Sinan Aral

· David Austin Professor of ManagementVerified

Massachusetts Institute of Technology · Information Technology

Active 2002–2025

h-index44

Citations25.7k

Papers13138 last 5y

Funding$476k

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About

Sinan Aral is the Director of the MIT Initiative on the Digital Economy (IDE) and a Professor at the MIT Sloan School of Management. He leads the IDE, which focuses on research related to the digital economy, including areas such as applied AI, technology-driven organizations, digital culture, data analytics, AI in marketplaces and labor economics, AI in financial markets and decision making, data privacy, and human-first AI. As a faculty member at MIT Sloan, Aral contributes to advancing understanding of how digital technologies impact economic and social systems. His leadership role at the IDE positions him at the forefront of interdisciplinary research exploring the intersection of technology, economics, and management.

Research topics

Political Science
Business
Computer Science
Economics
Engineering ethics
Medicine
Social psychology
Microeconomics
Psychology
Data science
Internal medicine
Engineering
Virology
Physics
Management science

Selected publications

Collaborating with AI Agents: Field Experiments on Teamwork, Productivity, and Performance
ArXiv.org · 2025-03-23 · 1 citations
preprintOpen accessSenior author
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 collaboration. We randomly assigned 2,234 participants to human-human and human-AI teams that produced 11,024 ads for a think tank. We evaluated the ads using independent human ratings and a field experiment on X which garnered ~5M impressions. We found human-AI teams produced 50% more ads per worker and higher text quality, while human-human teams produced higher image quality, suggesting a jagged frontier of AI agent capability. Human-AI teams also produced more homogeneous, or self-similar, outputs. The field experiment revealed higher text quality improved click-through rates and view-through duration, while higher image quality improved cost-per-click rates. We found three mechanisms explained these effects. First, human-AI collaboration was more task-oriented, with 25% more task-oriented messages and 18% fewer interpersonal messages. Second, human-AI collaboration displayed more delegation, as participants delegated 17% more work to AI agents than to human partners and performed 62% fewer direct text edits when working with AI. Third, recognition that the collaborator was an AI moderated these effects as participants who correctly identified they were working with AI were more task-oriented and more likely to delegate work. These mechanisms then explained performance as task-oriented communication improved ad quality, specifically when working with AI, while interpersonal communication reduced ad quality; delegation improved text quality but had no effect on image quality and was positively associated with diversity collapse, creating homogeneous outputs of higher average quality. The results suggest AI agents drive changes in productivity, performance, and output diversity by reshaping teamwork.
Publisher OA PDF DOI
Advancing AI Negotiations: New Theory and Evidence from a Large-Scale Autonomous Negotiations Competition
2025-03-11 · 1 citations
preprintOpen access
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 between these agents across multiple scenarios with diverse characteristics and objectives. Our findings revealed that principles from human negotiation theory remain crucial even in AI-AI contexts. Surprisingly, warmth—a traditionally human relationship-building trait—was consistently associated with superior outcomes across all key performance metrics. Dominant agents, meanwhile, were especially effective at claiming value. Our analysis also revealed unique dynamics in AI-AI negotiations not fully explained by existing theory, including AI-specific technical strategies like chain-of-thought reasoning, prompt injection, and strategic concealment. When we applied natural language processing (NLP) methods to the full transcripts of all negotiations we found positivity, gratitude and question-asking (associated with warmth) were strongly associated with reaching deals as well as objective and subjective value, whereas conversation lengths (associated with dominance) were strongly associated with impasses. The results suggest the need to establish a new theory of AI negotiation, which integrates classic negotiation theory with AI-specific negotiation theories to better understand autonomous negotiations and optimize agent performance.
Publisher OA PDF DOI
Collaborating with AI Agents: Field Experiments on Teamwork, Productivity, and Performance
SSRN Electronic Journal · 2025-01-01 · 5 citations
preprintOpen accessSenior author
Publisher DOI
Human Trust in AI Search: A Large-Scale Experiment
SSRN Electronic Journal · 2025-01-01 · 1 citations
preprintOpen accessSenior author
Publisher DOI
Are Crypto Ecosystems (De)centralizing? A Framework for Longitudinal Analysis
ArXiv.org · 2025-06-02
preprintOpen accessSenior author
Blockchain technology relies on decentralization to resist faults and attacks while operating without trusted intermediaries. Although industry experts have touted decentralization as central to their promise and disruptive potential, it is still unclear whether the crypto ecosystems built around blockchains are becoming more or less decentralized over time. As crypto plays an increasing role in facilitating economic transactions and peer-to-peer interactions, measuring their decentralization becomes even more essential. We thus propose a systematic framework for measuring the decentralization of crypto ecosystems over time and compare commonly used decentralization metrics. We applied this framework to seven prominent crypto ecosystems, across five distinct subsystems and across their lifetime for over 15 years. Our analysis revealed that while crypto has largely become more decentralized over time, recent trends show a shift toward centralization in the consensus layer, NFT marketplaces, and developers. Our framework and results inform researchers, policymakers, and practitioners about the design, regulation, and implementation of crypto ecosystems and provide a systematic, replicable foundation for future studies.
Publisher OA PDF DOI
Effects of video ad features on audience engagement: evidence from a large-scale video platform
Journal of Research in Interactive Marketing · 2025-10-21
articleSenior author
