The Role of General Collective Intelligence in Optimizing Future Educational Environments

Abstract

From a functional perspective, an analysis of an educational environment serves a useful function to the degree it can reliably improve educational outcomes. However, both the choice of outcomes to optimize, and the choice of how to measure impact of educational initiatives on that optimization, might often be both subjective. General Collective Intelligence or GCI is a group decision-making system with the potential to significantly increase impact on any general outcome through increasing a group’s general problem-solving ability (intelligence). Having general problem-solving ability, a GCI must have the capacity to not only choose the optimal solution to problems, but also to choose the optimal problem to solve. However, while the subject of education has driven seeming endless analysis, the education of a nation’s children might often be too sensitive and too subjective of a topic for discussion to converge on consensus regarding such fundamental issues. As a result, without the ability to agree on what the goals (targeted outcomes) of education should even be, the problem of optimizing educational outcomes might not be susceptible to any analysis with the capacity to reliably achieve a significant improvement in such outcomes. However, with functional modeling this is poised to change. This paper explores how the properties of educational environments might be represented with functional modeling so that a General Collective Intelligence (GCI) might reliably optimize educational outcomes to a far greater degree than possible today.