MSR NYC Data Science Seminar Series: From "In" to "Over"

Опубликовано 7 июля 2016, 23:56

MSR NYC Data Science Seminar Series: From "In" to "Over": Behavioral Experiments on Whole-Network Computation
We report on a series of behavioral experiments in human computation on three different tasks over networks: graph coloring, community detection (or graph clustering), and competitive contagion. While these tasks share similar action spaces and interfaces, they capture a diversity of computational challenges: graph coloring is a search problem, clustering is an optimization problem, and competitive contagion is a game-theoretic problem. In contrast with much of the recent literature on human-subject experiments in networks, in which collectives of subjects are embedded “in” the network, and have only local information and interactions, here individual subjects have a global (or “over”) view and must solve “whole network” problems alone. Our primary findings are that subject performance is impressive across all three problem types; that subjects find diverse and novel strategies for solving each task; and that collective performance can often be strongly correlated with known algorithms. Joint work with Lili Dworkin.