How is decision-making shaped by our cognitive and neural architecture? How are we capable of solving tremendously complex problems and yet suffer from persistent cognitive biases? What does this mean for business and policy?
My goal is to figure out the deep unifying principles that let us explain both rationality and irrationality. To do this, I take a highly interdisciplinary approach that combines cognitive science, computational neuroscience, and behavioral economics.
I develop and test theories of decision-making using a mix of computational modeling, laboratory experiments, and large-scale field data. This also means covering a diverse array of topics. Here are a few examples of questions I’ve studied:
- Why do we feel disappointed when things fall short of our expectations? (What does this have to do the way our brains efficiently process information?)
- Do we spend the right amount of time making decisions, or do we overthink them? (How would we even be able to tell?)
- How do we make good choices from among hundreds of options? (Which restaurants do we order food from online?)
- When do we interpret seemingly negative information in a positive light? (What made the famous slogan from Avis, “We’re No. 2—that means we try harder,” convincing?)
I’m especially interested in the idea that people are “computationally rational”—we are doing as best as we can, given constraints on time, energy, and data. This idea can be formalized using frameworks like information theory, Bayesian inference, and reinforcement learning. Such an approach lets us understand how real people can behave adaptively in our complex world, and how the same cognitive processes can also lead to classic anomalies in decision-making.
I’m also interested in the roles of culture and biology, so I’ve worked with collaborators to investigate agents that are at different stages of physiological development like adolescents, or from different kinds of societies like hunter-gatherers, or from different species like chimpanzees. This wide-angle view is essential for a comprehensive behavioral science.
