Omer Lev

Yaron Salman, Tamir Tassa, Omer Lev et al.

Cake-cutting algorithms, which aim to fairly allocate a continuous resource based on individual agent preferences, have seen significant progress over the past two decades. Much of the research has concentrated on fairness, with comparatively less attention given to other important aspects. Chen et al. (2010) introduced an algorithm that, in addition to ensuring fairness, was strategyproof -- meaning agents had no incentive to misreport their valuations. However, even in the absence of strategic incentives to misreport, agents may still hesitate to reveal their true preferences due to privacy concerns (e.g., when allocating advertising time between firms, revealing preferences could inadvertently expose planned marketing strategies or product launch timelines). In this work, we extend the strategyproof algorithm of Chen et al. by introducing a privacy-preserving dimension. To the best of our knowledge, we present the first private cake-cutting protocol, and, in addition, this protocol is also envy-free and strategyproof. Our approach replaces the algorithm's centralized computation with a novel adaptation of cryptographic techniques, enabling privacy without compromising fairness or strategyproofness. Thus, our protocol encourages agents to report their true preferences not only because they are not incentivized to lie, but also because they are protected from having their preferences exposed.

Harper Lyon, Omer Lev, Nicholas Mattei

Peer selection, the evaluation and selection of agents by their peers, is an important problem in the field of computational social choice; with applications to grading in massively online courses (MOOCs) and academic peer review. Current existing algorithmic and empirical work focuses on developing and analyzing novel \emph{strategyproof} mechanisms, wherein no agent has an incentive to misreport their evaluations. However, the majority of published mechanisms share a flaw: they do not \emph{reward} agents for any effort expended during the evaluation process. In cases where high quality evaluations are costly to produce this missing incentive fails to align agents with an overall goal of accurate selection. To address this gap we first prove theoretically that incentivizing effort in peer selection requires information beyond a single evaluation. We then propose \textsc{PeerBTS}, a mechanism that combines a peer-prediction lottery, leveraging work on the Robust Bayesian Truth Serum, with any existing peer-selection mechanism to incentivize effort while remaining Bayes-Nash incentive compatible. We find that while an incentive-compatible peer-selection mechanism using agent predictions to incentivize effort is possible it requires adjustments to the assumed problem context and limits other mechanistics properties. We additionally present a series of non-strategic simulations to validate incentives and evaluate the performance of PeerBTS relative to existing strategyproof peer selection mechanisms. Finally, we discuss the results of an initial study on the validity of peer-prediction from a small academic workshop.

Roy Fairstein, Harper Lyon, Oshri Damty et al.

Peer-evaluation and selection systems are used when sets of agents evaluate each other in order to select the best $k$ among them. These are commonly used in real-world settings, including academic conferences where those reviewing papers are often the set of submitters. Conferences have attempted to better allocate their reviewing resources by moving to a two-stage mechanism, in which some papers are eliminated after a first stage of review and remaining papers receive additional reviewers. We investigate how two major strategyproof peer selection mechanisms, Partition and ExactDollarPartition, perform when adapted to a two-stage system, in order to try and understand the effect of the two-stage mechanism on which agents get selected. We also examine how the various parameters of the two-stage mechanism influence the outcome. We provide a theoretical basis by showing how a particular setting is influenced by the two stages. However, solving for the general case seems implausible at the moment, and we use extensive simulations of different scenarios and settings to observe which agents benefit and which are harmed by adopting two-stage mechanisms (and we vary this mechanisms parameters as well). We show that the two-stage mechanism's advantage depends the noisiness of reviewer beliefs. Borderline agents benefit most in a low noise environment, while high rank agents benefit more in noisy environments. We show that the effectiveness of these mechanisms is highly dependent on the number of chosen agents, the number of reviews requested from agents, and reviewers' correlation, indicating that organizers need to exercise caution when selecting these parameters for a reviewing process.

Omer Lev, Nicholas Mattei, Paolo Turrini et al.

