71 citations · 130 across the 47 of their papers we have counts for
15 papers · 1 filter
Computationally Efficient Collaborative Communication Via Regularity-Based Coarsening
Mark Bedaywi, Scott Emmons, Nika Haghtalab +1
Our results show that the existence of a short high-utility protocol already suffices for efficient communication. In particular, in a game with possible observations and a…
Pluralistic Leaderboards
Nika Haghtalab, Ariel D. Procaccia, Han Shao +2
Recent leaderboard-based evaluations of large language models aggregate user feedback by fitting a Bradley--Terry model to pairwise comparisons, producing a single global ranking b…
Leakage-Robust Bayesian Persuasion
Nika Haghtalab, Mingda Qiao, Kunhe Yang
This paper introduces leakage-robust Bayesian persuasion. Situated between public Bayesian persuasion [KG11] (and its multi-receiver variants [CCG23, Xu20]) and private Bayesian pe…
Is Knowledge Power? On the (Im)possibility of Learning from Strategic Interactions
Nivasini Ananthakrishnan, Nika Haghtalab, Chara Podimata +1
When learning in strategic environments, a key question is whether agents can overcome uncertainty about their preferences to achieve outcomes they could have achieved absent any u…
Platforms for Efficient and Incentive-Aware Collaboration
Nika Haghtalab, Mingda Qiao, Kunhe Yang
Collaboration is crucial for reaching collective goals. However, its effectiveness is often undermined by the strategic behavior of individual agents -- a fact that is captured by…
Smooth Nash Equilibria: Algorithms and Complexity
Constantinos Daskalakis, Noah Golowich, Nika Haghtalab +1
A fundamental shortcoming of the concept of Nash equilibrium is its computational intractability: approximating Nash equilibria in normal-form games is PPAD-hard. In this paper, in…
Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt +1
As the scale of machine learning models increases, trends such as scaling laws anticipate consistent downstream improvements in predictive accuracy. However, these trends take the…
Calibrated Stackelberg Games: Learning Optimal Commitments Against Calibrated Agents
Nika Haghtalab, Chara Podimata, Kunhe Yang
We introduce \emph{Calibrated Stackelberg Games (CSGs)}, a generalization of the standard Stackelberg Games (SGs) framework. In CSGs, a principal repeatedly interacts with an agent…
Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty
Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy +1
In online marketplaces, customers have access to hundreds of reviews for a single product. Buyers often use reviews from other customers that share their type -- such as height for…
Competition, Alignment, and Equilibria in Digital Marketplaces
Meena Jagadeesan, Michael I. Jordan, Nika Haghtalab
Competition between traditional platforms is known to improve user utility by aligning the platform's actions with user preferences. But to what extent is alignment exhibited in da…
Learning in Stackelberg Games with Non-myopic Agents
Nika Haghtalab, Thodoris Lykouris, Sloan Nietert +1
We study Stackelberg games where a principal repeatedly interacts with a non-myopic long-lived agent, without knowing the agent's payoff function. Although learning in Stackelberg…
Maximizing Welfare with Incentive-Aware Evaluation Mechanisms
Nika Haghtalab, Nicole Immorlica, Brendan Lucier +1
Motivated by applications such as college admission and insurance rate determination, we propose an evaluation problem where the inputs are controlled by strategic individuals who…
The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally
Lydia T. Liu, Ashia Wilson, Nika Haghtalab +3
The long-term impact of algorithmic decision making is shaped by the dynamics between the deployed decision rule and individuals' response. Focusing on settings where each individu…
Computing Stackelberg Equilibria of Large General-Sum Games
Avrim Blum, Nika Hagtalab, MohammadTaghi Hajiaghayi +1
We study the computational complexity of finding Stackelberg Equilibria in general-sum games, where the set of pure strategies of the leader and the followers are exponentially lar…
Weighted Voting Via No-Regret Learning
Nika Haghtalab, Ritesh Noothigattu, Ariel D. Procaccia
Voting systems typically treat all voters equally. We argue that perhaps they should not: Voters who have supported good choices in the past should be given higher weight than vote…