About

Pedro (Pete) Aceves is an assistant professor at the Johns Hopkins Carey Business School and a Fellow of the Knowledge Lab at the University of Chicago. He holds a PhD in Sociology from the University of Chicago, where he also served as an associate editor at the American Journal of Sociology. His research bridges multiple disciplinary domains, including organization theory, economic sociology, linguistics, cognitive science, and information theory. He uses computational and natural language processing tools to bring a dynamic and interactional perspective to the study of organizational and economic life. His research explores themes such as the linguistic relativity of team performance, communication speed and conversation breadth in human languages, and how word embedding models can inform measurement and theory within organization science. He studies these themes across various contexts, from small groups like mountaineering expeditions and innovation teams to large-scale social systems such as online platforms, markets, and scientific disciplines.

Research topics

• Artificial Intelligence
• Computer Science
• Social Science
• Sociology
• Mathematics
• Natural Language Processing
• Epistemology
• Data science
• Biology
• Linguistics
• Theoretical computer science
• Communication
• Psychology
• Management science
• Knowledge management

Selected publications

• Strategic Communication: Discourse and Framing Strategy for Collective Cognition and Performance

  Academy of Management Proceedings · 2025-07-01

  article

  This presenter symposium includes four research papers that explore corporate communication strategies to account for their effect on team and organizational performance. Each presenter develops new measures of discourse and framing features by deploying state-of-the-art computational content analysis methods and draws broadly from sense-making, collective cognition, and robust action theories to advance our understanding of strategic communication. Through research presentations, discussant comments, interactive panel discussions, and audience Q&As, this symposium highlights the importance of language as a strategy and facilitates a conversation between strategy scholars (STR), organizational and management theorists (OMT), and experts on management and organization cognition (MOC). Language Structure, Collective Cognition, and Team Performance Author: Pedro Aceves; Johns Hopkins University Author: Christopher G. Myers; Johns Hopkins University Language Ambiguity, Collective Cognition, and Social Evaluation Author: Ningzi Li; University of Chicago Author: Mukund Chari; University of Colorado-Boulder Author: James Evans; University of Chicago Ambiguous Entrepreneurial Communication and Mobilizing Audience Support for Novel Ideas Author: Jamie Seoyeon Song; ESMT - European School of Management and Technology - Berlin Author: Derek Harmon; University of Michigan Going Public, Going Inclusive: Diversity Messaging in Job Postings Author: Beril Yalcinkaya; University of Maryland College Park

  PublisherDOI

• Learning from Inconsistent Performance Feedback

  Organization Science · 2024-12-12 · 11 citations

  articleSenior author

  Organizations and the decision makers within them are increasingly subject to inconsistent performance feedback—feedback that contains elements that are incompatible with each other—which can lead to multiple interpretations of performance feedback. When this occurs, decision makers often recode inconsistent performance feedback as successful and continue with their current strategies, which allows them to avoid any self-threat from negative elements of performance feedback but implies that they do not learn from inconsistent performance feedback because they do not change. In contrast, we explore whether decision makers can learn from inconsistent performance feedback. Leveraging over 10 years of complete behavioral records in an online community and a laboratory experiment, we study how decision makers respond to inconsistent performance feedback stemming from multiple evaluators who do not agree on performance quality. Consistent with prior work, we find that decision makers change their strategies less after inconsistent performance feedback. Departing from prior work, we show a corresponding increase in clarification efforts aimed at better understanding which performance strategies work well. Importantly, clarification efforts mediate improved future performance. Our results suggest that inconsistent performance feedback can trigger deeper learning and enhanced performance, contributing to performance feedback theory and research on the microfoundations of organizational learning. Funding: The authors thank the University of São Paulo, Johns Hopkins University, and SKEMA Business School for institutional and financial support. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.16833 .

  PublisherDOI

• Conditional Word Count and Huffman Code Size are Two Sides of the Same Coin: Response to Koplenig

  2024-03-13 · 1 citations

  preprintOpen access1st authorCorresponding

  Conditional Word Count and Huffman Code Size are Two Sides of the Same Coin: Response to Koplenig

  PublisherOA PDFDOI

• Inconsistent rating scales decrease social influence bias and enhance crowd wisdom

  Computers in Human Behavior · 2024-11-14

  article1st author
  PublisherDOI

• Human languages with greater information density have higher communication speed but lower conversation breadth

  Nature Human Behaviour · 2024 · 19 citations

  1st authorCorresponding
  • Computer Science
  • Natural Language Processing
  • Artificial Intelligence
  PublisherDOI

• Mobilizing Conceptual Spaces: How Word Embedding Models Can Inform Measurement and Theory Within Organization Science

  Organization Science · 2023 · 47 citations

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Word embedding models are a powerful approach for representing the multidimensional conceptual spaces within which communicated concepts relate, combine, and compete with one another. This class of models represent a recent advance in machine learning allowing scholars to efficiently encode complex systems of meaning with minimal semantic distortion based on local and global word co-occurrences from large-scale text data. Although their use has the potential to broaden theoretical possibilities within organization science, embeddings are largely unknown to organizational scholars, where known they have only been mobilized for a narrow set of uses, and they remain unlinked to a theoretical scaffolding that can enable cumulative theory building within the organizations community. Our goal is to demonstrate the promise embedding models hold for organization science by providing a practical roadmap for users to mobilize the methodology in their research and a theoretical guide for consumers of that research to evaluate and conceptually link embedded representations with theoretical significance and potential. We begin by explicitly defining the notions of concept and conceptual space before proceeding to show how these can be represented and measured with word embedding models, noting strengths and weaknesses of the approach. We then provide a set of embedding measurements along with their theoretical interpretation and flexible extension. Our aim is to extract the operational and conceptual significance from technical treatments of word embeddings and place them within a practical, theoretical framework to accelerate research committed to understanding how individuals, teams, and broader collectives represent, communicate, and deploy meaning in organizational life. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.1686 .

