How do people trade off risk and reward? How do people decide how long they are willing to wait for a good thing? How do societies converge on good strategies for making decisions? For a long time, answering these questions has been the project of psychologists, economists and sociologists. However, the answers to these questions are becoming increasingly important to other scientists. An engineer trying to build a self-driving car needs to understand the decisions that people make on the road. A doctor trying to predict the course of a pandemic needs to know when people will make a short-term sacrifice for a long-term gain. Both of them need high-precision models of human decision-making.
Even as the study of decision-making begins to have an impact on a wider range of scientific disciplines, those disciplines are beginning to offer new tools for making sense of human behavior. In particular, recent advances in computer science have resulted in software that makes it possible to collect data at unprecedented scales and machine learning methods that can be used to automatically identify the patterns in those data. These technologies create a unique opportunity to study the human mind in a new way.
The Computer Science of Human Decisions project aims to seize that opportunity, integrating computer science with psychology to develop high-precision models of decision-making. The outcome of this research project will not just be better models for predicting human decisions, but a deeper integration of the classic tools of the social and behavioral sciences with those of computer science.
The project is being led by Tom Griffiths at Princeton University (Princeton, US).
