Venue: ICLR 2023 Conference

Reviews:

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: The paper examines how to design credible auctions (i.e. auctions where the auctioneer is not incentivized to deviate from their proposed mechanism), with a focus on repeated auctions. The setup is that the auctioneer proposes an allocation rule and a payment function at the beginning of the game, and then they run a multi-item auction at each time step. The paper proposes using a cryptographic verification scheme and penalties at randomly chosen set of time steps to guarantee credibility. 

The paper then considers the design of revenue-maximizing auctions in the repeated auction setup in the case where not all bidders necessarily participate in the auction at every time step (they change according to a stationary probability distribution). They propose a method for training revenue-maximizing auctions in this model and evaluate the method in simulations on small auction setups. 
strength_and_weaknesses: Strengths:
- The idea of using cryptographic verification schemes to guarantee commitment of the auctioneer is interesting. It provides a different approach to ensure credibility than is typically studied in market design (it is obtained by relaxing the assumption of no bidder communication). 

Weaknesses:
- The theoretical contribution is limited. The proof of credibility is straightforward, since the penalty is set so high that it guarantees that the auctioneer would rather stick to the proposed mechanism than deviate. It may be more interesting to investigate tradeoffs between approximate credibility and penalties. 
- The empirical contribution—designing revenue-maximizing auctions in the repeated auction setup—seems incremental relative to existing work in the automated auction design literature (e.g. Duetting et al., 2019, Rahme et al., 2020). 
- The two contributions in this paper—proposing a scheme to guarantee credibility and training revenue-maximizing auctions in the repeated auction setup when bidders can change between rounds—are somewhat disjointed. It is perhaps confusing to combine these investigations into a single paper. 

clarity,_quality,_novelty_and_reproducibility: The paper studies an interesting setup, but both the theoretical and empirical contributions are somewhat limited. The paper is a bit difficult to follow since there appear to be two separate and somewhat disjointed contributions. Moreover, the notation a bit awkward, with the superscripts appearing to the left of the variables. 
summary_of_the_review: The paper studies an interesting setup of designing credible auctions in the repeated auctions setup. However, both the theoretical and empirical contributions are somewhat incremental relative to prior work.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: It is known that no credible, sealed-bid, and incentive compatible exists, assuming no communication between the bidders. In this work, the authors relax this assumption and propose a framework to run efficient, credible, and revenue-optimal repeated auctions with cryptographic tools. Their main contributions are as follows:
1. The authors show how an auction can be made credible with just a verification scheme and a penalty. This verification scheme could simply be to bring in a trusted third party for auditing. They also show how to set a penalty that is independent of the auctioneer being untruthful.
2. The authors also propose a stationary bidder participation model and a method for training revenue-maximizing auctions in this context. They demonstrate the efficacy of this approach through a couple of experiments.
strength_and_weaknesses: **Strengths:** The proposed framework is quite novel. The authors also perform sufficient experiments to illustrate the efficiency of the training procedure. This direction of research of also quite relevant and is of significant importance especially given that a lot of tech companies generate revenue through auctions.

**Weakness:** The approach is not quite scalable, although I think this is a good first step.

clarity,_quality,_novelty_and_reproducibility: The paper is well-written and easy to read. The claims made in the paper are sufficiently demonstrated through experiments. The contributions are quite novel as well.
summary_of_the_review: Overall this is a good paper. The use of differential economics for running credible, incentive-compatible, and revenue-maximizing auctions is a new and an exciting direction to pursue.
review_title: null

final_score: rating: 8: accept, good paper
review_content: summary_of_the_paper: The paper studies how to combine cryptographic tools with neural auction design to provide credible optimal (repeated) auctions. The idea is to adding a verification step in repeated auctions to check the credibility of the auctioneer, while with properly design penalty, only excuting verification for a limited times is enough to prevent auctioneer's cheating. The repeated auction desgin through deep learning is transfered from previous work for single round auction, with an aggregation under bidders' stationary participantion. The approximate revenue maximization is supported by experiments.
strength_and_weaknesses: Strength:
1. The issue of credibility is important for the field of differentiable economics. 
2. The idea of introducing cryptographic tools, although straightforward, is quite convicing. The combination of verification and repeated auctions is properly designed, with clear theoretical analysis for credibility guarentee and corresponding revenue.

Weaknesses:
1. The assumption of stationary participation may be kind of strong. Nevertheless, I am not sure whether there has been related works on this issue.
clarity,_quality,_novelty_and_reproducibility: The paper is well-organized and easy to follow. The study on credibility for neural auction design, especially combining cryptographic tools, is novel. The experiments are clearly described with high reproducibility.
summary_of_the_review: The paper consider an important issue on credibiliy in differentiable auctions. The designed protocol is convincing with both theoretical analysis and experiments. Overall, I would recommend acception.
review_title: null

final_score: rating: 3: reject, not good enough
review_content: summary_of_the_paper: The authors consider the problem of a monopolist seller auctioning multiple items to many buyers, repeatedly over many time periods. They propose to learn the single-shot approximately IC mechanism using essentially off-the-shelf deep learning approaches (I guess this is why they submitted to ICLR?). Then they propose to solve the seller’s credibility issue by revisiting a random time period, revealing the bids and punishing the seller if they violated the auction terms.
strength_and_weaknesses: 1.	You abstract the audit process, and essentially use an off-the-shelf auction learning algorithm. It feels like there isn’t much novelty left in your submission. 
2.	I don’t think that your bid-revealing protocol resolves the seller credibility issue. An incredible seller could have a dummy buyer participating in the auction and submitting bids optimized to raise the price for the winning bidder. See e.g. https://arxiv.org/abs/2205.14758 on how to do it right.
3.	When considering repeated auctions, it is sub-optimal to use even the “optimal” single-shot auction many times. There is a rich literature on dynamic mechanisms that you should go read.
4.	It is not clear from your model how do bidders choose bids. Do they also do some kind of regret minimization? What happens when the auction uses a lottery?

**Typos:**

•	“Forty years of little analytical results” – what?! There are hundreds of papers on this. They don’t get fully optimal solutions for every case, but this is super well studied.

•	“Despite differentiable econ..” -> “Although…”

•	I can’t find the reference for Mor (2022)

•	“bids(i.e. “ -> “bids (i.e.~”

•	“the forgo the cost”

•	Algo 1 pseudocode: what is t not-greater-than T?! Did you mean \le?

•	“pay for together”

•	“and ran it” -> run

•	“the the weights”

clarity,_quality,_novelty_and_reproducibility: See numbered items above
summary_of_the_review: See numbered items above
review_title: null

Meta-review: metareview:_summary,_strengths_and_weaknesses: (a) Provides a cryptographic mechanism that incentivizes an auctioneer not to cheat in repeated auctions. Also shows how to use gradient descent to design a repeated auction when bidders may be different in different rounds.

(b) Highly relevant to a $100B industry. Likely correct.

(c) Reviewers are split and two experts out of four have many criticisms.

Overall, the authors can earn from the reviews and resubmit. They may want to take the advice to divide into two papers.

Comment from the AC: The authors say "Overall, we don't expect bidders dropping out to be an issue unless the application setting is radically different from the model we studied." Well, this is a common mechanism design on Wall Street, called "last look." In general, the authors should pay attention not just to ad auctions as the application area, but also to financial trading.


justification_for_why_not_higher_score: Two experts have multiple justified requests for improvement.
justification_for_why_not_lower_score: Sensible paper, important topic, authors have expertise.
Decision: Reject