UC Berkeley

The greatest human achievements are never completed alone. However, social interaction is complex and successful collaboration even more so. Tremendous amounts of information and processing are involved in predicting, interpreting, and working with others. These calculations are implicit, deeply embedded in the human psyche and not easily accessible for analysis or improvement. Knowing what algorithms the mind is solving when collaborating with others would be invaluable, both for our own knowledge and improvement of social collaboration between people, and to reconstruct these algorithms in systems outside ourselves. People increasingly interact with artificial intelligence systems, and developing models of social interaction could enable these systems to seamlessly assist us. This premise motivates my work: that by understanding the algorithms behind social collaboration, we can improve interactions between people, and between people and machines.

In this dissertation, I formalize and test computational models of collaboration, focusing on three problem areas. First, I investigate how people collaborate in recalling information. Though one might expect memory to be a solitary venture, researchers have long studied the differences in how people recall information in groups compared to alone, though only for small group sizes. Luhmann and Rajaram (2015) hypothesized mechanisms of large-scale recall using an agent-based model. I test the predictions of this model with an empirical experiment recruiting thousands of participants. Second, I investigate another key component of collaboration: people’s intuitive judgments of how shared resources should be allocated among people with different preferences. I collect empirical data from participants under a number of decision conditions, then use an inverse reinforcement learning model to determine what underlying mathematical fairness principles characterize people’s choices. Third, I investigate how people infer others’ preferences, a key component of collaboration. My coauthors and I present a rational model for inferring preferences from response times, using a Drift Diffusion Model to characterize how preferences influence response time and Bayesian inference to invert this relationship. We then compare the model’s predictions to collected empirical data. These three case studies comprise novel models of social interaction, tested with behavioral experiments, aimed at improving human and technological collaboration.