Resources

Daniel’s research extends to organizational design in the financial services and US hospital industries. In addition, he develops computer simulation models to study cognition, complex decision-making, and innovation, utilizing algorithms informed by psychology and neuroscience studies as effective, simplified representations of human learning processes. Many of these algorithms build the basic foundation of prominent AI models, such as neural networks that underly large language models. His findings have been published in prestigious academic journals and his work on mental models, learning, and decision-making earned him and his collaborators several prestigious awards.