Purpose Understanding video advertising effectiveness is essential, given advertisers’ substantial investment in the format and its ubiquitous presence in our daily lives. But understanding video ad feature effectiveness is challenging due to the limited availability of video ad data and the complexity of video features. Design/methodology/approach We therefore collected video ad and performance data on more than 90,000 video ad campaigns, registering 1 trillion impressions, across more than 20 industry segments, on six popular social media platforms, including Facebook, YouTube, SnapChat, LinkedIn, Twitter and Pinterest. We establish a taxonomy of video ad features, based on the Elaboration Likelihood Model (ELM), using unsupervised clustering to explore how different features cluster across our sample. We then perform a feature importance analysis, using group Lasso and Random Forest models, and employ multilevel linear models of video feature effects on ad performance. Findings We find, for example, that the presence and early appearance of text in videos reduces view-related performance metrics, while the presence and early appearance of people improves view-related metrics. We also explore the heterogeneity of video ad feature performance effects across platforms, industries and campaign objectives. As predicted by ELM, for example, text reduces ad performance in the luxury industry and increases performance in professional services ads. Originality/value To our knowledge, ours is the largest analysis of video ad features and their performance implications to date. We hope to provide a foundation for future causal studies of video ad features and their performance effects.
Publisher DOI
Teaching AI to Handle Exceptions: Supervised Fine-Tuning with Human-Aligned Judgment
PNAS Nexus · 2025-01-01 · 1 citations
preprintOpen accessSenior author
Abstract 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, while their generative capabilities are well-documented, their decision-making processes remain poorly understood. This is particularly evident when testing targeted decision-making: for instance, how models handle exceptions, a critical and challenging aspect of decision-making made relevant by the inherent incompleteness of contracts. Here we demonstrate that LLMs, even ones that excel at reasoning, deviate significantly from human judgments because they adhere strictly to policies, even when such adherence is impractical, suboptimal, or even counterproductive. We then evaluate three approaches to tuning AI agents to handle exceptions: ethical framework prompting, chain-of-thought reasoning, and supervised fine-tuning. We find that while ethical framework prompting fails and chain-of-thought prompting provides only slight improvements, supervised fine-tuning—specifically with human explanations—yields markedly better results. Surprisingly, in our experiments, supervised fine-tuning even enabled models to generalize human-like decision-making to novel scenarios, demonstrating transfer learning of human-aligned decision-making across contexts. Furthermore, fine-tuning with explanations, not just labels, was critical for alignment, suggesting that aligning LLMs with human judgment requires explicit training on how decisions are made, not just which decisions are made. These findings highlight the need to address LLMs' shortcomings in handling exceptions in order to guide the development of agentic AI toward models that can effectively align with human judgment and simultaneously adapt to novel contexts.
Publisher DOI
Advancing AI Negotiations: New Theory and Evidence from a Large-Scale Autonomous Negotiations Competition
SSRN Electronic Journal · 2025-01-01 · 4 citations
preprintOpen access
Publisher DOI
Human Trust in AI Search: A Large-Scale Experiment
ArXiv.org · 2025-04-08
preprintOpen accessSenior author
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 trust generative artificial intelligence (GenAI) can therefore influence what we buy, how we vote and our health. Unfortunately, no work establishes the causal effect of generative search designs on human trust. Here we execute ~12,000 search queries across seven countries, generating ~80,000 real-time GenAI and traditional search results, to understand the extent of current global exposure to GenAI search. We then use a preregistered, randomized experiment on a large study sample representative of the U.S. population to show that while participants trust GenAI search less than traditional search on average, reference links and citations significantly increase trust in GenAI, even when those links and citations are incorrect or hallucinated. Uncertainty highlighting, which reveals GenAI's confidence in its own conclusions, makes us less willing to trust and share generative information whether that confidence is high or low. Positive social feedback increases trust in GenAI while negative feedback reduces trust. These results imply that GenAI designs can increase trust in inaccurate and hallucinated information and reduce trust when GenAI's certainty is made explicit. Trust in GenAI varies by topic and with users' demographics, education, industry employment and GenAI experience, revealing which sub-populations are most vulnerable to GenAI misrepresentations. Trust, in turn, predicts behavior, as those who trust GenAI more click more and spend less time evaluating GenAI search results. These findings suggest directions for GenAI design to safely and productively address the AI "trust gap."
Publisher OA PDF DOI
Are Crypto Ecosystems (De)centralizing? A Framework for Longitudinal Analysis
Communications of the ACM · 2025-11-24
articleSenior author
Blockchain technology relies on decentralization to resist faults and attacks while operating without trusted intermediaries. Although industry experts have touted decentralization as central to their promise and disruptive potential, it is still unclear whether the crypto ecosystems built around blockchains are becoming more or less decentralized over time. As crypto plays an increasing role in facilitating economic transactions and peer-to-peer interactions, measuring their decentralization becomes even more essential. We thus propose a systematic framework for measuring the decentralization of crypto ecosystems over time and compare commonly used decentralization metrics. We applied this framework to seven prominent crypto ecosystems, across five distinct subsystems and across their lifetime for over 15 years. Our analysis revealed that while crypto has largely become more decentralized over time, recent trends show a shift toward centralization in the consensus layer, NFT marketplaces, and developers. Our framework and results inform researchers, policymakers, and practitioners about the design, regulation, and implementation of crypto ecosystems and provide a systematic, replicable foundation for future studies.
Publisher DOI