25 citations · 35 across the 15 of their papers we have counts for
19 papers
Pairit: A Platform for Live Experiments on Human-AI Collaboration
Harang Ju, Sinan Aral
Organizational design in the era of artificial intelligence requires experimental methods that can test how human-AI groups coordinate, delegate, and make decisions. Programmable p…
The Augmentation Trap: AI Productivity and the Cost of Cognitive Offloading
Michael Caosun, Sinan Aral
Experimental evidence suggests that AI tools raise worker productivity, but also that sustained offloading can erode expertise. This creates a tradeoff when expertise is a compleme…
The Rise of AI Search: Implications for Information Markets and Human Judgement at Scale
Sinan Aral, Haiwen Li, Rui Zuo
We executed 24,000 search queries in 243 countries, generating 2.8 million AI and traditional search results in 2024 and 2025. We found a rapid global expansion of AI search and ke…
Personality pairing improves human-AI collaboration
Harang Ju, Sinan Aral
Here we examine how AI agent "personalities" interact with human personalities to shape human-AI collaboration and performance. In a large-scale, preregistered randomized experimen…
Measuring Social Media Network Effects
Sinan Aral, Seth G Benzell, Avinash Collis +1
Network effects -- the utility gains from additional consumers of a good -- are widely regarded as critical to the digital economy. Yet recent theory and evidence suggest that loca…
Are Crypto Ecosystems (De)centralizing? A Framework for Longitudinal Analysis
Harang Ju, Ehsan Valavi, Madhav Kumar +1
Blockchain technology relies on decentralization to resist faults and attacks while operating without trusted intermediaries. Although industry experts have touted decentralization…
Explaining Sustained Blockchain Decentralization with Quasi-Experiments: The Resource Flexibility of Consensus Mechanisms
Harang Ju, Madhav Kumar, Ehsan Valavi +1
Decentralization is a fundamental design element of the Web3 economy. Blockchains and distributed consensus mechanisms are touted as fault-tolerant, attack-resistant, and collusion…
Human Trust in AI Search: A Large-Scale Experiment
Haiwen Li, Sinan Aral
Large Language Models (LLMs) increasingly power generative search engines which, in turn, drive human information seeking and decision making at scale. The extent to which humans t…
Advertising Spillovers in Mobile Apps: Evidence from Ad Shutoffs and Store Rankings
Harang Ju, Michael Zhao, Sinan Aral
Using advertising campaign data from a large US-based mobile game developer, the authors study a global advertising shutoff in the context of mobile app install ads. Contrary to pr…
Collaborating with AI Agents: Field Experiments on Teamwork, Productivity, and Performance
Harang Ju, Sinan Aral
We examined the mechanisms underlying productivity and performance gains from AI agents using a large-scale experiment on Pairit, a platform we developed to study human-AI collabor…
Advancing AI Negotiations: A Large-Scale Autonomous Negotiation Competition
Michelle Vaccaro, Michael Caosun, Harang Ju +2
We conducted an International AI Negotiation Competition in which participants designed and refined prompts for AI negotiation agents. We then facilitated over 180,000 negotiations…
Teaching AI to Handle Exceptions: Supervised Fine-Tuning with Human-Aligned Judgment
Matthew DosSantos DiSorbo, Harang Ju, Sinan Aral
Large language models (LLMs), initially developed for generative AI, are now evolving into agentic AI systems, which make decisions in complex, real-world contexts. Unfortunately,…
Modeling Dynamic User Interests: A Neural Matrix Factorization Approach
Paramveer Dhillon, Sinan Aral
In recent years, there has been significant interest in understanding users' online content consumption patterns. But, the unstructured, high-dimensional, and dynamic nature of suc…
Targeting for long-term outcomes
Jeremy Yang, Dean Eckles, Paramveer Dhillon +1
Decision makers often want to target interventions so as to maximize an outcome that is observed only in the long-term. This typically requires delaying decisions until the outcome…
Reducing Interference Bias in Online Marketplace Pricing Experiments
David Holtz, Ruben Lobel, Inessa Liskovich +1
Online marketplace designers frequently run A/B tests to measure the impact of proposed product changes. However, given that marketplaces are inherently connected, total average tr…
Limiting Bias from Test-Control Interference in Online Marketplace Experiments
David Holtz, Sinan Aral
In an A/B test, the typical objective is to measure the total average treatment effect (TATE), which measures the difference between the average outcome if all users were treated a…
The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify
David Holtz, Benjamin Carterette, Praveen Chandar +3
It remains unknown whether personalized recommendations increase or decrease the diversity of content people consume. We present results from a randomized field experiment on Spoti…
Scalable bundling via dense product embeddings
Madhav Kumar, Dean Eckles, Sinan Aral
Bundling, the practice of jointly selling two or more products at a discount, is a widely used strategy in industry and a well examined concept in academia. Historically, the focus…
Selection Effects in Online Sharing: Consequences for Peer Adoption
Sean J. Taylor, Eytan Bakshy, Sinan Aral
Most models of social contagion take peer exposure to be a corollary of adoption, yet in many settings, the visibility of one's adoption behavior happens through a separate decisio…