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Found 12 papers, 1 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.
Persuading a Learning Agent
We study a repeated Bayesian persuasion problem (and more generally, any generalized principal-agent problem with complete information) where the principal does not have commitment power and the agent uses algorithms to learn to respond to the principal's signals.
Multi-Sender Persuasion -- A Computational Perspective
The core solution concept here is the Nash equilibrium of senders' signaling policies.
Equilibrium of Data Markets with Externality
Starting with a simple setting where buyers know their valuations a priori, we characterize both the existence and welfare properties of the pure Nash equilibrium in the presence of such externality.
Persuading a Behavioral Agent: Approximately Best Responding and Learning
We show that, under natural assumptions, (1) the sender can find a signaling scheme that guarantees itself an expected utility almost as good as its optimal utility in the classic model, no matter what approximately best-responding strategy the receiver uses; (2) on the other hand, there is no signaling scheme that gives the sender much more utility than its optimal utility in the classic model, even if the receiver uses the approximately best-responding strategy that is best for the sender.
Learning When to Advise Human Decision Makers
Artificial intelligence (AI) systems are increasingly used for providing advice to facilitate human decision making in a wide range of domains, such as healthcare, criminal justice, and finance.
Optimal Scoring Rule Design under Partial Knowledge
We further remark that widely used scoring rules, such as the quadratic and log rules, as well as previously identified optimal scoring rules under full knowledge, can be far from optimal in our partial knowledge settings.
Cursed yet Satisfied Agents
In contrast, we show that for sum-concave valuations, which include weighted-sum valuations and l_p-norms, the welfare optimal EPBB mechanism obtains half of the optimal welfare as the number of agents grows large.
Algorithmic Risk Assessments Can Alter Human Decision-Making Processes in High-Stakes Government Contexts
In the government loans setting of our experiment, the risk assessment made participants more risk-averse; this shift reduced government aid by 8. 3%.
Learning Strategy-Aware Linear Classifiers
We address the question of repeatedly learning linear classifiers against agents who are strategically trying to game the deployed classifiers, and we use the Stackelberg regret to measure the performance of our algorithms.
Randomized Wagering Mechanisms
We show that a broad family of randomized wagering mechanisms satisfy all desirable theoretical properties.
Strategyproof Linear Regression in High Dimensions
This paper is part of an emerging line of work at the intersection of machine learning and mechanism design, which aims to avoid noise in training data by correctly aligning the incentives of data sources.
