Publications

Cameron Martel | MIT Sloan

Mohsen Mosleh | MIT Sloan

This paper asks under what conditions do social corrections promote engagement from corrected users, allowing for greater insight into how users respond to debunking messages (even if such responses are negative).

Alexander K. Saeri

Jess Graham

Michael Noetel, et al.

This paper reports results from a three-round Delphi study conducted late 2025 with 272 international AI experts who rated 24 AI risks on harm probability and severity, sector and actor vulnerability, actor responsibility, and overall concern.

Useful social science theories predict behavior across settings. However, applying a theory to make predictions in new settings is challenging: rarely can it be done without ad hoc modifications to account for setting-specific factors. We argue that AI agents put in simulations of those novel settings offer an alternative for applying theory, requiring minimal or no modifications.

Abigail Sussman

Yujin Yang

This paper finds that people rely on a competence-judgment tradeoff to choose between human and AI advisors, evaluating whether the benefit of expertise outweighs the cost of social judgment.

Michelle Vaccaro

Michael Caosun

Jared R. Curhan

This research reveals that principles from human negotiation theory remain crucial even in AI–AI contexts and that AI-specific technical strategies also create success. The results suggest the need to establish a new theory of AI negotiation that integrates classic negotiation theory with AI-specific negotiation theories to better understand autonomous negotiations and optimize agent performance.

Maja S. Svanberg
Brian C. Goehring

This paper’s findings suggest that AI job displacement will be substantial, but also gradual – and therefore there is room for policy and retraining to mitigate unemployment impacts.

Emanuele Del Sozzo

Kenneth Flamm

This paper examines technical progress in NVIDIA datacenter GPUs from the mid-2000s through 2025. 

Natalia Fischl-Lanzoni

Do leading LLM developers possess a proprietary “secret sauce,” or is LLM performance driven by scaling up compute? Using training and benchmark data for 809 models released between 2022 and 2025, the authors estimate scaling-law regressions with release-date and developer fixed effects.