  PublisherDOI

• Mobilizing Conceptual Spaces: How Word Embedding Models Can Inform Measurement and Theory within Organization Science

  2022 · 9 citations

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Word embedding models are a powerful approach for representing the multi-dimensional conceptual spaces within which communicated concepts relate, combine, and compete with one another. This class of models represent a recent advance in machine learning allowing scholars to efficiently encode complex systems of meaning with minimal semantic distortion based on local and global word co-occurrences from large-scale text data. While their use has the potential to broaden theoretical possibilities within organization science, embeddings are largely unknown to organizational scholars, where known they have only been mobilized for a narrow set of uses, and they remain unlinked to a theoretical scaffolding that can enable cumulative theory building within the organizations community. Our goal is to demonstrate the promise embedding models hold for organization science by providing a practical roadmap for users to mobilize the methodology in their research and a theoretical guide for consumers of that research to evaluate and conceptually link embedded representations with theoretical significance and potential. We begin by explicitly defining the notions of concept and conceptual space before proceeding to show how these can be represented and measured with word embedding models, noting strengths and weaknesses of the approach. We then provide a set of embedding measurements along with their theoretical interpretation and flexible extension. Our aim is to extract the operational and conceptual significance from technical treatments of word embeddings and place them within a practical, theoretical framework to accelerate research committed to understanding how individuals, teams, and broader collectives represent, communicate, and deploy meaning in organizational life.

  PublisherOA PDFDOI

• Computational Text Analysis: Value Added to Strategy and Organization Research

  Academy of Management Proceedings · 2021-07-26

  article1st authorCorresponding

  In organization theory and strategy, the study of culture, institutions, and cognition has been an active research space (Kaplan, 2011). Work in the area has attempted to capture collective representations and shared (institutional) meanings in the content of texts. Notably, scholars have approached this challenge from multiple perspectives, attempting to map patterns of words in groups, populations, and fields (Carley, 2002; Huff, 1990; Kennedy, 2008; Porac et al., 1995). The extent to which meanings have been effectively captured has been a deep perennial issue. With the increased availability of digital text data and adoption of methods from computational linguistics, techniques for measuring meanings have begun to infiltrate strategy and organizational research on a larger scale, once again intensifying the concerns capturing meaning (Hannigan et al., 2019; Evans & Aceves, 2016). In this symposium, we examine two leading approaches that are seemingly bifurcating: word embedding methods and topic modeling rendering methods. We invite a group of scholars in management with expertise doing cutting edge work across both approaches do engage in discussion. Our primary goal is to use these two approaches to explore whether this increased sophistication adds value to organization and strategy research above and beyond the existing methods in the field?

  PublisherDOI

• Human Languages with Greater Information Density Increase Communication Speed, but Decrease Conversation Breadth

  arXiv (Cornell University) · 2021-12-15 · 3 citations

  preprintOpen access1st author

  Human languages vary widely in how they encode information within circumscribed semantic domains (e.g., time, space, color, human body parts and activities), but little is known about the global structure of semantic information and nothing about its relation to human communication. We first show that across a sample of ~1,000 languages, there is broad variation in how densely languages encode information into their words. Second, we show that this language information density is associated with a denser configuration of semantic information. Finally, we trace the relationship between language information density and patterns of communication, showing that informationally denser languages tend toward (1) faster communication, but (2) conceptually narrower conversations within which topics of conversation are discussed at greater depth. These results highlight an important source of variation across the human communicative channel, revealing that the structure of language shapes the nature and texture of human engagement, with consequences for human behavior across levels of society.

  PublisherOA PDFDOI

• How Insufficient Recognition Shapes the Rate and Scope of Contributions to the Commons

  Academy of Management Proceedings · 2021-07-26

  article

  Scholars have long extolled the power of reputation for maintaining contributions toward collective resources. Incentivized to achieve reputations as good citizens of the collective, individuals are less inclined to freeride off the contributions of others. However, in examining the effects of reputation, researchers have largely relied on no-deception experimental designs wherein the granting of recognition for contributions to the collective is error-free and reputation is perfectly calculated. Reality is more complicated, with individuals often receiving insufficient recognition for their contributions, and we know little about how such insufficient recognition shapes the subsequent rate and scope of contributions to the collective. Leveraging a unique data feature from one of the largest online question-and-answer knowledge communities, we examine how occurrences of insufficient recognition shape subsequent contributions. Contrary to our expectations, we find that individuals contribute more after insufficient recognition and are less likely to venture away from their knowledge domain. Individuals with higher reputations display higher increases in contributions, but do not differ in their range of content. Results reveal a double-edged sword of insufficient recognition on cooperation—it increases future contributions, but narrows their conceptual scope relative to prior activity. We discuss the implications of these findings for maintaining diverse knowledge commons.

  PublisherDOI

Frequent coauthors

• James A. Evans

  University of Chicago

  7 shared

• Marlon Fernandes Rodrigues Alves

  Groupe de Recherche en Droit, Économie, Gestion

  2 shared

• Cassandra R. Chambers

  Johns Hopkins University

  2 shared

Awards & honors

• INFORMS/Organization Science Best Dissertation Proposal Comp…
• Best Paper Award from the Managerial and Organizational Cogn…
• Winner, Managerial and Organizational Cognition Best Paper A…
• National Science Foundation Dissertation Improvement Grant (…