The human problem solution process has attracted an increasing amount of interest by educators, managers, computer scientists, and others. However, it has suffered from the lack of stochastic tools to quantitatively capture both the fine steps of the problem solution process and the diversity of human thinking. To model stochastically the human problem solution, this thesis presents influence modeling, which attempts to capture how an individual person navigates from one memory chunk randomly to another related memory chunk, and how a group of people randomly remind one another of memory chunks that could be individually uncommon.
As applications of influence modeling, this thesis shows how groups play 20-questions games based on ConceptNet (a common-sense database) and signals about their behavior are collected by embedded devices; how group interaction processes such as discussions on mission survival could be automatically monitored with embedded devices; how group performance could be facilitated; and how we can map group behavior and performance from the macroscopic to microscopic level in experiments in measuring collective intelligence. Influence modeling works because we understand how a group could outperform an individual; we are able to monitor the status of problem solution; and we are able to direct group interaction in more fruitful ways.
Host/Chair: Alex 'Sandy' Pentland
Henry LiebermanDavid Lazer