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Found 17 papers, 0 papers with code
Online Algorithms with Limited Data Retention
We also show a nearly matching lower bound on the retention required to guarantee error $\epsilon$.
Impact of Decentralized Learning on Player Utilities in Stackelberg Games
To better understand such cases, we examine the learning dynamics of the two-agent system and the implications for each agent's objective.
Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms
To understand the total impact on the content landscape, we study a game between content creators competing on the basis of engagement metrics and analyze the equilibrium decisions about investment in quality and gaming.
Algorithmic Persuasion Through Simulation
After a fixed number of queries, the sender commits to a messaging policy and the receiver takes the action that maximizes her expected utility given the message she receives.
Efficiency in Collective Decision-Making via Quadratic Transfers
Consider the following collective choice problem: a group of budget constrained agents must choose one of several alternatives.
Content Filtering with Inattentive Information Consumers
We develop a model of content filtering as a game between the filter and the content consumer, where the latter incurs information costs for examining the content.
Communicating with Anecdotes
We study a communication game between a sender and receiver where the sender has access to a set of informative signals about a state of the world.
Social Learning under Platform Influence: Consensus and Persistent Disagreement
In this paper, we introduce an opinion dynamics model where agents are connected in a social network, and update their opinions based on their neighbors' opinions and on the content shown to them by the platform.
Making Auctions Robust to Aftermarkets
We show that when using balanced prices, both these approaches ensure high equilibrium welfare in the combined market.
Dynamic Weighted Matching with Heterogeneous Arrival and Departure Rates
The value of a match is determined by the types of the matched agents.
Computer Science and Game Theory Data Structures and Algorithms
Maximizing Welfare with Incentive-Aware Evaluation Mechanisms
The goal is to design an evaluation mechanism that maximizes the overall quality score, i. e., welfare, in the population, taking any strategic updating into account.
Bayesian Exploration with Heterogeneous Agents
We consider Bayesian Exploration: a simple model in which the recommendation system (the "principal") controls the information flow to the users (the "agents") and strives to incentivize exploration via information asymmetry.
Adversarial Bandits with Knapsacks
We suggest a new algorithm for the stochastic version, which builds on the framework of regret minimization in repeated games and admits a substantially simpler analysis compared to prior work.
Incentivizing Exploration with Selective Data Disclosure
We propose and design recommendation systems that incentivize efficient exploration.
The Disparate Effects of Strategic Manipulation
When consequential decisions are informed by algorithmic input, individuals may feel compelled to alter their behavior in order to gain a system's approval.
Unleashing Linear Optimizers for Group-Fair Learning and Optimization
Most systems and learning algorithms optimize average performance or average loss -- one reason being computational complexity.
Decoupled classifiers for fair and efficient machine learning
When it is ethical and legal to use a sensitive attribute (such as gender or race) in machine learning systems, the question remains how to do so.
