How do social, linguistic, and technological factors influence processes of collective cognition such as collective creativity, problem-solving, judgment, knowledge generation, innovation, evaluation, attention, perception, and interpretation?
How do these collective cognition processes influence organizational and market outcomes?
How do these collective cognition processes influence organizational and market outcomes?
RESEARCH INTERESTS
My research investigates how social, linguistic, and technological factors influence processes of collective cognition, and how these processes then affect organizational and market outcomes. I take groups as the basic units of analysis and apply the principal metaphor of cognitive science—that of cognition as computation—to their operation, tracing their information processing activities in fine detail.
By taking advantage of recent advances in computational and natural language processing techniques, I theorize and empirically trace how collectives innovate, problem-solve, coordinate, evaluate, judge, and decide. I study these patterns of collective cognition in social systems ranging from small groups such as mountaineering expedition teams to large-scale social systems such as markets and scientific disciplines. In so doing, my research bridges multiple disciplinary domains, including organization theory, economic sociology, cognitive science, linguistics, and information theory.
PUBLICATIONS
Evans, James A., and Pedro Aceves. 2016. “Machine Translation: Mining Text for Social Theory.” Annual Review of Sociology.
More of the social world lives within electronic text than ever before, from collective activity on the web, social media, and instant messaging to online transactions, government intelligence, and digitized libraries. This supply of text has elicited demand for natural language processing and machine learning tools to filter, search, and translate text into valuable data. We survey some of the most exciting computational approaches to text analysis, highlighting both supervised methods that extend old theories to new data and unsupervised techniques that discover hidden regularities worth theorizing. We then review recent research that uses these tools to develop social insight by exploring (a) collective attention and reasoning through content from communication; (b) social relationships through the process of communication; and (c) social states, roles, and moves identified through heterogeneous signals within communication. We highlight social questions for which these advances could offer powerful new insight.
More of the social world lives within electronic text than ever before, from collective activity on the web, social media, and instant messaging to online transactions, government intelligence, and digitized libraries. This supply of text has elicited demand for natural language processing and machine learning tools to filter, search, and translate text into valuable data. We survey some of the most exciting computational approaches to text analysis, highlighting both supervised methods that extend old theories to new data and unsupervised techniques that discover hidden regularities worth theorizing. We then review recent research that uses these tools to develop social insight by exploring (a) collective attention and reasoning through content from communication; (b) social relationships through the process of communication; and (c) social states, roles, and moves identified through heterogeneous signals within communication. We highlight social questions for which these advances could offer powerful new insight.
WORKING PAPERS
Aceves, Pedro. “Language Structure, Collective Cognition, and Team Performance.”
Finalist, Managerial and Organizational Cognition Best Paper Award, 2019 Academy of Management
Aceves, Pedro and Cassandra Chambers. “Amplifying and Dampening Social Influence: How the Structure of Rating Systems Affects Evaluative Judgments.” (Under Review)
Chambers, Cassandra, Maxim Sytch, and Pedro Aceves. “Forever Following the Crowd: When Decreasing Uncertainty Increases Imitation.”
DATA ANALYSIS
Aceves, Pedro, and James A. Evans. “Conceptually Dense Languages Communicate Faster.”
Aceves, Pedro, and Amanda Sharkey. “The Shifting Conceptual Ecology of Capitalism.”
Evans, James A., Jacob Foster, Pedro Aceves, and Christopher Rhea. “Inequality All the Way Down? The Structure of Attention in 20th Century Physics.”
Aceves, Pedro, Cassandra Chambers, and Marlon Rodrigues. “How Insufficient Recognition Affects Systems of Cooperation.”
Aceves, Pedro, Cassandra Chambers, and Marlon Rodrigues. “Rule-Sticklers, Cheaters, Cooperators: Creating Robust Knowledge Communities.”
Chambers, Cassandra, Pedro Aceves, and Chang Liu. “A Needle in a Haystack: Optimal Non-Distinctiveness for Sustainable Advantage.”
DISSERTATION
Title: “The Linguistic Relativity of Collective Cognition and Group Performance”
Committee: James A. Evans (Chair), John Levi Martin, Amanda Sharkey, Sameer Srivastava
The long-researched linguistic relativity hypothesis predicts that the structure of a person’s language influences their cognition. While this hypothesis has only been pursued in the context of how language affects individual cognition, my dissertation brings this argument into sociological territory by asking how the structure of language influences group performance. The idea is that the structural characteristics of a language can constrain some patterns of communication and collective cognition, while enabling others. I ask: 1) Can differences in language structure affect the performance of groups? If so, 2) what mechanisms account for this performance difference? To answer these questions, I trace the social interaction and collective cognition effects of a novel language structure attribute. I create estimates of information density, the average amount of conceptual information contained within words of a language, across the world’s languages. I then use computational, archival, and experimental methodologies to trace how the information density of a language affects the nature of social interactions and collective cognition as well as the way information flows through those interactions, thereby affecting overall rates of group performance.
Committee: James A. Evans (Chair), John Levi Martin, Amanda Sharkey, Sameer Srivastava
- National Science Foundation DDRI Grant
- Winner, INFORMS/Organization Science Dissertation Proposal Competition, 2017
The long-researched linguistic relativity hypothesis predicts that the structure of a person’s language influences their cognition. While this hypothesis has only been pursued in the context of how language affects individual cognition, my dissertation brings this argument into sociological territory by asking how the structure of language influences group performance. The idea is that the structural characteristics of a language can constrain some patterns of communication and collective cognition, while enabling others. I ask: 1) Can differences in language structure affect the performance of groups? If so, 2) what mechanisms account for this performance difference? To answer these questions, I trace the social interaction and collective cognition effects of a novel language structure attribute. I create estimates of information density, the average amount of conceptual information contained within words of a language, across the world’s languages. I then use computational, archival, and experimental methodologies to trace how the information density of a language affects the nature of social interactions and collective cognition as well as the way information flows through those interactions, thereby affecting overall rates of group performance.
