To advance the science of decision-making as it pertains to how people learn to make decisions and how this process can be captured computationally, we are specifically addressing the challenge of how nonlinear decisions can be learned from data, experience, and even interactions with other decision-makers. Nonlinear thinking is a prized ability we, humans, have that is ubiquitously applied across any and all domains when the problems are challenging, and known solutions or ways of addressing the problems all fail to provide an adequate solution – e.g., All available choices are bad choices, must we settle for the least bad one? The ability to discover a new choice has been called being nonlinear, innovative, intuitive, emergent, or “outside-the-box.” It is well-documented that humans can often excel at such thinking in situations when there is a scarcity/overflow of data, significant uncertainty, and numerous contradictions in what is known or provided. However, how this can be replicated computationally for a machine has yet to be fully addressed or understood in extant research. (See also Innovative reasoning and emergent learning)
Faculty contact: Eugene Santos Jr.