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Found 38 papers, 9 papers with code
Social Environment Design
Artificial Intelligence (AI) holds promise as a technology that can be used to improve government and economic policy-making.
Easy as ABCs: Unifying Boltzmann Q-Learning and Counterfactual Regret Minimization
We propose ABCs (Adaptive Branching through Child stationarity), a best-of-both-worlds algorithm combining Boltzmann Q-learning (BQL), a classic reinforcement learning algorithm for single-agent domains, and counterfactual regret minimization (CFR), a central algorithm for learning in multi-agent domains.
Optimal Automated Market Makers: Differentiable Economics and Strong Duality
The role of a market maker is to simultaneously offer to buy and sell quantities of goods, often a financial asset such as a share, at specified prices.
Predictive Churn with the Set of Good Models
And we characterize expected churn over model updates via the Rashomon set, pairing our analysis with empirical results on real-world datasets -- showing how our approach can be used to better anticipate, reduce, and avoid churn in consumer-facing applications.
Multi-Sender Persuasion -- A Computational Perspective
The core solution concept here is the Nash equilibrium of senders' signaling policies.
Chain-of-Thought Reasoning is a Policy Improvement Operator
We introduce SECToR (Self-Education via Chain-of-Thought Reasoning), a proof-of-concept demonstration that language models can teach themselves new skills using chain-of-thought reasoning.
Generative Social Choice
Traditionally, social choice theory has only been applicable to choices among a few predetermined alternatives but not to more complex decisions such as collectively selecting a textual statement.
Reinforcement Learning with Stepwise Fairness Constraints
AI methods are used in societally important settings, ranging from credit to employment to housing, and it is crucial to provide fairness in regard to algorithmic decision making.
Oracles & Followers: Stackelberg Equilibria in Deep Multi-Agent Reinforcement Learning
Stackelberg equilibria arise naturally in a range of popular learning problems, such as in security games or indirect mechanism design, and have received increasing attention in the reinforcement learning literature.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Explainable Reinforcement Learning via Model Transforms
This has given rise to a variety of approaches to explainability in RL that aim to reconcile discrepancies that may arise between the behavior of an agent and the behavior that is anticipated by an observer.
Predictive Multiplicity in Probabilistic Classification
We demonstrate the incidence and prevalence of predictive multiplicity in real-world tasks.
Learning to Mitigate AI Collusion on Economic Platforms
Algorithmic pricing on online e-commerce platforms raises the concern of tacit collusion, where reinforcement learning algorithms learn to set collusive prices in a decentralized manner and through nothing more than profit feedback.
CrowdPlay: Crowdsourcing human demonstration data for offline learning in Atari games
Human demonstrations of video game play can serve as vital surrogate representations of real-world behaviors, access to which would facilitate rapid progress in several complex learning settings (e. g. behavior classification, imitation learning, offline RL etc.).
The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning
Here we show that machine-learning-based economic simulation is a powerful policy and mechanism design framework to overcome these limitations.
Deep Learning for Two-Sided Matching
What is of most interest is to use machine learning to understand the possibility of new tradeoffs between strategy-proofness and stability.
Reinforcement Learning of Sequential Price Mechanisms
We introduce the use of reinforcement learning for indirect mechanisms, working with the existing class of sequential price mechanisms, which generalizes both serial dictatorship and posted price mechanisms and essentially characterizes all strongly obviously strategyproof mechanisms.
Decision-Aware Conditional GANs for Time Series Data
We introduce the decision-aware time-series conditional generative adversarial network (DAT-CGAN) as a method for time-series generation.
Defending Against Malicious Reorgs in Tezos Proof-of-Stake
As an example, an attacker with 40% of the staking power is able to execute a 20-block malicious reorg at an average rate of once per day, and the attack probability increases super-linearly as the staking power grows beyond 40%.
Cryptography and Security
From Predictions to Decisions: Using Lookahead Regularization
Machine learning is a powerful tool for predicting human-related outcomes, from credit scores to heart attack risks.
The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies
In experiments conducted on MTurk, an AI tax policy provides an equality-productivity trade-off that is similar to that provided by the Saez framework along with higher inverse-income weighted social welfare.
Too many cooks: Bayesian inference for coordinating multi-agent collaboration
Underlying the human ability to collaborate is theory-of-mind, the ability to infer the hidden mental states that drive others to act.
A Kernel of Truth: Determining Rumor Veracity on Twitter by Diffusion Pattern Alone
Recent work in the domain of misinformation detection has leveraged rich signals in the text and user identities associated with content on social media.
Finding Friend and Foe in Multi-Agent Games
Here we develop the DeepRole algorithm, a multi-agent reinforcement learning agent that we test on The Resistance: Avalon, the most popular hidden role game.
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation
We prove that all three algorithms achieve a regret upper bound $\mathcal{O}(\max \{ B, K\ln T\})$ where $B$ is the total budget across arms, $K$ is the total number of arms and $T$ is length of the time horizon.
Ridesharing with Driver Location Preferences
We study revenue-optimal pricing and driver compensation in ridesharing platforms when drivers have heterogeneous preferences over locations.
Multiagent Systems Computer Science and Game Theory
Learning Representations by Humans, for Humans
When machine predictors can achieve higher performance than the human decision-makers they support, improving the performance of human decision-makers is often conflated with improving machine accuracy.
Learning to Collaborate in Markov Decision Processes
We consider a two-agent MDP framework where agents repeatedly solve a task in a collaborative setting.
The Capacity Constrained Facility Location problem
The capacity constrained setting leads to a new strategic environment where a facility serves a subset of the population, which is endogenously determined by the ex-post Nash equilibrium of an induced subgame and is not directly controlled by the mechanism designer.
Multi-View Decision Processes: The Helper-AI Problem
We consider a two-player sequential game in which agents have the same reward function but may disagree on the transition probabilities of an underlying Markovian model of the world.
Calibrated Fairness in Bandits
In addition, we define the {\em fairness regret}, which corresponds to the degree to which an algorithm is not calibrated, where perfect calibration requires that the probability of selecting an arm is equal to the probability with which the arm has the best quality realization.
Optimal Auctions through Deep Learning: Advances in Differentiable Economics
Designing an incentive compatible auction that maximizes expected revenue is an intricate task.
Learnability of Influence in Networks
We establish PAC learnability of influence functions for three common influence models, namely, the Linear Threshold (LT), Independent Cascade (IC) and Voter models, and present concrete sample complexity results in each case.
Long-term causal effects via behavioral game theory
Planned experiments are the gold standard in reliably comparing the causal effect of switching from a baseline policy to a new policy.
A Statistical Decision-Theoretic Framework for Social Choice
In our framework, we are given a statistical ranking model, a decision space, and a loss function defined on (parameter, decision) pairs, and formulate social choice mechanisms as decision rules that minimize expected loss.
Generalized Method-of-Moments for Rank Aggregation
In this paper we propose a class of efficient Generalized Method-of-Moments(GMM) algorithms for computing parameters of the Plackett-Luce model, where the data consists of full rankings over alternatives.
Contrastive Learning Using Spectral Methods
In many natural settings, the analysis goal is not to characterize a single data set in isolation, but rather to understand the difference between one set of observations and another.
Generalized Random Utility Models with Multiple Types
We propose a model for demand estimation in multi-agent, differentiated product settings and present an estimation algorithm that uses reversible jump MCMC techniques to classify agents' types.
Preference Elicitation For General Random Utility Models
We also prove uni-modality of the likelihood functions for a class of GRUMs.
