Search Results for author:
Found 18 papers, 3 papers with code
Maximal Procurement under a Budget
We study the problem of a principal who wants to influence an agent's observable action, subject to an ex-post budget.
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.
Autobidders with Budget and ROI Constraints: Efficiency, Regret, and Pacing Dynamics
We study a game between autobidding algorithms that compete in an online advertising platform.
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.
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
ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool
Algorithm configuration procedures optimize parameters of a given algorithm to perform well over a distribution of inputs.
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.
Black-box Methods for Restoring Monotonicity
In this work we develop algorithms that are able to restore monotonicity in the parameters of interest.
Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration
Unfortunately, Structured Procrastination is not $\textit{adaptive}$ to characteristics of the parameterized algorithm: it treats every input like the worst case.
Online Pandora's Boxes and Bandits
We consider online variations of the Pandora's box problem (Weitzman.
Robust Optimization for Non-Convex Objectives
We consider robust optimization problems, where the goal is to optimize in the worst case over a class of objective functions.
Bertrand Networks
We study scenarios where multiple sellers of a homogeneous good compete on prices, where each seller can only sell to some subset of the buyers.
Computer Science and Game Theory J.4; F.2.2
Maximizing Social Influence in Nearly Optimal Time
Finally, we show that this runtime is optimal (up to logarithmic factors) for any beta and fixed seed size k.
Data Structures and Algorithms Social and Information Networks Physics and Society F.2.2; J.4
