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Found 37 papers, 7 papers with code
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.
Can AI Model the Complexities of Human Moral Decision-Making? A Qualitative Study of Kidney Allocation Decisions
A growing body of work in Ethical AI attempts to capture human moral judgments through simple computational models.
Learning and Computation of $Φ$-Equilibria at the Frontier of Tractability
At the heart of our approach is a polynomial-time algorithm for computing an expected fixed point of any $\phi : \mathcal{X} \to \mathcal{X}$ based on the ellipsoid against hope (EAH) algorithm of Papadimitriou and Roughgarden (JACM '08).
Expected Variational Inequalities
Variational inequalities (VIs) encompass many fundamental problems in diverse areas ranging from engineering to economics and machine learning.
Multi-Agent Risks from Advanced AI
The rapid development of advanced AI agents and the imminent deployment of many instances of these agents will give rise to multi-agent systems of unprecedented complexity.
An Interpretable Automated Mechanism Design Framework with Large Language Models
The code generation capabilities of LLMs enable the discovery of novel and interpretable solutions, bridging the symbolic logic of mechanism design and the generative power of modern AI.
Computing Game Symmetries and Equilibria That Respect Them
Strategic interactions can be represented more concisely, and analyzed and solved more efficiently, if we are aware of the symmetries within the multiagent system.
Observation Interference in Partially Observable Assistance Games
We show that this incentive for interference goes away if the human is playing optimally, or if we introduce a communication channel for the human to communicate their preferences to the assistant.
Efficiently Solving Turn-Taking Stochastic Games with Extensive-Form Correlation
Our algorithm for SEFCE is the first polynomial-time algorithm for equilibrium computation with commitment in such a general class of stochastic games.
Characterising Simulation-Based Program Equilibria
Finally, we explore the limits of simulation-based program equilibrium, showing that the Tennenholtz folk theorem cannot be attained by simulation-based programs without access to shared randomness.
Can CDT rationalise the ex ante optimal policy via modified anthropics?
If so, then causal decision theory might recommend one-boxing in order to cause the predictor to fill the opaque box.
Game Theory with Simulation in the Presence of Unpredictable Randomisation
As positive results, we establish that mixed-strategy simulation can improve social welfare if the simulator has the option to scale their level of trust, if the players face challenges with both trust and coordination, or if maintaining some level of privacy is essential for enabling cooperation.
On The Stability of Moral Preferences: A Problem with Computational Elicitation Methods
If participants' moral responses are unstable in such ways, it would raise important methodological and theoretical issues for how participants' true moral preferences, opinions, and judgments can be ascertained.
On the Pros and Cons of Active Learning for Moral Preference Elicitation
In this work, we argue that the use of active learning for moral preference elicitation relies on certain assumptions about the underlying moral preferences, which can be violated in practice.
Why should we ever automate moral decision making?
Given this, why should we ever automate moral decision making -- is it not better to leave all moral decision making to humans?
Imperfect-Recall Games: Equilibrium Concepts and Their Complexity
We investigate optimal decision making under imperfect recall, that is, when an agent forgets information it once held before.
Social Choice Should Guide AI Alignment in Dealing with Diverse Human Feedback
Foundation models such as GPT-4 are fine-tuned to avoid unsafe or otherwise problematic behavior, such as helping to commit crimes or producing racist text.
Recursive Joint Simulation in Games
In this paper, we study an interaction between AI agents where the agents run a recursive joint simulation.
A Theory of Bounded Inductive Rationality
In this paper, we develop a theory of rational decision making that does not assume logical omniscience.
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.
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.
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.
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.
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 $\varepsilon$.
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.
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.
Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization
We then consider the broader ethical issues involved.
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.
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.
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.
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.
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.
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.
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
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.