In the peer selection problem a group of agents must select a subset of themselves as winners for, e.g., peer-reviewed grants or prizes. Here, we take a Condorcet view of this aggregation problem, i.e., that there is a ground-truth ordering over the agents and we wish to select the best set of agents, subject to the noisy assessments of the peers. Given this model, some agents may be unreliable, while others might be self-interested, attempting to influence the outcome in their favour. In this paper we extend PeerNomination, the most accurate peer reviewing algorithm to date, into WeightedPeerNomination, which is able to handle noisy and inaccurate agents. To do this, we explicitly formulate assessors' reliability weights in a way that does not violate strategyproofness, and use this information to reweight their scores. We show analytically that a weighting scheme can improve the overall accuracy of the selection significantly. Finally, we implement several instances of reweighting methods and show empirically that our methods are robust in the face of noisy assessments.

Vignesh Viswanathan, Omer Lev, Neel Patel et al.

Equilibrium computation in markets usually considers settings where player valuation functions are known. We consider the setting where player valuations are unknown; using a PAC learning-theoretic framework, we analyze some classes of common valuation functions, and provide algorithms which output direct PAC equilibrium allocations, not estimates based on attempting to learn valuation functions. Since there exist trivial PAC market outcomes with an unbounded worst-case efficiency loss, we lower-bound the efficiency of our algorithms. While the efficiency loss under general distributions is rather high, we show that in some cases (e.g., unit-demand valuations), it is possible to find a PAC market equilibrium with significantly better utility.

Omer Lev, Moshe Tennenholtz

We introduce an axiomatic approach to group recommendations, in line of previous work on the axiomatic treatment of trust-based recommendation systems, ranking systems, and other foundational work on the axiomatic approach to internet mechanisms in social choice settings. In group recommendations we wish to recommend to a group of agents, consisting of both opinionated and undecided members, a joint choice that would be acceptable to them. Such a system has many applications, such as choosing a movie or a restaurant to go to with a group of friends, recommending games for online game players, & other communal activities. Our method utilizes a given social graph to extract information on the undecided, relying on the agents influencing them. We first show that a set of fairly natural desired requirements (a.k.a axioms) leads to an impossibility, rendering mutual satisfaction of them unreachable. However, we also show a modified set of axioms that fully axiomatize a group variant of the random-walk recommendation system, expanding a previous result from the individual recommendation case.

Omer Lev, Moshe Tennenholtz, Aviv Zohar

Information delivery in a network of agents is a key issue for large, complex systems that need to do so in a predictable, efficient manner. The delivery of information in such multi-agent systems is typically implemented through routing protocols that determine how information flows through the network. Different routing protocols exist each with its own benefits, but it is generally unclear which properties can be successfully combined within a given algorithm. We approach this problem from the axiomatic point of view, i.e., we try to establish what are the properties we would seek to see in such a system, and examine the different properties which uniquely define common routing algorithms used today. We examine several desirable properties, such as robustness, which ensures adding nodes and edges does not change the routing in a radical, unpredictable ways; and properties that depend on the operating environment, such as an "economic model", where nodes choose their paths based on the cost they are charged to pass information to the next node. We proceed to fully characterize minimal spanning tree, shortest path, and weakest link routing algorithms, showing a tight set of axioms for each.

Haris Aziz, Omer Lev, Nicholas Mattei et al.

Peer reviews, evaluations, and selections are a fundamental aspect of modern science. Funding bodies the world over employ experts to review and select the best proposals from those submitted for funding. The problem of peer selection, however, is much more general: a professional society may want to give a subset of its members awards based on the opinions of all members; an instructor for a Massive Open Online Course (MOOC) or an online course may want to crowdsource grading; or a marketing company may select ideas from group brainstorming sessions based on peer evaluation. We make three fundamental contributions to the study of peer selection, a specific type of group decision-making problem, studied in computer science, economics, and political science. First, we propose a novel mechanism that is strategyproof, i.e., agents cannot benefit by reporting insincere valuations. Second, we demonstrate the effectiveness of our mechanism by a comprehensive simulation-based comparison with a suite of mechanisms found in the literature. Finally, our mechanism employs a randomized rounding technique that is of independent interest, as it solves the apportionment problem that arises in various settings where discrete resources such as parliamentary representation slots need to be divided proportionally.