Where Next for Understanding and Predicting Human Decision Making?

After early work in psychology in the 1920s and major econometric developments in the 1960s and 1970s, the mathematical modelling of human decision-making has evolved substantially in recent decades. Novel model structures have been put forward, alternative data sources have been explored, and the methods have gained a foothold in new application areas, notably health and environmental economics. At the same time, our traditional thinking is being challenged by ideas from behavioural economics and mathematical psychology, and important work remains to be done on improving the quality of our forecasts. Developments in one area of application also regularly fail to make the transition into other fields, and the impact of data quality on results is often not discussed.

Drawing on experiences of working in both theoretical and applied choice modelling across different fields, Dr. Stephane Hess presents a critical view of the reliance on cross-sectional (and often hypothetical) choice data, making the case for moving beyond treating individual decisions in isolation and discussing the potential benefits and pitfalls of moving away from models grounded in economic theory.

To register email Caroline Kurdian at ckurdian@healthpolicy.usc.edu