Eden Saig, M.Sc. Thesis Seminar
Monday, 20.11.2017, 15:30
The goal of this work is to develop collaborative mechanisms which help people gain understanding of complex phenomena. We start by presenting an online, collaborative system for the study of child development. Moving from practice to theory, we proceed by introducing an abstract mathematical model for user retention, facilitating the design of efficient crowd collaboration systems.
The first part of the work is dedicated to the Baby CROINC (CROwd INtelligence Curation) system, which is an online early-childhood development tracker designed to be both personalized and objective. To meet these goals, we rely on Crowd Curated Intelligence (CCI), a process in which experts curate personalized inputs to connect with the crowd's aggregate data, providing parents with objective and personalized feedback on their children's development. We describe Baby CROINC's design, with a focus on CCI, and assess the extent to which it meets its design goals of objectivity and personalization.
In the second part of the work, we present the Collaborative Discovery model of guru-follower dynamics, which explains why "smarter" gurus tend to retain a larger following in the face of competition and limited follower attention. We define a natural class of retentive scoring rules to model the way followers evaluate gurus they interact with, and show that these rules are tightly connected to the classical notion of truth-eliciting proper scoring rules studied in Decision Theory. We then move our attention from the dynamics of interaction between gurus and followers to the study of the intrinsic properties of distributions that deem them appropriate for instruction by a guru. Finally, we take a modest first step towards relating retention models to other established computational complexity measures, namely, dual distance and query complexity, when the phenomena in question can be modeled by a uniform distribution over a linear space.