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Found 21 papers, 7 papers with code
Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
We further consider the problem of restricting the joint probability that certain suspect pairs of reviewers are assigned to certain papers, and show that this problem is NP-hard for arbitrary constraints on these joint probabilities but efficiently solvable for a practical special case.
Adapting a Kidney Exchange Algorithm to Align with Human Values
In kidney exchanges, a central market maker allocates living kidney donors to patients in need of an organ.
Indecision Modeling
The philosophy and psychology literature shows that indecision is a measurable and nuanced behavior -- and that there are several different reasons people are indecisive.
A Dataset on Malicious Paper Bidding in Peer Review
The performance of these detection algorithms can be taken as a baseline for future research on detecting malicious bidding.
Complexity of Scheduling Charging in the Smart Grid
In the smart grid, the intent is to use flexibility in demand, both to balance demand and supply as well as to resolve potential congestion.
Philosophy in the Face of Artificial Intelligence
In this article, I discuss how the AI community views concerns about the emergence of superintelligent AI and related philosophical issues.
Distinguishing Distributions When Samples Are Strategically Transformed
In other settings, the principal may not even be able to observe samples directly; instead, she must rely on signals that the agent is able to send based on the samples that he obtains, and he will choose these signals strategically.
Learning the Valuations of a $k$-demand Agent
We study problems where a learner aims to learn the valuations of an agent by observing which goods he buys under varying price vectors.
Multiplicative Pacing Equilibria in Auction Markets
Although we show that computing either a social-welfare-maximizing or a revenue-maximizing pacing equilibrium is NP-hard, we present a mixed-integer program (MIP) that can be used to find equilibria optimizing several relevant objectives.
Computer Science and Game Theory
Classification with Strategically Withheld Data
To this end, we present {\sc IC-LR}, a modification of Logistic Regression that removes the incentive to strategically drop features.
Automated Mechanism Design for Classification with Partial Verification
We study the problem of automated mechanism design with partial verification, where each type can (mis)report only a restricted set of types (rather than any other type), induced by the principal's limited verification power.
Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization
We then consider the broader ethical issues involved.
Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing and Conference Experiment Design
Many scientific conferences employ a two-phase paper review process, where some papers are assigned additional reviewers after the initial reviews are submitted.
Efficient Algorithms for Planning with Participation Constraints
Our approach can also be extended to the (discounted) infinite-horizon case, for which we give an algorithm that runs in time polynomial in the size of the input and $\log(1/\varepsilon)$, and returns a policy that is optimal up to an additive error of $\varepsilon$.
Incentive-aware PAC learning
We give a sample complexity bound that is, curiously, independent of the hypothesis class, for the ERM principle restricted to incentivecompatible classifiers.
For Learning in Symmetric Teams, Local Optima are Global Nash Equilibria
In this work, we show that any locally optimal symmetric strategy profile is also a (global) Nash equilibrium.
Tradeoffs in Preventing Manipulation in Paper Bidding for Reviewer Assignment
Many conferences rely on paper bidding as a key component of their reviewer assignment procedure.
The Computational Complexity of Single-Player Imperfect-Recall Games
For such games, two natural equilibrium concepts have been proposed as alternative solution concepts to ex-ante optimality.
A Theory of Bounded Inductive Rationality
In this paper, we develop a theory of rational decision making that does not assume logical omniscience.
Recursive Joint Simulation in Games
In this paper, we study an interaction between AI agents where the agents run a recursive joint simulation.
