Multilayer Value Metrics Using Lexical Link Analysis and Game Theory for Discovering Innovation from Big Data and Crowd-Sourcing

Abstract

We demonstrated a machine learning and artificial intelligence method, i.e., lexical link analysis (LLA) to discover different layers of semantic network that contribute to innovative ideas from big data. The LLA is an unsupervised machine learning paradigm that does not require manually labeled training data. Multilayer value metrics are defined based on game theory for LLA. We showed the following results: 1) the value metrics generated from LLA in a use case of an internet game and crowd-sourcing; 2) the results from LLA are validated and correlated with the ground truth; 3) the game-theoretic LLA can help an information provider to present the information in the most valuable way. The information presentation can solve a problem (e.g., a search request of innovation) that no other information providers can solve (i.e., expertise). In addition, it ties also to a broader context that the unique value can propagate through the consensus. Based on the game-theoretic LLA, an information provider should not always present expertise content or authoritative content but rather with a mixed strategy where each type of content is presented with certain probabilities for the best value overall.