About

Thomas W. Malone is the Patrick J. McGovern Professor of Management and the Director of the MIT Center for Collective Intelligence at the Sloan School of Management. His work focuses on understanding and enhancing collective intelligence, which involves studying how groups, organizations, and networks can work together more effectively. As a leading figure in this field, Malone's research explores the ways in which technology and organizational design can improve decision-making, collaboration, and innovation within various social and technological systems.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Political Science
• Mathematics education
• Cognitive psychology
• Cognitive science
• Data science
• History
• Social psychology

Selected publications

• Where can AI be used? Insights from a deep ontology of work activities

  ArXiv.org · 2026-03-21

  articleOpen accessSenior author

  Artificial intelligence (AI) is poised to profoundly reshape how work is executed and organized, but we do not yet have deep frameworks for understanding where AI can be used. Here we provide a comprehensive ontology of work activities that can help systematically analyze and predict uses of AI. To do this, we disaggregate and then substantially reorganize the approximately 20K activities in the US Department of Labor's widely used O*NET occupational database. Next, we use this framework to classify descriptions of 13,275 AI software applications and a worldwide tally of 20.8 million robotic systems. Finally, we use the data about both these kinds of AI to generate graphical displays of how the estimated units and market values of all worldwide AI systems used today are distributed across the work activities that these systems help perform. We find a highly uneven distribution of AI market value across activities, with the top 1.6% of activities accounting for over 60% of AI market value. Most of the market value is used in information-based activities (72%), especially creating information (36%), and only 12% is used in physical activities. Interactive activities include both information-based and physical activities and account for 48% of AI market value, much of which (26%) involves transferring information. These results can be viewed as rough predictions of the AI applicability for all the different work activities down to very low levels of detail. Thus, we believe this systematic framework can help predict at a detailed level where today's AI systems can and cannot be used and how future AI capabilities may change this.

  PublisherOA PDF

• Copyright, Learnright, and Fair Use: Rethinking Compensation for AI Model Training

  SSRN Electronic Journal · 2025-01-01 · 1 citations

  articleOpen access
  PublisherDOI

• Agribusiness organization and management

  Elsevier eBooks · 2025-01-01

  book-chapter
  PublisherDOI

• An Analysis of Critical Midstream Electrical Assets to Mitigate Process Shutdown Failures

  2025-09-22

  article

  This paper examines the methodology and engineering decisions developed in response to a nine-day outage caused by an electrical thermal and arc flash failure at a compressor station in a major gas field. The resulting mitigation strategy aims to reduce repair time and improve the predictability of future failures by leveraging multiple technologies in both retrofit and new equipment designs.

  PublisherDOI

• Togedule: Scheduling Meetings with Large Language Models and Adaptive Representations of Group Availability

  Proceedings of the ACM on Human-Computer Interaction · 2025-10-16

  articleOpen accessSenior author

  Scheduling is a perennial-and often challenging-problem for many groups. Existing tools are mostly static, showing an identical set of choices to everyone, regardless of the current status of attendees' inputs and preferences. In this paper, we propose Togedule, an adaptive scheduling tool that uses large language models to dynamically adjust the pool of choices and their presentation format. With the initial prototype, we conducted a formative study (N=10) and identified the potential benefits and risks of such an adaptive scheduling tool. Then, after enhancing the system, we conducted two controlled experiments, one each for attendees and organizers (total N=66). For each experiment, we compared scheduling with verbal messages, shared calendars, or Togedule. Results show that Togedule significantly reduces the cognitive load of attendees indicating their availability and improves the speed and quality of the decisions made by organizers.

  PublisherDOI

• AGI-Elo: How Far Are We From Mastering A Task?

  ArXiv.org · 2025-05-19

  preprintOpen access

  As the field progresses toward Artificial General Intelligence (AGI), there is a pressing need for more comprehensive and insightful evaluation frameworks that go beyond aggregate performance metrics. This paper introduces a unified rating system that jointly models the difficulty of individual test cases and the competency of AI models (or humans) across vision, language, and action domains. Unlike existing metrics that focus solely on models, our approach allows for fine-grained, difficulty-aware evaluations through competitive interactions between models and tasks, capturing both the long-tail distribution of real-world challenges and the competency gap between current models and full task mastery. We validate the generalizability and robustness of our system through extensive experiments on multiple established datasets and models across distinct AGI domains. The resulting rating distributions offer novel perspectives and interpretable insights into task difficulty, model progression, and the outstanding challenges that remain on the path to achieving full AGI task mastery.

