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 am especially interested in tracing how attributes of a social system can condition patterns of social interaction that then aggregate up into consequential system-level cognitive dynamics. My research builds on the fundamental premise that cognition exists not only inside the brains of people, but also exists across the interactions that people have with each other and with artifacts in their environment. I trace these patterns of collective cognition in collectives ranging from small groups such as mountaineering expedition teams to large-scale social systems such as online platforms, markets, and scientific disciplines.
By taking advantage of recent advances in computational tools and big data sources of textual and behavioral data, I theorize and empirically trace how the structure of language and the structure of information environments condition the way collectives problem-solve, generate new knowledge, navigate conceptual space, innovate, evaluate, judge, distribute attention, represent, perceive, and interpret. Much of my research then proceeds to trace how these collective-level cognitive processes impact group, organization, and market outcomes. In so doing, my research bridges multiple disciplinary domains, including organization theory, economic sociology, linguistics, cognitive science, and information theory. By bridging these domains of scientific work, I aim to bring a dynamic and interactional lens to the study of organizational and economic life.
By taking advantage of recent advances in computational tools and big data sources of textual and behavioral data, I theorize and empirically trace how the structure of language and the structure of information environments condition the way collectives problem-solve, generate new knowledge, navigate conceptual space, innovate, evaluate, judge, distribute attention, represent, perceive, and interpret. Much of my research then proceeds to trace how these collective-level cognitive processes impact group, organization, and market outcomes. In so doing, my research bridges multiple disciplinary domains, including organization theory, economic sociology, linguistics, cognitive science, and information theory. By bridging these domains of scientific work, I aim to bring a dynamic and interactional lens to the study of organizational and economic life.
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.
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
- Winner, Best Paper Award from the Managerial and Organizational Cognition division of AoM.
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.
