no code implementations • 23 Apr 2024 • Nicole Immorlica, Nicholas Wu, Brendan Lucier
We study the problem of a principal who wants to influence an agent's observable action, subject to an ex-post budget.
no code implementations • 17 Apr 2024 • Nicole Immorlica, Brendan Lucier, Markus Mobius, James Siderius
We also show a nearly matching lower bound on the retention required to guarantee error $\epsilon$.
no code implementations • 29 Feb 2024 • Kate Donahue, Nicole Immorlica, Meena Jagadeesan, Brendan Lucier, Aleksandrs Slivkins
To better understand such cases, we examine the learning dynamics of the two-agent system and the implications for each agent's objective.
no code implementations • 18 Jan 2024 • Nicole Immorlica, Meena Jagadeesan, Brendan Lucier
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.
no code implementations • 29 Nov 2023 • Keegan Harris, Nicole Immorlica, Brendan Lucier, Aleksandrs Slivkins
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.
no code implementations • 15 Jan 2023 • Jon X. Eguia, Nicole Immorlica, Steven P. Lalley, Katrina Ligett, Glen Weyl, Dimitrios Xefteris
Consider the following collective choice problem: a group of budget constrained agents must choose one of several alternatives.
no code implementations • 27 May 2022 • Ian Ball, James Bono, Justin Grana, Nicole Immorlica, Brendan Lucier, Aleksandrs Slivkins
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.
no code implementations • 26 May 2022 • Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Markus Mobius, Divyarthi Mohan
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.
no code implementations • 25 Feb 2022 • Ozan Candogan, Nicole Immorlica, Bar Light, Jerry Anunrojwong
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.
no code implementations • 13 Jul 2021 • Moshe Babaioff, Nicole Immorlica, Yingkai Li, Brendan Lucier
We show that when using balanced prices, both these approaches ensure high equilibrium welfare in the combined market.
no code implementations • 1 Dec 2020 • Natalie Collina, Nicole Immorlica, Kevin Leyton-Brown, Brendan Lucier, Neil Newman
The value of a match is determined by the types of the matched agents.
Computer Science and Game Theory Data Structures and Algorithms
no code implementations • 3 Nov 2020 • Nika Haghtalab, Nicole Immorlica, Brendan Lucier, Jack Z. Wang
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.
no code implementations • 19 Feb 2019 • Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu
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.
no code implementations • 28 Nov 2018 • Nicole Immorlica, Karthik Abinav Sankararaman, Robert Schapire, Aleksandrs Slivkins
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.
no code implementations • 14 Nov 2018 • Nicole Immorlica, Jieming Mao, Aleksandrs Slivkins, Zhiwei Steven Wu
We propose and design recommendation systems that incentivize efficient exploration.
no code implementations • 27 Aug 2018 • Lily Hu, Nicole Immorlica, Jennifer Wortman Vaughan
When consequential decisions are informed by algorithmic input, individuals may feel compelled to alter their behavior in order to gain a system's approval.
no code implementations • 11 Apr 2018 • Daniel Alabi, Nicole Immorlica, Adam Tauman Kalai
Most systems and learning algorithms optimize average performance or average loss -- one reason being computational complexity.
no code implementations • 20 Jul 2017 • Cynthia Dwork, Nicole Immorlica, Adam Tauman Kalai, Max Leiserson
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.