders*
    152