  PublisherOA PDFDOI

• Interaction Configurations and Prompt Guidance in Conversational AI for Question Answering in Human-AI Teams

  Proceedings of the ACM on Human-Computer Interaction · 2025-10-16 · 4 citations

  preprintOpen accessSenior author

  Understanding the dynamics of human-AI interaction in question answering is crucial for enhancing collaborative efficiency. Extending from our initial formative study, which revealed challenges in human utilization of conversational AI support, we designed two configurations for prompt guidance: a Nudging approach, where the AI suggests potential responses for human agents, and a Highlight strategy, emphasizing crucial parts of reference documents to aid human responses. Through two controlled experiments, the first involving 31 participants and the second involving 106 participants, we compared these configurations against traditional human-only approaches, both with and without AI assistance. Our findings suggest that effective human-AI collaboration can enhance response quality, though merely combining human and AI efforts does not ensure improved outcomes. In particular, the Nudging configuration was shown to help improve the quality of the output when compared to AI alone. This paper delves into the development of these prompt guidance paradigms, offering insights for refining human-AI collaborations in conversational question-answering contexts and contributing to a broader understanding of human perceptions and expectations in AI partnerships.

  PublisherDOI

• Augmenting Collaborative Problem-Solving: Exploring the Design and Use of GenAI for Groupwork

  2025-10-17

  articleOpen accessSenior author

  CSCW Companion ’25, Bergen, Norway

  PublisherOA PDFDOI

• A Taxonomy for Human-LLM Interaction Modes: An Initial Exploration

  2024-05-11 · 42 citations

  preprintOpen accessSenior author

  With ChatGPT’s release, conversational prompting has become the most popular form of human-LLM interaction. However, its effectiveness is limited for more complex tasks involving reasoning, creativity, and iteration. Through a systematic analysis of HCI papers published since 2021, we identified four key phases in the human-LLM interaction flow—planning, facilitating, iterating, and testing—to precisely understand the dynamics of this process. Additionally, we have developed a taxonomy of four primary interaction modes: Mode 1: Standard Prompting, Mode 2: User Interface, Mode 3: Context-based, and Mode 4: Agent Facilitator. This taxonomy was further enriched using the “5W1H” guideline method, which involved a detailed examination of definitions, participant roles (Who), the phases that happened (When), human objectives and LLM abilities (What), and the mechanics of each interaction mode (How). We anticipate this taxonomy will contribute to the future design and evaluation of human-LLM interaction.

  PublisherOA PDFDOI

• When Are Combinations of Humans and AI Useful?

  2024-05-09 · 9 citations

  preprintOpen accessSenior author

  Inspired by the increasing use of AI to augment humans, researchers have studied human-AI systems involving different tasks, systems, and populations. Despite such a large body of work, we lack a broad conceptual understanding of when combinations of humans and AI are better than either alone. Here, we addressed this question by conducting a meta-analysis of over 100 recent experimental studies reporting over 300 effect sizes. First, we found that, on average, human-AI combinations performed significantly worse than the best of humans or AI alone. Second, we found performance losses in tasks that involved making decisions and significantly greater gains in tasks that involved creating content. Finally, when humans outperformed AI alone, we found performance gains in the combination, but when the AI outperformed humans alone we found losses. These findings highlight the heterogeneity of the effects of human-AI collaboration and point to promising avenues for improving human-AI systems.

  PublisherOA PDFDOI

Recent grants

• Collaborative Research: Measuring Collective Intelligence

  NSF · $538k · 2010–2014

• VOSS: Collaborative Research: Is Larger Smarter? Investigating the Effect of Group Size on Collective Intelligence

  NSF · $328k · 2013–2017

• CyberSEES:Type 2: Collaborative Research: Combining Experts and Crowds to Address Challenging Societal Problems

  NSF · $599k · 2015–2018

• SoCS: The Climate Collaboratorium: A Tool for Large-Scale Model-Centric Collective Decision-Making

  NSF · $249k · 2010–2012

• EAGER: The Climate CoLab: A System for Very Large-Scale Model-Based Group Problem-Solving

  NSF · $300k · 2011–2013

Frequent coauthors

• Kevin Crowston

  24 shared

• Robert Laubacher

  Massachusetts Institute of Technology

  24 shared

• Jintae Lee

  Yeungnam University

  14 shared

• Anita Williams Woolley

  Carnegie Mellon University

  12 shared

• Kum‐Yew Lai

  9 shared

• George Herman

  Eaton (United States)

  9 shared

• Kenneth R. Grant

  8 shared

• Robert I. Benjamin

  7 shared

Labs

• MIT Center for Collective IntelligencePI

Awards & honors

• Roosevelt “Rosey” Thompson Award from the U.S. Presidential…