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Cognitive Foundations of Collaboration

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2026-05-12

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Xiang, Yang. 2026. Cognitive Foundations of Collaboration. Doctoral Dissertation, Harvard University Graduate School of Arts and Sciences.

Abstract

Collaboration enables humans to achieve goals that are beyond the reach of a single person by integrating the efforts of people who differ in what they know, what they want, and what they can do. This integration is at the heart of both the greatest benefits of collaboration and its greatest challenges—collaborators can disagree, they can work on the wrong things, or they can misunderstand one another. What cognitive capacities enable humans to navigate these challenges? In this dissertation, I propose the Collaborative Utility Calculus as a cognitive framework for understanding human collaboration. According to this framework, humans have an intuitive theory of competence and effort, which they knit together with their intuitive theory of belief-desire reasoning to make joint inferences and plan joint actions. Chapter 1 introduces the Collaborative Utility Calculus framework and shows that people make joint inferences about collaborators’ competence and effort from minimal observations. The framework also captures how these inferences guide collaborative decisions, such as how much effort to invest in a task, how to incentivize collaborators to put in their fair share, and whom to recruit to a team. Chapter 2 shows that these inferences about competence and effort are also crucial for evaluating collaborators. In particular, people assign responsibility to collaborators based on a combination of their actual effort—how much effort they actually exerted—and counterfactual effort—how much effort they could have exerted to change the outcome, given their competence. Chapter 3 shows that people care not just about how much effort their collaborators expend, but also about why they do so. People make deeper trait inferences about collaborators’ willingness to contribute, and are more sensitive to effort when it is voluntarily chosen than when it is required. While Chapters 1–3 focus on static competence, Chapter 4 turns to people’s reasoning about how competence changes with training. This chapter shows that people do not simply recruit the most competent collaborators for the job, nor invest in training collaborators who stand to improve the most. Rather, they plan ways to optimize competence training in the service of collaboration, balancing the benefits of successful collaboration against the costs of recruitment and training. These decisions, too, are captured by the Collaborative Utility Calculus. Together, these studies offer empirical support for the Collaborative Utility Calculus as a theoretical framework for understanding how commonsense psychological reasoning gives rise to collaborative achievements.

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Cognitive Science, Collaboration, Competence, Computational Modeling, Effort, Social Cognition, Psychology, Cognitive psychology, Experimental psychology

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