novel and would need experimentation and experience.

Author Incentives. Our first mechanism addresses A1 using peer pressure. It requires the conference to publish not only the list of accepted papers, but also, for each author, the author’s acceptance rate for that conference. For example, if an author were to submit two papers and none were accepted, the conference would report an acceptance rate of 0, and if one was accepted, the author would have an acceptance rate of 0.5. Because no author would like to be perceived to have a low acceptance ratio, we think this peer pressure will enforce A1.

Our second mechanism addresses A2 by raising the prestige of reviewing. For example, conferences can have a best reviewer award for the reviewer with the best review score^b or give them a discount in the registration fee.

A more radical step would be to solve A1 and A2 simultaneously by means of a virtual economy, where tokens are paid for reviews, and spent to allow submission of papers.^c Specifically, assuming each paper requires three reviews on average, reviewers are granted one token per review, independent of the conference, and the authors of a paper together pay three tokens to submit each paper. We recognize that this assumes all conferences expect the same level of reviewing: one could pervert this scheme by appropriate choice of reviewing venues. We ignore this fact for now, in the interests of simplicity. Continuing with our scheme, authors of accepted papers would be refunded one, two, or all their tokens depending on their review score. Authors of the top papers would therefore incur no cost, whereas authors of rejected papers would have spent all three of their tokens. Clearly, this scheme forces authors to become reviewers, and to be careful in using the tokens thus earned, solving A1 and A2.

We note that we obviously need to make tokens non-forgeable, non-repli-cable, and perhaps transferable. E-cash systems for achieving these goals are

b See the subsection Reviewer Incentives for details on review scoring.

c We have been informed that this scheme was first suggested by Jim Gray, though we cannot find a citation to this work.

the goal would be to
have a standard way
for members of the
community to review
and rank papers and
authors both before
and after publication.

well known^d—they merely need to be adapted to a non-traditional purpose. We recognize that regulating the economy is not trivial. Over-damping the system would lead to conferences with too few papers, or too few reviewers. Underestimating the value of tokens would only slightly mitigate the current problems, but would add a lot of expensive overhead in the form of these mechanisms. Moreover, it is not clear how this system can be implemented. Indeed, even if it was, it would not be obvious how it can be bootstrapped, or whether it would have unintended consequences. One possible technique would be to start by publishing signed reviews and rely on technologies such as Citeseer and Google Scholar as we describe here in more detail.

Reviewer Incentives. We first discuss dealing with R1 and R3. We propose that authors should rate the reviews they receive for their papers, while preserving reviewer confidentiality. Average ( non-anonymized) reviewer scores would then be circulated among the PC. No PC member wants to look bad in front of his or her peers, so peer pressure should enforce R1 and R3 (PC collusion will damage the conference reputation). Note that we expect most authors to rate detailed but unfavorable reviews highly.

An even more radical alternative is for reviews to be openly published with the name of the reviewer. The idea is that reviewers who are not willing to publish a review about a paper

d For example, David Chaum’s seminal work “Blind signatures for untraceable payments,”

Advances in Cryptology Crypto ’ 82, Springer-

Verlag (1983), 199–203.

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